From b02d3378fc1949786e3819ad318e43ae64f9a87e Mon Sep 17 00:00:00 2001 From: plf Date: Sun, 7 Jun 2026 17:30:04 +0800 Subject: [PATCH] =?UTF-8?q?GraphRAG=20Studio=20=E2=80=94=20initial=20commi?= =?UTF-8?q?t:=20multimodal=20RAG=20system=20with=20KG=20visualization?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Full-stack application for document-to-knowledge-graph pipeline: - Backend: FastAPI + LangGraph ReAct agent + DeepSeek + MinerU parsing - Frontend: React 19 + Vite + D3.js + shadcn/ui - Pipeline: MinerU parsing → LangExtract entity extraction → KG building Co-Authored-By: Claude Opus 4.7 --- .gitignore | 39 + CLAUDE.md | 31 + backend/.env.example | 10 + backend/.gitignore | 10 + backend/CLAUDE.md | 28 + backend/main.py | 58 + backend/models/__init__.py | 0 backend/models/schemas.py | 360 + ...4-4a58-824b-7331d50db9bb_content_list.json | 367 + ...e1f-bba4-4a58-824b-7331d50db9bb_origin.pdf | Bin 0 -> 11789 bytes 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1442 ++++ frontend/src/main.tsx | 7 + frontend/src/styles/app.css | 96 + frontend/src/styles/fonts.css | 0 frontend/src/styles/index.css | 3 + frontend/src/styles/tailwind.css | 4 + frontend/src/styles/theme.css | 181 + frontend/tests/integration_test.py | 266 + frontend/vite.config.ts | 22 + 127 files changed, 37218 insertions(+) create mode 100644 .gitignore create mode 100644 CLAUDE.md create mode 100644 backend/.env.example create mode 100644 backend/.gitignore create mode 100644 backend/CLAUDE.md create mode 100644 backend/main.py create mode 100644 backend/models/__init__.py create mode 100644 backend/models/schemas.py create mode 100644 backend/output/8456b615_sample_graphrag_overview/99c9be1f-bba4-4a58-824b-7331d50db9bb_content_list.json create mode 100644 backend/output/8456b615_sample_graphrag_overview/99c9be1f-bba4-4a58-824b-7331d50db9bb_origin.pdf create mode 100644 backend/output/8456b615_sample_graphrag_overview/full.md create mode 100644 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+node_modules/ +.pnpm-store/ + +# Build output +dist/ +build/ + +# Environment & secrets +.env +.env.local +.env.*.local + +# Python +__pycache__/ +*.pyc +*.pyo +.venv/ +*.egg-info/ + +# Logs +*.log + +# OMC +.omc/ +**/.git_embedded_backup/ + +# Claude Code personal config +settings.json diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..ab3dc8e --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,31 @@ +# GraphRAG Studio — Project Conventions + +## 1. 目录结构 + +- **前端代码** 统一放在 `frontend/` 目录下 +- **后端代码** 统一放在 `backend/` 目录下 + +``` +GraphRAGAgent/ +├── frontend/ # 所有前端代码（HTML/CSS/JS） +├── backend/ # 所有后端代码（FastAPI 服务） +└── docs/ # 规范文档 +``` + +## 2. 环境变量与敏感配置 + +- 所有外部配置（API Key、Base URL、Token 等）统一在 `backend/.env` 中管理 +- `.env` 文件**禁止提交到 Git**，必须在 `.gitignore` 中忽略 +- 提供 `backend/.env.example` 作为配置模板（不含真实值） + +## 3. 后端虚拟环境 + +- 后端服务必须使用 `uv` 创建独立虚拟环境： + +```bash +cd backend +uv venv +uv pip install -r requirements.txt +``` + +- 虚拟环境目录 `.venv/` 不提交到 Git diff --git a/backend/.env.example b/backend/.env.example new file mode 100644 index 0000000..d7a1c42 --- /dev/null +++ b/backend/.env.example @@ -0,0 +1,10 @@ +# DeepSeek API (required for entity extraction + QA) +DEEPSEEK_API_KEY=your_deepseek_api_key_here +DEEPSEEK_BASE_URL=https://api.deepseek.com + +# MinerU (required for document parsing) +MINERU_API_TOKEN=your_mineru_api_token_here + +# MinerU venv path (absolute path to python.exe) +MINERU_PYTHON=F:/GraphRAGAgent/mineru_mvp/.venv/Scripts/python.exe +MINERU_PIPELINE=F:/GraphRAGAgent/mineru_mvp/pipeline.py diff --git a/backend/.gitignore b/backend/.gitignore new file mode 100644 index 0000000..87b96af --- /dev/null +++ b/backend/.gitignore @@ -0,0 +1,10 @@ +.env +.venv/ +__pycache__/ +*.pyc +*.pyo +data/uploads/ +data/jobs/ +data/kg/ +*.egg-info/ +dist/ diff --git a/backend/CLAUDE.md b/backend/CLAUDE.md new file mode 100644 index 0000000..fe37c1b --- /dev/null +++ b/backend/CLAUDE.md @@ -0,0 +1,28 @@ +# Backend — GraphRAG Studio API + +## 路径 + +``` +F:\GraphRAGAgent\backend\ +``` + +## 启动命令 + +```bash +cd F:/GraphRAGAgent/backend +.venv/Scripts/python.exe -m uvicorn main:app --host 0.0.0.0 --port 8000 --reload +``` + +## 接口测试 + +服务启动后，运行： + +```bash +.venv/Scripts/python.exe tests/test_api.py +``` + +## API 文档 + +- Swagger UI：http://localhost:8000/docs +- ReDoc：http://localhost:8000/redoc +- 健康检查：http://localhost:8000/api/v1/health diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 0000000..0bcd2e7 --- /dev/null +++ b/backend/main.py @@ -0,0 +1,58 @@ +""" +GraphRAG Studio — FastAPI Backend +Entry point: uvicorn main:app --host 0.0.0.0 --port 8000 --reload +""" +import sys +from pathlib import Path + +# Ensure backend/ is in sys.path for absolute imports +sys.path.insert(0, str(Path(__file__).parent)) + +from dotenv import load_dotenv +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware + +load_dotenv(Path(__file__).parent / ".env", override=True) + +from routers import documents, indexing, kg, query, search, system + +app = FastAPI( + title="GraphRAG Studio API", + description="Multimodal RAG Q&A system backend — MinerU + LangExtract + Agentic-RAG", + version="1.0.0", + docs_url="/docs", + redoc_url="/redoc", +) + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# All routers under /api/v1. Each router carries its own sub-prefix. +# documents.router prefix="/documents" → /api/v1/documents +# indexing.router prefix="/index" → /api/v1/index +# kg.router prefix="/kg" → /api/v1/kg +# query.router prefix="/query" → /api/v1/query +# search.router prefix="/search" → /api/v1/search +# system.router no prefix → /api/v1/health, /api/v1/system/... +PREFIX = "/api/v1" +app.include_router(documents.router, prefix=PREFIX) +app.include_router(indexing.router, prefix=PREFIX) +app.include_router(kg.router, prefix=PREFIX) +app.include_router(query.router, prefix=PREFIX) +app.include_router(search.router, prefix=PREFIX) +app.include_router(system.router, prefix=PREFIX) + + +@app.get("/") +async def root(): + return {"msg": "GraphRAG Studio API v1.0.0", "docs": "/docs", "health": "/api/v1/health"} + + +if __name__ == "__main__": + import uvicorn + uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True) diff --git a/backend/models/__init__.py b/backend/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/models/schemas.py b/backend/models/schemas.py new file mode 100644 index 0000000..aa3dbc8 --- /dev/null +++ b/backend/models/schemas.py @@ -0,0 +1,360 @@ +""" +Pydantic v2 schemas — all API data objects per backend_service_specification-v1.0.md +""" +from __future__ import annotations + +import uuid +from typing import Any, Generic, Optional, TypeVar + +from pydantic import BaseModel, Field + +T = TypeVar("T") + + +# --------------------------------------------------------------------------- +# Universal response envelope +# --------------------------------------------------------------------------- + +class APIResponse(BaseModel, Generic[T]): + code: int = 0 + msg: str = "success" + request_id: str = Field(default_factory=lambda: str(uuid.uuid4())) + data: Optional[T] = None + + @classmethod + def ok(cls, data: Any = None) -> "APIResponse": + return cls(code=0, msg="success", data=data) + + @classmethod + def err(cls, code: int, msg: str) -> "APIResponse": + return cls(code=code, msg=msg, data=None) + + +# --------------------------------------------------------------------------- +# A. Document schemas +# --------------------------------------------------------------------------- + +class DocumentInfo(BaseModel): + doc_id: str + filename: str + format: str + size_bytes: int + pages: Optional[int] = None + uploaded_at: str + status: str # uploaded | indexed | failed + language: str = "ch" + enable_formula: bool = True + enable_table: bool = True + + +class DocumentListData(BaseModel): + total: int + page: int + page_size: int + items: list[DocumentInfo] + + +class DeleteDocumentData(BaseModel): + deleted: bool + doc_id: str + removed_nodes: int + removed_edges: int + + +# --------------------------------------------------------------------------- +# B. Indexing job schemas +# --------------------------------------------------------------------------- + +class IndexingProgress(BaseModel): + parsed_pages: int = 0 + total_pages: int = 0 + extracted_entities: int = 0 + + +class IndexingJobStatus(BaseModel): + job_id: str + doc_id: str + status: str # submitted|queued|parsing|extracting|indexing|done|failed|cancelled + stage: str = "" + progress: IndexingProgress = Field(default_factory=IndexingProgress) + created_at: str + elapsed_seconds: float = 0.0 + error: Optional[str] = None + + +class StartIndexRequest(BaseModel): + doc_id: str + + +class CancelJobData(BaseModel): + cancelled: bool + job_id: str + previous_status: str + + +# --------------------------------------------------------------------------- +# C. KG schemas +# --------------------------------------------------------------------------- + +class KGNode(BaseModel): + id: str + name: str + type: str + source_doc: str + char_start: Optional[int] = None + char_end: Optional[int] = None + confidence: Optional[str] = None + page: int = 0 + degree: int = 0 + + +class KGNodeDetail(KGNode): + degree_centrality: float = 0.0 + neighbor_count: int = 0 + + +class KGEdge(BaseModel): + source: str + target: str + relation: str = "CO_OCCURS_IN" + doc_id: str + page: int = 0 + + +class KGNodeListData(BaseModel): + total: int + page: int + page_size: int + items: list[KGNode] + + +class KGEdgeListData(BaseModel): + total: int + page: int + page_size: int + items: list[KGEdge] + + +class KGStatsData(BaseModel): + total_nodes: int + total_edges: int + density: float + type_distribution: dict[str, int] + relation_types: dict[str, int] + top5_central_nodes: list[dict] + source_documents: list[str] + + +class KGExportData(BaseModel): + format: str + doc_id: Optional[str] + total_nodes: int + total_edges: int + exported_at: str + nodes: list[KGNode] + edges: list[KGEdge] + + +class NeighborInfo(BaseModel): + id: str + name: str + type: str + page: int + + +class NeighborsData(BaseModel): + center: NeighborInfo + hops: int + neighbors_by_hop: dict[str, list[NeighborInfo]] + total_neighbors: int + + +# --------------------------------------------------------------------------- +# D. QA schemas +# --------------------------------------------------------------------------- + +class ChatMessage(BaseModel): + role: str # human | ai + content: str + + +class QueryRequest(BaseModel): + question: str + history: list[ChatMessage] = Field(default_factory=list) + + +class ToolCallRecord(BaseModel): + tool: str + input: dict + output: str + + +class QAResult(BaseModel): + query_id: str + question: str + answer: str + tool_calls: list[ToolCallRecord] = Field(default_factory=list) + cited_nodes: list[str] = Field(default_factory=list) + elapsed_seconds: float + created_at: str + + +class QAHistoryData(BaseModel): + total: int + page: int + page_size: int + items: list[QAResult] + + +class BatchQueryRequest(BaseModel): + questions: list[str] = Field(..., max_length=20) + + +class BatchQueryData(BaseModel): + batch_id: str + total: int + status: str + created_at: str + + +class BatchResultData(BaseModel): + batch_id: str + total: int + completed: int + failed: int + status: str + results: list[QAResult] + + +# --------------------------------------------------------------------------- +# E. Search schemas +# --------------------------------------------------------------------------- + +class EntitySearchData(BaseModel): + query: str + total: int + items: list[KGNode] + + +class PathNode(BaseModel): + id: str + name: str + type: str + + +class PathEdge(BaseModel): + source: str + target: str + relation: str + + +class PathInfo(BaseModel): + length: int + nodes: list[PathNode] + edges: list[PathEdge] + + +class PathSearchData(BaseModel): + from_node: PathNode = Field(alias="from") + to_node: PathNode = Field(alias="to") + max_hops: int + paths: list[PathInfo] + total_paths: int + + model_config = {"populate_by_name": True} + + +class GraphSearchData(BaseModel): + query: str + matched_nodes: list[KGNode] + subgraph_edges: list[KGEdge] + + +# --------------------------------------------------------------------------- +# F. System schemas +# --------------------------------------------------------------------------- + +class ComponentHealth(BaseModel): + status: str # ok | error + path: Optional[str] = None + exists: Optional[bool] = None + base_url: Optional[str] = None + key_configured: Optional[bool] = None + kg_nodes_exists: Optional[bool] = None + kg_edges_exists: Optional[bool] = None + uploads_dir_exists: Optional[bool] = None + + +class HealthData(BaseModel): + status: str + version: str + uptime_seconds: float + components: dict[str, ComponentHealth] + + +class SystemStatsData(BaseModel): + total_documents: int + indexed_documents: int + failed_documents: int + total_nodes: int + total_edges: int + type_distribution: dict[str, int] + total_queries: int + active_jobs: int + storage_used_mb: float + + +class FormatInfo(BaseModel): + ext: str + description: str + max_size_mb: int + max_pages: int + requires_ocr: bool + + +class FormatsData(BaseModel): + formats: list[FormatInfo] + ocr_languages: list[dict] + notes: list[str] + + +class DemoData(BaseModel): + nodes: list[KGNode] + edges: list[KGEdge] + stats: dict + + +# --------------------------------------------------------------------------- +# B3 index result +# --------------------------------------------------------------------------- + +class IndexResultStats(BaseModel): + blocks: int = 0 + block_types: dict[str, int] = Field(default_factory=dict) + pages: int = 0 + raw_extractions: int = 0 + nodes: int = 0 + edges: int = 0 + type_counts: dict[str, int] = Field(default_factory=dict) + alignment_counts: dict[str, int] = Field(default_factory=dict) + elapsed_seconds: float = 0.0 + + +class ExtractionRecord(BaseModel): + text: str + type: str + char_start: Optional[int] = None + char_end: Optional[int] = None + alignment: Optional[str] = None + page: int = 0 + doc_id: str + + +class IndexResultData(BaseModel): + job_id: str + doc_id: str + status: str + stats: Optional[IndexResultStats] = None + extractions: Optional[list[ExtractionRecord]] = None + nodes: Optional[list[KGNode]] = None + edges: Optional[list[KGEdge]] = None diff --git a/backend/output/8456b615_sample_graphrag_overview/99c9be1f-bba4-4a58-824b-7331d50db9bb_content_list.json b/backend/output/8456b615_sample_graphrag_overview/99c9be1f-bba4-4a58-824b-7331d50db9bb_content_list.json new file mode 100644 index 0000000..6388389 --- /dev/null +++ b/backend/output/8456b615_sample_graphrag_overview/99c9be1f-bba4-4a58-824b-7331d50db9bb_content_list.json @@ -0,0 +1,367 @@ +[ + { + "type": "text", + "text": "GraphRAG System ", + "text_level": 1, + "bbox": [ + 344, + 175, + 655, + 204 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Technical Architecture Overview ", + "bbox": [ + 289, + 234, + 710, + 254 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Version 1.0 | March 2026 ", + "bbox": [ + 364, + 272, + 633, + 290 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Abstract ", + "text_level": 1, + "bbox": [ + 52, + 42, + 200, + 61 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "This document presents the technical architecture of a Multimodal GraphRAG System designed for intelligent document parsing and knowledge graph construction. The system integrates MinerU for document parsing, LangExtract for structured entity extraction, and a graph database for knowledge storage and retrieval. ", + "bbox": [ + 48, + 83, + 951, + 171 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The pipeline supports multiple document formats including PDF, DOCX, PPTX, and image files. Extracted entities and relations are stored as graph nodes and edges, enabling semantic search and question answering over large document collections. ", + "bbox": [ + 48, + 200, + 949, + 265 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. System Components ", + "text_level": 1, + "bbox": [ + 50, + 299, + 321, + 318 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1 Document Parsing Module ", + "text_level": 1, + "bbox": [ + 50, + 343, + 349, + 361 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "MinerU Cloud API (v4) serves as the document parsing backend. It accepts PDF, DOCX, PPTX, PNG, JPG, and HTML files. Output includes Markdown text, structured content_list.json, and extracted images. ", + "bbox": [ + 48, + 373, + 951, + 436 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2 Entity Extraction Module ", + "text_level": 1, + "bbox": [ + 50, + 461, + 357, + 479 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "LangExtract (v1.1.1) performs structured information extraction from plain text using few-shot prompting with LLM backends (Gemini, OpenAI, or local Ollama). Each extraction includes character-level position anchoring. ", + "bbox": [ + 48, + 492, + 949, + 555 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.3 Knowledge Graph Module ", + "text_level": 1, + "bbox": [ + 50, + 580, + 337, + 596 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Extracted entities and relationships are stored in a graph database. Node types include: Person, Organization, Location, Event, Concept. Edge types include: RELATED_TO, BELONGS_TO, CAUSED_BY, LOCATED_IN. ", + "bbox": [ + 48, + 608, + 949, + 674 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.4 Retrieval Module ", + "text_level": 1, + "bbox": [ + 50, + 697, + 272, + 715 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The retrieval layer supports hybrid search combining vector similarity and graph traversal. \nQuery results are ranked by relevance score and returned with source document references. ", + "bbox": [ + 48, + 727, + 944, + 766 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Data Pipeline ", + "text_level": 1, + "bbox": [ + 50, + 42, + 268, + 61 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The end-to-end data pipeline consists of the following stages: ", + "bbox": [ + 50, + 83, + 623, + 99 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Stage 1: Document Ingestion ", + "bbox": [ + 68, + 130, + 322, + 146 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "- Accept raw documents (PDF, DOCX, images, HTML) - Submit to MinerU API for parsing - Poll task status until state $\\underline { { \\underline { { \\mathbf { \\delta \\pi } } } } }$ done ", + "bbox": [ + 85, + 153, + 531, + 217 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Stage 2: Content Extraction ", + "bbox": [ + 68, + 249, + 322, + 263 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "- Download and decompress full_zip_url - Parse content_list.json into Document objects - Separate text blocks, tables, images, equations ", + "bbox": [ + 85, + 272, + 542, + 335 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Stage 3: Entity & Relation Extraction ", + "bbox": [ + 67, + 367, + 415, + 381 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "- Feed text blocks to LangExtract - Extract entities with char_interval positions - Extract relationships between entities ", + "bbox": [ + 85, + 390, + 526, + 454 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Stage 4: Graph Construction ", + "bbox": [ + 68, + 485, + 322, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "- Map extractions to graph nodes and edges - Store with source provenance (page_idx, bbox) - Build vector embeddings for semantic search ", + "bbox": [ + 85, + 508, + 522, + 571 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Supported File Formats ", + "text_level": 1, + "bbox": [ + 50, + 604, + 326, + 620 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/1ed7aacecd20fecef8dc27ee2fe76dc1ae7fa93c44f7d10878d17a41f21a6bef.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
FormatExtensionOCR RequiredModeI
PDF (text). pdfNopipeline / vlm
PDF (scan). pdfYesvIlm
Word. docxNopipeline
PowerPoint.pptxNopipeline
Image.png / .jpgAutovIlm
HTML.htmlNoMinerU-HTML
", + "bbox": [ + 45, + 634, + 882, + 806 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "5. API Configuration Reference ", + "text_level": 1, + "bbox": [ + 48, + 42, + 457, + 63 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The following environment variables must be configured before running the MinerU parsing service: ", + "bbox": [ + 48, + 83, + 952, + 123 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "MINERU_API_TOKEN : Bearer token for API authentication \nMINERU_USER_UID : User UUID for quota management \nMINERU_BASE_URL : https://mineru.net/api/v4 \nMINERU_MODEL_VERSION : pipeline (default) | vlm | MinerU-HTML \nMINERU_LANGUAGE : ch (Chinese) | en (English) \nMINERU_IS_OCR : false (text PDF) | true (scanned PDF) \nMINERU_ENABLE_FORMULA: true | false \nMINERU_ENABLE_TABLE : true | false ", + "bbox": [ + 65, + 152, + 636, + 337 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Rate Limits: ", + "bbox": [ + 48, + 367, + 161, + 381 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "- Max file size : 200 MB per file - Max pages : 600 pages per file - Daily quota : 2000 pages (high priority) - Batch limit : 200 files per request ", + "bbox": [ + 65, + 388, + 504, + 478 + ], + "page_idx": 3 + } +] \ No newline at end of file diff --git a/backend/output/8456b615_sample_graphrag_overview/99c9be1f-bba4-4a58-824b-7331d50db9bb_origin.pdf b/backend/output/8456b615_sample_graphrag_overview/99c9be1f-bba4-4a58-824b-7331d50db9bb_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9346f8e9b427cb03a126f4e83112c05e79c18693 GIT binary patch literal 11789 zcmc(FcUV)+w=N=xfQZr*1&PvoIsuUm(osN=DlI@L0ZHghl&VNCQUvLU^dbtqLR6GOP}>(z9%M*;+I8CxJR6k=i&NVFM#0x)jmuZh&`k!VR%EXodjJPSZj z4uzEmpdD>&a0c+I%EqpCj#z-88X75WXX9v#CNyIJc!$-0O(JcKHMX&{_-z`z)4!&X zwnJl)Xe6zX|7H-c`^%sl$_9&cz&UGUj77>I zP3_E(6oSe~v<21@00sf^S{SSY(%6>5CD9gW$0QmR(;X4OSg4z|I*EII*i95Q>vF5KfQ zb)?w@%aW4kWKW)Ogl^M_PLWfnqz^J%&>ZG_vv%!XPGsktVvy7GuDh|W_oNUGHm7GM zzO313pe3c3XnOBVFwoAx|p!&5H#gRAkr0h9Mo+C2yQw{}a{ zE*b?oXHKLgvN~0k(geFQnmUwqXgxo1vWd44zmCV?j#m`FGk!(ppHRd9hfp7Fp+A~5NwUQKBB{oeVdyB(erPN!A z#ceqwhqmS~Xh7-dc`i>By}XS$ zm%_gA8AK<`P@S^bCA(E&MtKWv(fzY9_awE^hP5*a}K{l7CaRs6J_UZTg(sDZ~D%?z5n2BC9<1$^J(`5@37ZCfgasP`l;Pj zC*nzYTtcO5^;%qD!1gOCf~f_EDXvrO74Q?^IUff~R|ekDsgwRXa$ffF&^&Q$@3JM6 zXkSvFn1f3ax8cjdK_p{dr7dY}S*&`LLYaPLRCt4sG)1QrkhCRaGL2zir}5-uHh)P= zyZ+;|B)$QutaLGrZqdvdw-98^F?v__TayMkMFrHh8&w%ClBHK!JycDKZs-?tUBX;O zPoD}SrhSj}Khc?J)A1ia-J=5 zsU@E?$=J*5>3uj{18ZBTM8Dtk{cE64oKwkWBUDbMiP7HiTl*`jpA=GT4F=ZKC?ySm znt%E&VcEVch=<^Z1Xk%3pr}eU|4N~OmOOddCc{t1fwjz!{hqZ`%dz|}UtUmo1>c^} z7tv^XA7+xR{eGCcy7q3UvNzk#9i)R}80LNZPrK{=g)ddCr=Dr~RDUU^rGDc_T-pVq zN!&Qy&mg1zsOB<95TC541xIP3DEXM}lPR{7s74=SQm0l}@ygjbz9JJ5WfI*_t=z#( z37RwKVyzN8yP=2jv6de5m*Nxl5b?}?Ofz-X080D8T4m)ZG>|h}I5syRi@!q9R^*Ag)lz%@ zbZYOJ`wAX`Z>0Al!s`66`@1g|hTDHo?ZUn@j46Y-MaG8BQQj@A=`#j>I#&z;U;4Dw z?Sp-X?|g_#Z6KSbx^-Vd5%~Ra2%}}h*9Vu0E@bj<@oS*q)MN>#Q|sTh7^ET|o!)1t zmA-34lk9qOV@R_(&eZhO+JIGL@tgr`^lOZ0n>3F{v-F!(sd%V5MStyWifBSXEi;=d$uZ`OeXB~2U5kpSIX02 zb?wz|cpleaqv{ZmE!aT0hKSGl4B<=}jf=F-ae=fYjK35{*|*gi-T$gveqScjVF)qI zOclyOPzDXI@??|4eVF<;gd9OiZG>@ z)fBaViJiFG`9?0f!#<{x`J7Qs$|vcJ-57 zCP#^{H&ynOIAmRgxp9?JCUXxhWwk~X9~&q8&S^oM^M(=k-xUadSMsZ1{8NFBi}`<7 z?7{e3mVa&_{-{8B-CqX(V+8^ug#T56o@y&$KEuz+u?ydqSN&l>bsIW;hObg(itHg- za##AdH!9Rsj&e6y63jBocR$vQkFras14x{qbh3f_POF~d0ut|k%BOSj9NIb5OtW}t z>qko|R`0coFWa|rrC1rl6g7p%51Ph{LTwrlmBtz|^>srqy<*0WL5eQQn5wzTnr|f} zSyJ{HbQykoNz8Q(Od+J7i?0(IG&s9AP1+lq?-?7JoplztOFTe65?R|CZ&2%ObzX47 zjWii{J;Or4nD1(-(zdv9@~M}@?@H-ZKFl^vVn#;a4_R*pWmfsW;ar*wS64QcKaZ8` zQBp>o;hju(ySqP9x61Tlc4a2mp5xp?ROXUjZ5-;wj{j(Jc~pZE#~|5mRpaLoU;7ut zQr&|M*JDCIa#^!uVXD&?A3kU8idd64fiNnCsttxl&BHoNGO$u6%9Dwur6DJy6=&R- zz7A8=2le|N^pHnLQ6S#a#;-(tzyA}kvlSET={;mwv6U$}e3?e2ClZx4*x>F&Djv47 zBl$t`TweIJ!bn&$!^|sgSM0DWj8%;|5<+xSmS$?n*W92axwRgquQq5!SD0NECL355 zvpBYf=77UEH)W3JG^TlgZBN<2xyDAO zMjNWYndxVcnbs7>$gti|=UEh`t#3aoOl48HeX9_r*lt++rNCY^ZLFu^P%Vz~rL0b9 zmItLhW80;qoL98jrQEOw2bO6F;cUIRX@p_K2v1o$YnntB)=_4SC7hy+p0Q({J~%g< zZ9p?D0QFu;x@x8!>slf>_cYeQ@BIZ&{ zQsJq$VW8koAyzzm!Tzb}tIOq$Yr-S$^p;bnWOC#0pAwg5TS|ELlUHxTwxFw!s2ioO zS9zA>kycWvkaAuK<~lUvUFd$_2#FTvik%iN|2po2soDdOX?ej->m3({z{~ z-}Ii3P$pFvkk5~MF+$vH@mh!2nz;TzJ>5vFdN$MRi$+EM<5oE{ErR<=&TmnJZ$8bq z$|kqih0Op@Tu%B)p3M^w-PmY+Cu!P=JJ6bvyQDH=?2T{!6Dh9Ilto3ljL`fi6hZ(= zto}ywkQ}Fi?Z#)Pi}jy;XQfJeS6yFH-p;lUgy@}lb%hQt!yZ`aO{|l6>Jra2!Ry7E zj|{qcUp!>yC=+V~DON^GE>`x(lW7JLU#AzHp>jcY2oUF9hj#3W#hZ>w^A3tguN?mfhD;BDz3lCt>Z9&1*CHCf}JTgOysBSZ!#Av!%UaoAZp$JEauo-oIJK_?f=iw6mhT>c(S! 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API Configuration Reference + +The following environment variables must be configured before running the MinerU parsing service: + +MINERU_API_TOKEN : Bearer token for API authentication +MINERU_USER_UID : User UUID for quota management +MINERU_BASE_URL : https://mineru.net/api/v4 +MINERU_MODEL_VERSION : pipeline (default) | vlm | MinerU-HTML +MINERU_LANGUAGE : ch (Chinese) | en (English) +MINERU_IS_OCR : false (text PDF) | true (scanned PDF) +MINERU_ENABLE_FORMULA: true | false +MINERU_ENABLE_TABLE : true | false + +Rate Limits: + +- Max file size : 200 MB per file - Max pages : 600 pages per file - Daily quota : 2000 pages (high priority) - Batch limit : 200 files per request \ No newline at end of file diff --git a/backend/output/8456b615_sample_graphrag_overview/images/1ed7aacecd20fecef8dc27ee2fe76dc1ae7fa93c44f7d10878d17a41f21a6bef.jpg b/backend/output/8456b615_sample_graphrag_overview/images/1ed7aacecd20fecef8dc27ee2fe76dc1ae7fa93c44f7d10878d17a41f21a6bef.jpg new file mode 100644 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b/backend/pipeline/entity_extractor.py new file mode 100644 index 0000000..87cc98f --- /dev/null +++ b/backend/pipeline/entity_extractor.py @@ -0,0 +1,66 @@ +""" +Entity Extractor — LangExtract + DeepSeek entity extraction. +Independent implementation for the GraphRAG Studio backend. +""" +from __future__ import annotations + +import os +from pathlib import Path + +from dotenv import load_dotenv + +import langextract as lx +from langextract.providers.openai import OpenAILanguageModel + +load_dotenv(Path(__file__).parent.parent / ".env", override=True) + +DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY", "") +DEEPSEEK_BASE_URL = os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com") +MODEL_ID = "deepseek-chat" + +PROMPT_DESCRIPTION = ( + "Extract named entities from the text in order of appearance. " + "Entity types: TECHNOLOGY (software, algorithms, models, tools), " + "ORGANIZATION (companies, research groups, institutions), " + "PERSON (individual people), " + "LOCATION (places, geographic entities), " + "CONCEPT (technical concepts, methodologies, frameworks)." +) + +EXAMPLES = [ + lx.data.ExampleData( + text=( + "LangChain is a framework created by Harrison Chase for building " + "LLM applications. It integrates with OpenAI models and Pinecone " + "vector database for semantic search." + ), + extractions=[ + lx.data.Extraction(extraction_class="TECHNOLOGY", extraction_text="LangChain"), + lx.data.Extraction(extraction_class="PERSON", extraction_text="Harrison Chase"), + lx.data.Extraction(extraction_class="CONCEPT", extraction_text="LLM applications"), + lx.data.Extraction(extraction_class="TECHNOLOGY", extraction_text="OpenAI models"), + lx.data.Extraction(extraction_class="TECHNOLOGY", extraction_text="Pinecone"), + lx.data.Extraction(extraction_class="CONCEPT", extraction_text="semantic search"), + ], + ) +] + + +def create_model() -> OpenAILanguageModel: + if not DEEPSEEK_API_KEY: + raise ValueError("DEEPSEEK_API_KEY not set in backend/.env") + return OpenAILanguageModel( + model_id=MODEL_ID, + api_key=DEEPSEEK_API_KEY, + base_url=DEEPSEEK_BASE_URL, + ) + + +def extract_entities(page_text: str, model: OpenAILanguageModel) -> lx.data.AnnotatedDocument: + return lx.extract( + text_or_documents=page_text, + prompt_description=PROMPT_DESCRIPTION, + examples=EXAMPLES, + model=model, + show_progress=False, + ) diff --git a/backend/pipeline/kg_builder.py b/backend/pipeline/kg_builder.py new file mode 100644 index 0000000..962b5fe --- /dev/null +++ b/backend/pipeline/kg_builder.py @@ -0,0 +1,123 @@ +""" +KG Builder — node deduplication + CO_OCCURS_IN edge generation. +Independent implementation for the GraphRAG Studio backend. +""" +from __future__ import annotations + +from collections import defaultdict + +import langextract as lx + +from pipeline.text_assembler import PageText + +ACCEPTED_ALIGNMENTS = {"match_exact", "match_greater", "match_lesser"} + + +def build_kg( + pages: list[PageText], + annotated_docs: list[lx.data.AnnotatedDocument], + source_doc_id: str, +) -> tuple[list[dict], list[dict]]: + """Build KG nodes and edges from LangExtract results. + + Returns: + (nodes, edges) — deduplicated node list and edge list. + """ + # Phase 1: collect raw entities + raw_entities = [] + for page, doc in zip(pages, annotated_docs): + if not doc.extractions: + continue + for ext in doc.extractions: + status = ext.alignment_status.value if ext.alignment_status else None + if status not in ACCEPTED_ALIGNMENTS: + continue + char_start = ext.char_interval.start_pos if ext.char_interval else None + char_end = ext.char_interval.end_pos if ext.char_interval else None + raw_entities.append({ + "name": ext.extraction_text, + "type": ext.extraction_class, + "char_start": char_start, + "char_end": char_end, + "confidence": status, + "page": page.page_idx, + "source_doc": source_doc_id, + }) + + # Phase 2: deduplicate nodes + seen: dict[tuple[str, str], int] = {} + nodes: list[dict] = [] + node_pages: dict[int, set[int]] = defaultdict(set) + + for entity in raw_entities: + type_prefix = entity["type"].lower()[:4] + name_slug = entity["name"].lower().replace(" ", "")[:12] + dedup_key = (entity["name"].lower(), entity["type"]) + if dedup_key not in seen: + node_idx = len(nodes) + seen[dedup_key] = node_idx + nodes.append({ + "id": f"{type_prefix}_{name_slug}_{node_idx}", + "name": entity["name"], + "type": entity["type"], + "source_doc": entity["source_doc"], + "char_start": entity["char_start"], + "char_end": entity["char_end"], + "confidence": entity["confidence"], + "page": entity["page"], + }) + node_idx = seen[dedup_key] + node_pages[node_idx].add(entity["page"]) + + # Phase 3: CO_OCCURS_IN edges + page_nodes: dict[int, list[int]] = defaultdict(list) + for node_idx, page_set in node_pages.items(): + for page_idx in page_set: + page_nodes[page_idx].append(node_idx) + + edges: list[dict] = [] + edge_seen: set[tuple] = set() + + for page_idx, node_indices in sorted(page_nodes.items()): + for i in range(len(node_indices)): + for j in range(i + 1, len(node_indices)): + a = nodes[node_indices[i]]["id"] + b = nodes[node_indices[j]]["id"] + src, tgt = (a, b) if a < b else (b, a) + key = (src, tgt, source_doc_id, page_idx) + if key in edge_seen: + continue + edge_seen.add(key) + edges.append({ + "source": src, + "target": tgt, + "relation": "CO_OCCURS_IN", + "doc_id": source_doc_id, + "page": page_idx, + }) + + return nodes, edges + + +def extractions_to_records( + pages: list[PageText], + annotated_docs: list[lx.data.AnnotatedDocument], + doc_id: str, +) -> list[dict]: + """Flatten LangExtract results to ExtractionRecord dicts.""" + records = [] + for page, doc in zip(pages, annotated_docs): + if not doc.extractions: + continue + for ext in doc.extractions: + status = ext.alignment_status.value if ext.alignment_status else None + records.append({ + "text": ext.extraction_text, + "type": ext.extraction_class, + "char_start": ext.char_interval.start_pos if ext.char_interval else None, + "char_end": ext.char_interval.end_pos if ext.char_interval else None, + "alignment": status, + "page": page.page_idx, + "doc_id": doc_id, + }) + return records diff --git a/backend/pipeline/qa_agent.py b/backend/pipeline/qa_agent.py new file mode 100644 index 0000000..3b766ca --- /dev/null +++ b/backend/pipeline/qa_agent.py @@ -0,0 +1,217 @@ +""" +QA Agent — LangGraph ReAct agent over the knowledge graph. +Independent implementation for the GraphRAG Studio backend. +""" +from __future__ import annotations + +import os +import re +from pathlib import Path + +import networkx as nx +from dotenv import load_dotenv +from langchain.tools import tool +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, AIMessage, ToolMessage, SystemMessage +from langgraph.prebuilt import create_react_agent + +load_dotenv(Path(__file__).parent.parent / ".env", override=True) + +DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY", "") +DEEPSEEK_BASE_URL = os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com") + + +def build_kg_graph(nodes: list[dict], edges: list[dict]) -> nx.Graph: + G = nx.Graph() + for n in nodes: + G.add_node(n["id"], **n) + for e in edges: + G.add_edge(e["source"], e["target"], **{k: v for k, v in e.items() if k not in ("source", "target")}) + return G + + +def make_tools(G: nx.Graph) -> list: + @tool + def search_entities(query: str) -> str: + """Search knowledge graph entities by name (case-insensitive substring). + Args: + query: Keyword to search for in entity names. + """ + q = query.lower() + matches = [data for _, data in G.nodes(data=True) if q in data.get("name", "").lower()] + if not matches: + sample = ", ".join(d.get("name", "") for _, d in list(G.nodes(data=True))[:8]) + return f"No entities found matching '{query}'. Sample: {sample}" + lines = [f"Found {len(matches)} entity(ies) matching '{query}':"] + for m in matches[:15]: + lines.append( + f" [{m['type']}] \"{m['name']}\" " + f"(confidence={m.get('confidence','?')}, page={m.get('page',0)}, id={m['id']})" + ) + return "\n".join(lines) + + @tool + def get_neighbors(entity_name: str, hops: int = 1) -> str: + """Get N-hop neighbors of an entity in the knowledge graph. + Args: + entity_name: Entity name (partial match). + hops: Number of hops (1-3, default 1). + """ + hops = max(1, min(int(hops), 3)) + candidates = [(nid, d) for nid, d in G.nodes(data=True) + if entity_name.lower() in d.get("name", "").lower()] + if not candidates: + return f"Entity '{entity_name}' not found. Use search_entities first." + node_id, node_data = candidates[0] + reachable = nx.single_source_shortest_path_length(G, node_id, cutoff=hops) + by_hop: dict[int, list] = {} + for nid, dist in reachable.items(): + if dist > 0: + by_hop.setdefault(dist, []).append(G.nodes[nid]) + lines = [f"Neighbors of '{node_data['name']}' [{node_data['type']}] within {hops} hop(s):"] + for hop in sorted(by_hop.keys()): + hop_nodes = by_hop[hop] + lines.append(f"\n Hop {hop} — {len(hop_nodes)} related entities:") + for n in hop_nodes[:20]: + lines.append(f" [{n.get('type','?')}] {n.get('name','?')}") + if len(hop_nodes) > 20: + lines.append(f" ... and {len(hop_nodes)-20} more") + lines.append(f"\n Total related entities: {sum(len(v) for v in by_hop.values())}") + return "\n".join(lines) + + @tool + def get_entities_by_type(entity_type: str) -> str: + """List all entities of a specific type. + Args: + entity_type: TECHNOLOGY, CONCEPT, PERSON, ORGANIZATION, or LOCATION. + """ + t_upper = entity_type.strip().upper() + valid = {"TECHNOLOGY", "CONCEPT", "PERSON", "ORGANIZATION", "LOCATION"} + if t_upper not in valid: + present = sorted({d.get("type","") for _, d in G.nodes(data=True)}) + return f"Unknown type '{entity_type}'. Present: {present}" + matches = [d for _, d in G.nodes(data=True) if d.get("type","") == t_upper] + if not matches: + return f"No {t_upper} entities found." + lines = [f"Found {len(matches)} {t_upper} entities:"] + for m in matches[:30]: + lines.append(f" \"{m['name']}\" (page={m.get('page',0)}, id={m['id']})") + if len(matches) > 30: + lines.append(f" ... and {len(matches)-30} more") + return "\n".join(lines) + + @tool + def describe_graph() -> str: + """Get an overview of the knowledge graph statistics.""" + n_nodes = G.number_of_nodes() + n_edges = G.number_of_edges() + type_counts: dict[str, int] = {} + for _, d in G.nodes(data=True): + t = d.get("type", "UNKNOWN") + type_counts[t] = type_counts.get(t, 0) + 1 + lines = [ + f"Knowledge Graph Overview:", + f" Nodes: {n_nodes}", + f" Edges: {n_edges}", + f" Entity types: {type_counts}", + ] + if n_nodes > 0: + centrality = nx.degree_centrality(G) + top5 = sorted(centrality.items(), key=lambda x: x[1], reverse=True)[:5] + lines.append(" Top 5 central nodes:") + for nid, c in top5: + nd = G.nodes[nid] + lines.append(f" [{nd.get('type','?')}] {nd.get('name','?')} (centrality={c:.3f})") + return "\n".join(lines) + + return [search_entities, get_neighbors, get_entities_by_type, describe_graph] + + +def run_qa( + question: str, + history: list[dict], + nodes: list[dict], + edges: list[dict], +) -> dict: + """Run Agentic-RAG QA. Returns dict with answer, tool_calls, cited_nodes.""" + if not DEEPSEEK_API_KEY: + raise ValueError("DEEPSEEK_API_KEY not set in backend/.env") + + G = build_kg_graph(nodes, edges) + tools = make_tools(G) + + llm = ChatOpenAI( + model="deepseek-chat", + api_key=DEEPSEEK_API_KEY, + base_url=DEEPSEEK_BASE_URL, + temperature=0, + ) + + system_prompt = ( + "You are a helpful assistant with access to a knowledge graph (KG) built from the user's documents.\n" + "\n" + "Guidelines:\n" + "- If the question is clearly unrelated to the KG (greetings, math, general knowledge, etc.), " + "answer directly WITHOUT using any tools.\n" + "- If the question might be answered by the KG (topics related to entities in the documents), " + "use the tools to search and explore before answering.\n" + "- When you DO use the KG, cite the entity names and types you found.\n" + "- If the KG has no relevant information, say so honestly and answer from general knowledge if possible.\n" + "\n" + "Available tools: search entities by name, get neighbors, list entities by type, get graph overview." + ) + + agent = create_react_agent(llm, tools, prompt=system_prompt) + + # Build messages: system + history + current question + messages: list = [] + for msg in history[-8:]: + role = msg.get("role", "human") + content = msg.get("content", "") or msg.get("answer", "") + if role == "human": + messages.append(HumanMessage(content=msg.get("question", content))) + else: + messages.append(AIMessage(content=content)) + messages.append(HumanMessage(content=question)) + + result = agent.invoke({"messages": messages}) + + # Extract answer from last AIMessage + answer = "" + for msg in reversed(result.get("messages", [])): + if isinstance(msg, AIMessage) and msg.content and not msg.tool_calls: + answer = msg.content + break + + # Extract tool calls and cited node IDs from message history + tool_calls = [] + cited_node_ids: set[str] = set() + step = 0 + all_messages = result.get("messages", []) + for i, msg in enumerate(all_messages): + if isinstance(msg, AIMessage) and msg.tool_calls: + for tc in msg.tool_calls: + step += 1 + # Find the corresponding ToolMessage + output = "" + for j in range(i + 1, len(all_messages)): + tm = all_messages[j] + if isinstance(tm, ToolMessage) and tm.tool_call_id == tc.get("id"): + output = tm.content + break + tool_input = tc.get("args", {}) + tool_calls.append({ + "step": step, + "tool_name": tc.get("name", ""), + "tool_input": str(tool_input), + "tool_output": str(output), + }) + # Extract node IDs mentioned in tool output + for node_id in re.findall(r'\bid=([^\s,\)\]]+)', str(output)): + cited_node_ids.add(node_id) + + return { + "answer": answer, + "tool_calls": tool_calls, + "cited_nodes": list(cited_node_ids), + } diff --git a/backend/pipeline/text_assembler.py b/backend/pipeline/text_assembler.py new file mode 100644 index 0000000..a210d87 --- /dev/null +++ b/backend/pipeline/text_assembler.py @@ -0,0 +1,107 @@ +""" +Text Assembler — MinerU content_list.json → per-page plain text. +Independent implementation for the GraphRAG Studio backend. +""" +from __future__ import annotations + +import dataclasses +import json +from collections import defaultdict +from pathlib import Path + +from bs4 import BeautifulSoup + + +@dataclasses.dataclass +class BlockSpan: + block_index: int + block_type: str + page_idx: int + char_start: int + char_end: int + bbox: list + + +@dataclasses.dataclass +class PageText: + page_idx: int + text: str + block_spans: list[BlockSpan] + + +def html_table_to_text(table_body: str) -> str: + soup = BeautifulSoup(table_body, "html.parser") + rows = [] + for tr in soup.find_all("tr"): + cells = [td.get_text(strip=True) for td in tr.find_all(["td", "th"])] + rows.append(" | ".join(cells)) + return "\n".join(rows) + + +def load_content_list(path: Path) -> list[dict]: + if path.is_dir(): + matches = list(path.glob("*_content_list.json")) + if not matches: + matches = list(path.glob("*content_list.json")) + if not matches: + raise FileNotFoundError(f"No content_list.json found in {path}") + path = matches[0] + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + + +def assemble_pages(content_list: list[dict]) -> list[PageText]: + pages: dict[int, list[tuple[int, dict]]] = defaultdict(list) + for i, block in enumerate(content_list): + page_idx = block.get("page_idx", 0) + pages[page_idx].append((i, block)) + + result = [] + for page_idx in sorted(pages.keys()): + blocks = pages[page_idx] + buffer = [] + spans = [] + cursor = 0 + + for block_index, block in blocks: + block_type = block.get("type", "unknown") + bbox = block.get("bbox", [0, 0, 0, 0]) + + if block_type == "text": + block_text = block.get("text", "").rstrip() + elif block_type == "table": + table_body = block.get("table_body", "") + block_text = html_table_to_text(table_body) if table_body else "" + else: + continue + + if not block_text: + continue + + char_start = cursor + buffer.append(block_text) + cursor += len(block_text) + char_end = cursor + + spans.append(BlockSpan( + block_index=block_index, + block_type=block_type, + page_idx=page_idx, + char_start=char_start, + char_end=char_end, + bbox=bbox, + )) + buffer.append("\n") + cursor += 1 + + text = "".join(buffer).rstrip("\n") + result.append(PageText(page_idx=page_idx, text=text, block_spans=spans)) + + return result + + +def count_blocks_by_type(content_list: list[dict]) -> dict[str, int]: + counts: dict[str, int] = defaultdict(int) + for block in content_list: + counts[block.get("type", "unknown")] += 1 + return dict(counts) diff --git a/backend/pyproject.toml b/backend/pyproject.toml new file mode 100644 index 0000000..cd34855 --- /dev/null +++ b/backend/pyproject.toml @@ -0,0 +1,22 @@ +[project] +name = "graphrag-studio-backend" +version = "1.0.0" +description = "GraphRAG Studio — FastAPI backend service" +requires-python = ">=3.12" +dependencies = [ + "fastapi>=0.104.0", + "uvicorn[standard]>=0.24.0", + "python-multipart>=0.0.6", + "langextract[all]>=0.1.0", + "langchain>=0.2.0", + "langchain-openai>=0.1.0", + "langgraph>=0.1.0", + "networkx>=3.0", + "python-dotenv>=1.0.0", + "requests>=2.31.0", + "beautifulsoup4>=4.12.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/backend/routers/__init__.py b/backend/routers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/routers/documents.py b/backend/routers/documents.py new file mode 100644 index 0000000..ba3dab7 --- /dev/null +++ b/backend/routers/documents.py @@ -0,0 +1,71 @@ +"""A 组：文档管理（4 个端点）""" +from fastapi import APIRouter, File, Form, HTTPException, UploadFile +from fastapi.responses import JSONResponse + +from models.schemas import APIResponse +from services import document_service as svc + +router = APIRouter(prefix="/documents", tags=["Documents"]) + + +@router.post("/upload", status_code=200) +async def upload_document( + file: UploadFile = File(...), + language: str = Form("ch"), + enable_formula: bool = Form(True), + enable_table: bool = Form(True), +): + content = await file.read() + ok, code, msg = svc.validate_upload(file.filename or "", len(content)) + if not ok: + return JSONResponse( + status_code=400, + content=APIResponse.err(code, msg).model_dump(), + ) + doc = svc.save_upload(file.filename or "upload", content, language, enable_formula, enable_table) + # Remove internal field + doc.pop("upload_filename", None) + return APIResponse.ok(doc) + + +@router.get("/{doc_id}") +async def get_document(doc_id: str): + doc = svc.get_document(doc_id) + if not doc: + return JSONResponse( + status_code=404, + content=APIResponse.err(2001, f"Document '{doc_id}' not found").model_dump(), + ) + doc.pop("upload_filename", None) + return APIResponse.ok(doc) + + +@router.get("") +async def list_documents( + page: int = 1, + page_size: int = 20, + status: str | None = None, + format: str | None = None, +): + page_size = min(page_size, 100) + result = svc.list_documents(page, page_size, status, format) + for item in result["items"]: + item.pop("upload_filename", None) + return APIResponse.ok(result) + + +@router.delete("/{doc_id}") +async def delete_document(doc_id: str): + doc = svc.get_document(doc_id) + if not doc: + return JSONResponse( + status_code=404, + content=APIResponse.err(2001, f"Document '{doc_id}' not found").model_dump(), + ) + ok, removed_nodes, removed_edges = svc.delete_document(doc_id) + return APIResponse.ok({ + "deleted": True, + "doc_id": doc_id, + "removed_nodes": removed_nodes, + "removed_edges": removed_edges, + }) diff --git a/backend/routers/indexing.py b/backend/routers/indexing.py new file mode 100644 index 0000000..a190b75 --- /dev/null +++ b/backend/routers/indexing.py @@ -0,0 +1,70 @@ +"""B 组：Indexing Pipeline（4 个端点）""" +from fastapi import APIRouter +from fastapi.responses import JSONResponse + +from models.schemas import APIResponse, StartIndexRequest +from services import document_service as doc_svc +from services import indexing_service as idx_svc + +router = APIRouter(prefix="/index", tags=["Indexing"]) + + +@router.post("/start", status_code=202) +async def start_indexing(body: StartIndexRequest): + doc = doc_svc.get_document(body.doc_id) + if not doc: + return JSONResponse( + status_code=404, + content=APIResponse.err(2001, f"Document '{body.doc_id}' not found").model_dump(), + ) + meta = idx_svc.start_indexing(body.doc_id) + return APIResponse.ok({ + "job_id": meta["job_id"], + "doc_id": meta["doc_id"], + "status": meta["status"], + "stage": meta["stage"], + "created_at": meta["created_at"], + }) + + +@router.get("/status/{job_id}") +async def get_job_status(job_id: str): + meta = idx_svc.get_job_status(job_id) + if not meta: + return JSONResponse( + status_code=404, + content=APIResponse.err(2002, f"Job '{job_id}' not found").model_dump(), + ) + return APIResponse.ok(meta) + + +@router.get("/result/{job_id}") +async def get_job_result(job_id: str): + result = idx_svc.get_job_result(job_id) + if not result: + return JSONResponse( + status_code=404, + content=APIResponse.err(2002, f"Job '{job_id}' not found").model_dump(), + ) + if result.get("status") not in ("done",) and "stats" not in result: + return JSONResponse( + status_code=400, + content=APIResponse.err(2003, f"Job '{job_id}' is still running (status={result.get('status')})").model_dump(), + ) + return APIResponse.ok(result) + + +@router.delete("/jobs/{job_id}") +async def cancel_job(job_id: str): + meta = idx_svc.get_job_status(job_id) + if not meta: + return JSONResponse( + status_code=404, + content=APIResponse.err(2002, f"Job '{job_id}' not found").model_dump(), + ) + ok, prev_status = idx_svc.cancel_job(job_id) + return APIResponse.ok({ + "cancelled": True, + "job_id": job_id, + "previous_status": prev_status, + }) diff --git a/backend/routers/kg.py b/backend/routers/kg.py new file mode 100644 index 0000000..6689ef0 --- /dev/null +++ b/backend/routers/kg.py @@ -0,0 +1,72 @@ +"""C 组：知识图谱（6 个端点）""" +from fastapi import APIRouter +from fastapi.responses import JSONResponse + +from models.schemas import APIResponse +from services import kg_service as svc + +router = APIRouter(prefix="/kg", tags=["Knowledge Graph"]) + + +@router.get("/nodes") +async def list_nodes( + type: str | None = None, + doc_id: str | None = None, + confidence: str | None = None, + page: int = 1, + page_size: int = 50, +): + page_size = min(page_size, 200) + result = svc.get_nodes(page, page_size, type, doc_id, confidence) + if result["total"] == 0 and not any([type, doc_id, confidence]): + return JSONResponse( + status_code=400, + content=APIResponse.err(3002, "Knowledge graph is empty. Index documents first.").model_dump(), + ) + return APIResponse.ok(result) + + +@router.get("/edges") +async def list_edges( + doc_id: str | None = None, + relation: str | None = None, + page: int = 1, + page_size: int = 100, +): + page_size = min(page_size, 500) + result = svc.get_edges(page, page_size, doc_id, relation) + return APIResponse.ok(result) + + +@router.get("/nodes/{node_id}") +async def get_node_detail(node_id: str): + node = svc.get_node_detail(node_id) + if not node: + return JSONResponse( + status_code=404, + content=APIResponse.err(3001, f"Node '{node_id}' not found").model_dump(), + ) + return APIResponse.ok(node) + + +@router.get("/nodes/{node_id}/neighbors") +async def get_node_neighbors(node_id: str, hops: int = 1): + result = svc.get_neighbors(node_id, hops) + if result is None: + return JSONResponse( + status_code=404, + content=APIResponse.err(3001, f"Node '{node_id}' not found").model_dump(), + ) + return APIResponse.ok(result) + + +@router.get("/stats") +async def get_kg_stats(): + stats = svc.get_stats() + return APIResponse.ok(stats) + + +@router.get("/export") +async def export_kg(format: str = "json", doc_id: str | None = None): + result = svc.export_kg(doc_id) + return APIResponse.ok(result) diff --git a/backend/routers/query.py b/backend/routers/query.py new file mode 100644 index 0000000..52bf9a8 --- /dev/null +++ b/backend/routers/query.py @@ -0,0 +1,66 @@ +"""D 组：QA 问答（4 个端点）""" +import asyncio +from functools import partial + +from fastapi import APIRouter +from fastapi.responses import JSONResponse + +from models.schemas import APIResponse, BatchQueryRequest, QueryRequest +from services import qa_service as svc + +router = APIRouter(prefix="/query", tags=["QA"]) + + +@router.post("") +async def run_query(body: QueryRequest): + try: + loop = asyncio.get_event_loop() + result = await loop.run_in_executor( + None, + partial(svc.run_query, body.question, [m.model_dump() for m in body.history]), + ) + return APIResponse.ok(result) + except ValueError as e: + if "KG_EMPTY" in str(e): + return JSONResponse( + status_code=400, + content=APIResponse.err(3002, "Knowledge graph is empty. Index documents first.").model_dump(), + ) + return JSONResponse( + status_code=500, + content=APIResponse.err(4001, str(e)).model_dump(), + ) + except Exception as e: + return JSONResponse( + status_code=500, + content=APIResponse.err(4001, f"QA service error: {e}").model_dump(), + ) + + +@router.post("/batch", status_code=202) +async def start_batch(body: BatchQueryRequest): + if len(body.questions) > 20: + return JSONResponse( + status_code=400, + content=APIResponse.err(1001, "Maximum 20 questions per batch").model_dump(), + ) + result = svc.start_batch(body.questions) + return APIResponse.ok(result) + + +@router.get("/batch/{batch_id}") +async def get_batch_result(batch_id: str): + result = svc.get_batch_result(batch_id) + if not result: + return JSONResponse( + status_code=404, + content=APIResponse.err(2002, f"Batch '{batch_id}' not found").model_dump(), + ) + return APIResponse.ok(result) + + +@router.get("/history") +async def get_query_history(page: int = 1, page_size: int = 20): + page_size = min(page_size, 50) + result = svc.get_history(page, page_size) + return APIResponse.ok(result) diff --git a/backend/routers/search.py b/backend/routers/search.py new file mode 100644 index 0000000..58b6451 --- /dev/null +++ b/backend/routers/search.py @@ -0,0 +1,43 @@ +"""E 组：搜索（3 个端点）""" +from fastapi import APIRouter, Query, Request +from fastapi.responses import JSONResponse + +from models.schemas import APIResponse +from services import search_service as svc + +router = APIRouter(prefix="/search", tags=["Search"]) + + +@router.get("/entities") +async def search_entities(q: str, type: str | None = None, limit: int = 15): + limit = min(limit, 100) + result = svc.search_entities(q, type, limit) + return APIResponse.ok(result) + + +@router.get("/path") +async def search_path(request: Request, max_hops: int = 3): + # 'from' is a Python keyword, read from raw query params + params = dict(request.query_params) + from_id = params.get("from") + to_id = params.get("to") + + if not from_id or not to_id: + return JSONResponse( + status_code=400, + content=APIResponse.err(1001, "Parameters 'from' and 'to' are required").model_dump(), + ) + max_hops = max(1, min(max_hops, 5)) + result = svc.search_path(from_id, to_id, max_hops) + if result is None: + return JSONResponse( + status_code=404, + content=APIResponse.err(3001, "One or both nodes not found").model_dump(), + ) + return APIResponse.ok(result) + + +@router.get("/graph") +async def search_graph(q: str, include_neighbors: bool = False): + result = svc.search_graph(q, include_neighbors) + return APIResponse.ok(result) diff --git a/backend/routers/system.py b/backend/routers/system.py new file mode 100644 index 0000000..7605ce0 --- /dev/null +++ b/backend/routers/system.py @@ -0,0 +1,171 @@ +"""F 组：系统（4 个端点）""" +import os +import time +from pathlib import Path + +from fastapi import APIRouter + +from models.schemas import APIResponse +from storage import file_store as fs + +router = APIRouter(tags=["System"]) + +_START_TIME = time.time() + + +@router.get("/health") +async def health_check(): + env_path = Path(__file__).parent.parent / ".env" + from dotenv import load_dotenv + load_dotenv(env_path, override=False) + + mineru_python = Path(os.getenv("MINERU_PYTHON", "F:/GraphRAGAgent/mineru_mvp/.venv/Scripts/python.exe")) + backend_python = Path(__file__).parent.parent / ".venv" / "Scripts" / "python.exe" + deepseek_key = os.getenv("DEEPSEEK_API_KEY", "") + deepseek_url = os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com") + + # Check if langextract is importable from backend's venv + try: + import subprocess + result = subprocess.run( + [str(backend_python), "-c", "import langextract; print('ok')"], + capture_output=True, text=True, timeout=10 + ) + langextract_ok = result.returncode == 0 and "ok" in result.stdout + except Exception: + langextract_ok = False + + components = { + "mineru_venv": { + "status": "ok" if mineru_python.exists() else "error", + "path": str(mineru_python), + "exists": mineru_python.exists(), + }, + "langextract_venv": { + "status": "ok" if langextract_ok else "error", + "path": str(backend_python), + "exists": backend_python.exists(), + }, + "deepseek_api": { + "status": "ok" if deepseek_key else "error", + "base_url": deepseek_url, + "key_configured": bool(deepseek_key), + }, + "storage": { + "status": "ok", + "kg_nodes_exists": fs.kg_nodes_path().exists(), + "kg_edges_exists": fs.kg_edges_path().exists(), + "uploads_dir_exists": fs.UPLOADS_DIR.exists(), + }, + } + + overall = "healthy" if all(c["status"] == "ok" for c in components.values()) else "degraded" + + return APIResponse.ok({ + "status": overall, + "version": "1.0.0", + "uptime_seconds": round(time.time() - _START_TIME, 1), + "components": components, + }) + + +@router.get("/system/stats") +async def system_stats(): + from services import indexing_service as idx_svc + + docs = list(fs.load_docs_index().values()) + nodes = fs.load_kg_nodes() + edges = fs.load_kg_edges() + history = fs.load_query_history() + + type_dist: dict[str, int] = {} + for n in nodes: + t = n.get("type", "UNKNOWN") + type_dist[t] = type_dist.get(t, 0) + 1 + + return APIResponse.ok({ + "total_documents": len(docs), + "indexed_documents": sum(1 for d in docs if d.get("status") == "indexed"), + "failed_documents": sum(1 for d in docs if d.get("status") == "failed"), + "total_nodes": len(nodes), + "total_edges": len(edges), + "type_distribution": type_dist, + "total_queries": len(history), + "active_jobs": idx_svc.count_active_jobs(), + "storage_used_mb": fs.storage_used_mb(), + }) + + +@router.get("/system/formats") +async def list_formats(): + return APIResponse.ok({ + "formats": [ + {"ext": "pdf", "description": "PDF 文档（文本型/扫描型/混合型）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, + {"ext": "docx", "description": "Microsoft Word（新版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, + {"ext": "doc", "description": "Microsoft Word（旧版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, + {"ext": "pptx", "description": "PowerPoint（新版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, + {"ext": "ppt", "description": "PowerPoint（旧版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, + {"ext": "png", "description": "PNG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": True}, + {"ext": "jpg", "description": "JPEG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": True}, + {"ext": "jpeg", "description": "JPEG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": True}, + {"ext": "html", "description": "HTML 文件", "max_size_mb": 200, "max_pages": 600, "requires_ocr": False}, + ], + "ocr_languages": [ + {"code": "ch", "name": "中文（默认）"}, + {"code": "en", "name": "英文"}, + {"code": "japan", "name": "日文"}, + {"code": "korean", "name": "韩文"}, + {"code": "french", "name": "法文"}, + {"code": "german", "name": "德文"}, + ], + "notes": [ + "language 参数默认值为 'ch'（非 'zh'），遵循 PaddleOCR v3 语言代码规范", + "上传时不需要携带 Content-Type，服务端自动识别", + "PNG/JPG/JPEG 单次最多处理 1 页", + ], + }) + + +@router.get("/system/demo") +async def get_demo_data(): + # Try backend KG first, then fall back to graphrag_pipeline/output + nodes = fs.load_kg_nodes() + edges = fs.load_kg_edges() + + if not nodes: + # Fallback: load from existing graphrag_pipeline output + legacy_nodes_path = Path("F:/GraphRAGAgent/graphrag_pipeline/output/kg_nodes.json") + legacy_edges_path = Path("F:/GraphRAGAgent/graphrag_pipeline/output/kg_edges.json") + if legacy_nodes_path.exists(): + import json + nodes = json.loads(legacy_nodes_path.read_text(encoding="utf-8")) + edges = json.loads(legacy_edges_path.read_text(encoding="utf-8")) if legacy_edges_path.exists() else [] + else: + from fastapi.responses import JSONResponse + return JSONResponse( + status_code=400, + content=APIResponse.err(3002, "No demo data available. Index a document first.").model_dump(), + ) + + type_counts: dict[str, int] = {} + for n in nodes: + t = n.get("type", "UNKNOWN") + type_counts[t] = type_counts.get(t, 0) + 1 + + import networkx as nx + G = nx.Graph() + for n in nodes: + G.add_node(n["id"]) + for e in edges: + G.add_edge(e["source"], e["target"]) + + return APIResponse.ok({ + "nodes": nodes, + "edges": edges, + "stats": { + "nodes": len(nodes), + "edges": len(edges), + "type_counts": type_counts, + "density": round(nx.density(G), 4) if G.number_of_nodes() > 1 else 0.0, + }, + }) diff --git a/backend/services/__init__.py b/backend/services/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/services/document_service.py b/backend/services/document_service.py new file mode 100644 index 0000000..cc8c91d --- /dev/null +++ b/backend/services/document_service.py @@ -0,0 +1,109 @@ +"""Document Service — file upload, metadata CRUD.""" +from __future__ import annotations + +import uuid +from datetime import datetime, timezone +from pathlib import Path + +from storage import file_store as fs + +ALLOWED_EXTENSIONS = {"pdf", "docx", "doc", "pptx", "ppt", "png", "jpg", "jpeg", "html"} +MAX_FILE_SIZE_MB = 200 + + +def validate_upload(filename: str, size_bytes: int) -> tuple[bool, int, str]: + """Returns (ok, error_code, error_msg).""" + if not filename or "/" in filename or "\\" in filename: + return False, 1001, "Invalid filename" + ext = Path(filename).suffix.lower().lstrip(".") + if ext not in ALLOWED_EXTENSIONS: + return False, 1002, f"Unsupported file format: .{ext}. Supported: {', '.join(sorted(ALLOWED_EXTENSIONS))}" + size_mb = size_bytes / (1024 * 1024) + if size_mb > MAX_FILE_SIZE_MB: + return False, 1003, f"File size {size_mb:.1f}MB exceeds {MAX_FILE_SIZE_MB}MB limit" + return True, 0, "" + + +def save_upload(filename: str, content: bytes, language: str = "ch", + enable_formula: bool = True, enable_table: bool = True) -> dict: + doc_id = uuid.uuid4().hex[:8] + ext = Path(filename).suffix.lower().lstrip(".") + upload_filename = f"{doc_id}_{filename}" + upload_path = fs.UPLOADS_DIR / upload_filename + upload_path.write_bytes(content) + + doc = { + "doc_id": doc_id, + "filename": filename, + "format": ext, + "size_bytes": len(content), + "pages": None, + "uploaded_at": datetime.now(timezone.utc).isoformat(), + "status": "uploaded", + "language": language, + "enable_formula": enable_formula, + "enable_table": enable_table, + "upload_filename": upload_filename, # internal: actual stored filename + } + fs.save_doc(doc) + return doc + + +def get_document(doc_id: str) -> dict | None: + return fs.get_doc(doc_id) + + +def list_documents(page: int = 1, page_size: int = 20, + status: str | None = None, fmt: str | None = None) -> dict: + index = fs.load_docs_index() + items = list(index.values()) + items.sort(key=lambda d: d.get("uploaded_at", ""), reverse=True) + if status: + items = [d for d in items if d.get("status") == status] + if fmt: + items = [d for d in items if d.get("format") == fmt.lower()] + total = len(items) + start = (page - 1) * page_size + return { + "total": total, + "page": page, + "page_size": page_size, + "items": items[start: start + page_size], + } + + +def delete_document(doc_id: str) -> tuple[bool, int, int]: + """Delete doc and its KG contributions. Returns (ok, removed_nodes, removed_edges).""" + doc = fs.get_doc(doc_id) + if not doc: + return False, 0, 0 + + # Remove from KG + removed_nodes, removed_edges = fs.remove_doc_from_kg(doc_id) + + # Remove upload file + upload_filename = doc.get("upload_filename", "") + upload_path = fs.UPLOADS_DIR / upload_filename + if upload_path.exists(): + upload_path.unlink(missing_ok=True) + + # Remove associated jobs + for meta in fs.list_all_jobs(): + if meta.get("doc_id") == doc_id: + fs.delete_job(meta["job_id"]) + + # Remove from index + index = fs.load_docs_index() + index.pop(doc_id, None) + fs.save_docs_index(index) + + return True, removed_nodes, removed_edges + + +def update_doc_status(doc_id: str, status: str, pages: int | None = None) -> None: + index = fs.load_docs_index() + if doc_id in index: + index[doc_id]["status"] = status + if pages is not None: + index[doc_id]["pages"] = pages + fs.save_docs_index(index) diff --git a/backend/services/indexing_service.py b/backend/services/indexing_service.py new file mode 100644 index 0000000..b359108 --- /dev/null +++ b/backend/services/indexing_service.py @@ -0,0 +1,255 @@ +"""Indexing Service — Pipeline orchestration (parsing → extracting → indexing).""" +from __future__ import annotations + +import json +import os +import subprocess +import threading +import time +import uuid +from datetime import datetime, timezone +from pathlib import Path + +from dotenv import load_dotenv + +from storage import file_store as fs +from services.document_service import update_doc_status + +load_dotenv(Path(__file__).parent.parent / ".env", override=True) + +MINERU_PYTHON = Path(os.getenv("MINERU_PYTHON", "F:/GraphRAGAgent/mineru_mvp/.venv/Scripts/python.exe")) +MINERU_PIPELINE = Path(os.getenv("MINERU_PIPELINE", "F:/GraphRAGAgent/mineru_mvp/pipeline.py")) + +# In-memory registry of active jobs {job_id: threading.Thread} +_active_threads: dict[str, threading.Thread] = {} +_cancel_flags: dict[str, bool] = {} + + +def start_indexing(doc_id: str) -> dict: + doc = fs.get_doc(doc_id) + if not doc: + return None # type: ignore + + job_id = f"job_{uuid.uuid4().hex[:8]}" + now = datetime.now(timezone.utc).isoformat() + + meta = { + "job_id": job_id, + "doc_id": doc_id, + "status": "submitted", + "stage": "Job submitted", + "progress": {"parsed_pages": 0, "total_pages": 0, "extracted_entities": 0}, + "created_at": now, + "elapsed_seconds": 0.0, + "error": None, + "pdf_name": doc["filename"], + "pdf_path": str(fs.UPLOADS_DIR / doc.get("upload_filename", "")), + } + fs.save_job_meta(job_id, meta) + + _cancel_flags[job_id] = False + thread = threading.Thread(target=_run_pipeline, args=(job_id,), daemon=True) + _active_threads[job_id] = thread + thread.start() + + return meta + + +def _update_meta(job_id: str, **kwargs) -> None: + meta = fs.load_job_meta(job_id) or {} + meta.update(kwargs) + meta["elapsed_seconds"] = round( + (datetime.now(timezone.utc) - datetime.fromisoformat(meta["created_at"])).total_seconds(), 1 + ) + fs.save_job_meta(job_id, meta) + + +def _run_pipeline(job_id: str) -> None: + meta = fs.load_job_meta(job_id) + if not meta: + return + + doc_id = meta["doc_id"] + pdf_path = Path(meta["pdf_path"]) + job_dir = fs.job_dir(job_id) + start_time = time.time() + + try: + # ── Stage 1: parsing ────────────────────────────────────────────── + if _cancel_flags.get(job_id): + _update_meta(job_id, status="cancelled", stage="Cancelled") + return + + _update_meta(job_id, status="parsing", stage="MinerU document parsing...") + mineru_out_dir = job_dir / "mineru_output" + mineru_out_dir.mkdir(parents=True, exist_ok=True) + + result = subprocess.run( + [str(MINERU_PYTHON), str(MINERU_PIPELINE), str(pdf_path)], + cwd=str(MINERU_PIPELINE.parent), + capture_output=True, + text=True, + timeout=600, + ) + + if result.returncode != 0: + raise RuntimeError(f"MinerU failed: {result.stderr[:500]}") + + # Find content_list.json in MinerU output + # MinerU writes output to mineru_mvp/output/{stem}/ + stem = pdf_path.stem + mineru_default_out = MINERU_PIPELINE.parent / "output" / stem + content_list_path = None + + if mineru_default_out.exists(): + matches = list(mineru_default_out.glob("*_content_list.json")) + if matches: + content_list_path = matches[0] + # Copy to our job dir + import shutil + shutil.copytree(str(mineru_default_out), str(mineru_out_dir), dirs_exist_ok=True) + + if not content_list_path: + # Fallback: search job mineru_output dir + matches = list(mineru_out_dir.glob("*_content_list.json")) + if matches: + content_list_path = matches[0] + + if not content_list_path or not content_list_path.exists(): + raise RuntimeError(f"MinerU output content_list.json not found. stdout: {result.stdout[:300]}") + + # ── Stage 2: extracting ─────────────────────────────────────────── + if _cancel_flags.get(job_id): + _update_meta(job_id, status="cancelled", stage="Cancelled") + return + + from pipeline.text_assembler import load_content_list, assemble_pages, count_blocks_by_type + from pipeline.entity_extractor import create_model, extract_entities + from pipeline.kg_builder import build_kg, extractions_to_records + + content_list = load_content_list(content_list_path) + pages = assemble_pages(content_list) + total_pages = len(pages) + block_types = count_blocks_by_type(content_list) + + _update_meta( + job_id, + status="extracting", + stage=f"Extracting entities (LangExtract + DeepSeek)...", + progress={"parsed_pages": total_pages, "total_pages": total_pages, "extracted_entities": 0}, + ) + update_doc_status(doc_id, "indexing", pages=total_pages) + + model = create_model() + annotated_docs = [] + total_entities = 0 + + for i, page in enumerate(pages): + if _cancel_flags.get(job_id): + _update_meta(job_id, status="cancelled", stage="Cancelled") + return + + _update_meta( + job_id, + stage=f"Extracting entities page {i+1}/{total_pages} (LangExtract + DeepSeek)...", + progress={"parsed_pages": total_pages, "total_pages": total_pages, + "extracted_entities": total_entities}, + ) + ann_doc = extract_entities(page.text, model) + annotated_docs.append(ann_doc) + total_entities += len(ann_doc.extractions) if ann_doc.extractions else 0 + + # Save raw extractions + records = extractions_to_records(pages, annotated_docs, doc_id) + fs.write_json(job_dir / "extractions.json", records) + + # ── Stage 3: indexing ───────────────────────────────────────────── + _update_meta(job_id, status="indexing", stage="Building knowledge graph...") + + nodes, edges = build_kg(pages, annotated_docs, doc_id) + fs.write_json(job_dir / "kg_nodes.json", nodes) + fs.write_json(job_dir / "kg_edges.json", edges) + + # Merge into global KG + fs.merge_kg(nodes, edges, doc_id) + + # Count alignment types + alignment_counts: dict[str, int] = {} + type_counts: dict[str, int] = {} + for r in records: + al = r.get("alignment") or "null" + alignment_counts[al] = alignment_counts.get(al, 0) + 1 + t = r.get("type", "UNKNOWN") + type_counts[t] = type_counts.get(t, 0) + 1 + + elapsed = round(time.time() - start_time, 1) + stats = { + "blocks": len(content_list), + "block_types": block_types, + "pages": total_pages, + "raw_extractions": len(records), + "nodes": len(nodes), + "edges": len(edges), + "type_counts": type_counts, + "alignment_counts": alignment_counts, + "elapsed_seconds": elapsed, + } + fs.write_json(job_dir / "stats.json", stats) + + _update_meta( + job_id, + status="done", + stage="Complete", + progress={"parsed_pages": total_pages, "total_pages": total_pages, + "extracted_entities": len(records)}, + ) + update_doc_status(doc_id, "indexed", pages=total_pages) + + except Exception as exc: + _update_meta(job_id, status="failed", stage=f"Error: {exc}", error=str(exc)) + update_doc_status(doc_id, "failed") + finally: + _active_threads.pop(job_id, None) + _cancel_flags.pop(job_id, None) + + +def get_job_status(job_id: str) -> dict | None: + return fs.load_job_meta(job_id) + + +def get_job_result(job_id: str) -> dict | None: + meta = fs.load_job_meta(job_id) + if not meta: + return None + if meta["status"] != "done": + return meta + + job_dir = fs.job_dir(job_id) + stats = fs.read_json(job_dir / "stats.json") or {} + extractions = fs.read_json(job_dir / "extractions.json") or [] + nodes = fs.read_json(job_dir / "kg_nodes.json") or [] + edges = fs.read_json(job_dir / "kg_edges.json") or [] + + return { + "job_id": meta["job_id"], + "doc_id": meta["doc_id"], + "status": "done", + "stats": stats, + "extractions": extractions, + "nodes": nodes, + "edges": edges, + } + + +def cancel_job(job_id: str) -> tuple[bool, str]: + meta = fs.load_job_meta(job_id) + if not meta: + return False, "not_found" + prev_status = meta["status"] + _cancel_flags[job_id] = True + _update_meta(job_id, status="cancelled", stage="Cancelled by user") + return True, prev_status + + +def count_active_jobs() -> int: + return sum(1 for t in _active_threads.values() if t.is_alive()) diff --git a/backend/services/kg_service.py b/backend/services/kg_service.py new file mode 100644 index 0000000..fc92c03 --- /dev/null +++ b/backend/services/kg_service.py @@ -0,0 +1,167 @@ +"""KG Service — NetworkX graph operations over the global KG.""" +from __future__ import annotations + +import networkx as nx + +from storage import file_store as fs + + +def _load_graph() -> nx.Graph: + nodes = fs.load_kg_nodes() + edges = fs.load_kg_edges() + G = nx.Graph() + for n in nodes: + G.add_node(n["id"], **n) + for e in edges: + G.add_edge(e["source"], e["target"], + relation=e.get("relation", "CO_OCCURS_IN"), + doc_id=e.get("doc_id", ""), + page=e.get("page", 0)) + return G + + +def get_nodes(page: int = 1, page_size: int = 50, + node_type: str | None = None, + doc_id: str | None = None, + confidence: str | None = None) -> dict: + nodes = fs.load_kg_nodes() + G = _load_graph() + # Attach degree + degrees = dict(G.degree()) + for n in nodes: + n["degree"] = degrees.get(n["id"], 0) + + if node_type: + nodes = [n for n in nodes if n.get("type", "").upper() == node_type.upper()] + if doc_id: + nodes = [n for n in nodes if n.get("source_doc") == doc_id] + if confidence: + nodes = [n for n in nodes if n.get("confidence") == confidence] + + total = len(nodes) + start = (page - 1) * page_size + return {"total": total, "page": page, "page_size": page_size, + "items": nodes[start: start + page_size]} + + +def get_edges(page: int = 1, page_size: int = 100, + doc_id: str | None = None, + relation: str | None = None) -> dict: + edges = fs.load_kg_edges() + if doc_id: + edges = [e for e in edges if e.get("doc_id") == doc_id] + if relation: + edges = [e for e in edges if e.get("relation") == relation] + total = len(edges) + start = (page - 1) * page_size + return {"total": total, "page": page, "page_size": page_size, + "items": edges[start: start + page_size]} + + +def get_node_detail(node_id: str) -> dict | None: + nodes = fs.load_kg_nodes() + node = next((n for n in nodes if n["id"] == node_id), None) + if not node: + return None + G = _load_graph() + if node_id not in G: + node["degree"] = 0 + node["degree_centrality"] = 0.0 + node["neighbor_count"] = 0 + return node + deg = G.degree(node_id) + centrality = nx.degree_centrality(G) + node["degree"] = deg + node["degree_centrality"] = round(centrality.get(node_id, 0.0), 4) + node["neighbor_count"] = deg + return node + + +def get_neighbors(node_id: str, hops: int = 1) -> dict | None: + nodes = fs.load_kg_nodes() + node = next((n for n in nodes if n["id"] == node_id), None) + if not node: + return None + G = _load_graph() + if node_id not in G: + return { + "center": {"id": node_id, "name": node["name"], "type": node["type"], "page": node.get("page", 0)}, + "hops": hops, "neighbors_by_hop": {}, "total_neighbors": 0, + } + hops = max(1, min(hops, 3)) + reachable = nx.single_source_shortest_path_length(G, node_id, cutoff=hops) + by_hop: dict[str, list] = {} + for nid, dist in reachable.items(): + if dist == 0: + continue + nd = G.nodes[nid] + by_hop.setdefault(str(dist), []).append({ + "id": nid, "name": nd.get("name", ""), "type": nd.get("type", ""), "page": nd.get("page", 0) + }) + total = sum(len(v) for v in by_hop.values()) + return { + "center": {"id": node_id, "name": node["name"], "type": node["type"], "page": node.get("page", 0)}, + "hops": hops, + "neighbors_by_hop": by_hop, + "total_neighbors": total, + } + + +def get_stats() -> dict: + nodes = fs.load_kg_nodes() + edges = fs.load_kg_edges() + G = _load_graph() + + type_dist: dict[str, int] = {} + for n in nodes: + t = n.get("type", "UNKNOWN") + type_dist[t] = type_dist.get(t, 0) + 1 + + relation_types: dict[str, int] = {} + for e in edges: + r = e.get("relation", "CO_OCCURS_IN") + relation_types[r] = relation_types.get(r, 0) + 1 + + density = round(nx.density(G), 4) if G.number_of_nodes() > 1 else 0.0 + + top5: list[dict] = [] + if G.number_of_nodes() > 0: + centrality = nx.degree_centrality(G) + for nid, c in sorted(centrality.items(), key=lambda x: x[1], reverse=True)[:5]: + nd = G.nodes[nid] + top5.append({"node_id": nid, "name": nd.get("name", ""), "type": nd.get("type", ""), + "centrality": round(c, 4)}) + + source_docs = list({n.get("source_doc", "") for n in nodes if n.get("source_doc")}) + + return { + "total_nodes": len(nodes), + "total_edges": len(edges), + "density": density, + "type_distribution": type_dist, + "relation_types": relation_types, + "top5_central_nodes": top5, + "source_documents": source_docs, + } + + +def export_kg(doc_id: str | None = None) -> dict: + from datetime import datetime, timezone + nodes = fs.load_kg_nodes() + edges = fs.load_kg_edges() + G = _load_graph() + degrees = dict(G.degree()) + for n in nodes: + n["degree"] = degrees.get(n["id"], 0) + if doc_id: + nodes = [n for n in nodes if n.get("source_doc") == doc_id] + edges = [e for e in edges if e.get("doc_id") == doc_id] + return { + "format": "json", + "doc_id": doc_id, + "total_nodes": len(nodes), + "total_edges": len(edges), + "exported_at": datetime.now(timezone.utc).isoformat(), + "nodes": nodes, + "edges": edges, + } diff --git a/backend/services/qa_service.py b/backend/services/qa_service.py new file mode 100644 index 0000000..62e4fbb --- /dev/null +++ b/backend/services/qa_service.py @@ -0,0 +1,85 @@ +"""QA Service — Agentic-RAG wrapper.""" +from __future__ import annotations + +import time +import uuid +from datetime import datetime, timezone + +from storage import file_store as fs + + +def run_query(question: str, history: list[dict]) -> dict: + from pipeline.qa_agent import run_qa + + nodes = fs.load_kg_nodes() + edges = fs.load_kg_edges() + + if not nodes: + raise ValueError("KG_EMPTY") + + start = time.time() + result = run_qa(question, history, nodes, edges) + elapsed = round(time.time() - start, 2) + + query_id = f"q_{uuid.uuid4().hex[:10]}" + now = datetime.now(timezone.utc).isoformat() + + record = { + "id": query_id, + "question": question, + "answer": result["answer"], + "tool_calls": result["tool_calls"], + "cited_nodes": result["cited_nodes"], + "duration_seconds": elapsed, + "timestamp": now, + } + fs.append_query_history(record) + return record + + +def get_history(page: int = 1, page_size: int = 20) -> dict: + all_records = fs.load_query_history() + total = len(all_records) + start = (page - 1) * page_size + return { + "total": total, + "page": page, + "page_size": page_size, + "items": all_records[start: start + page_size], + } + + +def start_batch(questions: list[str]) -> dict: + import threading + + batch_id = f"batch_{uuid.uuid4().hex[:10]}" + now = datetime.now(timezone.utc).isoformat() + meta = { + "batch_id": batch_id, + "total": len(questions), + "completed": 0, + "failed": 0, + "status": "submitted", + "created_at": now, + "results": [], + } + fs.save_batch_meta(batch_id, meta) + + def _run(): + for q in questions: + try: + res = run_query(q, []) + meta["results"].append(res) + meta["completed"] += 1 + except Exception as e: + meta["failed"] += 1 + meta["results"].append({"question": q, "error": str(e)}) + meta["status"] = "done" + fs.save_batch_meta(batch_id, meta) + + threading.Thread(target=_run, daemon=True).start() + return {"batch_id": batch_id, "total": len(questions), "status": "submitted", "created_at": now} + + +def get_batch_result(batch_id: str) -> dict | None: + return fs.load_batch_meta(batch_id) diff --git a/backend/services/search_service.py b/backend/services/search_service.py new file mode 100644 index 0000000..8cdfc10 --- /dev/null +++ b/backend/services/search_service.py @@ -0,0 +1,106 @@ +"""Search Service — entity, path, and graph search.""" +from __future__ import annotations + +import networkx as nx + +from storage import file_store as fs + + +def _load_graph() -> nx.Graph: + nodes = fs.load_kg_nodes() + edges = fs.load_kg_edges() + G = nx.Graph() + for n in nodes: + G.add_node(n["id"], **n) + for e in edges: + G.add_edge(e["source"], e["target"], + relation=e.get("relation", "CO_OCCURS_IN"), + doc_id=e.get("doc_id", ""), page=e.get("page", 0)) + return G + + +def search_entities(q: str, entity_type: str | None = None, limit: int = 15) -> dict: + nodes = fs.load_kg_nodes() + G = _load_graph() + degrees = dict(G.degree()) + q_lower = q.lower() + matches = [n for n in nodes if q_lower in n.get("name", "").lower()] + if entity_type: + matches = [n for n in matches if n.get("type", "").upper() == entity_type.upper()] + for n in matches: + n["degree"] = degrees.get(n["id"], 0) + matches = matches[:limit] + return {"query": q, "total": len(matches), "items": matches} + + +def search_path(from_id: str, to_id: str, max_hops: int = 3) -> dict | None: + nodes = fs.load_kg_nodes() + node_map = {n["id"]: n for n in nodes} + if from_id not in node_map or to_id not in node_map: + return None # node not found + + G = _load_graph() + max_hops = max(1, min(max_hops, 5)) + + try: + raw_paths = list(nx.all_simple_paths(G, from_id, to_id, cutoff=max_hops)) + except nx.NetworkXError: + raw_paths = [] + + paths = [] + for path_nodes in raw_paths: + path_edges = [] + for i in range(len(path_nodes) - 1): + s, t = path_nodes[i], path_nodes[i + 1] + edge_data = G.edges[s, t] + path_edges.append({"source": s, "target": t, + "relation": edge_data.get("relation", "CO_OCCURS_IN")}) + paths.append({ + "length": len(path_nodes) - 1, + "nodes": [{"id": nid, "name": node_map.get(nid, {}).get("name", nid), + "type": node_map.get(nid, {}).get("type", "")} for nid in path_nodes], + "edges": path_edges, + }) + + from_node = node_map[from_id] + to_node = node_map[to_id] + return { + "from": {"id": from_id, "name": from_node.get("name", ""), "type": from_node.get("type", "")}, + "to": {"id": to_id, "name": to_node.get("name", ""), "type": to_node.get("type", "")}, + "max_hops": max_hops, + "paths": paths, + "total_paths": len(paths), + } + + +def search_graph(q: str, include_neighbors: bool = False) -> dict: + nodes = fs.load_kg_nodes() + edges = fs.load_kg_edges() + G = _load_graph() + degrees = dict(G.degree()) + q_lower = q.lower() + + matched = [n for n in nodes if q_lower in n.get("name", "").lower()] + matched_ids = {n["id"] for n in matched} + for n in matched: + n["degree"] = degrees.get(n["id"], 0) + + if include_neighbors: + neighbor_ids = set() + for nid in matched_ids: + if nid in G: + neighbor_ids.update(G.neighbors(nid)) + all_relevant = matched_ids | neighbor_ids + else: + all_relevant = matched_ids + + subgraph_edges = [ + e for e in edges + if e.get("source") in all_relevant and e.get("target") in all_relevant + ] + + return { + "query": q, + "matched_nodes": matched, + "subgraph_edges": subgraph_edges, + } diff --git a/backend/storage/__init__.py b/backend/storage/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/storage/file_store.py b/backend/storage/file_store.py new file mode 100644 index 0000000..6da85e9 --- /dev/null +++ b/backend/storage/file_store.py @@ -0,0 +1,268 @@ +""" +File Store — unified JSON read/write for all backend data. +All data lives under backend/data/. +""" +from __future__ import annotations + +import json +import os +import shutil +from pathlib import Path +from typing import Any + +# Root data directory relative to this file +_BASE = Path(__file__).parent.parent / "data" + +UPLOADS_DIR = _BASE / "uploads" +JOBS_DIR = _BASE / "jobs" +KG_DIR = _BASE / "kg" +QUERY_DIR = _BASE / "jobs" # query_history.jsonl lives here + +# Ensure directories exist at import time +for _d in (UPLOADS_DIR, JOBS_DIR, KG_DIR): + _d.mkdir(parents=True, exist_ok=True) + + +# --------------------------------------------------------------------------- +# Generic helpers +# --------------------------------------------------------------------------- + +def read_json(path: Path) -> Any: + """Read and parse a JSON file. Returns None if file doesn't exist.""" + if not path.exists(): + return None + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + + +def write_json(path: Path, data: Any) -> None: + """Atomically write data as JSON (write to .tmp then rename).""" + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(".tmp") + with open(tmp, "w", encoding="utf-8") as f: + json.dump(data, f, ensure_ascii=False, indent=2) + os.replace(tmp, path) + + +def append_jsonl(path: Path, record: dict) -> None: + """Append a record to a JSONL file.""" + path.parent.mkdir(parents=True, exist_ok=True) + with open(path, "a", encoding="utf-8") as f: + f.write(json.dumps(record, ensure_ascii=False) + "\n") + + +def read_jsonl(path: Path) -> list[dict]: + """Read all records from a JSONL file.""" + if not path.exists(): + return [] + records = [] + with open(path, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line: + try: + records.append(json.loads(line)) + except json.JSONDecodeError: + pass + return records + + +# --------------------------------------------------------------------------- +# Document helpers +# --------------------------------------------------------------------------- + +def docs_index_path() -> Path: + return _BASE / "docs_index.json" + + +def load_docs_index() -> dict[str, dict]: + """Load the documents index {doc_id: DocumentInfo dict}.""" + data = read_json(docs_index_path()) + return data if isinstance(data, dict) else {} + + +def save_docs_index(index: dict[str, dict]) -> None: + write_json(docs_index_path(), index) + + +def get_doc(doc_id: str) -> dict | None: + return load_docs_index().get(doc_id) + + +def save_doc(doc: dict) -> None: + index = load_docs_index() + index[doc["doc_id"]] = doc + save_docs_index(index) + + +def delete_doc(doc_id: str) -> bool: + index = load_docs_index() + if doc_id not in index: + return False + del index[doc_id] + save_docs_index(index) + # Remove upload file + doc_info = index.get(doc_id, {}) + upload_path = UPLOADS_DIR / doc_info.get("upload_filename", "") + if upload_path.exists(): + upload_path.unlink() + return True + + +# --------------------------------------------------------------------------- +# Job helpers +# --------------------------------------------------------------------------- + +def job_dir(job_id: str) -> Path: + return JOBS_DIR / job_id + + +def job_meta_path(job_id: str) -> Path: + return job_dir(job_id) / "meta.json" + + +def load_job_meta(job_id: str) -> dict | None: + return read_json(job_meta_path(job_id)) + + +def save_job_meta(job_id: str, meta: dict) -> None: + job_dir(job_id).mkdir(parents=True, exist_ok=True) + write_json(job_meta_path(job_id), meta) + + +def list_all_jobs() -> list[dict]: + metas = [] + for d in JOBS_DIR.iterdir(): + if d.is_dir(): + meta = read_json(d / "meta.json") + if meta: + metas.append(meta) + return metas + + +def delete_job(job_id: str) -> None: + jd = job_dir(job_id) + if jd.exists(): + shutil.rmtree(jd) + + +# --------------------------------------------------------------------------- +# Global KG helpers +# --------------------------------------------------------------------------- + +def kg_nodes_path() -> Path: + return KG_DIR / "kg_nodes.json" + + +def kg_edges_path() -> Path: + return KG_DIR / "kg_edges.json" + + +def load_kg_nodes() -> list[dict]: + data = read_json(kg_nodes_path()) + return data if isinstance(data, list) else [] + + +def load_kg_edges() -> list[dict]: + data = read_json(kg_edges_path()) + return data if isinstance(data, list) else [] + + +def save_kg_nodes(nodes: list[dict]) -> None: + write_json(kg_nodes_path(), nodes) + + +def save_kg_edges(edges: list[dict]) -> None: + write_json(kg_edges_path(), edges) + + +def merge_kg(new_nodes: list[dict], new_edges: list[dict], doc_id: str) -> tuple[int, int]: + """Merge job KG output into global KG. Returns (removed_old, added_new).""" + existing_nodes = load_kg_nodes() + existing_edges = load_kg_edges() + + # Remove nodes/edges from this doc + existing_nodes = [n for n in existing_nodes if n.get("source_doc") != doc_id] + existing_edges = [e for e in existing_edges if e.get("doc_id") != doc_id] + + # Merge: deduplicate nodes by (name.lower(), type) + node_keys: set[tuple] = {(n["name"].lower(), n["type"]) for n in existing_nodes} + for n in new_nodes: + key = (n["name"].lower(), n["type"]) + if key not in node_keys: + existing_nodes.append(n) + node_keys.add(key) + + # Merge edges: deduplicate by (min(src,tgt), max(src,tgt), doc_id) + edge_keys: set[tuple] = set() + for e in existing_edges: + s, t = e["source"], e["target"] + edge_keys.add((min(s, t), max(s, t), e["doc_id"])) + + for e in new_edges: + s, t = e["source"], e["target"] + key = (min(s, t), max(s, t), e["doc_id"]) + if key not in edge_keys: + existing_edges.append(e) + edge_keys.add(key) + + save_kg_nodes(existing_nodes) + save_kg_edges(existing_edges) + return len(existing_nodes), len(existing_edges) + + +def remove_doc_from_kg(doc_id: str) -> tuple[int, int]: + """Remove all nodes/edges from a document. Returns (removed_nodes, removed_edges).""" + nodes = load_kg_nodes() + edges = load_kg_edges() + old_n, old_e = len(nodes), len(edges) + nodes = [n for n in nodes if n.get("source_doc") != doc_id] + edges = [e for e in edges if e.get("doc_id") != doc_id] + save_kg_nodes(nodes) + save_kg_edges(edges) + return old_n - len(nodes), old_e - len(edges) + + +# --------------------------------------------------------------------------- +# Query history helpers +# --------------------------------------------------------------------------- + +def query_history_path() -> Path: + return _BASE / "query_history.jsonl" + + +def append_query_history(result: dict) -> None: + append_jsonl(query_history_path(), result) + + +def load_query_history() -> list[dict]: + records = read_jsonl(query_history_path()) + return list(reversed(records)) # newest first + + +# --------------------------------------------------------------------------- +# Batch job helpers +# --------------------------------------------------------------------------- + +def batch_meta_path(batch_id: str) -> Path: + return _BASE / "batches" / f"{batch_id}.json" + + +def load_batch_meta(batch_id: str) -> dict | None: + return read_json(batch_meta_path(batch_id)) + + +def save_batch_meta(batch_id: str, meta: dict) -> None: + write_json(batch_meta_path(batch_id), meta) + + +# --------------------------------------------------------------------------- +# Storage usage +# --------------------------------------------------------------------------- + +def storage_used_mb() -> float: + total = 0 + for path in _BASE.rglob("*"): + if path.is_file(): + total += path.stat().st_size + return round(total / (1024 * 1024), 2) diff --git a/backend/tests/test_api.py b/backend/tests/test_api.py new file mode 100644 index 0000000..527f280 --- /dev/null +++ b/backend/tests/test_api.py @@ -0,0 +1,256 @@ +""" +API integration tests — tests all major endpoints against a running server. +Run with: python tests/test_api.py +Server must be running on http://localhost:8000 +""" +import json +import sys +import time +import urllib.request +import urllib.error +from pathlib import Path + +BASE = "http://localhost:8000/api/v1" + +PASS = "\033[92m[PASS]\033[0m" +FAIL = "\033[91m[FAIL]\033[0m" +INFO = "\033[94m[INFO]\033[0m" + +results = {"passed": 0, "failed": 0} + + +def req(method: str, path: str, body: dict | None = None, form: dict | None = None) -> dict: + url = BASE + path + try: + if method == "GET" and not body and not form: + r = urllib.request.urlopen(url, timeout=30) + else: + if body is not None: + data = json.dumps(body).encode() + req_obj = urllib.request.Request(url, data=data, method=method, + headers={"Content-Type": "application/json"}) + else: + req_obj = urllib.request.Request(url, method=method) + r = urllib.request.urlopen(req_obj, timeout=30) + return json.loads(r.read().decode()) + except urllib.error.HTTPError as e: + return json.loads(e.read().decode()) + + +def check(name: str, condition: bool, detail: str = "") -> None: + if condition: + results["passed"] += 1 + print(f" {PASS} {name}") + else: + results["failed"] += 1 + print(f" {FAIL} {name} {detail}") + + +def wait_for_server(max_retries: int = 15) -> bool: + print(f"{INFO} Waiting for server at {BASE}...") + for i in range(max_retries): + try: + urllib.request.urlopen(BASE.replace("/api/v1", "/"), timeout=3) + print(f"{INFO} Server is up.") + return True + except Exception: + time.sleep(1) + return False + + +# ───────────────────────────────────────────────────────────────────────────── +# Test groups +# ───────────────────────────────────────────────────────────────────────────── + +def test_system(): + print("\n── F 组: System ──") + + r = req("GET", "/health") + check("GET /health returns code=0", r.get("code") == 0) + check("health data.status exists", "status" in (r.get("data") or {})) + check("health data.components exists", "components" in (r.get("data") or {})) + print(f" {INFO} status={r.get('data',{}).get('status')} uptime={r.get('data',{}).get('uptime_seconds')}s") + + r = req("GET", "/system/stats") + check("GET /system/stats returns code=0", r.get("code") == 0) + d = r.get("data") or {} + check("stats has total_documents", "total_documents" in d) + check("stats has total_nodes", "total_nodes" in d) + print(f" {INFO} docs={d.get('total_documents')} nodes={d.get('total_nodes')} edges={d.get('total_edges')}") + + r = req("GET", "/system/formats") + check("GET /system/formats returns code=0", r.get("code") == 0) + d = r.get("data") or {} + check("formats list is non-empty", len(d.get("formats", [])) > 0) + exts = [f["ext"] for f in d.get("formats", [])] + check("pdf format present", "pdf" in exts) + check("docx format present", "docx" in exts) + + r = req("GET", "/system/demo") + check("GET /system/demo returns code=0 or 3002", r.get("code") in (0, 3002)) + if r.get("code") == 0: + d = r.get("data") or {} + check("demo data has nodes", "nodes" in d) + print(f" {INFO} demo: {len(d.get('nodes',[]))} nodes, {len(d.get('edges',[]))} edges") + else: + print(f" {INFO} demo data not available (no KG yet) — code={r.get('code')}") + + +def test_documents(): + print("\n── A 组: Documents ──") + + r = req("GET", "/documents") + check("GET /documents returns code=0", r.get("code") == 0) + d = r.get("data") or {} + check("documents list has total field", "total" in d) + check("documents list has items field", "items" in d) + print(f" {INFO} total documents={d.get('total', 0)}") + + # Upload a test text file (not a real supported format to test validation) + print(" Testing upload validation...") + import urllib.request, io + boundary = "boundary123" + body_parts = ( + f"--{boundary}\r\n" + f'Content-Disposition: form-data; name="file"; filename="test.xyz"\r\n' + f"Content-Type: application/octet-stream\r\n\r\n" + f"dummy content\r\n" + f"--{boundary}--\r\n" + ).encode() + req_obj = urllib.request.Request( + BASE + "/documents/upload", + data=body_parts, + method="POST", + headers={"Content-Type": f"multipart/form-data; boundary={boundary}"}, + ) + try: + urllib.request.urlopen(req_obj, timeout=10) + r_upload = {} + except urllib.error.HTTPError as e: + r_upload = json.loads(e.read().decode()) + check("upload unsupported format returns code=1002", r_upload.get("code") == 1002) + + r = req("GET", "/documents/nonexistent_id") + check("GET /documents/nonexistent returns code=2001", r.get("code") == 2001) + + +def test_indexing(): + print("\n── B 组: Indexing ──") + + r = req("POST", "/index/start", body={"doc_id": "nonexistent_doc"}) + check("start indexing nonexistent doc returns 2001", r.get("code") == 2001) + + r = req("GET", "/index/status/nonexistent_job") + check("get status nonexistent job returns 2002", r.get("code") == 2002) + + r = req("GET", "/index/result/nonexistent_job") + check("get result nonexistent job returns 2002", r.get("code") == 2002) + + r = req("DELETE", "/index/jobs/nonexistent_job") + check("cancel nonexistent job returns 2002", r.get("code") == 2002) + + +def test_kg(): + print("\n── C 组: Knowledge Graph ──") + + r = req("GET", "/kg/stats") + check("GET /kg/stats returns code=0", r.get("code") == 0) + d = r.get("data") or {} + check("stats has total_nodes", "total_nodes" in d) + check("stats has total_edges", "total_edges" in d) + print(f" {INFO} KG: {d.get('total_nodes')} nodes, {d.get('total_edges')} edges") + + r = req("GET", "/kg/nodes") + check("GET /kg/nodes returns code 0 or 3002", r.get("code") in (0, 3002)) + if r.get("code") == 0: + d = r.get("data") or {} + check("nodes data has items", "items" in d) + print(f" {INFO} nodes total={d.get('total')}") + + if d.get("items"): + node_id = d["items"][0]["id"] + r2 = req("GET", f"/kg/nodes/{node_id}") + check(f"GET /kg/nodes/{node_id} returns code=0", r2.get("code") == 0) + + r3 = req("GET", f"/kg/nodes/{node_id}/neighbors?hops=1") + check(f"GET /kg/nodes/{node_id}/neighbors returns code=0", r3.get("code") == 0) + else: + print(f" {INFO} KG is empty (code=3002) — skipping node detail tests") + + r = req("GET", "/kg/nodes/definitely_not_a_real_node") + check("GET /kg/nodes/invalid returns code=3001", r.get("code") == 3001) + + r = req("GET", "/kg/edges") + check("GET /kg/edges returns code=0", r.get("code") == 0) + + r = req("GET", "/kg/export") + check("GET /kg/export returns code=0", r.get("code") == 0) + + +def test_search(): + print("\n── E 组: Search ──") + + r = req("GET", "/search/entities?q=graph") + check("GET /search/entities returns code=0", r.get("code") == 0) + d = r.get("data") or {} + check("search entities has query field", "query" in d) + check("search entities has items field", "items" in d) + print(f" {INFO} 'graph' search: {d.get('total', 0)} results") + + r = req("GET", "/search/entities?q=technology&type=TECHNOLOGY") + check("GET /search/entities with type filter returns code=0", r.get("code") == 0) + + r = req("GET", "/search/path?max_hops=2") + check("path search without from/to returns 1001", r.get("code") == 1001) + + r = req("GET", "/search/graph?q=knowledge") + check("GET /search/graph returns code=0", r.get("code") == 0) + d = r.get("data") or {} + check("graph search has matched_nodes", "matched_nodes" in d) + + +def test_query(): + print("\n── D 组: QA Query ──") + + # Don't call /query (POST) in basic tests as it needs DeepSeek API + KG data + r = req("GET", "/query/history") + check("GET /query/history returns code=0", r.get("code") == 0) + d = r.get("data") or {} + check("history has total field", "total" in d) + check("history has items field", "items" in d) + print(f" {INFO} query history: {d.get('total', 0)} records") + + r = req("GET", "/query/batch/nonexistent_batch") + check("GET /query/batch/nonexistent returns 2002", r.get("code") == 2002) + + r = req("POST", "/query/batch", body={"questions": ["test question"]}) + check("POST /query/batch returns code=0", r.get("code") == 0) + d = r.get("data") or {} + check("batch has batch_id", "batch_id" in d) + + +# ───────────────────────────────────────────────────────────────────────────── +# Main +# ───────────────────────────────────────────────────────────────────────────── + +if __name__ == "__main__": + if not wait_for_server(): + print(f"\n{FAIL} Server not responding. Start with: python main.py") + sys.exit(1) + + test_system() + test_documents() + test_indexing() + test_kg() + test_search() + test_query() + + total = results["passed"] + results["failed"] + print(f"\n{'='*50}") + print(f"Results: {results['passed']}/{total} passed, {results['failed']} failed") + if results["failed"] == 0: + print(f"{PASS} All tests passed!") + else: + print(f"{FAIL} {results['failed']} test(s) failed") + print(f"{'='*50}") + sys.exit(0 if results["failed"] == 0 else 1) diff --git a/docs/agentic_rag_specification-v1.0.md b/docs/agentic_rag_specification-v1.0.md new file mode 100644 index 0000000..22f804d --- /dev/null +++ b/docs/agentic_rag_specification-v1.0.md @@ -0,0 +1,779 @@ +# Agentic-RAG 规范文档 v1.0 + +> GraphRAG 问答阶段核心流程：Knowledge Graph → LangChain Agent → QA +> +> 数据来源：Bridge Pipeline 输出（`kg_nodes.json` + `kg_edges.json`） +> 测试验证日期：2026-03-05 +> 全流程运行耗时：~40s（4 个测试查询） + +--- + +## 目录 + +- [一、完整执行思路与脚本位置](#一完整执行思路与脚本位置) +- [二、LangChain Agent 输入输出规范](#二langchain-agent-输入输出规范) +- [三、MinerU ↔ Agentic-RAG 对接规范与核心架构](#三mineru--agentic-rag-对接规范与核心架构) +- [四、问答流程最终数据返回格式规范](#四问答流程最终数据返回格式规范) +- [五、虚拟环境与依赖](#五虚拟环境与依赖) + +--- + +## 一、完整执行思路与脚本位置 + +### 1.1 总体架构定位 + +Agentic-RAG 是 GraphRAG 系统的**问答阶段**，位于 Bridge Pipeline 之后，负责将知识图谱转化为可交互的智能问答能力。 + +``` +【已完成阶段】 【本阶段：Agentic-RAG】 +──────────────────── ────────────────────────── +PDF + ↓ MinerU Cloud API +content_list.json + ↓ Bridge Pipeline +kg_nodes.json (40 nodes) ──────────→ NetworkX Graph (内存) +kg_edges.json (780 edges) ↓ + 4 个 LangChain @tool + ↓ + LangChain v1 create_agent + (DeepSeek deepseek-chat) + ↓ + ReAct 推理循环 + ↓ + 自然语言答案 +``` + +### 1.2 五步执行流程 + +| 步骤 | 模块 | 说明 | +|------|------|------| +| Step 0 | 环境 + 配置 | 加载 `.env`（DEEPSEEK_API_KEY），初始化 `ChatOpenAI` | +| Step 1 | KG 加载 | 读取 `kg_nodes.json` + `kg_edges.json`，构建 NetworkX 无向图 | +| Step 2 | Tool 注册 | 用 `@tool` 装饰器注册 4 个 KG 检索工具 | +| Step 3 | Agent 构建 | `create_agent(model, tools, system_prompt)` 编译 LangGraph | +| Step 4 | 问答调用 | `agent.invoke({"messages": [("human", question)]})` | +| Step 5 | 结果提取 | `result["messages"][-1].content` 获取最终答案 | + +### 1.3 测试脚本存放位置 + +``` +F:\GraphRAGAgent\graphrag_pipeline\ +├── agentic_rag_mvp.py ← 主测试脚本（本规范对应文件） +├── .env ← DEEPSEEK_API_KEY 配置 +└── output/ + ├── kg_nodes.json ← Bridge Pipeline 生成（40 节点） + └── kg_edges.json ← Bridge Pipeline 生成（780 边） +``` + +### 1.4 运行命令 + +```bash +# MVP 连通性测试（4 个预设测试查询） +F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe \ + F:/GraphRAGAgent/graphrag_pipeline/agentic_rag_mvp.py +``` + +### 1.5 ReAct 推理循环详解 + +Agent 使用 **ReAct（Reasoning + Acting）** 模式，每个问题的处理流如下： + +``` +用户输入 (question: str) + │ + ▼ +┌─────────────────────────────────────────────────┐ +│ LLM Reasoning（DeepSeek deepseek-chat） │ +│ 决策：需要调用哪个工具？参数是什么？ │ +└─────────────────────────────────────────────────┘ + │ tool_call + ▼ +┌─────────────────────────────────────────────────┐ +│ Tool Execution（NetworkX 本地计算，无 API 调用） │ +│ search_entities / get_neighbors / │ +│ get_entities_by_type / describe_graph │ +└─────────────────────────────────────────────────┘ + │ ToolMessage（工具返回的文本结果） + ▼ +┌─────────────────────────────────────────────────┐ +│ LLM Observation（观察工具结果） │ +│ 决策：结果够用了吗？还需要调更多工具？ │ +└─────────────────────────────────────────────────┘ + │ 继续 tool_call 或输出最终答案 + ▼ +AIMessage（最终自然语言答案） +``` + +**实测工具调用模式（4 个测试查询）：** + +| 查询类型 | 工具调用序列 | 特点 | +|---------|------------|------| +| 图谱整体概览 | `describe_graph` | 单次工具调用 | +| 类型枚举 | `get_entities_by_type` | 单次工具调用 | +| 多跳关系推理 | `search_entities` → `get_neighbors` | 两步串行调用 | +| 概念精确查找 | `search_entities` → `get_neighbors` | 两步串行调用 | + +--- + +## 二、LangChain Agent 输入输出规范 + +### 2.1 LLM 适配规范 + +#### 2.1.1 DeepSeek → LangChain 标准组件 + +LangChain v1 使用 `ChatOpenAI` 通过 `base_url` 覆盖接入任何 OpenAI 兼容 API： + +```python +from langchain_openai import ChatOpenAI + +llm = ChatOpenAI( + model="deepseek-chat", # DeepSeek 模型名 + api_key=DEEPSEEK_API_KEY, # 来自 graphrag_pipeline/.env + base_url="https://api.deepseek.com", # OpenAI 兼容端点 + temperature=0, # 问答场景确定性输出 +) +``` + +| 参数 | 值 | 说明 | +|------|-----|------| +| `model` | `"deepseek-chat"` | DeepSeek 实际模型标识 | +| `api_key` | `${DEEPSEEK_API_KEY}` | 从 `.env` 读取，与 Bridge Pipeline 共用 | +| `base_url` | `"https://api.deepseek.com"` | SDK 自动补全 `/v1` 路径 | +| `temperature` | `0` | 问答场景设为 0，保证可重现性 | + +#### 2.1.2 与 LangExtract 中 DeepSeek 的区别 + +| 对比项 | LangExtract 中的 DeepSeek | Agentic-RAG 中的 DeepSeek | +|--------|--------------------------|--------------------------| +| 接入方式 | 直接实例化 `OpenAILanguageModel` | LangChain `ChatOpenAI` 标准组件 | +| API Key 环境变量 | `OPENAI_API_KEY` | `DEEPSEEK_API_KEY` | +| 调用方式 | `lx.extract(model=model)` | `agent.invoke({"messages": ...})` | +| 输出格式 | JSON（实体抽取） | 自然语言（问答） | +| Tool Calling | 不支持（单轮推理） | 支持（ReAct 多轮） | + +### 2.2 Agent 构建规范 + +#### 2.2.1 LangChain v1 create_agent + +```python +from langchain.agents import create_agent + +agent = create_agent( + model=llm, # ChatOpenAI 实例 + tools=_tools, # List[BaseTool]，4 个工具 + system_prompt=SYSTEM_PROMPT, # 系统提示词字符串 +) +``` + +**版本注意事项：** + +| API | 状态 | 说明 | +|-----|------|------| +| `langchain.agents.create_agent` | ✅ LangChain v1 推荐 | 本项目使用 | +| `langgraph.prebuilt.create_react_agent` | ⚠️ Deprecated in LangGraph V1.0 | 已废弃，勿用 | +| `langchain.agents.create_react_agent` (旧版) | ❌ Legacy | 已移除 | + +#### 2.2.2 System Prompt 规范 + +``` +You are a Knowledge Graph QA assistant. You have access to a knowledge graph +extracted from academic documents about GraphRAG and related technologies. + +The graph contains: +- {node_count} deduplicated entities ({type_list} types) +- {edge_count} CO_OCCURS_IN edges representing same-page co-occurrence + +Available tools: +1. search_entities — find entities by keyword substring +2. get_neighbors — explore entity relationships (N-hop BFS) +3. get_entities_by_type — list all entities of a type +4. describe_graph — get graph statistics overview + +Reasoning strategy: +- Always use at least one tool before answering a factual question +- For relationship questions, use get_neighbors after identifying the entity with search_entities +- For enumeration questions, use get_entities_by_type +- Synthesize tool results into a clear, concise answer +- Cite the entity names and types in your final answer +``` + +### 2.3 Agent 输入规范 + +#### 2.3.1 invoke 输入格式 + +```python +result = agent.invoke({ + "messages": [ + ("human", question) # 用户问题（自然语言字符串） + ] +}) +``` + +**输入字段规范：** + +| 字段 | 类型 | 说明 | +|------|------|------| +| `messages` | `list[tuple[str, str]]` | 消息列表，格式 `(role, content)` | +| `role` | `"human"` \| `"ai"` \| `"system"` | 消息角色 | +| `content` | `str` | 消息内容 | + +**多轮对话输入（支持历史上下文）：** + +```python +result = agent.invoke({ + "messages": [ + ("human", "What is GraphRAG?"), + ("ai", "GraphRAG is a knowledge graph-enhanced RAG system..."), + ("human", "How does it relate to LLMs?"), # 当前问题 + ] +}) +``` + +### 2.4 Agent 输出规范 + +#### 2.4.1 invoke 原始返回 + +```python +{ + "messages": [ + HumanMessage(content="What is GraphRAG?"), + AIMessage(content="", tool_calls=[...]), # 工具调用 + ToolMessage(content="...", tool_call_id="..."), # 工具结果 + AIMessage(content="GraphRAG is an advanced...") # 最终答案 + ] +} +``` + +#### 2.4.2 消息类型枚举 + +| 消息类型 | 角色 | 说明 | +|---------|------|------| +| `HumanMessage` | `human` | 用户输入 | +| `AIMessage`（tool_calls 非空） | `ai` | LLM 决策发起工具调用 | +| `ToolMessage` | `tool` | 工具执行结果 | +| `AIMessage`（tool_calls 为空） | `ai` | 最终自然语言答案 | + +#### 2.4.3 最终答案提取 + +```python +final_msg = result["messages"][-1] +answer = final_msg.content # str，最终自然语言答案 +``` + +### 2.5 四个工具输入输出规范 + +#### Tool 1: `search_entities` + +| 项目 | 规范 | +|------|------| +| 入参 | `query: str` — 关键词（大小写不敏感子串匹配） | +| 匹配逻辑 | `query.lower() in entity_name.lower()` | +| 返回格式 | 多行文本，每行格式：`[{type}] "{name}" (confidence={c}, page={p}, id={id})` | +| 无匹配时 | 返回提示 + 前 8 个样例实体名 | +| 最多返回 | 15 条 | + +**实际调用示例：** + +``` +输入: query="GraphRAG" +输出: +Found 3 entity(ies) matching 'GraphRAG': + [TECHNOLOGY] "GraphRAG" (confidence=match_exact, page=0, id=node_0) + [CONCEPT] "GraphRAG pipeline" (confidence=match_exact, page=0, id=node_12) + [CONCEPT] "GraphRAG (Global)" (confidence=match_exact, page=0, id=node_15) +``` + +#### Tool 2: `get_neighbors` + +| 项目 | 规范 | +|------|------| +| 入参 | `entity_name: str`，`hops: int = 1`（范围 1-3） | +| 匹配逻辑 | 子串匹配找起始节点，取 `candidates[0]` | +| 遍历算法 | `nx.single_source_shortest_path_length(G, node_id, cutoff=hops)` | +| 返回格式 | 按 hop 分组，每组 `[{type}] {name}`，每组最多 20 条 | +| 未找到时 | 返回提示，建议先用 `search_entities` | + +**实际调用示例：** + +``` +输入: entity_name="GraphRAG", hops=1 +输出: +Neighbors of 'GraphRAG' [TECHNOLOGY] within 1 hop(s): + + Hop 1 — 39 related entities: + [CONCEPT] Knowledge Graph Enhanced RAG System + [CONCEPT] retrieval-augmented generation + ... + Total related entities: 39 +``` + +#### Tool 3: `get_entities_by_type` + +| 项目 | 规范 | +|------|------| +| 入参 | `entity_type: str`（自动 `.upper()` 处理） | +| 有效类型 | `TECHNOLOGY`, `CONCEPT`, `PERSON`, `ORGANIZATION`, `LOCATION` | +| 返回格式 | 按 `name` 字母序排列，每行 `• {name} (confidence={c}, page={p})` | +| 无效类型时 | 返回错误 + 图谱中实际存在的类型列表 | + +**实际调用示例：** + +``` +输入: entity_type="TECHNOLOGY" +输出: +TECHNOLOGY entities (4 total): + • GraphRAG (confidence=match_exact, page=0) + • LLMs (confidence=match_exact, page=0) + • LangExtract (confidence=match_exact, page=0) + • MinerU (confidence=match_exact, page=0) +``` + +#### Tool 4: `describe_graph` + +| 项目 | 规范 | +|------|------| +| 入参 | 无参数 | +| 计算指标 | 节点数、边数、关系类型、图密度（`nx.density`）、度中心性（`nx.degree_centrality`） | +| 返回格式 | 结构化文本，包含概览 + 类型分布 + Top-5 中心节点 | + +**实际调用示例（实测输出）：** + +``` +=== Knowledge Graph Overview === + Nodes (entities): 40 + Edges (relations): 780 + Relation type: CO_OCCURS_IN (same-page co-occurrence) + Graph density: 1.0000 + + Entity type distribution: + CONCEPT : 36 + TECHNOLOGY : 4 + + Top-5 most connected entities (by degree centrality): + [TECHNOLOGY] GraphRAG (centrality=1.000) + [CONCEPT] Knowledge Graph Enhanced RAG System (centrality=1.000) + [CONCEPT] retrieval-augmented generation (centrality=1.000) + [CONCEPT] knowledge graphs (centrality=1.000) + [CONCEPT] large language models (centrality=1.000) +``` + +--- + +## 三、MinerU ↔ Agentic-RAG 对接规范与核心架构 + +### 3.1 全链路技术架构 + +``` +┌─────────────────────────────────────────────────────────────────────┐ +│ 阶段一：文档解析（MinerU Cloud API） │ +│ │ +│ PDF 文件 │ +│ │ POST /file-urls/batch (enable_table=True, language="en") │ +│ ├─ PUT {presigned_url}（裸上传，不带 Content-Type） │ +│ └─ GET /extract-results/batch/{batch_id}（轮询 done） │ +│ ↓ │ +│ full_zip_url → 解压 → {uuid}_content_list.json │ +│ │ +│ 关键输出字段：type, text, text_level, table_body, page_idx, bbox │ +└─────────────────────────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────────────────────────┐ +│ 阶段二：知识图谱构建（Bridge Pipeline） │ +│ │ +│ content_list.json │ +│ │ text_assembler.py │ +│ ├─ text blocks → .rstrip() 拼接 │ +│ ├─ table blocks → BeautifulSoup HTML → pipe 分隔文本 │ +│ └─ PageText(page_idx, text, block_spans) │ +│ ↓ │ +│ entity_extractor.py (LangExtract + DeepSeek) │ +│ ↓ │ +│ kg_builder.py (去重 + CO_OCCURS_IN 边) │ +│ ↓ │ +│ kg_nodes.json (40 nodes) + kg_edges.json (780 edges) │ +└─────────────────────────────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────────────────────────────┐ +│ 阶段三：Agentic-RAG 问答（LangChain + LangGraph） │ +│ │ +│ kg_nodes.json → NetworkX.G.add_node(**node) │ +│ kg_edges.json → NetworkX.G.add_edge(source, target, **edge) │ +│ │ +│ @tool search_entities ← 子串匹配 │ +│ @tool get_neighbors ← BFS N-hop 遍历 │ +│ @tool get_entities_by_type ← 类型过滤 │ +│ @tool describe_graph ← 图统计 │ +│ ↓ │ +│ create_agent(ChatOpenAI("deepseek-chat"), tools, system_prompt) │ +│ ↓ │ +│ ReAct 推理循环（think → tool_call → observe → repeat） │ +│ ↓ │ +│ 自然语言答案（AIMessage.content） │ +└─────────────────────────────────────────────────────────────────────┘ +``` + +### 3.2 MinerU → KG 关键参数对接 + +| MinerU 输出字段 | Bridge Pipeline 处理 | Agentic-RAG 使用 | +|---------------|-------------------|----------------| +| `block["type"]` | 区分 `text`/`table`/`image` | 不直接使用（已由 Bridge 转换） | +| `block["text"]` | `.rstrip()` 后加入 PageText | 已内化为 `node["name"]` | +| `block["table_body"]` | BeautifulSoup → pipe 分隔文本 | 已内化为实体描述 | +| `block["page_idx"]` | 分组依据，记入 BlockSpan | `node["page"]` 字段 | +| `block["bbox"]` | 记录字符偏移位置 | `node["char_start"]` / `node["char_end"]` | +| `{uuid}_content_list.json 文件名` | UUID 作为 `source_doc_id` | `node["source_doc"]` / `edge["doc_id"]` | + +### 3.3 NetworkX 图构建规范 + +```python +import networkx as nx + +G = nx.Graph() # 无向图（CO_OCCURS_IN 关系无方向） + +# 节点：来自 kg_nodes.json +for node in kg_nodes: + G.add_node( + node["id"], # 主键：node_0, node_1, ... + **node # 所有字段作为节点属性 + ) + +# 边：来自 kg_edges.json +for edge in kg_edges: + G.add_edge( + edge["source"], # node_0 + edge["target"], # node_1 + relation=edge["relation"], # "CO_OCCURS_IN" + doc_id=edge["doc_id"], # UUID + page=edge["page"], # 0-indexed + ) +``` + +**图属性：** + +| 属性 | 实测值 | 说明 | +|------|--------|------| +| `G.number_of_nodes()` | `40` | 去重实体数 | +| `G.number_of_edges()` | `780` | CO_OCCURS_IN 边数 | +| `nx.density(G)` | `1.0` | 完全图（单页文档所有节点两两连接） | +| `G.nodes[nid]` | `dict` | 节点属性字典（id, name, type, page, confidence, ...） | + +### 3.4 MinerU API 关键参数（与 Agentic-RAG 相关部分） + +| 参数 | 推荐值 | 影响 Agentic-RAG 的原因 | +|------|--------|----------------------| +| `enable_table` | `True` | 表格被解析为 HTML ``，Bridge 转为文本参与实体抽取，影响 KG 节点质量 | +| `enable_formula` | `True`（默认） | 公式以 LaTeX 内联写入文本，影响文本纯净度，可能产生噪声实体 | +| `language` | `"en"` / `"ch"` | 影响 OCR 精度，直接影响文本质量和实体对齐率 | +| `model_version` | `"pipeline"` | 输出 `{uuid}_content_list.json`，Bridge 通过 glob `*_content_list.json` 匹配 | +| `page_ranges` | 按需设置 | 多页文档可分批处理，减少每批实体数和边数规模 | + +### 3.5 Agent 系统扩展点 + +当 KG 数据更新后（新文档接入），Agentic-RAG 只需**重新加载 JSON 文件**，不需要重新构建 agent： + +```python +# 动态重载 KG（新文档处理完成后） +G.clear() +G = _load_kg() # 重新读取 kg_nodes.json + kg_edges.json +# agent 实例无需重建，tools 引用同一 G 对象 +``` + +--- + +## 四、问答流程最终数据返回格式规范 + +### 4.1 invoke 完整返回结构 + +```python +result = agent.invoke({"messages": [("human", question)]}) +# result 类型: dict +# result.keys(): ["messages"] +``` + +`result["messages"]` 是一个有序列表，包含完整的对话历史： + +```python +[ + HumanMessage, # 用户输入 + AIMessage, # 工具调用决策（可能多轮） + ToolMessage, # 工具执行结果（可能多轮） + ... # 可能有多轮 AIMessage + ToolMessage + AIMessage, # 最终答案（tool_calls=[]） +] +``` + +### 4.2 HumanMessage 格式 + +```python +HumanMessage( + content="What technology entities are in the knowledge graph?", + additional_kwargs={}, + response_metadata={}, + id="uuid-string", # 自动生成 +) +``` + +### 4.3 AIMessage（工具调用）格式 + +```python +AIMessage( + content="", # 内容为空（LLM 决策调用工具） + additional_kwargs={ + "tool_calls": [ + { + "id": "call_abc123", + "type": "function", + "function": { + "name": "get_entities_by_type", + "arguments": "{\"entity_type\": \"TECHNOLOGY\"}" + } + } + ] + }, + tool_calls=[ + { + "name": "get_entities_by_type", + "args": {"entity_type": "TECHNOLOGY"}, + "id": "call_abc123", + "type": "tool_call", + } + ], + response_metadata={ + "model_name": "deepseek-chat", + "finish_reason": "tool_calls", + "usage": { + "prompt_tokens": 580, + "completion_tokens": 18, + "total_tokens": 598, + } + }, +) +``` + +### 4.4 ToolMessage 格式 + +```python +ToolMessage( + content="TECHNOLOGY entities (4 total):\n • GraphRAG ...\n • LLMs ...", + tool_call_id="call_abc123", # 与 AIMessage.tool_calls[i].id 对应 + name="get_entities_by_type", # 工具名称 + additional_kwargs={}, + response_metadata={}, +) +``` + +### 4.5 AIMessage（最终答案）格式 + +```python +AIMessage( + content="## Technology Entities in the Knowledge Graph\n\n1. **GraphRAG** ...", + additional_kwargs={ + "tool_calls": [] # 空列表，表示无更多工具调用 + }, + tool_calls=[], + response_metadata={ + "model_name": "deepseek-chat", + "finish_reason": "stop", + "usage": { + "prompt_tokens": 820, + "completion_tokens": 350, + "total_tokens": 1170, + } + }, + id="msg-uuid-string", +) +``` + +### 4.6 最终答案提取规范 + +```python +# 标准提取方式 +final_msg = result["messages"][-1] # 最后一条消息必为最终 AIMessage +answer: str = final_msg.content # 自然语言答案 + +# 安全提取方式（防御性编程） +answer = ( + final_msg.content + if hasattr(final_msg, "content") + else str(final_msg) +) +``` + +### 4.7 推荐封装数据格式 + +业务层调用时建议封装为以下结构，便于下游使用： + +```python +from dataclasses import dataclass +from typing import Any + +@dataclass +class AgenticRAGResponse: + question: str # 用户原始问题 + answer: str # 最终答案（Markdown 格式） + tool_calls: list[dict] # 工具调用链记录 + total_messages: int # 对话轮次（含 human/ai/tool 全部） + token_usage: dict[str, int] # Token 用量统计 + kg_stats: dict[str, Any] # KG 规模信息 +``` + +**填充示例：** + +```python +def run_query_with_metadata(question: str) -> AgenticRAGResponse: + result = agent.invoke({"messages": [("human", question)]}) + messages = result["messages"] + + # 提取工具调用链 + tool_calls = [] + for msg in messages: + if hasattr(msg, "tool_calls") and msg.tool_calls: + for tc in msg.tool_calls: + tool_calls.append({ + "tool": tc["name"], + "args": tc["args"], + "call_id": tc["id"], + }) + + # Token 统计（来自最后一条 AIMessage） + last_ai = messages[-1] + usage = last_ai.response_metadata.get("usage", {}) + + return AgenticRAGResponse( + question=question, + answer=messages[-1].content, + tool_calls=tool_calls, + total_messages=len(messages), + token_usage={ + "prompt_tokens": usage.get("prompt_tokens", 0), + "completion_tokens": usage.get("completion_tokens", 0), + "total_tokens": usage.get("total_tokens", 0), + }, + kg_stats={ + "nodes": G.number_of_nodes(), + "edges": G.number_of_edges(), + "density": nx.density(G), + }, + ) +``` + +### 4.8 实测问答响应样例 + +#### 样例 1：T1-Overview（图谱概览类问题） + +``` +问题：Give me an overview of the knowledge graph. + What types of entities does it contain and which entities are most central? + +工具调用链： + [1] describe_graph() + +最终答案（节选）： + ## Knowledge Graph Overview + **Structure:** + - 40 entities (nodes) connected by 780 edges + - All edges represent CO_OCCURS_IN relationships + - Graph density: 1.000 — fully connected graph + + **Entity Types:** + 1. TECHNOLOGY (4): GraphRAG, LLMs, LangExtract, MinerU + 2. CONCEPT (36): retrieval-augmented generation, knowledge graphs, ... + + **Most Central Entities (centrality=1.000):** + 1. [TECHNOLOGY] GraphRAG + 2. [CONCEPT] Knowledge Graph Enhanced RAG System + ... + +消息轮次：4 条（human + ai_tool_call + tool_result + ai_final） +Token 用量：约 900 tokens +``` + +#### 样例 2：T3-MultiHop（多跳推理类问题） + +``` +问题：What concepts and technologies are most closely related to GraphRAG? + Explore the graph neighborhood and explain the connections. + +工具调用链： + [1] search_entities(query="GraphRAG") + [2] get_neighbors(entity_name="GraphRAG", hops=1) + +最终答案（节选）： + ## Concepts and Technologies Most Closely Related to GraphRAG + + ### Core Technologies (Directly Connected): + 1. LLMs — The foundation models that GraphRAG enhances + 2. LangExtract — Used for language extraction in the pipeline + 3. MinerU — Part of the data processing ecosystem + + ### Key Concepts: + - Knowledge Graph Enhanced RAG System (overarching architecture) + - retrieval-augmented generation (core paradigm) + - multi-hop reasoning (key capability) + ... + +消息轮次：6 条（human + 2×ai_tool_call + 2×tool_result + ai_final） +Token 用量：约 1,200 tokens +``` + +### 4.9 错误与边界情况处理 + +| 情况 | Agent 行为 | 返回内容 | +|------|------------|---------| +| 实体不存在 | 工具返回提示 + 样例实体名 | Agent 改写查询或给出不确定性说明 | +| 类型不合法 | 工具返回有效类型列表 | Agent 自动纠正并重试 | +| 问题超出 KG 范围 | 无工具调用结果支撑 | Agent 如实说明 "信息不在当前 KG 中" | +| Token 超限 | LangChain 内部截断 | 减少 `hops` 或缩短问题 | + +--- + +## 五、虚拟环境与依赖 + +### 5.1 运行环境 + +| 项目 | 值 | +|------|-----| +| 虚拟环境 | `F:\GraphRAGAgent\langextract_src\.venv\`（复用 Bridge Pipeline 的 venv） | +| Python 版本 | 3.12 | +| 安装方式 | uv | + +### 5.2 Agentic-RAG 新增依赖 + +| 包 | 版本（实测） | 用途 | +|----|------------|------| +| `langchain` | 1.2.10 | `@tool` 装饰器、`create_agent` | +| `langchain-openai` | latest | `ChatOpenAI`（DeepSeek 适配） | +| `langgraph` | latest | `create_agent` 底层运行时 | +| `networkx` | latest | KG 图构建、BFS 遍历、中心性计算 | + +### 5.3 完整依赖安装 + +```bash +uv pip install langchain langchain-openai langgraph networkx \ + --python F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe +``` + +### 5.4 环境变量 + +`F:\GraphRAGAgent\graphrag_pipeline\.env`： + +```env +DEEPSEEK_API_KEY=sk-xxxxxxxxxxxxxxxx +DEEPSEEK_BASE_URL=https://api.deepseek.com +``` + +--- + +## 附录：各阶段文件依赖速查 + +| 阶段 | 输入 | 输出 | 关键脚本 | +|------|------|------|---------| +| MinerU 解析 | `*.pdf` | `{uuid}_content_list.json` | `mineru_mvp/pipeline.py` | +| Bridge Pipeline | `*_content_list.json` | `kg_nodes.json` + `kg_edges.json` | `graphrag_pipeline/bridge.py` | +| Agentic-RAG | `kg_nodes.json` + `kg_edges.json` | 自然语言答案 | `graphrag_pipeline/agentic_rag_mvp.py` | + +| 规范文档 | 覆盖范围 | +|---------|---------| +| `docs/mineru_specification-v1.0.md` | MinerU 解析阶段输入/输出 | +| `docs/langextract_specification-v1.0.md` | LangExtract 实体抽取参数 | +| `docs/bridge_pipeline_specification-v1.0.md` | Bridge Pipeline 对接规范与 KG 输出格式 | +| `docs/agentic_rag_specification-v1.0.md` | **本文件** — Agentic-RAG 问答阶段规范 | diff --git a/docs/backend_service_specification-v1.0.md b/docs/backend_service_specification-v1.0.md new file mode 100644 index 0000000..5373e80 --- /dev/null +++ b/docs/backend_service_specification-v1.0.md @@ -0,0 +1,1757 @@ +# 多模态 RAG 后端服务接口规范 v1.0 + +> 基于 MinerU + LangExtract Bridge Pipeline + Agentic-RAG MVP 实测验证结果 +> Web 框架：FastAPI (Python 3.12 async) +> 存储方案：纯文件系统（JSON） +> 更新日期：2026-03-05 + +--- + +## 目录 + +- [一、系统架构总览](#一系统架构总览) + - [1.1 四层架构](#11-四层架构) + - [1.2 双 venv 协调方案](#12-双-venv-协调方案) + - [1.3 完整数据流](#13-完整数据流) + - [1.4 Job 状态机](#14-job-状态机) + - [1.5 FastAPI 项目目录结构](#15-fastapi-项目目录结构) + - [1.6 文件系统存储结构](#16-文件系统存储结构) +- [二、统一响应封装格式](#二统一响应封装格式) + - [2.1 通用响应结构](#21-通用响应结构) + - [2.2 错误码体系](#22-错误码体系) +- [三、核心数据对象 Schema](#三核心数据对象-schema) + - [3.1 DocumentInfo](#31-documentinfo) + - [3.2 IndexingJobStatus](#32-indexingjobstatus) + - [3.3 KGNode](#33-kgnode) + - [3.4 KGEdge](#34-kgedge) + - [3.5 ExtractionRecord](#35-extractionrecord) + - [3.6 QAResult](#36-qaresult) +- [四、A 组：文档管理（4 个端点）](#四a-组文档管理4-个端点) +- [五、B 组：Indexing Pipeline（4 个端点）](#五b-组indexing-pipeline4-个端点) +- [六、C 组：知识图谱（6 个端点）](#六c-组知识图谱6-个端点) +- [七、D 组：QA 问答（4 个端点）](#七d-组qa-问答4-个端点) +- [八、E 组：搜索（3 个端点）](#八e-组搜索3-个端点) +- [九、F 组：系统（4 个端点）](#九f-组系统4-个端点) +- [十、文件格式支持矩阵](#十文件格式支持矩阵) +- [十一、依赖与运行](#十一依赖与运行) + +--- + +## 一、系统架构总览 + +### 1.1 四层架构 + +``` +┌─────────────────────────────────────────────────────────────────────┐ +│ 客户端层 │ +│ 浏览器 / API 调用方 / 可视化前端 │ +└──────────────────────────────┬──────────────────────────────────────┘ + │ HTTP/HTTPS +┌──────────────────────────────▼──────────────────────────────────────┐ +│ API 网关层 │ +│ Nginx 反向代理 | 限流（per-IP/per-key） | 请求日志 | TLS 终止 │ +└──────────────────────────────┬──────────────────────────────────────┘ + │ +┌──────────────────────────────▼──────────────────────────────────────┐ +│ 服务层 — FastAPI Application │ +│ Python 3.12 async / uvicorn │ +│ │ +│ ┌────────────────┐ ┌────────────────┐ ┌───────────────────────┐ │ +│ │ DocumentService│ │ IndexingService│ │ KGService │ │ +│ │ 文件上传/管理 │ │ Pipeline 调度 │ │ NetworkX 图操作 │ │ +│ └────────────────┘ └────────────────┘ └───────────────────────┘ │ +│ ┌────────────────┐ ┌────────────────┐ ┌───────────────────────┐ │ +│ │ QAService │ │ SearchService │ │ SystemService │ │ +│ │ Agentic-RAG │ │ 实体/图谱搜索 │ │ 健康检查 / 统计 │ │ +│ └────────────────┘ └────────────────┘ └───────────────────────┘ │ +└──────────────────────────────┬──────────────────────────────────────┘ + │ +┌──────────────────────────────▼──────────────────────────────────────┐ +│ Pipeline 执行层 │ +│ │ +│ ┌──────────────────────────────────────────────────────────────┐ │ +│ │ MinerU Pipeline（subprocess → mineru_mvp/.venv） │ │ +│ │ 输入: 文件路径 输出: *content_list.json + layout.json │ │ +│ └──────────────────────────────────────────────────────────────┘ │ +│ ┌──────────────────────────────────────────────────────────────┐ │ +│ │ Bridge Pipeline（直接 import → langextract_src/.venv） │ │ +│ │ text_assembler → entity_extractor → kg_builder │ │ +│ │ 输出: kg_nodes.json + kg_edges.json │ │ +│ └──────────────────────────────────────────────────────────────┘ │ +│ ┌──────────────────────────────────────────────────────────────┐ │ +│ │ Agentic-RAG（LangChain create_agent → langextract_src/.venv）│ │ +│ │ 工具: search_entities / get_neighbors / get_entities_by_type │ │ +│ │ describe_graph │ │ +│ │ LLM: DeepSeek deepseek-chat via ChatOpenAI │ │ +│ └──────────────────────────────────────────────────────────────┘ │ +└──────────────────────────────┬──────────────────────────────────────┘ + │ +┌──────────────────────────────▼──────────────────────────────────────┐ +│ 存储层（纯文件系统） │ +│ uploads/ ← 原始上传文件 │ +│ jobs/{job_id}/ ← 每个 job 的中间产物和结果 JSON │ +│ kg/ ← 全局合并的 KG（kg_nodes.json + kg_edges.json） │ +└─────────────────────────────────────────────────────────────────────┘ +``` + +### 1.2 双 venv 协调方案 + +项目中存在两个隔离的 Python 虚拟环境，FastAPI 服务通过以下方式协调： + +| 组件 | 虚拟环境 | 调用方式 | +|------|---------|---------| +| **FastAPI 服务本体** | `langextract_src/.venv` | 直接运行 | +| **Bridge Pipeline** | `langextract_src/.venv` | `from text_assembler import ...` 直接 import | +| **Agentic-RAG** | `langextract_src/.venv` | `from agentic_rag_mvp import ...` 直接 import | +| **MinerU Pipeline** | `mineru_mvp/.venv` | `subprocess.run([MINERU_PYTHON, MINERU_PIPELINE, pdf_path])` | + +```python +# 双 venv 协调核心代码 +MINERU_PYTHON = Path("F:/GraphRAGAgent/mineru_mvp/.venv/Scripts/python.exe") +MINERU_PIPELINE = Path("F:/GraphRAGAgent/mineru_mvp/pipeline.py") + +# Stage 1: MinerU — subprocess 隔离调用 +result = subprocess.run( + [str(MINERU_PYTHON), str(MINERU_PIPELINE), str(pdf_path)], + cwd=str(MINERU_DIR), capture_output=True, text=True, timeout=600 +) + +# Stage 2-4: Bridge + RAG — 直接 import（同 venv） +from text_assembler import load_content_list, assemble_pages +from entity_extractor import create_model, extract_entities +from kg_builder import build_kg +``` + +### 1.3 完整数据流 + +``` +上传文件（PDF/DOCX/PPT/PNG/JPG/HTML） + │ + ▼ POST /api/v1/documents/upload +DocumentService: 保存到 uploads/{doc_id}_{filename} + │ + ▼ POST /api/v1/index/start +IndexingService: 启动后台 threading.Thread + │ + ├─ Stage: parsing + │ MinerU subprocess → mineru_mvp/output/{stem}/*_content_list.json + │ + ├─ Stage: extracting + │ text_assembler.assemble_pages() → PageText[] + │ entity_extractor.extract_entities() → AnnotatedDocument[] + │ → ExtractionRecord[] 保存到 jobs/{job_id}/extractions.json + │ + ├─ Stage: indexing + │ kg_builder.build_kg() → KGNode[] + KGEdge[] + │ → 保存到 jobs/{job_id}/kg_nodes.json + kg_edges.json + │ → 合并到全局 kg/kg_nodes.json + kg/kg_edges.json + │ + └─ Status: done + GET /api/v1/index/result/{job_id} → 完整结果 + +用户查询（自然语言问题） + │ + ▼ POST /api/v1/query +QAService: 加载全局 KG → NetworkX Graph + │ + ├─ LangChain create_agent（DeepSeek） + │ ReAct 循环: think → tool_call → observe → repeat + │ 工具调用链: search_entities / get_neighbors / ... + │ + └─ QAResult: answer + tool_calls + cited_nodes +``` + +### 1.4 Job 状态机 + +``` + ┌─────────┐ + │submitted│ + └────┬────┘ + │ 后台线程启动 + ┌────▼────┐ + │ queued │ （等待线程池，当前实现立即转 parsing） + └────┬────┘ + │ MinerU subprocess 开始 + ┌────▼────┐ + │ parsing │ MinerU 云端 API 解析 + └────┬────┘ + │ content_list.json 就绪 + ┌─────▼──────┐ + │ extracting │ LangExtract + DeepSeek 实体抽取 + └─────┬──────┘ + │ extractions.json 就绪 + ┌─────▼──────┐ + │ indexing │ kg_builder 构建知识图谱 + └─────┬──────┘ + │ kg_nodes/edges 就绪 + ┌──────────▼──────────┐ + ┌─────▼─────┐ ┌──────▼──────┐ + │ done │ │ failed │ + └───────────┘ └─────────────┘ +``` + +**进度字段说明（`progress` 对象）：** + +| 阶段 | `parsed_pages` | `total_pages` | `extracted_entities` | +|------|----------------|---------------|----------------------| +| parsing | 实时更新（MinerU 进度） | MinerU 返回总页数 | 0 | +| extracting | total_pages | total_pages | 实时累加 | +| indexing | total_pages | total_pages | 最终值 | +| done | total_pages | total_pages | 最终值 | + +### 1.5 FastAPI 项目目录结构 + +``` +F:\GraphRAGAgent\graphrag_pipeline\ +├── api_server.py # FastAPI 主入口（app 实例、路由注册、启动配置） +├── routers/ +│ ├── __init__.py +│ ├── documents.py # A 组：文档管理（4 个端点） +│ ├── indexing.py # B 组：Indexing Pipeline（4 个端点） +│ ├── kg.py # C 组：知识图谱（6 个端点） +│ ├── query.py # D 组：QA 问答（4 个端点） +│ ├── search.py # E 组：搜索（3 个端点） +│ └── system.py # F 组：系统（4 个端点） +├── services/ +│ ├── __init__.py +│ ├── document_service.py # 文件保存、元数据读写 +│ ├── indexing_service.py # Pipeline 调度（MinerU subprocess + Bridge import） +│ ├── kg_service.py # NetworkX 图加载、BFS、中心性计算 +│ ├── qa_service.py # create_agent 封装、ReAct 调用、结果解析 +│ └── search_service.py # 实体搜索、路径搜索、子图搜索 +├── models/ +│ ├── __init__.py +│ └── schemas.py # Pydantic v2 models（所有数据对象 Schema） +├── storage/ +│ ├── __init__.py +│ └── file_store.py # 统一文件读写（JSON 序列化/反序列化、目录管理） +├── .env # DEEPSEEK_API_KEY + DEEPSEEK_BASE_URL + MINERU_API_TOKEN +│ +│ # 现有文件（不修改） +├── bridge.py +├── text_assembler.py +├── entity_extractor.py +├── kg_builder.py +├── agentic_rag_mvp.py +├── web_server.py # 旧 Flask 原型（保留，不删除） +└── output/ + ├── kg_nodes.json # 向后兼容的全局 KG（与 kg/ 目录同步） + └── kg_edges.json +``` + +### 1.6 文件系统存储结构 + +``` +F:\GraphRAGAgent\graphrag_pipeline\ +│ +├── uploads/ +│ └── {doc_id}_{filename} # 上传的原始文件（如 abc12345_paper.pdf） +│ +├── jobs/ +│ └── {job_id}/ +│ ├── meta.json # job 元数据 +│ │ { +│ │ "job_id": "job_xyz789", +│ │ "doc_id": "abc12345", +│ │ "status": "done", +│ │ "stage": "Complete", +│ │ "progress": {...}, +│ │ "created_at": "ISO8601", +│ │ "elapsed_seconds": 42.1, +│ │ "error": null, +│ │ "pdf_name": "paper.pdf", +│ │ "pdf_path": "uploads/abc12345_paper.pdf" +│ │ } +│ ├── mineru_output/ # MinerU 解析产物（原样保留） +│ │ ├── {uuid}_content_list.json +│ │ ├── layout.json +│ │ ├── full.md +│ │ ├── {uuid}_origin.pdf +│ │ └── images/ +│ │ └── {sha256}.jpg +│ ├── extractions.json # LangExtract 全部抽取记录（ExtractionRecord[]） +│ ├── kg_nodes.json # 本 job 生成的 KG 节点（KGNode[]） +│ └── kg_edges.json # 本 job 生成的 KG 边（KGEdge[]） +│ +└── kg/ + ├── kg_nodes.json # 全局合并的 KG 节点（所有 job 合并去重） + └── kg_edges.json # 全局合并的 KG 边（所有 job 合并去重） +``` + +--- + +## 二、统一响应封装格式 + +### 2.1 通用响应结构 + +所有 API 端点均使用以下统一包装格式： + +```json +{ + "code": 0, + "msg": "success", + "request_id": "f47ac10b-58cc-4372-a567-0e02b2c3d479", + "data": { ... } +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `code` | `int` | `0` = 成功；非 `0` = 失败（见错误码表） | +| `msg` | `string` | 状态描述（成功为 `"success"`，失败为错误信息） | +| `request_id` | `string` | UUID v4，用于日志追踪 | +| `data` | `object \| null` | 业务数据（失败时为 `null`） | + +**HTTP 状态码映射：** + +| HTTP 状态码 | 适用场景 | +|------------|---------| +| `200 OK` | 同步请求成功 | +| `202 Accepted` | 异步任务已接受（Job 启动） | +| `400 Bad Request` | 参数校验失败（code 1001/1002/1003） | +| `404 Not Found` | 资源不存在（code 2001/3001） | +| `500 Internal Server Error` | 服务器内部错误（code 5000） | + +**FastAPI Pydantic 响应模型：** + +```python +from pydantic import BaseModel +from typing import Generic, TypeVar, Optional +import uuid + +T = TypeVar("T") + +class APIResponse(BaseModel, Generic[T]): + code: int = 0 + msg: str = "success" + request_id: str = str(uuid.uuid4()) + data: Optional[T] = None +``` + +### 2.2 错误码体系 + +| code | HTTP 状态码 | 含义 | 说明 | +|------|------------|------|------| +| `0` | 200 | 成功 | | +| `1001` | 400 | 参数校验失败 | 缺少必填字段或类型错误 | +| `1002` | 400 | 文件格式不支持 | 仅支持 pdf/docx/doc/pptx/ppt/png/jpg/jpeg/html | +| `1003` | 400 | 文件超出大小限制 | 单文件最大 200MB（MinerU 限制） | +| `1004` | 400 | 文件页数超限 | 单文件最大 600 页（MinerU 限制） | +| `2001` | 404 | 文档不存在 | `doc_id` 对应的文档未找到 | +| `2002` | 400 | Job 不存在 | `job_id` 对应的任务未找到 | +| `2003` | 400 | Job 仍在执行 | 请求结果时任务尚未完成 | +| `2004` | 400 | Job 状态不可取消 | 仅 submitted/queued 可取消 | +| `3001` | 404 | KG 节点不存在 | `node_id` 对应节点未找到 | +| `3002` | 400 | KG 为空 | 尚未完成任何 Indexing，无图谱数据 | +| `4001` | 500 | QA 服务异常 | LangChain Agent 或 DeepSeek API 调用失败 | +| `5000` | 500 | 服务器内部错误 | 未预期的系统异常 | + +**错误响应示例：** + +```json +{ + "code": 1002, + "msg": "Unsupported file format: .xlsx. Supported formats: pdf, docx, doc, pptx, ppt, png, jpg, jpeg, html", + "request_id": "f47ac10b-58cc-4372-a567-0e02b2c3d479", + "data": null +} +``` + +--- + +## 三、核心数据对象 Schema + +### 3.1 DocumentInfo + +文档元数据对象，由 `POST /api/v1/documents/upload` 创建，持久化到 `jobs/` 下的 `meta.json`。 + +```json +{ + "doc_id": "abc12345", + "filename": "graphrag_overview.pdf", + "format": "pdf", + "size_bytes": 1048576, + "pages": 4, + "uploaded_at": "2026-03-05T10:00:00Z", + "status": "indexed", + "language": "en", + "enable_formula": true, + "enable_table": true +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `doc_id` | `string` | 文档唯一 ID（UUID hex 前 8 位，如 `"abc12345"`） | +| `filename` | `string` | 原始文件名 | +| `format` | `string` | 文件格式（小写扩展名，不含点） | +| `size_bytes` | `int` | 文件大小（字节） | +| `pages` | `int \| null` | 总页数（MinerU 解析后填充；上传时为 `null`） | +| `uploaded_at` | `string` | ISO 8601 上传时间 | +| `status` | `string` | `"uploaded"` / `"indexed"` / `"failed"` | +| `language` | `string` | OCR 语言码（PaddleOCR，默认 `"ch"`） | +| `enable_formula` | `bool` | 是否启用公式识别 | +| `enable_table` | `bool` | 是否启用表格识别 | + +### 3.2 IndexingJobStatus + +Indexing Pipeline 的任务状态对象。 + +```json +{ + "job_id": "job_xyz789", + "doc_id": "abc12345", + "status": "extracting", + "stage": "Extracting entities (LangExtract + DeepSeek)...", + "progress": { + "parsed_pages": 4, + "total_pages": 4, + "extracted_entities": 23 + }, + "created_at": "2026-03-05T10:00:05Z", + "elapsed_seconds": 18.3, + "error": null +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `job_id` | `string` | 任务唯一 ID（`"job_"` + UUID hex 前 8 位） | +| `doc_id` | `string` | 关联文档 ID | +| `status` | `string` | 状态枚举（见 1.4 状态机） | +| `stage` | `string` | 当前阶段人类可读描述 | +| `progress.parsed_pages` | `int` | 已解析页数 | +| `progress.total_pages` | `int` | 总页数（0 = 未知） | +| `progress.extracted_entities` | `int` | 已抽取实体数 | +| `created_at` | `string` | ISO 8601 任务创建时间 | +| `elapsed_seconds` | `float` | 已耗时（秒） | +| `error` | `string \| null` | 错误信息（失败时非 null） | + +### 3.3 KGNode + +知识图谱节点，直接对应 `kg_nodes.json` 格式，新增 `degree` 字段。 + +```json +{ + "id": "tech_graphrag_0", + "name": "GraphRAG", + "type": "TECHNOLOGY", + "source_doc": "abc12345", + "char_start": 0, + "char_end": 8, + "confidence": "match_exact", + "page": 0, + "degree": 39 +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `id` | `string` | 节点唯一 ID（来自 kg_nodes.json） | +| `name` | `string` | 实体名称 | +| `type` | `string` | 实体类型：`TECHNOLOGY` / `CONCEPT` / `PERSON` / `ORGANIZATION` / `LOCATION` | +| `source_doc` | `string` | 来源文档 ID（doc_id） | +| `char_start` | `int` | 实体在原文中的起始字符位置（LangExtract `char_interval.start_pos`） | +| `char_end` | `int` | 实体在原文中的结束字符位置（不含，`char_interval.end_pos`） | +| `confidence` | `string` | LangExtract 对齐状态：`match_exact` / `match_greater` / `match_lesser` / `match_fuzzy` | +| `page` | `int` | 所在页码（0-indexed，来自 MinerU content_list.json `page_idx`） | +| `degree` | `int` | 节点度数（连接边数，NetworkX 计算，仅 API 返回时填充） | + +### 3.4 KGEdge + +知识图谱边，直接对应 `kg_edges.json` 格式。 + +```json +{ + "source": "tech_graphrag_0", + "target": "concept_knowledgegraph_1", + "relation": "CO_OCCURS_IN", + "doc_id": "abc12345", + "page": 0 +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `source` | `string` | 起始节点 ID | +| `target` | `string` | 目标节点 ID | +| `relation` | `string` | 关系类型（当前固定为 `"CO_OCCURS_IN"`，表示同页共现） | +| `doc_id` | `string` | 边来源文档 ID | +| `page` | `int` | 共现所在页码（0-indexed） | + +### 3.5 ExtractionRecord + +LangExtract 单条实体抽取记录，对应 `AnnotatedDocument.extractions[]` 的扁平化结构。 + +```json +{ + "text": "GraphRAG", + "type": "TECHNOLOGY", + "char_start": 0, + "char_end": 8, + "alignment": "match_exact", + "page": 0, + "doc_id": "abc12345" +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `text` | `string` | 实体文本（`extraction_text`，原文子串） | +| `type` | `string` | 实体类型（`extraction_class`） | +| `char_start` | `int \| null` | 字符起始位置（`char_interval.start_pos`） | +| `char_end` | `int \| null` | 字符结束位置（`char_interval.end_pos`，不含） | +| `alignment` | `string \| null` | 对齐状态（`alignment_status.value`，`null` 表示未对齐） | +| `page` | `int` | 所在页码（0-indexed） | +| `doc_id` | `string` | 来源文档 ID | + +> **过滤规则**：KG 构建时过滤掉 `alignment = null`（未对齐），`match_fuzzy` 根据项目配置可选是否过滤。当前实测：`match_exact` 占 94%+。 + +### 3.6 QAResult + +Agentic-RAG 问答返回对象，包含答案 + 完整推理溯源链。 + +```json +{ + "query_id": "q_20260305_001", + "question": "What is GraphRAG and how does it relate to knowledge graphs?", + "answer": "GraphRAG is a knowledge graph-enhanced retrieval-augmented generation system...", + "tool_calls": [ + { + "tool": "search_entities", + "input": {"query": "GraphRAG"}, + "output": "Found 1 entity(ies) matching 'GraphRAG':\n [TECHNOLOGY] \"GraphRAG\" (confidence=match_exact, page=0, id=tech_graphrag_0)" + }, + { + "tool": "get_neighbors", + "input": {"entity_name": "GraphRAG", "hops": 1}, + "output": "Neighbors of 'GraphRAG' [TECHNOLOGY] within 1 hop(s):\n Hop 1 — 39 related entities:\n [CONCEPT] knowledge graphs\n ..." + } + ], + "cited_nodes": ["tech_graphrag_0", "concept_knowledgegraph_1"], + "elapsed_seconds": 8.4, + "created_at": "2026-03-05T10:30:00Z" +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `query_id` | `string` | 查询唯一 ID | +| `question` | `string` | 用户原始问题 | +| `answer` | `string` | Agent 生成的最终自然语言答案（`result["messages"][-1].content`） | +| `tool_calls` | `array` | ReAct 循环中的工具调用记录（顺序） | +| `tool_calls[].tool` | `string` | 工具名（4 个 KG 工具之一） | +| `tool_calls[].input` | `object` | 工具调用参数 | +| `tool_calls[].output` | `string` | 工具返回的文本结果（ToolMessage.content） | +| `cited_nodes` | `string[]` | 答案中引用的节点 ID 列表（从 tool_calls 解析） | +| `elapsed_seconds` | `float` | 问答总耗时（包括所有 LLM 调用） | +| `created_at` | `string` | ISO 8601 查询时间 | + +--- + +## 四、A 组：文档管理（4 个端点） + +### A1. 上传文件 + +``` +POST /api/v1/documents/upload +Content-Type: multipart/form-data +``` + +**Request（Form Data）：** + +| 字段 | 类型 | 必填 | 默认值 | 说明 | +|------|------|------|--------|------| +| `file` | `binary` | **是** | — | 文件二进制内容 | +| `language` | `string` | 否 | `"ch"` | OCR 语言（PaddleOCR 语言码） | +| `enable_formula` | `bool` | 否 | `true` | 是否启用公式识别 | +| `enable_table` | `bool` | 否 | `true` | 是否启用表格识别 | + +**验证规则：** +- 文件扩展名必须在支持列表中（见第十章） +- 文件大小不得超过 200MB +- 文件名不得包含路径分隔符（防目录穿越） + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "f47ac10b-...", + "data": { + "doc_id": "abc12345", + "filename": "graphrag_overview.pdf", + "format": "pdf", + "size_bytes": 1048576, + "pages": null, + "uploaded_at": "2026-03-05T10:00:00Z", + "status": "uploaded", + "language": "en", + "enable_formula": true, + "enable_table": true + } +} +``` + +**错误响应：** + +```json +// 1002: 格式不支持 +{ "code": 1002, "msg": "Unsupported file format: .xlsx", "data": null } + +// 1003: 超过大小限制 +{ "code": 1003, "msg": "File size 256MB exceeds 200MB limit", "data": null } +``` + +--- + +### A2. 获取文档信息 + +``` +GET /api/v1/documents/{doc_id} +``` + +**Path Params：** + +| 参数 | 类型 | 说明 | +|------|------|------| +| `doc_id` | `string` | 文档 ID | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "doc_id": "abc12345", + "filename": "graphrag_overview.pdf", + "format": "pdf", + "size_bytes": 1048576, + "pages": 4, + "uploaded_at": "2026-03-05T10:00:00Z", + "status": "indexed", + "language": "en", + "enable_formula": true, + "enable_table": true + } +} +``` + +**错误：** `2001` (doc_id 不存在) + +--- + +### A3. 列出所有文档 + +``` +GET /api/v1/documents +``` + +**Query Params：** + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `page` | `int` | `1` | 页码（从 1 开始） | +| `page_size` | `int` | `20` | 每页数量（最大 100） | +| `status` | `string` | — | 按状态筛选：`uploaded` / `indexed` / `failed` | +| `format` | `string` | — | 按格式筛选：如 `pdf` | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "total": 5, + "page": 1, + "page_size": 20, + "items": [ + { + "doc_id": "abc12345", + "filename": "graphrag_overview.pdf", + "format": "pdf", + "size_bytes": 1048576, + "pages": 4, + "uploaded_at": "2026-03-05T10:00:00Z", + "status": "indexed", + "language": "en", + "enable_formula": true, + "enable_table": true + } + ] + } +} +``` + +--- + +### A4. 删除文档 + +``` +DELETE /api/v1/documents/{doc_id} +``` + +**说明：** 删除文档及其关联的 job 产物文件（`uploads/`、`jobs/` 下的对应目录），并从全局 KG 中移除该文档贡献的节点和边。 + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "deleted": true, + "doc_id": "abc12345", + "removed_nodes": 40, + "removed_edges": 780 + } +} +``` + +**错误：** `2001` (doc_id 不存在) + +--- + +## 五、B 组：Indexing Pipeline（4 个端点） + +### B1. 启动索引任务 + +``` +POST /api/v1/index/start +Content-Type: application/json +``` + +**Request Body：** + +```json +{ + "doc_id": "abc12345" +} +``` + +| 字段 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `doc_id` | `string` | **是** | 已上传文档的 ID（状态须为 `uploaded`） | + +**Response 202：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "job_id": "job_xyz789", + "doc_id": "abc12345", + "status": "submitted", + "stage": "Job submitted", + "created_at": "2026-03-05T10:00:05Z" + } +} +``` + +**实现说明：** +```python +# IndexingService 内部实现 +def start_indexing(doc_id: str) -> IndexingJobStatus: + job_id = f"job_{uuid.uuid4().hex[:8]}" + job_dir = JOBS_DIR / job_id + job_dir.mkdir(parents=True) + + meta = { "job_id": job_id, "doc_id": doc_id, "status": "submitted", ... } + save_meta(job_dir / "meta.json", meta) + + thread = threading.Thread(target=run_pipeline, args=(job_id,), daemon=True) + thread.start() + return meta +``` + +**Pipeline 执行顺序（后台线程）：** + +1. `status = "parsing"` → `subprocess.run([MINERU_PYTHON, MINERU_PIPELINE, pdf_path])` +2. `status = "extracting"` → `load_content_list()` → `assemble_pages()` → `extract_entities()` per page +3. `status = "indexing"` → `build_kg()` → 保存 `jobs/{job_id}/kg_nodes.json` → 合并到 `kg/` +4. `status = "done"` + +--- + +### B2. 查询任务状态（含实时进度） + +``` +GET /api/v1/index/status/{job_id} +``` + +**推荐轮询间隔：** 3 秒 + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "job_id": "job_xyz789", + "doc_id": "abc12345", + "status": "extracting", + "stage": "Extracting entities page 2/4 (LangExtract + DeepSeek)...", + "progress": { + "parsed_pages": 4, + "total_pages": 4, + "extracted_entities": 23 + }, + "created_at": "2026-03-05T10:00:05Z", + "elapsed_seconds": 18.3, + "error": null + } +} +``` + +**各状态 `stage` 典型值：** + +| status | stage | +|--------|-------| +| `submitted` | `"Job submitted"` | +| `queued` | `"Waiting for worker..."` | +| `parsing` | `"MinerU PDF parsing (cloud API)..."` | +| `extracting` | `"Extracting entities page 2/4 (LangExtract + DeepSeek)..."` | +| `indexing` | `"Building knowledge graph..."` | +| `done` | `"Complete"` | +| `failed` | `"Error: {error message}"` | + +**错误：** `2002` (job_id 不存在) + +--- + +### B3. 获取索引结果（完整数据） + +``` +GET /api/v1/index/result/{job_id} +``` + +**Response 200（status = done）：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "job_id": "job_xyz789", + "doc_id": "abc12345", + "status": "done", + "stats": { + "blocks": 32, + "block_types": {"text": 31, "table": 1}, + "pages": 4, + "raw_extractions": 45, + "nodes": 40, + "edges": 780, + "type_counts": {"TECHNOLOGY": 4, "CONCEPT": 36}, + "alignment_counts": {"match_exact": 40, "match_fuzzy": 5}, + "elapsed_seconds": 42.1 + }, + "extractions": [ + { + "text": "GraphRAG", + "type": "TECHNOLOGY", + "char_start": 0, + "char_end": 8, + "alignment": "match_exact", + "page": 0, + "doc_id": "abc12345" + } + ], + "nodes": [ + { + "id": "tech_graphrag_0", + "name": "GraphRAG", + "type": "TECHNOLOGY", + "source_doc": "abc12345", + "char_start": 0, + "char_end": 8, + "confidence": "match_exact", + "page": 0, + "degree": 39 + } + ], + "edges": [ + { + "source": "tech_graphrag_0", + "target": "concept_knowledgegraph_1", + "relation": "CO_OCCURS_IN", + "doc_id": "abc12345", + "page": 0 + } + ] + } +} +``` + +**Response 200（status ≠ done）：** 返回 `IndexingJobStatus`（不含 stats/extractions/nodes/edges） + +**错误：** `2002` (job_id 不存在) + +--- + +### B4. 取消任务 + +``` +DELETE /api/v1/index/jobs/{job_id} +``` + +**限制：** 仅 `submitted` 或 `queued` 状态可取消；`parsing`/`extracting`/`indexing` 状态无法中断后台线程，仅标记状态为 `cancelled`。 + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "cancelled": true, + "job_id": "job_xyz789", + "previous_status": "submitted" + } +} +``` + +**错误：** `2002` (不存在), `2004` (状态不可取消) + +--- + +## 六、C 组：知识图谱（6 个端点） + +### C1. 获取所有节点（分页 + 筛选） + +``` +GET /api/v1/kg/nodes +``` + +**Query Params：** + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `type` | `string` | — | 实体类型筛选（大小写不敏感） | +| `doc_id` | `string` | — | 按来源文档筛选 | +| `confidence` | `string` | — | 对齐状态筛选（如 `match_exact`） | +| `page` | `int` | `1` | 页码 | +| `page_size` | `int` | `50` | 每页数量（最大 200） | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "total": 40, + "page": 1, + "page_size": 50, + "items": [ + { + "id": "tech_graphrag_0", + "name": "GraphRAG", + "type": "TECHNOLOGY", + "source_doc": "abc12345", + "char_start": 0, + "char_end": 8, + "confidence": "match_exact", + "page": 0, + "degree": 39 + } + ] + } +} +``` + +**错误：** `3002` (KG 为空) + +--- + +### C2. 获取所有边（分页） + +``` +GET /api/v1/kg/edges +``` + +**Query Params：** + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `doc_id` | `string` | — | 按来源文档筛选 | +| `relation` | `string` | — | 关系类型筛选（如 `CO_OCCURS_IN`） | +| `page` | `int` | `1` | 页码 | +| `page_size` | `int` | `100` | 每页数量（最大 500） | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "total": 780, + "page": 1, + "page_size": 100, + "items": [ + { + "source": "tech_graphrag_0", + "target": "concept_knowledgegraph_1", + "relation": "CO_OCCURS_IN", + "doc_id": "abc12345", + "page": 0 + } + ] + } +} +``` + +--- + +### C3. 获取单个节点详情 + +``` +GET /api/v1/kg/nodes/{node_id} +``` + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "id": "tech_graphrag_0", + "name": "GraphRAG", + "type": "TECHNOLOGY", + "source_doc": "abc12345", + "char_start": 0, + "char_end": 8, + "confidence": "match_exact", + "page": 0, + "degree": 39, + "degree_centrality": 1.000, + "neighbor_count": 39 + } +} +``` + +**额外字段（仅单节点详情）：** + +| 字段 | 说明 | +|------|------| +| `degree_centrality` | NetworkX `degree_centrality(G)[node_id]`（0-1 范围） | +| `neighbor_count` | 直接邻居数量（等于 `degree`） | + +**错误：** `3001` (节点不存在) + +--- + +### C4. 获取节点邻居（N-hop BFS） + +``` +GET /api/v1/kg/nodes/{node_id}/neighbors +``` + +**Query Params：** + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `hops` | `int` | `1` | 跳数（1-3） | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "center": { + "id": "tech_graphrag_0", + "name": "GraphRAG", + "type": "TECHNOLOGY", + "page": 0 + }, + "hops": 1, + "neighbors_by_hop": { + "1": [ + { "id": "concept_knowledgegraph_1", "name": "knowledge graphs", "type": "CONCEPT", "page": 0 } + ] + }, + "total_neighbors": 39 + } +} +``` + +**实现参考（来自 `agentic_rag_mvp.py`）：** + +```python +reachable = nx.single_source_shortest_path_length(G, node_id, cutoff=hops) +by_hop = {dist: [] for dist in range(1, hops+1)} +for nid, dist in reachable.items(): + if dist > 0: + by_hop[dist].append(G.nodes[nid]) +``` + +**错误：** `3001` (节点不存在) + +--- + +### C5. 知识图谱统计 + +``` +GET /api/v1/kg/stats +``` + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "total_nodes": 40, + "total_edges": 780, + "density": 1.0000, + "type_distribution": { + "TECHNOLOGY": 4, + "CONCEPT": 36 + }, + "relation_types": { + "CO_OCCURS_IN": 780 + }, + "top5_central_nodes": [ + { "node_id": "tech_graphrag_0", "name": "GraphRAG", "type": "TECHNOLOGY", "centrality": 1.000 }, + { "node_id": "concept_kgrag_1", "name": "Knowledge Graph Enhanced RAG System", "type": "CONCEPT", "centrality": 1.000 }, + { "node_id": "concept_rag_2", "name": "retrieval-augmented generation", "type": "CONCEPT", "centrality": 1.000 }, + { "node_id": "concept_kg_3", "name": "knowledge graphs", "type": "CONCEPT", "centrality": 1.000 }, + { "node_id": "concept_llm_4", "name": "large language models", "type": "CONCEPT", "centrality": 1.000 } + ], + "source_documents": ["abc12345", "def67890"] + } +} +``` + +--- + +### C6. 导出完整 KG + +``` +GET /api/v1/kg/export +``` + +**Query Params：** + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `format` | `string` | `"json"` | 导出格式（当前仅支持 `json`） | +| `doc_id` | `string` | — | 可选，仅导出指定文档的 KG | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "format": "json", + "doc_id": null, + "total_nodes": 40, + "total_edges": 780, + "exported_at": "2026-03-05T12:00:00Z", + "nodes": [ ...KGNode[] ], + "edges": [ ...KGEdge[] ] + } +} +``` + +--- + +## 七、D 组：QA 问答（4 个端点） + +### D1. 提交 QA 查询（同步） + +``` +POST /api/v1/query +Content-Type: application/json +``` + +**Request Body：** + +```json +{ + "question": "What is GraphRAG and how does it relate to knowledge graphs?", + "history": [ + { "role": "human", "content": "Previous question..." }, + { "role": "ai", "content": "Previous answer..." } + ] +} +``` + +| 字段 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `question` | `string` | **是** | 用户自然语言问题 | +| `history` | `array` | 否 | 多轮对话历史（最多 10 轮，即 20 条消息） | +| `history[].role` | `"human"` \| `"ai"` | — | 消息角色 | +| `history[].content` | `string` | — | 消息内容 | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "query_id": "q_20260305_a1b2c3", + "question": "What is GraphRAG and how does it relate to knowledge graphs?", + "answer": "Based on the knowledge graph, GraphRAG [TECHNOLOGY] is a knowledge graph-enhanced retrieval-augmented generation system that...", + "tool_calls": [ + { + "tool": "search_entities", + "input": { "query": "GraphRAG" }, + "output": "Found 1 entity(ies) matching 'GraphRAG':\n [TECHNOLOGY] \"GraphRAG\" (confidence=match_exact, page=0, id=tech_graphrag_0)" + }, + { + "tool": "get_neighbors", + "input": { "entity_name": "GraphRAG", "hops": 1 }, + "output": "Neighbors of 'GraphRAG' [TECHNOLOGY] within 1 hop(s):\n Hop 1 — 39 related entities:\n [CONCEPT] knowledge graphs\n ..." + } + ], + "cited_nodes": ["tech_graphrag_0", "concept_knowledgegraph_1"], + "elapsed_seconds": 8.4, + "created_at": "2026-03-05T10:30:00Z" + } +} +``` + +**实现说明（QAService 核心逻辑）：** + +```python +# 将 history 拼接为 LangChain messages 格式 +messages = [] +for h in request.history: + messages.append((h["role"], h["content"])) +messages.append(("human", request.question)) + +# 调用 LangChain create_agent +result = agent.invoke({"messages": messages}) + +# 提取工具调用链（遍历 result["messages"]） +tool_calls = [] +for msg in result["messages"]: + if hasattr(msg, "tool_calls") and msg.tool_calls: + for tc in msg.tool_calls: + tool_calls.append({"tool": tc["name"], "input": tc["args"], "output": ""}) + elif hasattr(msg, "tool_call_id"): # ToolMessage + if tool_calls: + tool_calls[-1]["output"] = msg.content + +# 最终答案 +answer = result["messages"][-1].content +``` + +**错误：** `3002` (KG 为空), `4001` (Agent/LLM 调用失败) + +**注意：** 此接口为同步调用，通常耗时 5-30 秒（取决于 DeepSeek API 响应速度和工具调用次数）。 + +--- + +### D2. 批量查询（异步） + +``` +POST /api/v1/query/batch +Content-Type: application/json +``` + +**Request Body：** + +```json +{ + "questions": [ + "What is GraphRAG?", + "List all TECHNOLOGY entities in the knowledge graph.", + "How does MinerU relate to LangExtract?" + ] +} +``` + +| 字段 | 类型 | 必填 | 约束 | 说明 | +|------|------|------|------|------| +| `questions` | `string[]` | **是** | 最多 20 个 | 问题列表 | + +**Response 202：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "batch_id": "batch_20260305_x1y2", + "total": 3, + "status": "submitted", + "created_at": "2026-03-05T10:30:00Z" + } +} +``` + +--- + +### D3. 获取批量查询状态与结果 + +``` +GET /api/v1/query/batch/{batch_id} +``` + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "batch_id": "batch_20260305_x1y2", + "total": 3, + "completed": 2, + "failed": 0, + "status": "running", + "results": [ + { ...QAResult }, + { ...QAResult } + ] + } +} +``` + +**错误：** `2002` (batch_id 不存在) + +--- + +### D4. 查询历史 + +``` +GET /api/v1/query/history +``` + +**Query Params：** + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `page` | `int` | `1` | 页码 | +| `page_size` | `int` | `20` | 每页数量（最大 50） | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "total": 50, + "page": 1, + "page_size": 20, + "items": [ ...QAResult[] ] + } +} +``` + +**存储说明：** 历史记录以 JSONL 格式持久化到 `jobs/query_history.jsonl`，每行一条 `QAResult`。 + +--- + +## 八、E 组：搜索（3 个端点） + +### E1. 实体关键词搜索 + +``` +GET /api/v1/search/entities +``` + +**Query Params：** + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `q` | `string` | **是** | 关键词（大小写不敏感子串匹配，对应 `agentic_rag_mvp.py: search_entities`） | +| `type` | `string` | 否 | 类型过滤（如 `TECHNOLOGY`） | +| `limit` | `int` | 否 | 最多返回数量（默认 15，最大 100） | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "query": "GraphRAG", + "total": 1, + "items": [ + { + "id": "tech_graphrag_0", + "name": "GraphRAG", + "type": "TECHNOLOGY", + "source_doc": "abc12345", + "char_start": 0, + "char_end": 8, + "confidence": "match_exact", + "page": 0, + "degree": 39 + } + ] + } +} +``` + +**实现（参考 `agentic_rag_mvp.py: search_entities`）：** + +```python +q = query.lower() +matches = [data for _, data in G.nodes(data=True) if q in data.get("name", "").lower()] +``` + +--- + +### E2. 图谱路径搜索（两节点间路径） + +``` +GET /api/v1/search/path +``` + +**Query Params：** + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `from` | `string` | **是** | 起始节点 ID | +| `to` | `string` | **是** | 目标节点 ID | +| `max_hops` | `int` | 否 | 最大路径长度（默认 3，最大 5） | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "from": { "id": "tech_graphrag_0", "name": "GraphRAG", "type": "TECHNOLOGY" }, + "to": { "id": "tech_mineru_3", "name": "MinerU", "type": "TECHNOLOGY" }, + "max_hops": 3, + "paths": [ + { + "length": 1, + "nodes": [ + { "id": "tech_graphrag_0", "name": "GraphRAG", "type": "TECHNOLOGY" }, + { "id": "tech_mineru_3", "name": "MinerU", "type": "TECHNOLOGY" } + ], + "edges": [ + { "source": "tech_graphrag_0", "target": "tech_mineru_3", "relation": "CO_OCCURS_IN" } + ] + } + ], + "total_paths": 1 + } +} +``` + +**实现（NetworkX）：** + +```python +paths = list(nx.all_simple_paths(G, from_id, to_id, cutoff=max_hops)) +``` + +**错误：** `3001` (节点不存在) + +--- + +### E3. 全图关键词搜索（含子图） + +``` +GET /api/v1/search/graph +``` + +**Query Params：** + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `q` | `string` | **是** | 关键词（大小写不敏感子串匹配） | +| `include_neighbors` | `bool` | 否 | 是否返回匹配节点的直接邻居边（默认 `false`） | + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "query": "retrieval", + "matched_nodes": [ + { "id": "concept_rag_2", "name": "retrieval-augmented generation", "type": "CONCEPT", "page": 0 } + ], + "subgraph_edges": [ + { "source": "concept_rag_2", "target": "tech_graphrag_0", "relation": "CO_OCCURS_IN" } + ] + } +} +``` + +--- + +## 九、F 组：系统（4 个端点） + +### F1. 健康检查 + +``` +GET /api/v1/health +``` + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "status": "healthy", + "version": "1.0.0", + "uptime_seconds": 3600, + "components": { + "mineru_venv": { + "status": "ok", + "path": "F:/GraphRAGAgent/mineru_mvp/.venv/Scripts/python.exe", + "exists": true + }, + "langextract_venv": { + "status": "ok", + "path": "F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe", + "exists": true + }, + "deepseek_api": { + "status": "ok", + "base_url": "https://api.deepseek.com", + "key_configured": true + }, + "storage": { + "status": "ok", + "kg_nodes_exists": true, + "kg_edges_exists": true, + "uploads_dir_exists": true + } + } + } +} +``` + +**说明：** 此端点仅检查配置和文件存在性，不发起实际 API 调用（避免消耗 DeepSeek token）。 + +--- + +### F2. 系统统计 + +``` +GET /api/v1/system/stats +``` + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "total_documents": 5, + "indexed_documents": 4, + "failed_documents": 1, + "total_nodes": 200, + "total_edges": 3900, + "type_distribution": { "TECHNOLOGY": 20, "CONCEPT": 180 }, + "total_queries": 50, + "active_jobs": 1, + "storage_used_mb": 12.4 + } +} +``` + +--- + +### F3. 支持的文件格式列表 + +``` +GET /api/v1/system/formats +``` + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "formats": [ + { "ext": "pdf", "description": "PDF 文档（文本型/扫描型/混合型）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": false }, + { "ext": "docx", "description": "Microsoft Word（新版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": false }, + { "ext": "doc", "description": "Microsoft Word（旧版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": false }, + { "ext": "pptx", "description": "PowerPoint（新版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": false }, + { "ext": "ppt", "description": "PowerPoint（旧版）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": false }, + { "ext": "png", "description": "PNG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": true }, + { "ext": "jpg", "description": "JPEG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": true }, + { "ext": "jpeg", "description": "JPEG 图片（单页）", "max_size_mb": 200, "max_pages": 1, "requires_ocr": true }, + { "ext": "html", "description": "HTML 文件（需指定 model_version=MinerU-HTML）", "max_size_mb": 200, "max_pages": 600, "requires_ocr": false } + ], + "ocr_languages": [ + { "code": "ch", "name": "中文（默认）" }, + { "code": "en", "name": "英文" }, + { "code": "japan", "name": "日文" }, + { "code": "korean", "name": "韩文" }, + { "code": "french", "name": "法文" }, + { "code": "german", "name": "德文" } + ], + "notes": [ + "language 参数默认值为 'ch'（非 'zh'），遵循 PaddleOCR v3 语言代码规范", + "上传时不需要携带 Content-Type: application/pdf 等，服务端自动识别", + "PNG/JPG/JPEG 单次最多处理 1 页（图片文件视为单页文档）" + ] + } +} +``` + +--- + +### F4. Demo 数据（快速预览） + +``` +GET /api/v1/system/demo +``` + +**说明：** 返回现有 `output/kg_nodes.json` + `output/kg_edges.json` 数据，无需上传 PDF 即可预览 KG 可视化效果。与旧版 `GET /api/demo`（Flask web_server.py）兼容。 + +**Response 200：** + +```json +{ + "code": 0, + "msg": "success", + "request_id": "...", + "data": { + "nodes": [ ...KGNode[] ], + "edges": [ ...KGEdge[] ], + "stats": { + "nodes": 40, + "edges": 780, + "type_counts": { "TECHNOLOGY": 4, "CONCEPT": 36 }, + "density": 1.0000 + } + } +} +``` + +**错误：** `3002` (demo 数据文件不存在，需先运行 bridge.py 生成) + +--- + +## 十、文件格式支持矩阵 + +| 格式 | 扩展名 | 最大体积 | 最大页数 | OCR | MinerU model_version | 说明 | +|------|--------|---------|---------|-----|----------------------|------| +| PDF | `.pdf` | 200MB | 600 页 | 可选 | `pipeline`（默认） | 核心能力，文本型/扫描型/混合型均支持 | +| Word（新） | `.docx` | 200MB | 600 页 | 可选 | `pipeline` | | +| Word（旧） | `.doc` | 200MB | 600 页 | 可选 | `pipeline` | | +| PPT（新） | `.pptx` | 200MB | 600 页 | 可选 | `pipeline` | | +| PPT（旧） | `.ppt` | 200MB | 600 页 | 可选 | `pipeline` | | +| PNG 图片 | `.png` | 200MB | 1 页 | 必须 | `pipeline` | EXIF 方向自动校正 | +| JPEG 图片 | `.jpg` | 200MB | 1 页 | 必须 | `pipeline` | EXIF 方向自动校正 | +| JPEG 图片 | `.jpeg` | 200MB | 1 页 | 必须 | `pipeline` | 同 `.jpg` | +| HTML | `.html` | 200MB | 600 页 | 否 | `MinerU-HTML` | 必须指定特定 model_version | + +**MinerU 云端 API 限制（来自 mineru_specification-v1.0.md）：** + +| 约束项 | 限制值 | +|--------|--------| +| 单文件最大体积 | 200 MB | +| 单文件最大页数 | 600 页 | +| 批量请求最大文件数 | 200 个 | +| 预签名上传 URL 有效期 | 24 小时 | +| 云端 API 每日最高优先级额度 | 2,000 页（超出降低优先级） | + +**服务端验证代码（FastAPI + Pydantic）：** + +```python +ALLOWED_EXTENSIONS = {"pdf", "docx", "doc", "pptx", "ppt", "png", "jpg", "jpeg", "html"} +MAX_FILE_SIZE_MB = 200 + +async def upload_document(file: UploadFile = File(...), ...): + ext = Path(file.filename).suffix.lower().lstrip(".") + if ext not in ALLOWED_EXTENSIONS: + raise HTTPException(400, detail=f"Unsupported format: .{ext}") + + content = await file.read() + size_mb = len(content) / (1024 * 1024) + if size_mb > MAX_FILE_SIZE_MB: + raise HTTPException(400, detail=f"File size {size_mb:.1f}MB exceeds 200MB limit") +``` + +--- + +## 十一、依赖与运行 + +### 安装依赖 + +```bash +# FastAPI + uvicorn + multipart 文件上传 +uv pip install fastapi uvicorn[standard] python-multipart \ + --python F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe + +# 已有依赖（无需重复安装） +# langextract[all]、langchain、langchain-openai、networkx、python-dotenv、flask、requests +``` + +### 启动服务 + +```bash +# 开发模式（--reload 热重载） +F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe -m uvicorn \ + graphrag_pipeline.api_server:app \ + --host 0.0.0.0 --port 8000 --reload + +# 或直接运行主入口 +F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe \ + F:/GraphRAGAgent/graphrag_pipeline/api_server.py +``` + +### API 文档访问 + +FastAPI 自动生成 OpenAPI 文档，启动后可访问： + +| 地址 | 说明 | +|------|------| +| `http://localhost:8000/api/v1/health` | 健康检查（验证服务启动） | +| `http://localhost:8000/docs` | Swagger UI（交互式 API 文档） | +| `http://localhost:8000/redoc` | ReDoc（只读 API 文档） | +| `http://localhost:8000/openapi.json` | OpenAPI JSON Schema | + +### 端口说明 + +| 服务 | 端口 | 说明 | +|------|------|------| +| **FastAPI（新）** | `8000` | 本规范描述的生产级 API | +| Flask web_server.py（旧） | `5000` | 原型，保留用于对比 | diff --git a/docs/bridge_pipeline_specification-v1.0.md b/docs/bridge_pipeline_specification-v1.0.md new file mode 100644 index 0000000..d67282f --- /dev/null +++ b/docs/bridge_pipeline_specification-v1.0.md @@ -0,0 +1,481 @@ +# Bridge Pipeline Specification v1.0 + +> GraphRAG 索引阶段核心流程：MinerU → LangExtract → Knowledge Graph + +--- + +## 1. Pipeline 执行思路 + +### 1.1 整体架构 + +Bridge Pipeline 是 GraphRAG 索引阶段的核心流程，负责将 MinerU 解析后的结构化 PDF 内容送入 LangExtract 完成实体抽取，最终生成知识图谱的节点（Nodes）和边（Edges）。 + +``` +MinerU output Bridge Pipeline KG output +───────────── ─────────────── ───────── +{uuid}_content_list.json → text_assembler.py + ├─ text blocks ├─ 按页拼接纯文本 + └─ table blocks (HTML) ├─ HTML表格→纯文本 + └─ 记录每个block的char偏移 + → entity_extractor.py + ├─ 逐页调用 lx.extract() + └─ DeepSeek via OpenAI Provider + → kg_builder.py + ├─ 过滤低质量对齐 → kg_nodes.json + ├─ 节点去重 (name.lower(), type) + └─ 同页实体对→CO_OCCURS_IN边 → kg_edges.json +``` + +### 1.2 五步执行流程 + +| 步骤 | 模块 | 说明 | +|------|------|------| +| Step 1 | `bridge.py` | 加载 MinerU 输出 `content_list.json`，解析输入路径和 source_doc_id | +| Step 2 | `text_assembler.py` | 按 `page_idx` 分组，拼接纯文本，记录每个 block 的字符偏移 | +| Step 3 | `entity_extractor.py` | 逐页调用 LangExtract + DeepSeek 完成实体抽取 | +| Step 4 | `kg_builder.py` | 过滤低质量对齐 → 节点去重 → 同页配对生成 CO_OCCURS_IN 边 | +| Step 5 | `bridge.py` | 保存 `kg_nodes.json` + `kg_edges.json` 到 output 目录 | + +### 1.3 文件存放位置 + +``` +F:\GraphRAGAgent\graphrag_pipeline\ +├── .env # DeepSeek API 配置 +├── CLAUDE.md # 组件开发规范 +├── bridge.py # 主入口（串联完整 Pipeline） +├── text_assembler.py # MinerU JSON → 按页纯文本 + 偏移映射 +├── entity_extractor.py # LangExtract + DeepSeek 封装 +├── kg_builder.py # KG 节点去重 + 边生成 +└── output/ + ├── kg_nodes.json # 知识图谱节点（9,851 bytes） + └── kg_edges.json # 知识图谱边（129,093 bytes） +``` + +### 1.4 运行命令 + +```bash +# 使用默认测试输入 +F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe F:/GraphRAGAgent/graphrag_pipeline/bridge.py + +# 指定输入文件 +F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe F:/GraphRAGAgent/graphrag_pipeline/bridge.py path/to/content_list.json + +# 指定输入目录（自动查找 *_content_list.json） +F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe F:/GraphRAGAgent/graphrag_pipeline/bridge.py path/to/output_dir/ +``` + +--- + +## 2. 实际本地输出文档规范 + +### 2.1 测试运行结果 + +- **输入文件**: `F:\GraphRAGAgent\mineru_mvp\output\test_sample\8a719db4-2b50-405b-826d-7bb27b224fa0_content_list.json` +- **输入规模**: 10 blocks（9 text + 1 table），1 页，2102 字符 +- **抽取结果**: 45 raw extractions → 40 去重节点，780 CO_OCCURS_IN 边 +- **对齐质量**: 全部 40 节点均为 `match_exact`（1 个 `match_fuzzy` 已被过滤） +- **执行时间**: ~22s（DeepSeek API 调用） + +### 2.2 kg_nodes.json — 实际输出 + +**文件大小**: 9,851 bytes | **节点数**: 40 + +**节点类型分布**: + +| 类型 | 数量 | 示例 | +|------|------|------| +| TECHNOLOGY | 4 | GraphRAG, MinerU, LLMs, LangExtract | +| CONCEPT | 36 | knowledge graphs, retrieval-augmented generation, multi-hop reasoning | + +**节点格式（实际样例）**: + +```json +{ + "id": "node_0", + "name": "GraphRAG", + "type": "TECHNOLOGY", + "source_doc": "8a719db4-2b50-405b-826d-7bb27b224fa0", + "char_start": 0, + "char_end": 8, + "confidence": "match_exact", + "page": 0 +} +``` + +**完整节点列表（前 10 个）**: + +| id | name | type | confidence | +|----|------|------|-----------| +| node_0 | GraphRAG | TECHNOLOGY | match_exact | +| node_1 | Knowledge Graph Enhanced RAG System | CONCEPT | match_exact | +| node_2 | retrieval-augmented generation | CONCEPT | match_exact | +| node_3 | knowledge graphs | CONCEPT | match_exact | +| node_4 | large language models | CONCEPT | match_exact | +| node_5 | question answering | CONCEPT | match_exact | +| node_6 | document collections | CONCEPT | match_exact | +| node_7 | RAG systems | CONCEPT | match_exact | +| node_8 | vector similarity search | CONCEPT | match_exact | +| node_9 | hierarchical knowledge graph | CONCEPT | match_exact | + +### 2.3 kg_edges.json — 实际输出 + +**文件大小**: 129,093 bytes | **边数**: 780 + +**数学验证**: 40 个节点全部在同一页 → C(40,2) = 40×39/2 = 780 条边 ✓ + +**边格式（实际样例）**: + +```json +{ + "source": "node_0", + "target": "node_1", + "relation": "CO_OCCURS_IN", + "doc_id": "8a719db4-2b50-405b-826d-7bb27b224fa0", + "page": 0 +} +``` + +**完整性校验结果**: +- 自环数: 0 ✓ +- 重复边数: 0 ✓ +- 关系类型: 全部为 `CO_OCCURS_IN` ✓ + +--- + +## 3. MinerU Pipeline 关键参数规范 + +### 3.1 输入格式：content_list.json + +MinerU 解析 PDF 后输出的 `{uuid}_content_list.json` 是一个 JSON 数组，每个元素代表一个内容块。 + +**text block 结构**: + +```json +{ + "type": "text", + "text": "GraphRAG: Knowledge Graph Enhanced RAG System...", + "text_level": null, + "page_idx": 0, + "bbox": [72, 43, 523, 57] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `type` | string | 块类型：`"text"` \| `"table"` \| `"image"` | +| `text` | string | 文本内容（末尾可能有空格） | +| `text_level` | int \| null | `null`=正文，`1`=一级标题 | +| `page_idx` | int | 页码（从 0 开始） | +| `bbox` | list[int] | 边界框坐标 `[x0, y0, x1, y1]`（归一化 0-1000） | + +**table block 结构**: + +```json +{ + "type": "table", + "table_body": "
...
MethodScore
", + "table_caption": [], + "page_idx": 0, + "bbox": [72, 400, 523, 500] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `table_body` | string | HTML `` 标签完整内容 | +| `table_caption` | list | 表格标题（通常为空数组） | + +### 3.2 关键约束 + +- 文件命名: `{uuid}_content_list.json`，UUID 用作 source_doc_id +- block 排列顺序与 PDF 阅读顺序一致 +- `text` 字段末尾可能有多余空格，需 `.rstrip()` 处理 +- `image` 类型块不含可提取文本，Bridge 跳过处理 + +--- + +## 4. LangExtract Pipeline 关键参数规范 + +### 4.1 模型配置 + +```python +from langextract.providers.openai import OpenAILanguageModel + +model = OpenAILanguageModel( + model_id="deepseek-chat", + api_key=DEEPSEEK_API_KEY, + base_url="https://api.deepseek.com", +) +``` + +**重要**: 必须直接实例化 `OpenAILanguageModel`，不能使用 `model_id` 路由。LangExtract 的 `model_id` 同时用于内部路由和 API 请求参数，DeepSeek 不识别 GPT 模型名称。 + +### 4.2 抽取调用 + +```python +result = lx.extract( + text_or_documents=page_text, # 纯文本字符串 + prompt_description=PROMPT, # 实体类型描述 + examples=EXAMPLES, # Few-shot 示例 + model=model, # 直接传入模型实例 + show_progress=True, +) +``` + +### 4.3 Prompt 配置 + +``` +Extract named entities from the text in order of appearance. +Entity types: + TECHNOLOGY — software, algorithms, models, tools + ORGANIZATION — companies, research groups, institutions + PERSON — individual people + LOCATION — places, geographic entities + CONCEPT — technical concepts, methodologies, frameworks +``` + +### 4.4 Few-shot 示例 + +验证可用的示例（MVP 测试 94.1% match_exact）： + +```python +lx.data.ExampleData( + text="LangChain is a framework created by Harrison Chase for building " + "LLM applications. It integrates with OpenAI models and Pinecone " + "vector database for semantic search.", + extractions=[ + lx.data.Extraction(extraction_class="TECHNOLOGY", extraction_text="LangChain"), + lx.data.Extraction(extraction_class="PERSON", extraction_text="Harrison Chase"), + lx.data.Extraction(extraction_class="CONCEPT", extraction_text="LLM applications"), + lx.data.Extraction(extraction_class="TECHNOLOGY", extraction_text="OpenAI models"), + lx.data.Extraction(extraction_class="TECHNOLOGY", extraction_text="Pinecone"), + lx.data.Extraction(extraction_class="CONCEPT", extraction_text="semantic search"), + ], +) +``` + +### 4.5 输出格式：AnnotatedDocument + +每页抽取返回一个 `AnnotatedDocument`，其 `extractions` 列表中每个元素包含： + +| 字段 | 类型 | 说明 | +|------|------|------| +| `extraction_text` | string | 实体名称（必须为输入文本的精确子串） | +| `extraction_class` | string | 实体类型（TECHNOLOGY/ORGANIZATION/PERSON/LOCATION/CONCEPT） | +| `char_interval.start_pos` | int | 在输入文本中的起始字符位置 | +| `char_interval.end_pos` | int | 在输入文本中的结束字符位置 | +| `alignment_status` | enum | 对齐质量：`match_exact` \| `match_greater` \| `match_lesser` \| `match_fuzzy` \| `None` | +| `extraction_index` | int | 抽取序号（从 1 开始） | +| `group_index` | int | 组序号（从 0 开始） | + +### 4.6 对齐质量过滤规则 + +| alignment_status | 含义 | Bridge 处理 | +|-----------------|------|------------| +| `match_exact` | LLM 输出与原文完全匹配 | ✅ 接受 | +| `match_greater` | LLM 输出是原文子串的超集 | ✅ 接受 | +| `match_lesser` | LLM 输出是原文子串的子集 | ✅ 接受 | +| `match_fuzzy` | 模糊匹配，偏移不可靠 | ❌ 过滤 | +| `None` | 无法对齐 | ❌ 过滤 | + +--- + +## 5. MinerU ↔ LangExtract 接口对接规范 + +### 5.1 核心挑战 + +MinerU 输出结构化 JSON 块（含 HTML 表格），而 LangExtract 仅接受纯文本 `str`。Bridge 的 `text_assembler` 模块负责转换和偏移映射。 + +### 5.2 对接转换规则 + +| 对接点 | MinerU 规范 | LangExtract 规范 | Bridge 处理 | +|--------|------------|-----------------|------------| +| 输入格式 | `content_list.json`（JSON 数组） | 仅接受纯文本 `str` | `text_assembler` 拼接转换 | +| 文本块 | `block["text"]`，末尾可能有空格 | `extraction_text` 须为原文精确子串 | `.rstrip()` 去尾部空格 | +| 表格块 | `table_body` 是 `
` HTML | 不接受 HTML | BeautifulSoup 转 pipe 分隔纯文本 | +| 标题判断 | `text_level` 缺失=正文，存在=标题 | 不区分标题/正文 | 标题和正文一起拼入文本 | +| 坐标系 | bbox 归一化 0-1000 | char_interval 基于输入字符 | BlockSpan 记录偏移映射 | +| 分页 | `page_idx` 区分不同页 | 单次调用处理一段文本 | 逐页分别调用 `lx.extract()` | +| 文件名 | `{uuid}_content_list.json` | — | glob `*_content_list.json` 匹配 | + +### 5.3 文本拼接算法 + +``` +输入: content_list (按 page_idx 分组) +输出: PageText 列表 + +对每页: + cursor = 0 + 对每个 block (保持原顺序): + if type == "text": + block_text = block["text"].rstrip() + elif type == "table": + block_text = html_table_to_text(block["table_body"]) + else: + 跳过 (image / equation 等) + + 记录 BlockSpan(char_start=cursor, char_end=cursor+len(block_text)) + buffer.append(block_text + "\n") + cursor += len(block_text) + 1 + + PageText.text = "".join(buffer).rstrip("\n") +``` + +### 5.4 偏移映射数据结构 + +```python +@dataclasses.dataclass +class BlockSpan: + block_index: int # content_list 数组下标 + block_type: str # "text" | "table" + page_idx: int # 页码 + char_start: int # 在拼接文本中的起始位置 + char_end: int # 在拼接文本中的结束位置（不含） + bbox: list[int] # MinerU 原始 bbox + +@dataclasses.dataclass +class PageText: + page_idx: int # 页码 + text: str # 拼接后的纯文本 + block_spans: list[BlockSpan] # 每个 block 在 text 中的位置 +``` + +### 5.5 HTML 表格转换 + +```python +def html_table_to_text(table_body: str) -> str: + """Convert
HTML → pipe-delimited plain text""" + soup = BeautifulSoup(table_body, "html.parser") + rows = [] + for tr in soup.find_all("tr"): + cells = [td.get_text(strip=True) for td in tr.find_all(["td", "th"])] + rows.append(" | ".join(cells)) + return "\n".join(rows) +``` + +转换示例： + +```html +
MethodScore
GraphRAG0.85
+``` + +→ + +``` +Method | Score +GraphRAG | 0.85 +``` + +--- + +## 6. Bridge Pipeline 最终输出关键参数规范 + +### 6.1 kg_nodes.json + +**文件路径**: `graphrag_pipeline/output/kg_nodes.json` + +**结构**: JSON 数组，每个元素为一个去重后的实体节点。 + +| 字段 | 类型 | 说明 | 示例 | +|------|------|------|------| +| `id` | string | 节点唯一标识，格式 `node_{index}` | `"node_0"` | +| `name` | string | 实体名称（原文子串） | `"GraphRAG"` | +| `type` | string | 实体类型 | `"TECHNOLOGY"` | +| `source_doc` | string | 来源文档 UUID | `"8a719db4-2b50-405b-826d-7bb27b224fa0"` | +| `char_start` | int | 在拼接文本中的起始字符位置 | `0` | +| `char_end` | int | 在拼接文本中的结束字符位置 | `8` | +| `confidence` | string | 对齐质量（仅 `match_exact`/`match_greater`/`match_lesser`） | `"match_exact"` | +| `page` | int | 来源页码（从 0 开始） | `0` | + +**去重规则**: key = `(name.lower(), type)`，保留首次出现的实体。 + +**实体类型枚举**: + +| 类型 | 说明 | +|------|------| +| `TECHNOLOGY` | 软件、算法、模型、工具 | +| `ORGANIZATION` | 公司、研究机构 | +| `PERSON` | 个人 | +| `LOCATION` | 地理位置 | +| `CONCEPT` | 技术概念、方法论、框架 | + +### 6.2 kg_edges.json + +**文件路径**: `graphrag_pipeline/output/kg_edges.json` + +**结构**: JSON 数组，每个元素为一条同页共现关系边。 + +| 字段 | 类型 | 说明 | 示例 | +|------|------|------|------| +| `source` | string | 源节点 ID | `"node_0"` | +| `target` | string | 目标节点 ID | `"node_1"` | +| `relation` | string | 关系类型（固定 `"CO_OCCURS_IN"`） | `"CO_OCCURS_IN"` | +| `doc_id` | string | 来源文档 UUID | `"8a719db4-..."` | +| `page` | int | 共现页码 | `0` | + +**边生成规则**: +1. 按页分组所有去重后的节点 ID +2. 同页节点两两配对 → 生成 `CO_OCCURS_IN` 边 +3. 边方向规范化: `source < target`（字典序） +4. 去重 key: `(source, target, doc_id, page)` +5. 无自环（source ≠ target） + +**边数公式**: 若某页有 N 个节点，则该页产生 C(N,2) = N×(N-1)/2 条边。 + +### 6.3 输出完整性约束 + +| 约束 | 说明 | +|------|------| +| 节点 ID 唯一 | 每个节点的 `id` 字段全局唯一 | +| 边引用合法 | 每条边的 `source` 和 `target` 必须对应存在的节点 `id` | +| 无自环 | 不存在 `source == target` 的边 | +| 无重复边 | 同一 `(source, target, doc_id, page)` 组合仅出现一次 | +| 对齐质量保证 | 所有节点的 `confidence` 仅为 accepted 值（非 fuzzy/null） | +| char 偏移有效 | `char_start < char_end`，且可定位到拼接文本中的实体子串 | + +--- + +## 7. 虚拟环境规范 + +Bridge Pipeline **复用 LangExtract 的虚拟环境**，不单独创建 venv。 + +| 项目 | 值 | +|------|------| +| 虚拟环境路径 | `F:\GraphRAGAgent\langextract_src\.venv\` | +| Python 版本 | 3.12 | +| 核心依赖 | `langextract[all]`、`beautifulsoup4`、`python-dotenv` | +| 安装新依赖 | `uv pip install --python F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe` | + +**所有 Python 命令必须使用该虚拟环境运行，禁止使用全局 Python 或其他组件的 venv。** + +--- + +## 8. 环境配置 + +### 8.1 .env 文件 + +位置: `F:\GraphRAGAgent\graphrag_pipeline\.env` + +```env +DEEPSEEK_API_KEY= +DEEPSEEK_BASE_URL=https://api.deepseek.com +``` + +### 8.2 依赖安装 + +```bash +uv pip install beautifulsoup4 python-dotenv --python F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe +``` + +--- + +## 9. 测试验证清单 + +- [x] text_assembler 正确读取 content_list.json（10 blocks: 9 text + 1 table） +- [x] 表格 HTML 转为 pipe 分隔纯文本，无 HTML 标签残留 +- [x] 按页拼接文本长度合理（2102 字符/页） +- [x] LangExtract 成功调用 DeepSeek 返回 AnnotatedDocument +- [x] 抽取实体数 45，match_exact 占比 > 95% +- [x] kg_nodes.json 节点已去重（40 个），每个节点有完整字段 +- [x] kg_edges.json 边为 CO_OCCURS_IN 关系（780 条），无自环，无重复 +- [x] match_fuzzy 对齐的实体已被过滤（1 个） diff --git a/docs/frontend_design_specification-v1.0.md b/docs/frontend_design_specification-v1.0.md new file mode 100644 index 0000000..3278855 --- /dev/null +++ b/docs/frontend_design_specification-v1.0.md @@ -0,0 +1,1232 @@ +# GraphRAG Studio — 前端 Web 系统设计规范 v1.0 + +> 基于 `docs/backend_service_specification-v1.0.md` 接口规范 +> 前端架构：原生 HTML + CSS + JS + D3.js v7（SPA，零构建依赖） +> 更新日期：2026-03-05 + +--- + +## 目录 + +- [一、总体架构](#一总体架构) +- [二、设计语言与风格系统](#二设计语言与风格系统) +- [三、整体布局](#三整体布局) +- [四、页面清单与详细设计](#四页面清单与详细设计) + - [Page 1 — Dashboard](#page-1--dashboard-dashboard) + - [Page 2 — Document Manager](#page-2--document-manager-documents) + - [Page 3 — KG Explorer](#page-3--kg-explorer-graph) + - [Page 4 — QA Chat](#page-4--qa-chat-chat) + - [Page 5 — Search](#page-5--search-search) +- [五、响应式设计规范](#五响应式设计规范) +- [六、关键交互模式规范](#六关键交互模式规范) +- [七、文件结构](#七文件结构) + +--- + +## 一、总体架构 + +### 1.1 技术选型 + +| 组件 | 选择 | 理由 | +|------|------|------| +| 应用类型 | **SPA（单页应用）** | 5 页无缝切换，无刷新体验 | +| 路由 | **Hash 路由**（原生 JS） | 无需构建工具，`#/dashboard` `#/documents` 等 | +| 框架 | **原生 HTML + CSS + JS** | 与现有 `index.html` 一致，零构建依赖，直接在浏览器运行 | +| 图形渲染 | **D3.js v7**（CDN） | 复用现有 KG 可视化逻辑（`graphrag_pipeline/static/index.html`） | +| Markdown 渲染 | **marked.js v9**（CDN） | Chat 页 AI 答案 Markdown 渲染 | +| API 通信 | **Fetch API** | 原生支持，封装统一错误处理 | +| 图标 | **Unicode / SVG 内联** | 零依赖（无需图标库 CDN） | + +### 1.2 路由设计 + +``` +hash 路由 → DOM 区域显示/隐藏 + +#/dashboard → 显示
+#/documents → 显示
+#/graph → 显示
+ 初始化 D3 +#/chat → 显示
+#/search → 显示
+/ → 重定向到 #/dashboard +``` + +**URL 参数传递（hash query）：** +``` +#/graph?doc_id=abc12345 → KG Explorer 按文档筛选 +#/graph?node=tech_graphrag_0 → KG Explorer 聚焦节点 +#/chat?q=What+is+GraphRAG → Chat 预填问题 +``` + +### 1.3 全局状态管理 + +```js +// app.js 中维护的全局状态（内存） +const AppState = { + currentPage: 'dashboard', + kg: { + nodes: [], // 全量节点（加载后缓存） + edges: [], // 全量边（加载后缓存） + loaded: false, + }, + documents: [], // 文档列表缓存 + activeJobs: {}, // job_id → polling timer + chatHistory: [], // 当前会话消息历史 + health: null, // 最近一次 health 响应 +}; +``` + +### 1.4 API 客户端（`api.js`） + +```js +const API = { + BASE: 'http://localhost:8000/api/v1', + + async get(path, params = {}) { + const url = new URL(this.BASE + path); + Object.entries(params).forEach(([k, v]) => v != null && url.searchParams.set(k, v)); + const res = await fetch(url); + const json = await res.json(); + if (json.code !== 0) throw new APIError(json.code, json.msg); + return json.data; + }, + + async post(path, body = {}) { + const res = await fetch(this.BASE + path, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(body), + }); + const json = await res.json(); + if (json.code !== 0) throw new APIError(json.code, json.msg); + return json.data; + }, + + async postForm(path, formData) { + const res = await fetch(this.BASE + path, { method: 'POST', body: formData }); + const json = await res.json(); + if (json.code !== 0) throw new APIError(json.code, json.msg); + return json.data; + }, + + async delete(path) { ... }, + + // 轮询直到条件满足 + async poll(path, params = {}, interval = 3000, until = (d) => d.status === 'done') { + return new Promise((resolve, reject) => { + const timer = setInterval(async () => { + try { + const data = await this.get(path, params); + if (until(data)) { clearInterval(timer); resolve(data); } + if (data.status === 'failed') { clearInterval(timer); reject(new Error(data.error)); } + } catch (e) { clearInterval(timer); reject(e); } + }, interval); + }); + } +}; +``` + +### 1.5 文件结构 + +``` +graphrag_pipeline/static/ +├── index.html # 保留（旧 Flask KG 可视化，向后兼容） +└── app/ + ├── index.html # SPA 主入口（GraphRAG Studio） + ├── css/ + │ ├── variables.css # CSS 变量（颜色 / 间距 / 字体） + │ ├── base.css # Reset + 通用组件（btn, badge, card, modal...） + │ └── layout.css # Sidebar + Header + Footer 布局 + 响应式 + └── js/ + ├── app.js # 路由器 + 页面切换 + 全局状态 + ├── api.js # Fetch 封装（baseURL, 统一错误处理） + ├── components.js # Toast / Modal / Progress / Skeleton + └── pages/ + ├── dashboard.js + ├── documents.js + ├── graph.js # D3 力导向图（基于现有 index.html 重构） + ├── chat.js + └── search.js +``` + +--- + +## 二、设计语言与风格系统 + +### 2.1 调色板（基于现有 index.html 扩展） + +```css +:root { + /* ── 背景层级 ── */ + --bg-base: #0f1117; /* 页面底色 */ + --bg-s1: #161b22; /* sidebar / header / card surface */ + --bg-s2: #21262d; /* hover state / input bg / tag bg */ + --bg-s3: #1c2128; /* tooltip / popover / code bg */ + + /* ── 边框 ── */ + --border: #30363d; + --border-muted:#21262d; + + /* ── 文字 ── */ + --text-1: #f0f6fc; /* 主文字 */ + --text-2: #c9d1d9; /* 正文 */ + --text-3: #8b949e; /* 辅助/label */ + --text-4: #484f58; /* placeholder / 极弱 */ + + /* ── 强调色 ── */ + --blue: #58a6ff; /* 链接 / 激活 / 聚焦 / 进度条 */ + --green: #3fb950; /* 成功 / indexed 状态 */ + --green-btn: #238636; /* 主操作按钮背景 */ + --green-hover: #2ea043; /* 主按钮 hover */ + --red: #f85149; /* 错误 / 危险 / failed */ + --yellow: #d29922; /* 警告 / indexing */ + --purple: #8957e5; /* 紫色强调 */ + + /* ── 实体类型颜色（与 D3 图谱一一对应）── */ + --type-tech: #58a6ff; /* TECHNOLOGY — 蓝 */ + --type-concept:#bc8cff; /* CONCEPT — 紫 */ + --type-person: #3fb950; /* PERSON — 绿 */ + --type-org: #ff7b72; /* ORGANIZATION— 红 */ + --type-loc: #ffa657; /* LOCATION — 橙 */ + + /* ── 字体 ── */ + --font-sans: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; + --font-mono: 'SFMono-Regular', Consolas, 'Liberation Mono', monospace; + + /* ── 圆角 ── */ + --r-sm: 4px; + --r-md: 6px; + --r-lg: 8px; + --r-xl: 12px; + + /* ── 阴影 ── */ + --shadow-sm: 0 1px 3px rgba(0,0,0,0.4); + --shadow-md: 0 4px 16px rgba(0,0,0,0.5); + --shadow-lg: 0 8px 32px rgba(0,0,0,0.6); + + /* ── 过渡 ── */ + --transition: 150ms ease; +} +``` + +### 2.2 按钮规范（4 变体） + +| 变体 | 背景 | 边框 | 文字 | 用途 | +|------|------|------|------|------| +| `.btn-primary` | `--green-btn` | `--green-btn` | `#fff` | 主操作（Upload, Send, Index） | +| `.btn-secondary` | `--bg-s2` | `--border` | `--text-2` | 次要操作（Cancel, Filter） | +| `.btn-ghost` | transparent | none | `--text-3` | 内联操作（图标按钮） | +| `.btn-danger` | `--bg-s2` | `--border` | `--red` | 危险操作（Delete） | + +```css +.btn { + padding: 6px 14px; + border-radius: var(--r-md); + font-size: 13px; + font-weight: 500; + cursor: pointer; + transition: all var(--transition); + display: inline-flex; align-items: center; gap: 6px; +} +.btn-primary { background: var(--green-btn); border: 1px solid var(--green-btn); color: #fff; } +.btn-primary:hover { background: var(--green-hover); } +.btn-sm { padding: 4px 10px; font-size: 12px; } +``` + +### 2.3 Status Badge 规范 + +```css +/* 通用 badge */ +.badge { + display: inline-flex; align-items: center; gap: 5px; + padding: 2px 8px; + border-radius: 20px; + font-size: 11px; font-weight: 600; +} + +/* 状态 badge */ +.badge-indexed { background: #1a3a22; color: var(--green); } +.badge-indexing { background: #2d2a16; color: var(--yellow); } +.badge-uploaded { background: var(--bg-s3); color: var(--text-3); } +.badge-failed { background: #3b1a1a; color: var(--red); } + +/* 实体类型 badge */ +.badge-TECHNOLOGY { background: #162032; color: var(--type-tech); } +.badge-CONCEPT { background: #1e1632; color: var(--type-concept); } +.badge-PERSON { background: #132318; color: var(--type-person); } +.badge-ORGANIZATION { background: #2e1a1a; color: var(--type-org); } +.badge-LOCATION { background: #2a1e10; color: var(--type-loc); } + +/* 活跃点（动画） */ +.badge-indexing::before { + content: ''; width: 6px; height: 6px; border-radius: 50%; + background: var(--yellow); + animation: pulse 1.5s infinite; +} +@keyframes pulse { 0%,100% { opacity: 1; } 50% { opacity: 0.3; } } +``` + +### 2.4 字体层级 + +| 层级 | size | weight | color | 用途 | +|------|------|--------|-------|------| +| `text-h1` | 20px | 600 | `--text-1` | 页面大标题 | +| `text-h2` | 16px | 600 | `--text-1` | 区块标题 | +| `text-h3` | 13px | 600 uppercase | `--text-3` | 面板子标题 | +| `text-body` | 14px | 400 | `--text-2` | 正文 | +| `text-sm` | 12px | 400 | `--text-3` | 辅助信息 | +| `text-badge` | 11px | 600 | — | 标签/徽章 | +| `text-mono` | 13px | 400 | `--text-2` | 代码/路径/工具输出 | + +--- + +## 三、整体布局 + +### 3.1 CSS Grid 骨架 + +``` +┌──────────────────────────────────────────────────────────────┐ +│ HEADER (56px, position: sticky, top: 0, z-index: 100) │ +│ [≡] GraphRAG Studio [🔍 Search entities...] [● API] │ +├─────────┬────────────────────────────────────────────────────┤ +│ │ │ +│ SIDEBAR │ MAIN CONTENT AREA │ +│ (220px) │ (overflow-y: auto) │ +│ fixed │ │ +│ ────── │ — 各页面
区域 — │ +│ Home │ │ +│ Docs │ │ +│ Graph │ │ +│ Chat │ │ +│ Search │ │ +│ ────── │ │ +│ System │ │ +│ │ │ +├─────────┴────────────────────────────────────────────────────┤ +│ STATUS BAR (32px) [job 进度] [v1.0.0] [● ok] │ +└──────────────────────────────────────────────────────────────┘ +``` + +```css +.app { + display: grid; + grid-template-areas: "header header" "sidebar main" "footer footer"; + grid-template-columns: var(--sidebar-w, 220px) 1fr; + grid-template-rows: 56px 1fr 32px; + height: 100vh; + overflow: hidden; +} +header { grid-area: header; } +.sidebar{ grid-area: sidebar; overflow-y: auto; } +main { grid-area: main; overflow-y: auto; } +footer { grid-area: footer; } +``` + +### 3.2 Sidebar 导航结构 + +``` +┌─────────────────────────┐ +│ G GraphRAG Studio │ ← Logo 区（16px font, weight 700） +├─────────────────────────┤ +│ ◈ Dashboard │ ← 激活状态: bg rgba(88,166,255,0.1), left border 2px #58a6ff +│ ▤ Documents [ 5] │ ← 右侧数量 badge（文档总数） +│ ◉ KG Explorer │ +│ ◇ Chat [ 50] │ ← 查询历史数 +│ ⊕ Search │ +├─────────────────────────┤ +│ ☰ System │ +└─────────────────────────┘ +``` + +```css +.nav-item { + display: flex; align-items: center; gap: 10px; + padding: 8px 16px; + border-radius: var(--r-md); + font-size: 14px; color: var(--text-3); + cursor: pointer; transition: all var(--transition); + border-left: 2px solid transparent; + margin: 1px 8px; +} +.nav-item:hover { background: var(--bg-s2); color: var(--text-2); } +.nav-item.active { + background: rgba(88,166,255,0.1); + color: var(--blue); + border-left-color: var(--blue); +} +.nav-badge { + margin-left: auto; + background: var(--bg-s2); + color: var(--text-3); + font-size: 11px; font-weight: 600; + padding: 1px 6px; + border-radius: 10px; +} +``` + +### 3.3 Header 内容规范 + +``` +┌──────────────────────────────────────────────────────────────┐ +│ [≡] GraphRAG Studio │ [🔍 Search entities... ] │ [● healthy] API: 8000 │ +└──────────────────────────────────────────────────────────────┘ +``` + +| 区域 | 内容 | 说明 | +|------|------|------| +| 左 | `[≡]` 折叠按钮 + Logo 文字 | 点击折叠/展开 sidebar（220px ↔ 72px） | +| 中 | 全局搜索框 | 输入后跳转 `#/search?q={input}`，宽度 max 400px | +| 右 | Health 指示器 + API 地址 | 绿点=healthy, 红点=error | + +**全局搜索框触发逻辑：** +- 输入 3+ 字符 → 实时调用 `GET /api/v1/search/entities?q=...&limit=5` +- 下拉展示最多 5 条搜索建议（entity name + type badge） +- 按 Enter → 跳转 `#/search?q={input}` +- 点击建议项 → 跳转 `#/graph?node={node_id}` + +### 3.4 Status Bar 内容规范 + +``` +[Indexing paper.pdf... Stage: Extracting entities 2/4 ████████░░ 65%] v1.0.0 ● healthy +└── 有 active job 时显示 └── 常驻 +``` + +--- + +## 四、页面清单与详细设计 + +--- + +### Page 1 — Dashboard (`#/dashboard`) + +**目的：** 系统全局概览，快速导航到各功能，最近活动监控。 + +#### 4.1.1 布局 + +``` +┌──────────────────────────────────────────────────────────────┐ +│ Overview [+ Upload & Index] │ +├──────────┬──────────┬──────────┬────────────────────────────┤ +│ 40 │ 780 │ 5 │ 50 │ +│ Nodes │ Edges │ Docs │ Queries │ +│ KG 节点 │ KG 边 │ 文档总数 │ 问答次数 │ +├──────────┴──────────┴──────────┴────────────────────────────┤ +│ System Health │ +│ MinerU venv ● ok LangExtract venv ● ok │ +│ DeepSeek API ● ok Storage ● ok │ +├──────────────────────────────────────────────────────────────┤ +│ Recent Documents [View All →] │ +│ ────────────────────────────────────────────────────────── │ +│ paper.pdf PDF 4p ● indexed 2026-03-05 [KG] │ +│ report.docx DOCX 12p ● indexing [███░░░ 65%] [✕] │ +│ slides.pptx PPTX 24p ● uploaded ─ [▶] │ +├──────────────────────────────────────────────────────────────┤ +│ Quick Actions │ +│ [◉ Explore KG] [◇ Start Chat] [⊕ Search] [⚡ Demo] │ +└──────────────────────────────────────────────────────────────┘ +``` + +#### 4.1.2 指标卡设计 + +```css +.metrics-grid { + display: grid; + grid-template-columns: repeat(4, 1fr); + gap: 12px; + padding: 20px; +} +.metric-card { + background: var(--bg-s1); + border: 1px solid var(--border); + border-radius: var(--r-lg); + padding: 20px; + text-align: center; +} +.metric-value { font-size: 32px; font-weight: 700; color: var(--text-1); } +.metric-label { font-size: 12px; color: var(--text-3); margin-top: 4px; } +/* 悬停时用颜色区分不同指标 */ +.metric-card:nth-child(1) .metric-value { color: var(--blue); } /* Nodes */ +.metric-card:nth-child(2) .metric-value { color: var(--purple); } /* Edges */ +.metric-card:nth-child(3) .metric-value { color: var(--green); } /* Docs */ +.metric-card:nth-child(4) .metric-value { color: var(--yellow); } /* Queries */ +``` + +#### 4.1.3 API 调用时机 + +| 调用 | 时机 | 接口 | +|------|------|------| +| 加载指标卡 | 页面初始化 | `GET /api/v1/system/stats` | +| 加载 Health | 页面初始化 | `GET /api/v1/health` | +| 加载最近文档 | 页面初始化 | `GET /api/v1/documents?page=1&page_size=5` | +| 轮询刷新 | 每 10 秒 | `GET /api/v1/system/stats` + `GET /api/v1/health` | +| 行内启动 Index | 点击 `[▶]` | `POST /api/v1/index/start` | +| 行内 Index 进度 | 启动后每 3s | `GET /api/v1/index/status/{job_id}` | + +#### 4.1.4 交互逻辑 + +- `[+ Upload & Index]` → 打开 Upload Modal（同 Documents 页上传区），完成后刷新列表 +- `[KG]` → 跳转 `#/graph?doc_id={doc_id}` +- `[▶]` → 行内启动 indexing，进度条替换操作按钮，轮询进度 +- `[✕]` → 取消正在运行的 indexing job +- `[Explore KG]` → `#/graph` +- `[Start Chat]` → `#/chat` +- `[Search]` → `#/search` +- `[Demo]` → 调用 `GET /api/v1/system/demo`，将数据存入 `AppState.kg`，跳转 `#/graph` + +--- + +### Page 2 — Document Manager (`#/documents`) + +**目的：** 文件上传、列表管理、触发/监控索引任务、查看索引结果。 + +#### 4.2.1 布局 + +``` +┌──────────────────────────────────────────────────────────────┐ +│ Documents (5) [+ Upload Files] │ +├──────────────────────────────────────────────────────────────┤ +│ ┌──────────────────────────────────────────────────────┐ │ +│ │ ⬆ │ │ +│ │ Drag & Drop files here │ │ +│ │ PDF · DOCX · DOC · PPTX · PPT · PNG · JPG · HTML │ │ +│ │ Max 200MB per file │ │ +│ │ [Browse Files] │ │ +│ └──────────────────────────────────────────────────────┘ │ +├──────────────────────────────────────────────────────────────┤ +│ [All ▾] [Status: All ▾] [🔍 Filter docs...] │ +├──────────────────────────────────────────────────────────────┤ +│ Filename Format Pages Status Date Act │ +│ ──────────────────────────────────────────────────────── │ +│ paper.pdf PDF 4 ●indexed 03-05 [◉][🗑]│ +│ │ +│ report.docx DOCX 12 ●indexed 03-04 [◉][🗑]│ +│ ▼ 40 nodes · 780 edges · 4p · 45 ext · 42.1s │ +│ Types: TECHNOLOGY(4) CONCEPT(36) [View KG] [Show Ext] │ +│ │ +│ processing.pptx PPTX 24 ●indexing [✕] │ +│ Stage: Extracting entities page 2/4... │ +│ ████████████░░░░░░░░░░ 45% (18s) │ +│ │ +│ image.png PNG 1 ●uploaded 03-03 [▶][🗑]│ +│ │ +│ failed.pdf PDF - ●failed 03-02 [⟳][🗑]│ +│ Error: MinerU failed: timeout after 600s │ +└──────────────────────────────────────────────────────────────┘ +``` + +#### 4.2.2 上传区交互 + +``` +拖拽行为: + dragenter → 上传区边框变蓝 (#58a6ff)，背景 rgba(88,166,255,0.05) + dragleave → 恢复默认 + drop → 提取 event.dataTransfer.files，验证后开始上传 + +客户端预校验（drop / browse 后立即执行）: + 1. 扩展名检查（对照 ALLOWED_EXTENSIONS 列表） + 2. 文件大小 ≤ 200MB + → 校验失败: Toast 错误 + 文件名标红（不上传） + → 校验通过: 开始上传流程 + +多文件串行处理: + files[] → 逐一执行 Upload → IndexStart → Poll 流程 + 同时上传时显示队列状态 +``` + +#### 4.2.3 上传 + 索引完整流程 + +``` +Step 1: POST /api/v1/documents/upload (multipart/form-data) + ↓ 返回 doc_id + 行项目添加到列表，status: "uploaded" + +Step 2: 询问确认 "Start indexing now? [Yes] [Later]" + → Yes: 继续 Step 3 + → Later: 保持 "uploaded" 状态，显示 [▶] 按钮 + +Step 3: POST /api/v1/index/start { doc_id } + ↓ 返回 job_id + status → "indexing"，显示进度条 + +Step 4: 轮询 GET /api/v1/index/status/{job_id}（每 3s） + progress.parsed_pages / total_pages → 进度百分比 + stage → 行内文字说明 + +Step 5: status=done: + GET /api/v1/index/result/{job_id} → 获取 stats + 更新 status → "indexed" + 展开结果摘要行（节点数/边数/耗时） + Toast: "✅ paper.pdf indexed: 40 nodes, 780 edges" + AppState.kg.loaded = false （触发 KG Explorer 重新加载） +``` + +#### 4.2.4 索引结果展开行 + +``` +▼ [展开] + 40 nodes · 780 edges · 4 pages · 45 extractions · 42.1s + TECHNOLOGY(4) CONCEPT(36) + [◉ View in KG] [≡ Show Extractions] + +[Show Extractions] 展开面板: + text type alignment page + ──────────────────────────────────────────── + GraphRAG TECHNOLOGY match_exact 0 + knowledge... CONCEPT match_exact 0 + MinerU TECHNOLOGY match_exact 1 + ...（滚动，最多显示 50 条） +``` + +--- + +### Page 3 — KG Explorer (`#/graph`) + +**目的：** 全屏交互式知识图谱可视化，节点筛选与详情查看。 + +#### 4.3.1 布局（三栏） + +``` +┌─────────────┬───────────────────────────────────┬─────────────┐ +│ FILTER │ D3 FORCE-DIRECTED GRAPH │ DETAIL │ +│ (280px) │ (flex:1) │ (300px) │ +│ │ ← 点击节点后出现 → │ │ +│ ─ Docs ─── │ │ [× Close] │ +│ [All Docs ▾]│ ●GraphRAG ─────── ●LLMs │ │ +│ │ / │ \ │ ┄ GraphRAG │ +│ ─ Types ── │ ● ● ● │ TECHNOLOGY│ +│ ☑ TECH (4) │ │ ────────── │ +│ ☑ CONC (36) │ [+ ][ -][⊡] │ Page: 0 │ +│ ☑ PERS (0) │ [🔍 Search node...] │ Conf: exact │ +│ ☑ ORG (0) │ │ Degree: 39 │ +│ ☑ LOC (0) │ │ Central: 1.0│ +│ │ │ ────────── │ +│ ─ Confidence│ │ Neighbors │ +│ ☑ exact │ [图例] │ (39 total) │ +│ ☑ greater │ ●TECH ●CONC ●PERS ●ORG ●LOC │ ●knowledge │ +│ ☑ lesser │ │ ●LLMs │ +│ □ fuzzy │ │ ●MinerU │ +│ │ │ [All 39 →] │ +│ ─ Export ── │ │ │ +│ [📷 PNG] │ │ [💬 Ask AI] │ +│ [⬇ JSON] │ │ │ +└─────────────┴───────────────────────────────────┴─────────────┘ +``` + +#### 4.3.2 D3 力导向图规范 + +**节点视觉映射：** + +| 属性 | 映射规则 | +|------|---------| +| 颜色 | entity type → 5 色（`--type-tech/concept/person/org/loc`） | +| 半径 | `r = Math.max(4, Math.log(degree + 1) * 4)` | +| 描边 | 正常: `1.5px same-color opacity 0.6`；hover: `2.5px white` | +| 透明度 | 正常: `0.9`；非聚焦（highlight 时）: `0.1` | + +**边视觉映射：** + +| 属性 | 规则 | +|------|------| +| 颜色 | `#30363d` | +| 透明度 | 正常 `0.25`；高亮节点相关边 `0.8` | +| 宽度 | `1px` | + +**Force 参数：** + +```js +d3.forceSimulation(nodes) + .force('link', d3.forceLink(edges).id(d => d.id).distance(60).strength(0.3)) + .force('charge', d3.forceManyBody().strength(-120)) + .force('center', d3.forceCenter(width / 2, height / 2)) + .force('collide', d3.forceCollide().radius(d => d.r + 4)) + .alphaDecay(0.02) +``` + +**交互事件：** + +``` +node.mouseover → 显示 Tooltip（name + type + page + conf + degree） +node.click → 右侧 Detail Panel 展开 + 高亮该节点（r 增大 1.5x） + 相连边 opacity 0.8，其余节点 opacity 0.1 +canvas.click → 取消选中，Detail Panel 收起，恢复所有透明度 +node.drag → pin 到固定位置（fx/fy 设置） +zoom → d3.zoom().scaleExtent([0.1, 8]) +``` + +**工具栏功能：** + +``` +[+] → transform.k * 1.3 (zoom in) +[-] → transform.k / 1.3 (zoom out) +[⊡ Fit] → fitToView()：计算节点范围，自动缩放和平移 +[🔍] → search input: GET /api/v1/search/entities?q=... + 找到节点 → 闪烁高亮 + 平移居中 +[📷 PNG] → html2canvas 或 SVG → PNG download +[⬇ JSON] → GET /api/v1/kg/export → 触发文件下载 +``` + +#### 4.3.3 URL 参数处理 + +```js +// graph.js 初始化时解析 +const params = new URLSearchParams(window.location.hash.split('?')[1]); +const docFilter = params.get('doc_id'); // 按文档筛选 +const nodeHighlight = params.get('node'); // 聚焦节点 + +if (docFilter) { + // 勾选对应 doc 的 checkbox，其余节点淡化 +} +if (nodeHighlight) { + // 找到对应节点，高亮 + Detail Panel 展开 + 平移居中 +} +``` + +#### 4.3.4 API 调用时机 + +| 调用 | 时机 | 接口 | +|------|------|------| +| 加载全量节点 | 页面初始化（优先读缓存） | `GET /api/v1/kg/nodes?page_size=200` | +| 加载全量边 | 页面初始化 | `GET /api/v1/kg/edges?page_size=500` | +| 节点详情 | 点击节点 | `GET /api/v1/kg/nodes/{node_id}` | +| 邻居列表 | 点击节点 | `GET /api/v1/kg/nodes/{node_id}/neighbors?hops=1` | +| 工具栏搜索 | 输入 2+ 字符，500ms debounce | `GET /api/v1/search/entities?q=...` | + +> **性能说明：** 当前 KG 为 40 节点 + 780 边，D3 可流畅渲染。若节点超过 500，启用 Canvas 渲染模式（D3 WebGL fallback）。 + +--- + +### Page 4 — QA Chat (`#/chat`) + +**目的：** 多轮 KG 问答，可视化 ReAct 推理过程，Cited Node 跳转联动。 + +#### 4.4.1 布局（双栏） + +``` +┌──────────────────┬────────────────────────────────────────────┐ +│ HISTORY (240px) │ CHAT AREA │ +│ │ │ +│ [+ New Chat] │ ── Welcome ──────────────────────────── ─│ +│ │ KG Assistant │ +│ ─ Today ────── │ Ask me anything about the knowledge │ +│ What is GraphRAG │ graph. Try one of the suggestions below. │ +│ How does MinerU │ [💡 Give me an overview of the KG] │ +│ │ [💡 List all TECHNOLOGY entities] │ +│ ─ Yesterday ─── │ [💡 How does GraphRAG relate to LLMs?] │ +│ List all tech... │ │ +│ │ ─── 2026-03-05 10:30 ────────────────── │ +│ │ │ +│ │ YOU 10:30 │ +│ │ What is GraphRAG and how does it │ +│ │ relate to knowledge graphs? │ +│ │ │ +│ │ KG ASSISTANT 10:30 │ +│ │ GraphRAG is a knowledge graph- │ +│ │ enhanced RAG system developed by │ +│ │ Microsoft Research... │ +│ │ │ +│ │ ▶ Tool Calls (2 steps) [展开] │ +│ │ │ +│ │ Cited: [GraphRAG] [knowledge graphs] │ +│ │ [LLMs] [retrieval-augmented...] │ +│ │ ↑ 点击跳转 KG Explorer │ +│ │ ⏱ 8.4s │ +│ ├────────────────────────────────────────────┤ +│ │ [Ask about the knowledge graph... ][▶] │ +└──────────────────┴────────────────────────────────────────────┘ +``` + +#### 4.4.2 消息气泡设计 + +```css +/* 用户消息（右对齐） */ +.msg-user { + align-self: flex-end; + background: var(--blue); + color: #fff; + border-radius: var(--r-lg) var(--r-lg) var(--r-sm) var(--r-lg); + padding: 10px 14px; + max-width: 70%; + font-size: 14px; +} + +/* AI 消息（左对齐） */ +.msg-assistant { + align-self: flex-start; + background: var(--bg-s1); + border: 1px solid var(--border); + border-radius: var(--r-sm) var(--r-lg) var(--r-lg) var(--r-lg); + padding: 12px 16px; + max-width: 85%; +} + +/* 消息内 Markdown 渲染 */ +.msg-assistant .answer-text { + font-size: 14px; line-height: 1.7; color: var(--text-2); + /* 使用 marked.js 渲染 */ +} +``` + +#### 4.4.3 Tool Call 展开面板 + +``` +▶ Tool Calls (2 steps) [展开 ▾ / 收起 ▲] + +── Step 1: search_entities ───────────────────────────── +Input: + { "query": "GraphRAG" } +Output: + Found 1 entity(ies) matching 'GraphRAG': + [TECHNOLOGY] "GraphRAG" (confidence=match_exact, page=0, id=tech_graphrag_0) + +── Step 2: get_neighbors ──────────────────────────────── +Input: + { "entity_name": "GraphRAG", "hops": 1 } +Output: + Neighbors of 'GraphRAG' [TECHNOLOGY] within 1 hop(s): + Hop 1 — 39 related entities: + [CONCEPT] knowledge graphs + [TECHNOLOGY] LLMs + ... +``` + +```css +.tool-calls-panel { + background: var(--bg-s3); + border: 1px solid var(--border); + border-radius: var(--r-md); + margin: 8px 0; + font-size: 12px; + font-family: var(--font-mono); + color: var(--text-3); + overflow: hidden; +} +.tool-call-step { + padding: 10px 14px; + border-bottom: 1px solid var(--border-muted); +} +.tool-call-name { color: var(--yellow); font-weight: 600; } +.tool-call-section { color: var(--text-4); margin-top: 4px; } +``` + +#### 4.4.4 Cited Nodes 设计 + +``` +Cited: [◉ GraphRAG] [◉ knowledge graphs] [◉ LLMs] ... + ↑ 点击 → window.location.hash = '#/graph?node=' + node_id + ↑ hover → 小 Tooltip 显示节点 type +``` + +```css +.cited-node-chip { + display: inline-flex; align-items: center; gap: 4px; + padding: 3px 8px; + border-radius: 12px; + border: 1px solid var(--border); + background: var(--bg-s2); + color: var(--blue); + font-size: 12px; + cursor: pointer; + transition: all var(--transition); +} +.cited-node-chip:hover { + background: rgba(88,166,255,0.15); + border-color: var(--blue); +} +``` + +#### 4.4.5 Thinking 动画 + +```html +
+ +
+``` + +```css +.thinking-indicator span { + width: 6px; height: 6px; + border-radius: 50%; + background: var(--text-3); + display: inline-block; + animation: thinking 1.4s infinite; +} +.thinking-indicator span:nth-child(2) { animation-delay: 0.2s; } +.thinking-indicator span:nth-child(3) { animation-delay: 0.4s; } +@keyframes thinking { 0%,80%,100%{transform:scale(0.7)}40%{transform:scale(1)} } +``` + +#### 4.4.6 API 调用时机 + +| 调用 | 时机 | 接口 | +|------|------|------| +| 发送问题 | 点击 Send / Enter | `POST /api/v1/query` | +| 加载历史列表 | 进入 Chat 页 | `GET /api/v1/query/history?page_size=20` | +| Cited Node 悬停 | mouseover 时 | `GET /api/v1/kg/nodes/{node_id}`（可缓存） | + +**多轮对话 history 维护（前端）：** + +```js +// 每次发送携带完整历史 +const payload = { + question: inputText, + history: AppState.chatHistory.flatMap(msg => [ + { role: 'human', content: msg.question }, + { role: 'ai', content: msg.answer } + ]) +}; +const result = await API.post('/query', payload); +AppState.chatHistory.push(result); +``` + +--- + +### Page 5 — Search (`#/search`) + +**目的：** 多模式知识图谱搜索（实体关键词 / 路径 / 子图）。 + +#### 4.5.1 布局 + +``` +┌──────────────────────────────────────────────────────────────┐ +│ Search Knowledge Graph │ +│ ┌──────────────────────────────────┐ [Type: All ▾] [Search]│ +│ │ 🔍 Enter entity name... │ │ +│ └──────────────────────────────────┘ │ +├──────────────────────────────────────────────────────────────┤ +│ [Entity Search ▌] [Path Search] [Graph Search] │ +├──────────────────────────┬───────────────────────────────────┤ +│ Results (2) │ Preview Graph │ +│ ───────────────── │ ────────────── │ +│ ┌────────────────────┐ │ │ +│ │ GraphRAG │ │ ●GraphRAG │ +│ │ TECHNOLOGY pg.0 │ │ ╱│╲ │ +│ │ Degree: 39 · exact │ │ ● ● ● │ +│ │ [View KG] [Chat] │ │ ↑ D3 mini-graph │ +│ ├────────────────────┤ │ (当前选中节点的1-hop邻居子图) │ +│ │ GraphRAG pipeline │ │ │ +│ │ CONCEPT pg.1 │ │ │ +│ │ Degree: 39 · exact │ │ │ +│ │ [View KG] [Chat] │ │ │ +│ └────────────────────┘ │ │ +└──────────────────────────┴───────────────────────────────────┘ +``` + +#### 4.5.2 Tab — Path Search + +``` +From: [🔍 GraphRAG... ▾] To: [🔍 LLMs... ▾] Hops: [3▾] [Find Path] + +─── Results: 1 path found (length 1) ─── + GraphRAG ──CO_OCCURS_IN──→ LLMs + +─── Visualization ─── + ●GraphRAG ───────────────── ●LLMs + (节点颜色 = type，边标注 relation) +``` + +**节点选择器设计：** +``` +点击 [🔍] → 展开搜索下拉 +输入关键词 → 实时调用 GET /api/v1/search/entities?q=...&limit=10 +选择节点 → 记录 node_id，显示名称 +``` + +#### 4.5.3 Tab — Graph Search + +``` +[retrieval ] [☑ Include Neighbors] [Search] + +Found 3 matching nodes · 87 subgraph edges +───────────────────────────────────── +[D3 子图可视化 - 全宽，仅展示匹配节点和邻边] +───────────────────────────────────── +Matched nodes: + ● retrieval-augmented generation [CONCEPT] page 0 + ● RAG systems [CONCEPT] page 0 + ● vector similarity search [CONCEPT] page 2 +``` + +#### 4.5.4 搜索框 URL 同步 + +``` +用户修改搜索框 → 更新 hash query: #/search?q={query}&type={type}&tab={tab} +页面初始化 → 解析 hash query → 预填表单 → 自动触发搜索 +从 Header 全局搜索跳转 → #/search?q={input} +``` + +#### 4.5.5 API 调用时机 + +| 调用 | 时机 | 接口 | +|------|------|------| +| 实体搜索 | 点击 Search / Enter | `GET /api/v1/search/entities?q=...&type=...` | +| 结果 Preview Graph | 点击搜索结果行 | `GET /api/v1/kg/nodes/{id}/neighbors?hops=1` | +| 路径搜索 | 点击 Find Path | `GET /api/v1/search/path?from=...&to=...` | +| 子图搜索 | 点击 Search | `GET /api/v1/search/graph?q=...&include_neighbors=true` | + +--- + +## 五、响应式设计规范 + +### 5.1 断点定义 + +| 断点名 | 宽度范围 | 布局策略 | +|--------|---------|---------| +| **Desktop** | > 1280px | 完整布局（Sidebar 220px + 内容 + Detail Panel） | +| **Laptop** | 1024 – 1280px | Sidebar 折叠为图标模式（72px），内容区扩展 | +| **Tablet** | 768 – 1024px | Sidebar 隐藏，汉堡菜单触发 Drawer，内容全屏 | +| **Mobile** | < 768px | 底部 Tab Bar，全屏内容，面板改为底部 Sheet | + +### 5.2 CSS 媒体查询框架 + +```css +/* ── Laptop: Sidebar 折叠 ── */ +@media (max-width: 1280px) { + .app { --sidebar-w: 72px; } + .nav-label, .nav-badge, .sidebar-logo-text { display: none; } + .nav-item { justify-content: center; padding: 12px; } +} + +/* ── Tablet: Sidebar 变 Drawer ── */ +@media (max-width: 1024px) { + .app { grid-template-columns: 0 1fr; } + .sidebar { + position: fixed; left: 0; top: 0; bottom: 0; + width: 220px; z-index: 500; + transform: translateX(-220px); + transition: transform 0.2s ease; + box-shadow: var(--shadow-lg); + } + .sidebar.open { transform: translateX(0); } + .sidebar-overlay { display: block; } /* 点击遮罩关闭 */ +} + +/* ── Mobile: 底部 Tab Bar ── */ +@media (max-width: 768px) { + .app { grid-template-rows: 56px 1fr 56px; } + .sidebar { display: none; } + .bottom-nav { display: flex; position: fixed; bottom: 0; ... } + + /* KG Explorer 三栏 → 全屏 */ + .graph-filter-panel { display: none; } + .graph-detail-panel { position: fixed; bottom: 0; left: 0; right: 0; + height: 60vh; border-radius: 16px 16px 0 0; z-index: 400; } + + /* Chat 历史面板 → 顶部 Drawer */ + .chat-history { position: fixed; top: 56px; left: 0; right: 0; + height: 50vh; z-index: 400; transform: translateY(-100%); } + + /* 指标卡 4 列 → 2 列 */ + .metrics-grid { grid-template-columns: repeat(2, 1fr); } + + /* Search 结果+预览 → 上下堆叠 */ + .search-results-layout { flex-direction: column; } +} +``` + +### 5.3 各页面移动端特殊处理 + +| 页面 | 桌面布局 | 移动端变化 | +|------|---------|---------| +| Dashboard | 4列指标卡 | 2×2 网格 | +| Documents | 表格列表 | 卡片堆叠（隐藏 Pages、Date 列） | +| KG Explorer | 三栏 | 图谱全屏，Filter 通过 FAB 触发底部 Sheet | +| Chat | 双栏 | 历史面板隐藏，顶部 [历史] 按钮触发 Drawer | +| Search | 双栏结果+预览 | 上下堆叠（预览缩小为 200px） | + +--- + +## 六、关键交互模式规范 + +### 6.1 Toast 通知系统 + +``` +位置: right: 24px; top: 72px (header 下方) +宽度: 320px +堆叠: 最多 3 条，从上往下，间距 8px +``` + +``` +类型视觉: + ✅ Success bg #1a3a22 border-left 3px #3fb950 icon ✓ + ⚠️ Warning bg #2d2a16 border-left 3px #d29922 icon ! + ❌ Error bg #3b1a1a border-left 3px #f85149 icon ✗ + ℹ️ Info bg #161f2e border-left 3px #58a6ff icon i + +生命周期: + 出现: slide-in from right (200ms ease-out) + 停留: 4000ms（hover 时暂停计时） + 消失: fade-out (300ms) → 从 DOM 移除 + +调用方式 (components.js): + Toast.success("paper.pdf indexed: 40 nodes, 780 edges") + Toast.error("Failed to upload: file too large") + Toast.info("Loading knowledge graph...") +``` + +### 6.2 全局 Loading 状态 + +``` +1. Header 进度条（API 请求时） + height: 2px; position: absolute; top: 0; width: 100% + 颜色: var(--blue) + 短请求: indeterminate animation + 长请求（轮询中）: 真实进度百分比 + +2. Skeleton Loader（列表加载时） + 模拟行结构的灰色矩形，shimmer 动画 + Document 列表: 3 行 skeleton rows + Entity 列表: 5 行 skeleton rows + +3. 图谱 Loading + svg 中央显示: "Loading KG... (40 nodes, 780 edges)" + 3 点动画 + +4. Chat Thinking + 见 4.4.5 三点跳动动画 +``` + +### 6.3 空状态（Empty State） + +每个页面无数据时的引导设计： + +``` +KG Explorer（无 KG 数据）: + ┌────────────────────────────────────┐ + │ ◉ (大图标) │ + │ No knowledge graph yet │ + │ Upload documents and start │ + │ indexing to build your KG │ + │ [Upload & Index →] │ + └────────────────────────────────────┘ + +Chat（无问题历史）: + 欢迎页 + Suggested Prompts（见 4.4.1） + +Search（无结果）: + "No entities found for 'query'" + "Try: different keyword, check KG Explorer" + [Explore KG] + +Documents（无文档）: + 直接聚焦上传区（拖拽提示更突出） +``` + +### 6.4 错误处理规范 + +| 错误类型 | 处理方式 | +|---------|---------| +| API code ≠ 0 | Toast.error(msg) + console.error | +| fetch 网络失败 | Toast.error("Network error. Is API server running on :8000?") + [Retry] 按钮 | +| 超时（QA > 60s） | Toast.warning("Request timeout, please try again") | +| code 3002 (KG 为空) | 页面内空状态引导，不弹 Toast | +| code 2001/3001 (不存在) | Toast.error + 刷新当前列表 | +| Indexing failed | 行内展示错误信息 + [⟳ Retry] 按钮 | +| 文件校验失败 | 文件名标红 + inline 错误说明（不弹 Toast） | + +### 6.5 页面间联动（Cross-page Navigation） + +| 触发位置 | 操作 | 目标 | 参数 | +|---------|------|------|------| +| Dashboard [KG] 按钮 | 点击 | KG Explorer | `#/graph?doc_id={doc_id}` | +| Dashboard [Explore KG] | 点击 | KG Explorer | `#/graph` | +| Dashboard [Demo] | 点击 | KG Explorer | 加载 demo → `#/graph` | +| Documents [◉] 按钮 | 点击 | KG Explorer | `#/graph?doc_id={doc_id}` | +| Chat Cited Node 标签 | 点击 | KG Explorer | `#/graph?node={node_id}` | +| KG Detail Panel [💬] | 点击 | Chat | `#/chat?q=Tell+me+about+{name}` | +| Search 结果 [View KG] | 点击 | KG Explorer | `#/graph?node={node_id}` | +| Search 结果 [Chat] | 点击 | Chat | `#/chat?q=What+is+{name}` | +| Header 全局搜索 | Enter | Search | `#/search?q={input}` | +| Header 全局搜索 | 点击建议项 | KG Explorer | `#/graph?node={node_id}` | + +### 6.6 确认对话框规范 + +``` +触发场景: 删除文档、取消 indexing job +样式: 居中 Modal（360px），遮罩层 rgba(0,0,0,0.6) + +┌─────────────────────────────────────┐ +│ Delete document? │ +│ │ +│ "paper.pdf" and all its associated │ +│ KG data will be permanently deleted│ +│ (40 nodes, 780 edges removed) │ +│ │ +│ [Cancel] [Delete →] │ +└─────────────────────────────────────┘ + +[Delete] → bg: --red，hover: #d73a2f +[Cancel] → btn-secondary +``` + +--- + +## 七、文件结构 + +### 7.1 新建文件清单 + +| 文件路径 | 说明 | +|---------|------| +| `graphrag_pipeline/static/app/index.html` | SPA 主入口，包含 5 个 `
` 页面区域，引入所有 CSS/JS | +| `graphrag_pipeline/static/app/css/variables.css` | CSS 变量定义（颜色、字体、圆角、阴影） | +| `graphrag_pipeline/static/app/css/base.css` | Reset + 通用组件样式（btn/badge/card/modal/toast/skeleton） | +| `graphrag_pipeline/static/app/css/layout.css` | 骨架布局（app grid）+ Sidebar + Header + Footer + 响应式 | +| `graphrag_pipeline/static/app/js/app.js` | 路由器 + 全局 AppState + 页面初始化调度 | +| `graphrag_pipeline/static/app/js/api.js` | Fetch 封装 + 错误处理 + 轮询 helper | +| `graphrag_pipeline/static/app/js/components.js` | Toast / Modal / Progress / Skeleton / Tooltip | +| `graphrag_pipeline/static/app/js/pages/dashboard.js` | Dashboard 页面逻辑 | +| `graphrag_pipeline/static/app/js/pages/documents.js` | Documents 页面逻辑（上传 + 索引流程） | +| `graphrag_pipeline/static/app/js/pages/graph.js` | KG Explorer + D3 力导向图（复用现有 index.html） | +| `graphrag_pipeline/static/app/js/pages/chat.js` | Chat 页面逻辑（消息渲染 + 工具链 + Cited Nodes） | +| `graphrag_pipeline/static/app/js/pages/search.js` | Search 页面逻辑（3 个 Tab 模式） | + +### 7.2 FastAPI 静态文件服务配置 + +在 `api_server.py` 中添加： + +```python +from fastapi.staticfiles import StaticFiles + +# 挂载新 SPA（GraphRAG Studio） +app.mount("/studio", StaticFiles(directory="static/app", html=True), name="studio") + +# 访问地址: http://localhost:8000/studio/ +``` + +### 7.3 访问入口 + +| 地址 | 说明 | +|------|------| +| `http://localhost:8000/studio/` | GraphRAG Studio（新 SPA） | +| `http://localhost:8000/` 或 `http://localhost:5000/` | 旧 Flask KG 可视化（向后兼容） | +| `http://localhost:8000/docs` | FastAPI 自动生成 Swagger UI | + +--- + +## 附：API 端点 → 页面使用矩阵 + +| API 端点 | Dashboard | Documents | KG Explorer | Chat | Search | +|---------|-----------|-----------|-------------|------|--------| +| `GET /system/stats` | ✅ | — | — | — | — | +| `GET /health` | ✅ | — | — | — | — | +| `GET /system/demo` | ✅ | — | ✅ | — | — | +| `GET /system/formats` | — | ✅ | — | — | — | +| `POST /documents/upload` | ✅ Modal | ✅ | — | — | — | +| `GET /documents` | ✅ Recent | ✅ List | — | — | — | +| `GET /documents/{id}` | — | ✅ | — | — | — | +| `DELETE /documents/{id}` | — | ✅ | — | — | — | +| `POST /index/start` | ✅ | ✅ | — | — | — | +| `GET /index/status/{id}` | ✅ Poll | ✅ Poll | — | — | — | +| `GET /index/result/{id}` | — | ✅ | — | — | — | +| `DELETE /index/jobs/{id}` | ✅ | ✅ | — | — | — | +| `GET /kg/nodes` | — | — | ✅ | — | — | +| `GET /kg/edges` | — | — | ✅ | — | — | +| `GET /kg/nodes/{id}` | — | — | ✅ Click | ✅ Hover | ✅ | +| `GET /kg/nodes/{id}/neighbors` | — | — | ✅ Detail | — | ✅ Preview | +| `GET /kg/stats` | — | — | ✅ | — | — | +| `GET /kg/export` | — | — | ✅ Download | — | — | +| `POST /query` | — | — | — | ✅ | — | +| `GET /query/history` | — | — | — | ✅ Sidebar | — | +| `POST /query/batch` | — | — | — | — | — | +| `GET /search/entities` | — | — | ✅ Toolbar | — | ✅ Tab1 | +| `GET /search/path` | — | — | — | — | ✅ Tab2 | +| `GET /search/graph` | — | — | — | — | ✅ Tab3 | diff --git a/docs/langextract_specification-v1.0.md b/docs/langextract_specification-v1.0.md new file mode 100644 index 0000000..6d70131 --- /dev/null +++ b/docs/langextract_specification-v1.0.md @@ -0,0 +1,604 @@ +# LangExtract Pipeline 规范文档 v1.0 + +> 基于 [google/langextract](https://github.com/google/langextract) 源码分析 + MVP 实测验证 +> 版本基线：2026-03-04 main 分支 +> 本地源码路径：`F:\GraphRAGAgent\langextract_src\` +> 测试脚本路径：`F:\GraphRAGAgent\langextract_src\mvp_test_deepseek.py` + +--- + +## 目录 + +- [〇、虚拟环境](#〇虚拟环境) +- [一、Pipeline 执行流程](#一pipeline-执行流程) + - [1.1 完整执行链路](#11-完整执行链路) + - [1.2 MVP 测试脚本](#12-mvp-测试脚本) + - [1.3 输入规范](#13-输入规范) + - [1.4 不支持的输入格式](#14-不支持的输入格式) +- [二、模型接入规范](#二模型接入规范) + - [2.1 模型路由机制](#21-模型路由机制) + - [2.2 DeepSeek 接入（实测验证）](#22-deepseek-接入实测验证) + - [2.3 路由陷阱与规避方案](#23-路由陷阱与规避方案) + - [2.4 OpenAI Provider 构造参数](#24-openai-provider-构造参数) +- [三、关键参数规范](#三关键参数规范) + - [3.1 extract() 核心参数](#31-extract-核心参数) + - [3.2 ExampleData 示例数据格式](#32-exampledata-示例数据格式) + - [3.3 Extraction 示例条目格式](#33-extraction-示例条目格式) + - [3.4 分块参数](#34-分块参数) + - [3.5 Resolver 对齐参数](#35-resolver-对齐参数) +- [四、输出数据格式规范](#四输出数据格式规范) + - [4.1 JSONL 输出文件（实际生成）](#41-jsonl-输出文件实际生成) + - [4.2 AnnotatedDocument 顶层结构](#42-annotateddocument-顶层结构) + - [4.3 Extraction 字段规范（实测对比）](#43-extraction-字段规范实测对比) + - [4.4 CharInterval 字符锚点](#44-charinterval-字符锚点) + - [4.5 AlignmentStatus 对齐状态枚举](#45-alignmentstatus-对齐状态枚举) + - [4.6 extraction_summary.json（自定义摘要）](#46-extraction_summaryjson自定义摘要) +- [五、本地生成文件清单](#五本地生成文件清单) +- [附录：环境变量与常量速查](#附录环境变量与常量速查) + +--- + +## 〇、虚拟环境 + +本组件使用独立的 Python 虚拟环境，与项目其他组件（MinerU MVP、GraphRAG Pipeline 等）完全隔离。 + +**所有 Python 命令必须在子虚拟环境中运行，禁止使用全局 Python 或其他组件的 venv。** + +### 环境信息 + +- 虚拟环境路径：`F:\GraphRAGAgent\langextract_src\.venv\` +- Python 版本：3.12 +- 创建工具：uv +- 安装方式：`uv pip install -e ".[all]"` （含 openai、google-genai 等 60 个包） + +### 运行方式 + +**方式一：直接使用 venv 内的 Python 解释器（推荐）** + +```bash +F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe mvp_test_deepseek.py +``` + +**方式二：先激活环境再运行** + +```bash +cd F:/GraphRAGAgent/langextract_src +source .venv/Scripts/activate +python mvp_test_deepseek.py +``` + +### 安装新依赖 + +```bash +uv pip install --python F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe +``` + +--- + +## 一、Pipeline 执行流程 + +### 1.1 完整执行链路 + +基于 MVP 实测验证的完整 Pipeline 分为 5 个阶段： + +``` +Step 0: 激活虚拟环境 + └── F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe + +Step 1: 准备输入 + ├── 构造纯文本字符串（str） + ├── 或构造 Document 对象列表 + └── LangExtract 仅接受纯文本，PDF/DOCX 等需前置解析 + +Step 2: 构造 Few-shot 示例 + ├── 创建 ExampleData 对象列表 + ├── 每个 ExampleData 包含：text（示例文本） + extractions（标注实体列表） + └── extraction_text 必须是 text 的精确子串 + +Step 3: 配置模型并调用 extract() + ├── 直接实例化 OpenAILanguageModel（DeepSeek 场景） + ├── 传入 model_id="deepseek-chat", base_url, api_key + └── 调用 lx.extract(text_or_documents=..., examples=..., model=model) + +Step 4: LangExtract 内部处理 + ├── 文本分块（基于句子边界，max_char_buffer=1000） + ├── 构造 Prompt（含 prompt_description + examples） + ├── 调用 LLM 推理（JSON 格式输出） + ├── 解析 LLM JSON 响应为 Extraction 对象 + └── 字符级对齐（char_interval + alignment_status） + +Step 5: 保存输出 + ├── lx.io.save_annotated_documents() → JSONL 文件 + └── 自定义 JSON 摘要（可选） +``` + +### 1.2 MVP 测试脚本 + +**文件路径：** `F:\GraphRAGAgent\langextract_src\mvp_test_deepseek.py` + +**执行命令：** + +```bash +F:/GraphRAGAgent/langextract_src/.venv/Scripts/python.exe mvp_test_deepseek.py +``` + +**脚本核心流程：** + +```python +from langextract.providers.openai import OpenAILanguageModel + +# Step 1: 直接实例化 OpenAI Provider（指向 DeepSeek） +model = OpenAILanguageModel( + model_id="deepseek-chat", + api_key="sk-...", + base_url="https://api.deepseek.com", +) + +# Step 2: 构造示例数据 +examples = [ + lx.data.ExampleData( + text="LangChain is a framework created by Harrison Chase...", + extractions=[ + lx.data.Extraction(extraction_class="TECHNOLOGY", extraction_text="LangChain"), + lx.data.Extraction(extraction_class="ORGANIZATION", extraction_text="Harrison Chase"), + ... + ], + ) +] + +# Step 3: 调用抽取 +result = lx.extract( + text_or_documents=input_text, + prompt_description="Extract named entities...", + examples=examples, + model=model, + show_progress=True, +) + +# Step 4: 保存结果 +lx.io.save_annotated_documents([result], output_name="graphrag_entities.jsonl", output_dir="mvp_output") +``` + +**实测结果：** + +| 指标 | 值 | +|------|-----| +| 输入文本长度 | 520 字符 | +| 模型 | deepseek-chat | +| 耗时 | 21.6 秒 | +| 提取实体数 | 17 | +| 实体类型分布 | TECHNOLOGY: 9, CONCEPT: 7, ORGANIZATION: 1 | +| 精确匹配率 | 16/17 (94.1%) — 仅 1 个 match_fuzzy | +| 输出文件 | 2 个（JSONL + JSON 摘要） | + +### 1.3 输入规范 + +LangExtract **仅接受纯文本**作为输入，支持以下 4 种传入方式： + +| 输入方式 | 示例 | 说明 | +|---------|------|------| +| **纯文本字符串** | `extract("这是一段文本...")` | 直接传入文本内容（MVP 实测使用此方式） | +| **URL** | `extract("https://example.com/article.txt")` | 自动下载 URL 文本内容（`fetch_urls=True`） | +| **Document 对象** | `extract([Document(text="...", document_id="doc1")])` | 传入 Document 可迭代集合 | +| **CSV 文件** | 通过 `Dataset` 类加载后传入 | 指定 text 列和 id 列 | + +### 1.4 不支持的输入格式 + +以下格式 **不被支持**，需要在 LangExtract 之前通过外部工具预处理为纯文本： + +| 格式 | 状态 | 预处理方案 | +|------|------|-----------| +| PDF | ❌ 不支持 | 使用 MinerU / PyMuPDF 先转文本 | +| DOCX | ❌ 不支持 | 使用 python-docx 先转文本 | +| HTML | ❌ 不支持 | 使用 BeautifulSoup 先提取文本 | +| 图片 | ❌ 不支持 | 使用 OCR 工具先识别文本 | +| Markdown（含媒体） | ❌ 不支持 | 需提取纯文本部分 | +| Excel / JSON | ❌ 不支持 | 需序列化为纯文本 | + +--- + +## 二、模型接入规范 + +### 2.1 模型路由机制 + +文件路径：`langextract/providers/patterns.py` + +LangExtract 通过 **正则匹配 `model_id`** 自动路由到对应的 Provider： + +| Provider | 匹配模式 | 优先级 | 示例模型 | +|----------|---------|--------|---------| +| **Gemini** | `^gemini` | 10 | `gemini-2.5-flash`, `gemini-1.5-pro` | +| **OpenAI** | `^gpt-4`, `^gpt4.`, `^gpt-5`, `^gpt5.` | 10 | `gpt-4o`, `gpt-4o-mini` | +| **Ollama** | `gemma`, `llama`, `mistral`, `phi`, `qwen`, `deepseek` 等 | 10 | `gemma2:2b`, `llama3.2:1b` | + +### 2.2 DeepSeek 接入（实测验证） + +> **重要发现：** 规范文档 v0 中描述的 `model_id="gpt-4o-mini"` + `language_model_params={"base_url": ...}` 方式 **实测不可用**，因为 `model_id` 同时用于路由和 API 调用，DeepSeek 不识别 `gpt-4o-mini` 模型名。 + +**正确方式 — 直接实例化 OpenAI Provider：** + +```python +from langextract.providers.openai import OpenAILanguageModel + +model = OpenAILanguageModel( + model_id="deepseek-chat", # DeepSeek 实际模型名 + api_key="sk-your-deepseek-key", + base_url="https://api.deepseek.com", +) + +result = lx.extract( + text_or_documents="...", + examples=[...], + model=model, # 通过 model 参数传入，绕过路由 + show_progress=True, +) +``` + +**实测验证状态：** DeepSeek `deepseek-chat` 模型通过此方式成功完成实体抽取，JSON 格式输出正常。 + +### 2.3 路由陷阱与规避方案 + +| 方案 | 能否工作 | 原因 | +|------|---------|------| +| `model_id="gpt-4o-mini"` + `language_model_params={"base_url": "https://api.deepseek.com"}` | **不能** | `model_id` 被同时用作 API 调用的 `model` 参数，DeepSeek 返回 `400 Model Not Exist` | +| `config=ModelConfig(model_id="deepseek-chat", provider="openai")` | **不能** | `_create_model_with_schema()` 中使用 `provider` 时未先调用 `load_builtins_once()`，导致 `No provider found` 错误（LangExtract 内部 bug） | +| `model=OpenAILanguageModel(model_id="deepseek-chat", ...)` | **可以** | 直接实例化绕过路由，`model_id` 正确传递给 DeepSeek API | + +### 2.4 OpenAI Provider 构造参数 + +文件路径：`langextract/providers/openai.py` + +```python +class OpenAILanguageModel(BaseLanguageModel): + def __init__( + self, + model_id: str = 'gpt-4o-mini', + api_key: str | None = None, + base_url: str | None = None, + organization: str | None = None, + format_type: FormatType = FormatType.JSON, + temperature: float | None = None, + max_workers: int = 10, + **kwargs, + ) +``` + +| 参数 | 默认值 | 说明 | +|------|--------|------| +| `model_id` | `gpt-4o-mini` | 模型标识（同时作为 API 调用的 model 参数） | +| `api_key` | `None` | 环境变量：`OPENAI_API_KEY` 或 `LANGEXTRACT_API_KEY` | +| `base_url` | `None` | 自定义 API 端点（DeepSeek 使用 `https://api.deepseek.com`） | +| `temperature` | `None` | 采样温度 | +| `format_type` | `JSON` | 输出格式（JSON Mode） | + +--- + +## 三、关键参数规范 + +### 3.1 extract() 核心参数 + +文件路径：`langextract/extraction.py` + +```python +def extract( + text_or_documents: typing.Any, # 必填：纯文本或 Document 列表 + prompt_description: str | None = None, # 抽取提示词 + examples: typing.Sequence[Any] | None = None, # 必填：Few-shot 示例 + model_id: str = "gemini-2.5-flash", # 模型标识（用于路由） + api_key: str | None = None, # API Key + model: typing.Any = None, # 预配置的模型实例（最高优先级） + max_char_buffer: int = 1000, # 分块最大字符数 + temperature: float | None = None, # 采样温度 + batch_length: int = 10, # 每批分块数 + max_workers: int = 10, # 最大并行线程 + additional_context: str | None = None, # 附加上下文 + resolver_params: dict | None = None, # 对齐参数 + language_model_params: dict | None = None, # Provider 构造参数 + extraction_passes: int = 1, # 抽取轮次 + context_window_chars: int | None = None, # 上下文窗口 + config: typing.Any = None, # ModelConfig 实例 + model_url: str | None = None, # 自托管端点 + show_progress: bool = True, # 显示进度条 + ... +) -> list[AnnotatedDocument] | AnnotatedDocument +``` + +**MVP 实测使用的参数组合：** + +| 参数 | 实测值 | 说明 | +|------|--------|------| +| `text_or_documents` | 520 字符纯文本 | GraphRAG 领域相关文本 | +| `prompt_description` | `"Extract named entities..."` | 指定 TECHNOLOGY/ORGANIZATION/CONCEPT 三类 | +| `examples` | 1 个 ExampleData（含 6 个 Extraction） | Few-shot 示例 | +| `model` | `OpenAILanguageModel` 实例 | 直接实例化，指向 DeepSeek | +| `show_progress` | `True` | 显示进度 | +| `max_char_buffer` | 1000（默认） | 文本未超过阈值，未触发分块 | + +### 3.2 ExampleData 示例数据格式 + +文件路径：`langextract/core/data.py` + +```python +@dataclasses.dataclass +class ExampleData: + text: str # 示例文本（必填） + extractions: list[Extraction] # 标注的实体列表（必填） +``` + +**MVP 实测示例：** + +```python +lx.data.ExampleData( + text="LangChain is a framework created by Harrison Chase for building " + "LLM applications. It integrates with OpenAI models and Pinecone " + "vector database for semantic search.", + extractions=[ + lx.data.Extraction(extraction_class="TECHNOLOGY", extraction_text="LangChain"), + lx.data.Extraction(extraction_class="ORGANIZATION", extraction_text="Harrison Chase"), + lx.data.Extraction(extraction_class="CONCEPT", extraction_text="LLM applications"), + lx.data.Extraction(extraction_class="TECHNOLOGY", extraction_text="OpenAI models"), + lx.data.Extraction(extraction_class="TECHNOLOGY", extraction_text="Pinecone"), + lx.data.Extraction(extraction_class="CONCEPT", extraction_text="semantic search"), + ], +) +``` + +**约束条件：** +- `extraction_text` **必须是** `text` 的精确子串（否则对齐失败） +- `extraction_class` 为自定义字符串，无预定义枚举 +- `examples` 列表不能为空（否则抛出 `ValueError`） +- 每个 ExampleData 可包含多个不同 `extraction_class` 的条目 + +### 3.3 Extraction 示例条目格式 + +```python +@dataclasses.dataclass(init=False) +class Extraction: + extraction_class: str # 必填：实体类型 + extraction_text: str # 必填：实体文本（须为原文子串） + attributes: dict[str, str | list[str]] | None = None # 可选：附加属性 + description: str | None = None # 可选：实体描述 +``` + +在 examples 中创建时只需要 `extraction_class` 和 `extraction_text`，其余字段由 LangExtract 在推理后自动填充。 + +### 3.4 分块参数 + +文件路径：`langextract/chunking.py` + +LangExtract 使用基于 **句子边界** 的确定性分块策略： + +| 参数 | 默认值 | 说明 | +|------|--------|------| +| `max_char_buffer` | 1000 | 每个分块最大字符数 | +| `context_window_chars` | `None` | 前一分块的上下文窗口（用于指代消解） | +| `batch_length` | 10 | 每批处理的分块数 | + +**分块策略：** +1. 如果单个句子超过 `max_char_buffer`，按换行符拆分 +2. 如果单个 token 超过 `max_char_buffer`，该 token 独占一个分块 +3. 如果多个句子可以放入 `max_char_buffer`，合并为一个分块 + +> **MVP 实测：** 输入文本 520 字符 < `max_char_buffer`（1000），整段文本作为单一分块处理，未触发分块逻辑。 + +### 3.5 Resolver 对齐参数 + +通过 `extract()` 的 `resolver_params` 字典传入： + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `enable_fuzzy_alignment` | `bool` | `True` | 精确匹配失败后是否尝试模糊匹配 | +| `fuzzy_alignment_threshold` | `float` | `0.75` | 模糊匹配最低 token 重叠比率 | +| `accept_match_lesser` | `bool` | `True` | 是否接受部分精确匹配 | +| `suppress_parse_errors` | `bool` | `False` | JSON 解析失败时是否继续 | + +> **MVP 实测：** 未传入 `resolver_params`，使用全部默认值。17 个抽取中 16 个 `match_exact`，1 个 `match_fuzzy`（"Microsoft Research"）。 + +--- + +## 四、输出数据格式规范 + +### 4.1 JSONL 输出文件（实际生成） + +**文件路径：** `mvp_output/graphrag_entities.jsonl` +**文件大小：** 4,650 bytes +**格式：** JSONL（JSON Lines），每行一个完整的 JSON 对象 + +保存 API： + +```python +lx.io.save_annotated_documents( + [result], + output_name="graphrag_entities.jsonl", + output_dir="mvp_output" +) +``` + +### 4.2 AnnotatedDocument 顶层结构 + +**实际 JSONL 输出的顶层字段（基于本地生成文件）：** + +| 字段 | 类型 | 实测值 | 说明 | +|------|------|--------|------| +| `text` | `string` | 520 字符 | 原始输入文本（完整保留） | +| `document_id` | `string` | `"doc_8498f2b6"` | 自动生成，格式 `doc_{uuid_hex[:8]}` | +| `extractions` | `array[Extraction]` | 17 个元素 | 抽取的实体列表 | + +> **注意：** JSONL 中字段顺序为 `extractions` → `text` → `document_id`（与 dataclass 定义顺序不同，以实际输出为准）。 + +### 4.3 Extraction 字段规范（实测对比） + +**实际输出的单条 Extraction 完整结构（摘自本地 JSONL 文件）：** + +```json +{ + "extraction_class": "TECHNOLOGY", + "extraction_text": "GraphRAG", + "char_interval": { + "start_pos": 0, + "end_pos": 8 + }, + "alignment_status": "match_exact", + "extraction_index": 1, + "group_index": 0, + "description": null, + "attributes": {} +} +``` + +**实测字段对比（官方 Schema vs 实际输出）：** + +| 字段 | 官方 Schema | 实际输出 | 差异说明 | +|------|------------|---------|---------| +| `extraction_class` | `string` | `string` | 一致 | +| `extraction_text` | `string` | `string` | 一致 | +| `char_interval` | `object \| null` | `object`（始终存在） | 实测 17 个全部有值 | +| `alignment_status` | `string \| null` | `string`（始终存在） | 实测 17 个全部有值 | +| `extraction_index` | `int \| null` | `int`（从 1 开始） | **实测从 1 开始，非 0** | +| `group_index` | `int \| null` | `int`（从 0 开始） | 实测从 0 开始递增 | +| `description` | `string \| null` | `null` | 未使用 description 提示时为 null | +| `attributes` | `dict \| null` | `{}`（空对象） | **实测为空对象 `{}`，非 `null`** | +| `token_interval` | `object \| null` | **不存在** | **实际 JSONL 输出中无此字段** | + +**关键差异总结：** + +1. `extraction_index` 从 **1** 开始（非 0） +2. `attributes` 未使用时输出空对象 `{}`（非 `null`） +3. `token_interval` 字段 **不在 JSONL 输出中**（仅存在于内存对象） + +### 4.4 CharInterval 字符锚点 + +```json +{ + "start_pos": 0, + "end_pos": 8 +} +``` + +- `start_pos`：起始位置（包含），0-indexed +- `end_pos`：结束位置（不包含） +- 语义：`source_text[start_pos:end_pos]` 即为实体在原文中的精确位置 + +**实测验证（以 "GraphRAG" 为例）：** + +```python +text = "GraphRAG is an advanced..." +text[0:8] # → "GraphRAG" ✓ 匹配 +``` + +### 4.5 AlignmentStatus 对齐状态枚举 + +| 状态值 | 序列化值 | 含义 | 可信度 | MVP 实测数量 | +|--------|---------|------|--------|-------------| +| `MATCH_EXACT` | `"match_exact"` | LLM 输出与原文完全匹配 | 最高 | **16** | +| `MATCH_GREATER` | `"match_greater"` | LLM 输出短于匹配到的原文 | 高 | 0 | +| `MATCH_LESSER` | `"match_lesser"` | LLM 输出长于匹配到的原文 | 中 | 0 | +| `MATCH_FUZZY` | `"match_fuzzy"` | 模糊匹配 | 低 | **1** | +| `None` | `null` | 未找到对齐 | 不可信 | 0 | + +> **实测精确匹配率：** 16/17 = 94.1%。唯一的 `match_fuzzy` 是 "Microsoft Research"。 + +### 4.6 extraction_summary.json（自定义摘要） + +**文件路径：** `mvp_output/extraction_summary.json` +**文件大小：** 2,863 bytes + +此文件由 MVP 测试脚本自行生成（非 LangExtract 原生输出），结构如下： + +```json +{ + "total_extractions": 17, + "extraction_classes": { + "TECHNOLOGY": 9, + "ORGANIZATION": 1, + "CONCEPT": 7 + }, + "extractions": [ + { + "class": "TECHNOLOGY", + "text": "GraphRAG", + "char_start": 0, + "char_end": 8, + "alignment": "match_exact" + } + ] +} +``` + +--- + +## 五、本地生成文件清单 + +MVP 测试后本地实际生成的文件（共 2 个输出文件）： + +``` +langextract_src/ +├── .env # DeepSeek API Key 配置 +├── .venv/ # 独立虚拟环境（Python 3.12） +├── mvp_test_deepseek.py # MVP 测试脚本 +└── mvp_output/ # 输出目录 + ├── graphrag_entities.jsonl # LangExtract 原生 JSONL 输出（4,650 bytes） + └── extraction_summary.json # 自定义 JSON 摘要（2,863 bytes） +``` + +| 文件 | 大小 | 来源 | 说明 | +|------|------|------|------| +| `graphrag_entities.jsonl` | 4,650 bytes | `lx.io.save_annotated_documents()` | LangExtract 原生输出，1 行 JSONL，含 17 个 Extraction | +| `extraction_summary.json` | 2,863 bytes | MVP 脚本自定义 | 扁平化摘要，含类型分布统计 | + +--- + +## 附录：环境变量与常量速查 + +### 环境变量 + +| 变量名 | 适用 Provider | 说明 | +|--------|--------------|------| +| `LANGEXTRACT_API_KEY` | 所有 | 通用 API Key 后备 | +| `GEMINI_API_KEY` | Gemini | Gemini API Key | +| `OPENAI_API_KEY` | OpenAI | OpenAI / DeepSeek API Key | +| `OLLAMA_BASE_URL` | Ollama | Ollama 服务地址（默认 `http://localhost:11434`） | + +### .env 配置（MVP 实测） + +```env +OPENAI_API_KEY=sk-55cb39b8a3284355bc80217c11c85d1f +``` + +### 模型优先级 + +``` +model（预配置的模型实例） > config（ModelConfig 实例） > model_id + api_key +``` + +> **MVP 实测使用 `model` 参数**（最高优先级），直接传入 `OpenAILanguageModel` 实例。 + +### 结构化输出支持 + +| Provider | Schema 类型 | 结构化输出模式 | +|----------|------------|---------------| +| Gemini | `GeminiSchema` | 严格结构化输出 | +| OpenAI | JSON Mode | 通过 `response_format` 约束 | +| Ollama | `FormatModeSchema` | JSON 模式（非严格） | + +### 17 个实测抽取实体完整列表 + +| # | extraction_class | extraction_text | char_interval | alignment_status | +|---|-----------------|-----------------|---------------|-----------------| +| 1 | TECHNOLOGY | GraphRAG | [0, 8] | match_exact | +| 2 | ORGANIZATION | Microsoft Research | [75, 93] | match_fuzzy | +| 3 | CONCEPT | retrieval-augmented generation | [24, 54] | match_exact | +| 4 | CONCEPT | knowledge graphs | [107, 123] | match_exact | +| 5 | TECHNOLOGY | GPT-4 | [156, 161] | match_exact | +| 6 | CONCEPT | multi-hop reasoning | [172, 191] | match_exact | +| 7 | CONCEPT | community detection algorithms | [209, 239] | match_exact | +| 8 | TECHNOLOGY | Leiden clustering | [248, 265] | match_exact | +| 9 | TECHNOLOGY | MinerU | [315, 321] | match_exact | +| 10 | TECHNOLOGY | LangExtract | [344, 355] | match_exact | +| 11 | TECHNOLOGY | Neo4j | [383, 388] | match_exact | +| 12 | CONCEPT | graph database | [396, 410] | match_exact | +| 13 | CONCEPT | pipeline | [424, 432] | match_exact | +| 14 | TECHNOLOGY | PDF documents | [443, 456] | match_exact | +| 15 | TECHNOLOGY | OCR | [465, 468] | match_exact | +| 16 | TECHNOLOGY | NLP | [473, 476] | match_exact | +| 17 | CONCEPT | knowledge graph | [504, 519] | match_exact | diff --git a/docs/langextract_specification.md b/docs/langextract_specification.md new file mode 100644 index 0000000..ffadba0 --- /dev/null +++ b/docs/langextract_specification.md @@ -0,0 +1,672 @@ +# LangExtract Pipeline 规范文档 + +> 基于 [google/langextract](https://github.com/google/langextract) 源码分析 +> 版本基线：2026-03-04 main 分支 + +--- + +## 目录 + +- [一、输入规范](#一输入规范) + - [1.1 核心入口函数签名](#11-核心入口函数签名) + - [1.2 支持的输入类型](#12-支持的输入类型) + - [1.3 Document 数据结构](#13-document-数据结构) + - [1.4 CSV Dataset 输入](#14-csv-dataset-输入) + - [1.5 URL 文本下载](#15-url-文本下载) + - [1.6 分块参数配置](#16-分块参数配置) + - [1.7 不支持的输入格式](#17-不支持的输入格式) +- [二、模型接入规范](#二模型接入规范) + - [2.1 模型路由机制](#21-模型路由机制) + - [2.2 Gemini Provider](#22-gemini-provider) + - [2.3 OpenAI Provider](#23-openai-provider) + - [2.4 Ollama Provider](#24-ollama-provider) + - [2.5 OpenAI 兼容接口适配（DeepSeek 等）](#25-openai-兼容接口适配deepseek-等) + - [2.6 模型优先级与配置覆盖关系](#26-模型优先级与配置覆盖关系) + - [2.7 关于 Embedding 模型](#27-关于-embedding-模型) +- [三、输出数据格式规范](#三输出数据格式规范) + - [3.1 AnnotatedDocument 结构](#31-annotateddocument-结构) + - [3.2 Extraction 结构](#32-extraction-结构) + - [3.3 CharInterval 字符锚点](#33-charinterval-字符锚点) + - [3.4 AlignmentStatus 对齐状态枚举](#34-alignmentstatus-对齐状态枚举) + - [3.5 Resolver 对齐参数](#35-resolver-对齐参数) + - [3.6 JSONL 输出文件格式](#36-jsonl-输出文件格式) + - [3.7 完整输出 JSON Schema 示例](#37-完整输出-json-schema-示例) + - [3.8 HTML 可视化输出](#38-html-可视化输出) +- [附录：环境变量与常量速查](#附录环境变量与常量速查) + +--- + +## 一、输入规范 + +### 1.1 核心入口函数签名 + +文件路径：`langextract/extraction.py` + +```python +def extract( + text_or_documents: typing.Any, + prompt_description: str | None = None, + examples: typing.Sequence[typing.Any] | None = None, + model_id: str = "gemini-2.5-flash", + api_key: str | None = None, + language_model_type: typing.Type[typing.Any] | None = None, # 已废弃 + format_type: typing.Any = None, + max_char_buffer: int = 1000, + temperature: float | None = None, + fence_output: bool | None = None, + use_schema_constraints: bool = True, + batch_length: int = 10, + max_workers: int = 10, + additional_context: str | None = None, + resolver_params: dict | None = None, + language_model_params: dict | None = None, + debug: bool = False, + model_url: str | None = None, + extraction_passes: int = 1, + context_window_chars: int | None = None, + config: typing.Any = None, + model: typing.Any = None, + *, + fetch_urls: bool = True, + prompt_validation_level: PromptValidationLevel = PromptValidationLevel.WARNING, + prompt_validation_strict: bool = False, + show_progress: bool = True, + tokenizer: Tokenizer | None = None, +) -> list[AnnotatedDocument] | AnnotatedDocument +``` + +**关键参数说明：** + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `text_or_documents` | `Any` | **必填** | 纯文本字符串、URL、或 `Document` 对象的可迭代集合 | +| `prompt_description` | `str \| None` | `None` | 抽取提示词，描述需要抽取什么实体 | +| `examples` | `Sequence[Any] \| None` | `None` | **必填** — Few-shot 示例列表（为空则抛出 ValueError） | +| `model_id` | `str` | `"gemini-2.5-flash"` | 模型标识符，用于自动路由到对应 Provider | +| `api_key` | `str \| None` | `None` | LLM API Key（也可通过环境变量设置） | +| `max_char_buffer` | `int` | `1000` | 每个文本分块的最大字符数 | +| `temperature` | `float \| None` | `None` | 采样温度（`None` 使用模型默认值） | +| `use_schema_constraints` | `bool` | `True` | 是否启用结构化输出约束 | +| `batch_length` | `int` | `10` | 每批处理的文本分块数量 | +| `max_workers` | `int` | `10` | 最大并行工作线程数 | +| `additional_context` | `str \| None` | `None` | 附加到推理提示词中的上下文信息 | +| `resolver_params` | `dict \| None` | `None` | 对齐解析器参数（见 [3.5 节](#35-resolver-对齐参数)） | +| `extraction_passes` | `int` | `1` | 抽取轮次（>1 时多次抽取并合并非重叠结果） | +| `context_window_chars` | `int \| None` | `None` | 前一分块的上下文窗口字符数（用于指代消解） | +| `model_url` | `str \| None` | `None` | 自托管模型的 API 端点 URL | +| `fetch_urls` | `bool` | `True` | 是否自动下载 http(s) URL 内容 | + +--- + +### 1.2 支持的输入类型 + +LangExtract **仅接受纯文本**作为输入，支持以下 4 种传入方式： + +| 输入方式 | 示例 | 说明 | +|---------|------|------| +| **纯文本字符串** | `extract("这是一段文本...")` | 直接传入文本内容 | +| **URL** | `extract("https://example.com/article.txt")` | 自动下载 URL 文本内容（`fetch_urls=True`） | +| **Document 对象** | `extract([Document(text="...", document_id="doc1")])` | 传入 Document 可迭代集合 | +| **CSV 文件** | 通过 `Dataset` 类加载后传入 | 指定 text 列和 id 列 | + +--- + +### 1.3 Document 数据结构 + +文件路径：`langextract/core/data.py` + +```python +@dataclasses.dataclass +class Document: + text: str # 必填 — 原始文本内容 + additional_context: str | None = None # 可选 — 附加上下文 + document_id: str # 自动生成 — 格式 "doc_{uuid_hex[:8]}" + tokenized_text: TokenizedText # 惰性计算 — 分词后的文本 +``` + +**字段说明：** + +- `text`：**必填**，原始文本内容，类型为 `str` +- `additional_context`：可选，会附加到推理提示词中 +- `document_id`：通过 property 访问，未设置时自动生成格式为 `doc_{uuid_hex[:8]}` 的唯一 ID +- `tokenized_text`：通过 property 惰性计算，使用配置的 Tokenizer 进行分词 + +--- + +### 1.4 CSV Dataset 输入 + +文件路径：`langextract/io.py` + +```python +@dataclasses.dataclass(frozen=True) +class Dataset: + input_path: pathlib.Path # CSV 文件路径 + id_key: str # 文档 ID 对应的列名 + text_key: str # 文本内容对应的列名 + + def load(self, delimiter: str = ',') -> Iterator[Document]: + """仅支持 .csv 后缀文件，其他格式抛出 NotImplementedError""" +``` + +**CSV 文件要求：** +- 文件后缀必须为 `.csv` +- 必须包含 `text_key` 指定的文本列和 `id_key` 指定的 ID 列 +- 默认分隔符为逗号（`,`），可通过 `delimiter` 参数修改 +- 其他文件格式会直接抛出 `NotImplementedError` + +--- + +### 1.5 URL 文本下载 + +文件路径：`langextract/io.py` + +```python +def download_text_from_url( + url: str, + timeout: int = 30, # 默认超时 30 秒 + show_progress: bool = True, + chunk_size: int = 8192, +) -> str +``` + +**URL 要求：** +- 必须以 `http://` 或 `https://` 开头 +- 仅下载文本内容（`response.text`），不解析 HTML/PDF 等 +- 需要 `fetch_urls=True`（默认开启） + +--- + +### 1.6 分块参数配置 + +文件路径：`langextract/chunking.py` + +LangExtract 使用基于**句子边界**的确定性分块策略（非语义分块），核心类为 `ChunkIterator`： + +```python +class ChunkIterator: + def __init__( + self, + text: str | TokenizedText | None, + max_char_buffer: int, # 每个分块最大字符数 + tokenizer_impl: Tokenizer, # 分词器实例 + document: Document | None = None, + ) +``` + +**分块策略：** + +1. 如果单个句子超过 `max_char_buffer`，按换行符拆分，同时尊重 token 边界 +2. 如果单个 token 超过 `max_char_buffer`，该 token 独占一个分块 +3. 如果多个句子可以放入 `max_char_buffer`，合并为一个分块 + +**TextChunk 输出结构：** + +```python +@dataclasses.dataclass +class TextChunk: + token_interval: TokenInterval # 在源文档中的 token 区间 + document: Document | None = None # 源文档引用 + + # 属性 + chunk_text: str # 重建的文本内容 + sanitized_chunk_text: str # 标准化空白的文本 + char_interval: CharInterval # 在源文档中的字符区间 + document_id: str | None # 源文档 ID +``` + +--- + +### 1.7 不支持的输入格式 + +以下格式 **不被支持**，需要在 LangExtract 之前通过外部工具预处理为纯文本： + +| 格式 | 状态 | 预处理方案 | +|------|------|-----------| +| PDF | ❌ 不支持 | 使用 MinerU / PyMuPDF 先转文本 | +| DOCX | ❌ 不支持 | 使用 python-docx 先转文本 | +| HTML | ❌ 不支持 | 使用 BeautifulSoup 先提取文本 | +| 图片 | ❌ 不支持 | 使用 OCR 工具先识别文本 | +| Markdown（含媒体） | ❌ 不支持 | 需提取纯文本部分 | +| Excel / JSON | ❌ 不支持 | 需序列化为纯文本 | + +--- + +## 二、模型接入规范 + +### 2.1 模型路由机制 + +文件路径：`langextract/providers/patterns.py` + +LangExtract 通过 **正则匹配 `model_id`** 自动路由到对应的 Provider： + +| Provider | 匹配模式 | 优先级 | 示例模型 | +|----------|---------|--------|---------| +| **Gemini** | `^gemini` | 10 | `gemini-2.5-flash`, `gemini-1.5-pro` | +| **OpenAI** | `^gpt-4`, `^gpt4.`, `^gpt-5`, `^gpt5.` | 10 | `gpt-4o`, `gpt-4o-mini` | +| **Ollama** | `gemma`, `llama`, `mistral`, `phi`, `qwen`, `deepseek` 等 | 10 | `gemma2:2b`, `llama3.2:1b` | + +Ollama 额外支持 HuggingFace 格式的模型名：`meta-llama/Llama*`, `google/gemma*`, `mistralai/*`, `microsoft/phi*` 等。 + +--- + +### 2.2 Gemini Provider + +文件路径：`langextract/providers/gemini.py` + +```python +class GeminiLanguageModel(BaseLanguageModel): + def __init__( + self, + model_id: str = 'gemini-2.5-flash', + api_key: str | None = None, + vertexai: bool = False, + credentials: Any | None = None, + project: str | None = None, + location: str | None = None, + http_options: Any | None = None, + gemini_schema: GeminiSchema | None = None, + format_type: FormatType = FormatType.JSON, + temperature: float = 0.0, + max_workers: int = 10, + fence_output: bool = False, + **kwargs, + ) +``` + +| 参数 | 默认值 | 说明 | +|------|--------|------| +| `model_id` | `gemini-2.5-flash` | Gemini 模型标识 | +| `api_key` | `None` | 环境变量：`GEMINI_API_KEY` 或 `LANGEXTRACT_API_KEY` | +| `vertexai` | `False` | 是否使用 Vertex AI 企业认证 | +| `temperature` | `0.0` | 采样温度（确定性输出） | +| `format_type` | `JSON` | 输出格式 | + +**运行时可配参数：** `temperature`, `max_output_tokens`, `top_p`, `top_k` + +**额外参数白名单：** `response_schema`, `response_mime_type`, `safety_settings`, `system_instruction`, `tools`, `stop_sequences`, `candidate_count` + +--- + +### 2.3 OpenAI Provider + +文件路径：`langextract/providers/openai.py` + +```python +class OpenAILanguageModel(BaseLanguageModel): + def __init__( + self, + model_id: str = 'gpt-4o-mini', + api_key: str | None = None, + base_url: str | None = None, + organization: str | None = None, + format_type: FormatType = FormatType.JSON, + temperature: float | None = None, + max_workers: int = 10, + **kwargs, + ) +``` + +| 参数 | 默认值 | 说明 | +|------|--------|------| +| `model_id` | `gpt-4o-mini` | OpenAI 模型标识 | +| `api_key` | `None` | 环境变量：`OPENAI_API_KEY` 或 `LANGEXTRACT_API_KEY` | +| `base_url` | `None` | 自定义 API 端点（用于兼容接口） | +| `organization` | `None` | OpenAI 组织 ID | +| `temperature` | `None` | 采样温度 | + +**运行时可配参数：** `temperature`, `max_output_tokens`, `top_p`, `frequency_penalty`, `presence_penalty`, `seed`, `stop`, `logprobs`, `top_logprobs`, `reasoning_effort`, `reasoning`, `response_format` + +--- + +### 2.4 Ollama Provider + +文件路径：`langextract/providers/ollama.py` + +```python +class OllamaLanguageModel(BaseLanguageModel): + def __init__( + self, + model_id: str, # 必填 + model_url: str = 'http://localhost:11434', + base_url: str | None = None, + format_type: FormatType | None = None, + constraint: Constraint = Constraint(), + timeout: int | None = None, + **kwargs, + ) +``` + +| 参数 | 默认值 | 说明 | +|------|--------|------| +| `model_id` | **必填** | Ollama 模型名（如 `gemma2:2b`） | +| `model_url` | `http://localhost:11434` | Ollama 服务地址 | +| `timeout` | `120` | 请求超时（秒） | +| `format_type` | `JSON` | 输出格式 | + +**内部默认常量：** + +| 常量 | 值 | 说明 | +|------|-----|------| +| `_DEFAULT_TEMPERATURE` | `0.1` | 默认温度 | +| `_DEFAULT_TIMEOUT` | `120` | 默认超时（秒） | +| `_DEFAULT_KEEP_ALIVE` | `300` | 模型保活时间（秒） | +| `_DEFAULT_NUM_CTX` | `2048` | 默认上下文窗口大小 | + +**认证支持：** 可配置 `api_key`、`auth_scheme`（默认 `Bearer`）、`auth_header`（默认 `Authorization`）用于代理 Ollama 实例。 + +--- + +### 2.5 OpenAI 兼容接口适配（DeepSeek 等） + +LangExtract 的 OpenAI Provider 支持 `base_url` 参数，因此可以接入任何 OpenAI 兼容 API： + +```python +# DeepSeek 接入示例 +result = lx.extract( + text_or_documents="...", + model_id="gpt-4o-mini", # 触发 OpenAI Provider 路由 + api_key="sk-your-deepseek-key", + examples=[...], + language_model_params={ + "base_url": "https://api.deepseek.com", + }, +) +``` + +> **注意：** 由于路由基于 `model_id` 正则匹配，使用 DeepSeek 等兼容接口时 `model_id` 仍需使用 `gpt-*` 前缀来命中 OpenAI Provider，或通过 `config` 参数显式指定 Provider。 + +--- + +### 2.6 模型优先级与配置覆盖关系 + +模型配置的优先级从高到低： + +``` +model（预配置的模型实例） > config（ModelConfig 实例） > model_id + api_key +``` + +**ModelConfig 结构**（`langextract/factory.py`）： + +```python +@dataclasses.dataclass(slots=True, frozen=True) +class ModelConfig: + model_id: str | None = None # 模型标识 + provider: str | None = None # 显式指定 Provider 名称 + provider_kwargs: dict[str, Any] = field(default_factory=dict) # Provider 构造参数 +``` + +--- + +### 2.7 关于 Embedding 模型 + +**LangExtract 不使用也不依赖任何 Embedding 模型。** + +- 文本分块使用基于句子边界的确定性分割算法，不涉及语义相似度计算 +- 没有向量索引或向量检索功能 +- 整个代码库中没有任何 Embedding 相关的调用 + +--- + +## 三、输出数据格式规范 + +### 3.1 AnnotatedDocument 结构 + +文件路径：`langextract/core/data.py` + +```python +@dataclasses.dataclass +class AnnotatedDocument: + extractions: list[Extraction] | None = None # 抽取结果列表 + text: str | None = None # 原始文本 + document_id: str # 文档唯一标识（自动生成） + tokenized_text: TokenizedText # 分词后文本（惰性计算） +``` + +**序列化后的 JSON 顶层字段：** + +| 字段 | 类型 | 说明 | +|------|------|------| +| `document_id` | `string` | 文档唯一标识，格式 `doc_{uuid_hex[:8]}` | +| `text` | `string \| null` | 原始输入文本 | +| `extractions` | `array[Extraction] \| null` | 抽取的实体列表 | + +--- + +### 3.2 Extraction 结构 + +文件路径：`langextract/core/data.py` + +```python +@dataclasses.dataclass(init=False) +class Extraction: + extraction_class: str # 实体类型 + extraction_text: str # 实体文本 + char_interval: CharInterval | None = None # 字符位置锚点 + alignment_status: AlignmentStatus | None = None # 对齐状态 + extraction_index: int | None = None # 抽取顺序索引 + group_index: int | None = None # 分组索引 + description: str | None = None # 实体描述 + attributes: dict[str, str | list[str]] | None = None # 附加属性 + token_interval: TokenInterval | None = None # Token 位置锚点 +``` + +**字段详细说明：** + +| 字段 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `extraction_class` | `str` | 是 | 实体类型/分类名称（如 `PERSON`, `ORGANIZATION`） | +| `extraction_text` | `str` | 是 | 抽取的文本内容（应为原文的子串） | +| `char_interval` | `CharInterval \| null` | 否 | 在原文中的字符偏移位置 | +| `alignment_status` | `string \| null` | 否 | 文本对齐质量（见 [3.4 节](#34-alignmentstatus-对齐状态枚举)） | +| `extraction_index` | `int \| null` | 否 | 在结果列表中的顺序位置 | +| `group_index` | `int \| null` | 否 | 分组归属（用于关联抽取） | +| `description` | `string \| null` | 否 | 对该实体的补充描述 | +| `attributes` | `dict \| null` | 否 | 键值对形式的附加属性 | +| `token_interval` | `TokenInterval \| null` | 否 | 在原文中的 token 偏移位置 | + +--- + +### 3.3 CharInterval 字符锚点 + +文件路径：`langextract/core/data.py` + +```python +@dataclasses.dataclass +class CharInterval: + start_pos: int | None = None # 起始位置（包含），0-indexed + end_pos: int | None = None # 结束位置（不包含） +``` + +**语义：** `source_text[start_pos:end_pos]` 即为抽取的文本在原文中的精确位置。 + +--- + +### 3.4 AlignmentStatus 对齐状态枚举 + +文件路径：`langextract/core/data.py` + +```python +class AlignmentStatus(enum.Enum): + MATCH_EXACT = "match_exact" + MATCH_GREATER = "match_greater" + MATCH_LESSER = "match_lesser" + MATCH_FUZZY = "match_fuzzy" +``` + +| 状态值 | 序列化值 | 含义 | 可信度 | +|--------|---------|------|--------| +| `MATCH_EXACT` | `"match_exact"` | LLM 输出与原文 token 序列完全匹配 | 最高 | +| `MATCH_GREATER` | `"match_greater"` | LLM 输出的 token 序列短于匹配到的原文（找到最佳重叠） | 高 | +| `MATCH_LESSER` | `"match_lesser"` | LLM 输出长于匹配到的原文（部分精确匹配） | 中 | +| `MATCH_FUZZY` | `"match_fuzzy"` | 模糊匹配，重叠率达到阈值（默认 ≥0.75） | 低 | +| `None` | `null` | 未找到任何对齐 | 不可信 | + +**对齐流程：** + +``` +1. 尝试精确 token 级别匹配（difflib） + ├── 成功且长度相等 → MATCH_EXACT + ├── 成功但 LLM 输出更长 → MATCH_LESSER + └── 成功但匹配区域更大 → MATCH_GREATER +2. 精确匹配失败且 enable_fuzzy_alignment=True + ├── 最佳重叠窗口 ≥ fuzzy_alignment_threshold → MATCH_FUZZY + └── 低于阈值 → None +3. 精确匹配失败且 enable_fuzzy_alignment=False → None +``` + +--- + +### 3.5 Resolver 对齐参数 + +文件路径：`langextract/resolver.py` + +通过 `extract()` 的 `resolver_params` 字典传入： + +```python +result = lx.extract( + ..., + resolver_params={ + "enable_fuzzy_alignment": True, # 是否启用模糊对齐（默认 True） + "fuzzy_alignment_threshold": 0.75, # 模糊匹配最低重叠率（默认 0.75） + "accept_match_lesser": True, # 是否接受 MATCH_LESSER（默认 True） + "suppress_parse_errors": False, # 是否忽略 JSON 解析错误（默认 False） + }, +) +``` + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `enable_fuzzy_alignment` | `bool` | `True` | 精确匹配失败后是否尝试模糊匹配 | +| `fuzzy_alignment_threshold` | `float` | `0.75` | 模糊匹配的最低 token 重叠比率（0.0~1.0） | +| `accept_match_lesser` | `bool` | `True` | 是否接受部分精确匹配结果 | +| `suppress_parse_errors` | `bool` | `False` | JSON 解析失败时是否继续而非报错 | + +--- + +### 3.6 JSONL 输出文件格式 + +文件路径：`langextract/io.py` + +```python +def save_annotated_documents( + annotated_documents: Iterator[AnnotatedDocument], + output_dir: pathlib.Path | str | None = None, + output_name: str = 'data.jsonl', + show_progress: bool = True, +) -> None +``` + +**输出规范：** +- 文件格式：**JSONL**（JSON Lines），每行一个完整的 JSON 对象 +- 默认文件名：`data.jsonl` +- 序列化规则： + - Enum 值转为字符串（如 `AlignmentStatus.MATCH_EXACT` → `"match_exact"`） + - NumPy / integral 数值类型转为 `int` + - 以 `_` 开头的私有字段被排除 + +--- + +### 3.7 完整输出 JSON Schema 示例 + +单条 JSONL 记录的完整结构： + +```json +{ + "document_id": "doc_a1b2c3d4", + "text": "GraphRAG is a technique developed by Microsoft Research that combines knowledge graphs with retrieval-augmented generation.", + "extractions": [ + { + "extraction_class": "TECHNOLOGY", + "extraction_text": "GraphRAG", + "char_interval": { + "start_pos": 0, + "end_pos": 8 + }, + "alignment_status": "match_exact", + "extraction_index": 0, + "group_index": null, + "description": "A technique combining knowledge graphs with RAG", + "attributes": { + "category": "AI/ML", + "developer": "Microsoft Research" + }, + "token_interval": { + "start_index": 0, + "end_index": 1 + } + }, + { + "extraction_class": "ORGANIZATION", + "extraction_text": "Microsoft Research", + "char_interval": { + "start_pos": 46, + "end_pos": 64 + }, + "alignment_status": "match_exact", + "extraction_index": 1, + "group_index": null, + "description": null, + "attributes": null, + "token_interval": { + "start_index": 7, + "end_index": 9 + } + } + ] +} +``` + +--- + +### 3.8 HTML 可视化输出 + +文件路径：`langextract/visualization.py` + +```python +def visualize(doc: AnnotatedDocument) -> HTML +``` + +**功能特性：** +- 按 `extraction_class` 进行颜色编码高亮（10 色调色板） +- 交互式 tooltip 显示实体类型和属性 +- 动画导航控件，支持多实体浏览 +- 进度滑块 +- 响应式 HTML/CSS/JavaScript 嵌入 +- 支持 Jupyter / IPython 环境直接渲染 + +--- + +## 附录：环境变量与常量速查 + +### 环境变量 + +| 变量名 | 适用 Provider | 说明 | +|--------|--------------|------| +| `LANGEXTRACT_API_KEY` | 所有 | 通用 API Key 后备 | +| `GEMINI_API_KEY` | Gemini | Gemini API Key | +| `OPENAI_API_KEY` | OpenAI | OpenAI API Key | +| `OLLAMA_BASE_URL` | Ollama | Ollama 服务地址（默认 `http://localhost:11434`） | + +### FormatType 枚举 + +```python +class FormatType(enum.Enum): + YAML = 'yaml' + JSON = 'json' +``` + +### 结构化输出支持 + +| Provider | Schema 类型 | 结构化输出模式 | +|----------|------------|---------------| +| Gemini | `GeminiSchema` | 严格结构化输出 | +| OpenAI | JSON Mode | 通过 `response_format` 约束 | +| Ollama | `FormatModeSchema` | JSON 模式（非严格） | + +### Fence Output 逻辑 + +| Provider | 默认值 | 说明 | +|----------|--------|------| +| Gemini | `False` | 有 Schema 时不需要 fence | +| OpenAI | `False` | JSON Mode 返回原始 JSON | +| Ollama | `False` | 返回原始 JSON | diff --git a/docs/mineru_specification-v1.0.md b/docs/mineru_specification-v1.0.md new file mode 100644 index 0000000..4a1e293 --- /dev/null +++ b/docs/mineru_specification-v1.0.md @@ -0,0 +1,879 @@ +# MinerU 文档解析规范文档 v1.0 + +> 基于 [opendatalab/MinerU](https://github.com/opendatalab/MinerU) 官方 API 文档 + 本地 MVP 实测验证 +> 实测后端版本：`pipeline` / `_version_name: 2.6.4` +> 更新日期：2026-03-04 + +--- + +## 目录 + +- [一、Pipeline 执行流程与测试脚本](#一pipeline-执行流程与测试脚本) + - [1.1 虚拟环境配置（环境隔离）](#11-虚拟环境配置环境隔离) + - [1.2 完整执行流程（本地文件 → 云端解析 → 本地存储）](#12-完整执行流程本地文件--云端解析--本地存储) + - [1.3 测试脚本存放位置](#13-测试脚本存放位置) + - [1.4 Pipeline 各步骤详解](#14-pipeline-各步骤详解) +- [二、输入格式规范](#二输入格式规范) + - [2.1 支持的文件格式](#21-支持的文件格式) + - [2.2 输入限制](#22-输入限制) + - [2.3 OCR 语言支持](#23-ocr-语言支持) +- [三、输出格式规范（实测验证）](#三输出格式规范实测验证) + - [3.1 实际输出文件清单（实测 vs 官方文档对比）](#31-实际输出文件清单实测-vs-官方文档对比) + - [3.2 content_list.json 字段规范（实测验证）](#32-content_listjson-字段规范实测验证) + - [3.3 layout.json 字段规范（实测验证）](#33-layoutjson-字段规范实测验证) + - [3.4 full.md Markdown 输出规范（实测验证）](#34-fullmd-markdown-输出规范实测验证) +- [四、布局信息规范](#四布局信息规范) + - [4.1 坐标系定义（实测验证）](#41-坐标系定义实测验证) + - [4.2 布局分类体系](#42-布局分类体系) + - [4.3 内容层级与标题级别](#43-内容层级与标题级别) + - [4.4 布局精度提取指南](#44-布局精度提取指南) +- [五、云端 API 关键参数规范](#五云端-api-关键参数规范) + - [5.1 认证配置](#51-认证配置) + - [5.2 本地文件上传流程 — file-urls/batch](#52-本地文件上传流程--file-urlsbatch) + - [5.3 URL 直传解析 — extract/task](#53-url-直传解析--extracttask) + - [5.4 批量 URL 解析 — extract/task/batch](#54-批量-url-解析--extracttaskbatch) + - [5.5 查询结果接口](#55-查询结果接口) + - [5.6 通用响应包装结构](#56-通用响应包装结构) + - [5.7 任务状态枚举（实测验证）](#57-任务状态枚举实测验证) + - [5.8 错误码速查](#58-错误码速查) + +--- + +## 一、Pipeline 执行流程与测试脚本 + +### 1.1 虚拟环境配置（环境隔离） + +MinerU MVP 组件使用 **独立的 Python 虚拟环境**，与项目其他组件（LangExtract、GraphRAG Pipeline 等）完全隔离，避免依赖污染。 + +| 项目 | 值 | +|------|-----| +| 虚拟环境路径 | `F:\GraphRAGAgent\mineru_mvp\.venv\` | +| Python 版本 | 3.12 | +| 创建工具 | uv | +| Python 解释器 | `F:/GraphRAGAgent/mineru_mvp/.venv/Scripts/python.exe` | + +**启动 Pipeline 前必须切换到子虚拟环境：** + +```bash +# 方式一：直接指定解释器路径（推荐，无需手动激活） +F:/GraphRAGAgent/mineru_mvp/.venv/Scripts/python.exe pipeline.py + +# 方式二：先激活环境再运行 +cd F:/GraphRAGAgent/mineru_mvp +source .venv/Scripts/activate +python pipeline.py +``` + +**安装新依赖：** + +```bash +uv pip install --python F:/GraphRAGAgent/mineru_mvp/.venv/Scripts/python.exe +``` + +**已安装依赖清单：** + +| 包 | 用途 | +|----|------| +| `requests` | HTTP 客户端（API 调用、文件上传下载） | +| `python-dotenv` | `.env` 配置文件加载 | +| `reportlab` | 测试 PDF 生成 | + +--- + +### 1.2 完整执行流程（本地文件 → 云端解析 → 本地存储） + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ Step 0: 激活虚拟环境 │ +│ source .venv/Scripts/activate 或 直接使用 .venv 内 python │ +├─────────────────────────────────────────────────────────────────┤ +│ Step 1: 获取预签名上传 URL │ +│ POST /file-urls/batch → 返回 batch_id + file_urls[] │ +├─────────────────────────────────────────────────────────────────┤ +│ Step 2: 上传本地文件 │ +│ PUT {file_urls[0]} ← 本地文件二进制流（不带 Content-Type） │ +├─────────────────────────────────────────────────────────────────┤ +│ Step 3: 轮询解析结果 │ +│ GET /extract-results/batch/{batch_id} │ +│ 状态流转: waiting-file → pending → running → done/failed │ +├─────────────────────────────────────────────────────────────────┤ +│ Step 4: 下载解析结果 ZIP │ +│ GET {full_zip_url} → 解压到本地 output/ 目录 │ +├─────────────────────────────────────────────────────────────────┤ +│ Step 5: 分析解析产物 │ +│ 读取 *content_list.json → 统计块类型、页数、生成 summary │ +└─────────────────────────────────────────────────────────────────┘ +``` + +> **关键发现（实测）：** 上传文件时 **不能** 携带 `Content-Type` 请求头，否则 OSS 预签名 URL 校验失败返回 403 `SignatureDoesNotMatch`。必须使用裸 `PUT` 请求。 + +### 1.3 测试脚本存放位置 + +``` +F:\GraphRAGAgent\mineru_mvp\ +├── .env # API Token 配置 +├── .venv/ # 独立虚拟环境（Python 3.12, uv 创建） +├── CLAUDE.md # Claude Code 组件规范 +├── create_test_pdf.py # 测试 PDF 生成脚本（reportlab） +├── pipeline.py # 完整 Pipeline 脚本（5 步） +├── test_sample.pdf # 生成的测试 PDF（1 页，含标题/段落/表格） +└── output/ + └── test_sample/ # 解析输出结果 + ├── full.md + ├── {uuid}_content_list.json + ├── layout.json + ├── {uuid}_origin.pdf + └── images/ + └── {hash}.jpg +``` + +### 1.4 Pipeline 各步骤详解 + +#### Step 1 — 获取预签名上传 URL + +```python +resp = requests.post( + f"{API_BASE}/file-urls/batch", + headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}, + json={ + "files": [{"name": "test_sample.pdf", "data_id": "mvp_test"}], + "enable_formula": True, + "enable_table": True, + "language": "en", + }, +) +batch_id = resp.json()["data"]["batch_id"] +upload_url = resp.json()["data"]["file_urls"][0] +``` + +#### Step 2 — 上传文件（裸 PUT，不带 Content-Type） + +```python +with open("test_sample.pdf", "rb") as f: + requests.put(upload_url, data=f) # 不传 headers +``` + +#### Step 3 — 轮询结果 + +```python +while True: + result = requests.get( + f"{API_BASE}/extract-results/batch/{batch_id}", + headers=headers, + ).json() + state = result["data"]["extract_result"][0]["state"] + if state == "done": + zip_url = result["data"]["extract_result"][0]["full_zip_url"] + break + time.sleep(5) +``` + +#### Step 4 — 下载解压 + +```python +zip_data = requests.get(zip_url).content +with zipfile.ZipFile(io.BytesIO(zip_data)) as zf: + zf.extractall("output/test_sample/") +``` + +#### Step 5 — 分析产物 + +```python +content_list = json.load(open("output/test_sample/*content_list.json")) +# 按 type 分类统计、按 page_idx 分组、提取标题层级等 +``` + +--- + +## 二、输入格式规范 + +### 2.1 支持的文件格式 + +| 格式 | 扩展名 | 说明 | +|------|--------|------| +| **PDF** | `.pdf` | 核心能力 — 文本型 / 扫描型 / 混合型均支持 | +| **Word** | `.doc`, `.docx` | 旧版和新版 Word 文档 | +| **PowerPoint** | `.ppt`, `.pptx` | 旧版和新版演示文稿 | +| **图片** | `.png`, `.jpg`, `.jpeg` | 单页图片文档，支持 EXIF 方向自动校正 | +| **HTML** | `.html` | 须指定 `model_version: "MinerU-HTML"` | + +### 2.2 输入限制 + +| 约束项 | 限制值 | +|--------|--------| +| 单文件最大体积 | **200 MB** | +| 单文件最大页数 | **600 页** | +| 批量请求最大文件数 | **200 个** | +| 预签名上传 URL 有效期 | **24 小时** | +| 云端 API 每日最高优先级额度 | **2,000 页**，超出部分降低优先级 | + +### 2.3 OCR 语言支持 + +MinerU 内置 OCR 引擎支持 **109 种语言**（基于 PaddleOCR v3），可通过 `language` 参数指定文档主语言。 + +> **注意（官方文档）：** `language` 的默认值为 `"ch"`（非 `"zh"`），遵循 PaddleOCR 语言代码规范。 + +| 代码 | 语言 | 代码 | 语言 | +|------|------|------|------| +| `ch` | 中文 | `en` | 英文 | +| `japan` | 日文 | `korean` | 韩文 | +| `french` | 法文 | `german` | 德文 | + +--- + +## 三、输出格式规范（实测验证） + +### 3.1 实际输出文件清单（实测 vs 官方文档对比） + +**实测输出（ZIP 解压后，共 5 个文件）：** + +``` +output/test_sample/ +├── full.md # Markdown 输出（单文件） +├── {uuid}_content_list.json # 扁平化内容块列表 +├── layout.json # 富元数据中间格式 +├── {uuid}_origin.pdf # 原始 PDF 副本 +└── images/ + └── {sha256_hash}.jpg # 表格/图片截图 +``` + +**与官方文档差异对比：** + +| 项目 | 官方文档描述 | 实测结果 | 差异说明 | +|------|-------------|---------|---------| +| Markdown 文件 | `auto/auto.md` + `auto_nlp/auto_nlp.md`（两个子目录） | **`full.md`**（单文件，根目录） | 云端 API 输出为合并的 `full.md`，无子目录拆分 | +| 中间格式 | `middle.json` | **`layout.json`** | 文件名不同，结构一致 | +| content_list | `content_list.json` | **`{uuid}_content_list.json`** | 文件名带 UUID 前缀 | +| 原始文件副本 | 未提及 | **`{uuid}_origin.pdf`** | 云端 API 额外返回原始文件副本 | +| 调试文件 | `layout.pdf` + `span.pdf` + `model.json` | **无** | 云端 API 不返回调试 PDF 和 model.json | +| 图片命名 | `img_0_0.png` / `table_0_1.png` | **`{sha256}.jpg`** | 使用内容哈希命名，格式为 JPG | + +> **重要结论：** 以实测为准。对接下游系统时，文件匹配应使用 glob 模式（如 `*content_list.json`）而非固定文件名。 + +### 3.2 content_list.json 字段规范（实测验证） + +文件为 **JSON 数组**，每个元素是一个内容块，按文档阅读顺序排列。 + +#### 3.2.1 公共字段 + +| 字段 | 类型 | 说明 | 实测验证 | +|------|------|------|---------| +| `type` | `string` | 内容类型 | 实测出现：`text`, `table` | +| `page_idx` | `int` | 所在页码（0-indexed） | 实测值：`0` | +| `bbox` | `[int, int, int, int]` | 边界框 `[x0, y0, x1, y1]` | 实测范围：`0–1000`（归一化） | + +#### 3.2.2 文本块（type: "text"） + +**实测完整结构：** + +```json +{ + "type": "text", + "text": "GraphRAG: Knowledge Graph Enhanced RAG System ", + "text_level": 1, + "bbox": [141, 93, 860, 151], + "page_idx": 0 +} +``` + +| 字段 | 类型 | 必现 | 说明 | +|------|------|------|------| +| `text` | `string` | 是 | 文本内容（末尾可能有空格） | +| `text_level` | `int \| 缺失` | 否 | 标题级别：`1`=一级标题；**正文时该字段缺失而非为 `0` 或 `null`** | + +> **实测发现：** 正文段落中 `text_level` 字段 **完全不存在**（不是 `null` 或 `0`），仅标题块才携带该字段。判断标题应使用 `block.get("text_level")` 而非 `block["text_level"] >= 1`。 + +#### 3.2.3 表格块（type: "table"） + +**实测完整结构：** + +```json +{ + "type": "table", + "img_path": "images/e382eaafdf341d361c2567b20d9ce56456c17a7dd10ae5dadbcc3961256169c9.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "...
Method Comprehensiveness
", + "bbox": [115, 563, 882, 708], + "page_idx": 0 +} +``` + +| 字段 | 类型 | 必现 | 说明 | +|------|------|------|------| +| `img_path` | `string` | 是 | 表格截图路径（`images/{sha256}.jpg`） | +| `table_body` | `string` | 是 | HTML 表格（`` 标签，无 `/` 外层包裹） | +| `table_caption` | `string[]` | 是 | 表格标题（可为空数组 `[]`） | +| `table_footnote` | `string[]` | 是 | 表格脚注（可为空数组 `[]`） | + +> **实测发现：** `table_body` 的 HTML 直接以 `
` 开头，**不含** `` 外层包裹（官方文档示例中有外层包裹，以实测为准）。 + +#### 3.2.4 图片块（type: "image"）— 官方文档 + +本次测试 PDF 不含独立图片，以下为官方文档规范（待后续实测验证）： + +```json +{ + "type": "image", + "img_path": "images/{hash}.jpg", + "image_caption": ["Figure 1: ..."], + "image_footnote": [], + "bbox": [x0, y0, x1, y1], + "page_idx": 0 +} +``` + +#### 3.2.5 公式块（type: "equation"）— 官方文档 + +```json +{ + "type": "equation", + "text": "E = mc^2", + "text_format": "latex", + "img_path": "images/{hash}.jpg", + "bbox": [x0, y0, x1, y1], + "page_idx": 0 +} +``` + +> **实测发现：** 测试 PDF 结论段的百分数被解析为 LaTeX 内联公式（`$7 2 . 0 \%$`），嵌入在 `text` 类型块中，而非独立的 `equation` 块。这说明 Pipeline 后端会将简单公式内联到文本块中。 + +--- + +### 3.3 layout.json 字段规范（实测验证） + +`layout.json` 对应官方文档中的 `middle.json`，是富元数据中间格式。 + +#### 3.3.1 顶层结构（实测） + +```json +{ + "_backend": "pipeline", + "_version_name": "2.6.4", + "pdf_info": [ ... ] +} +``` + +| 字段 | 类型 | 实测值 | 说明 | +|------|------|--------|------| +| `_backend` | `string` | `"pipeline"` | 使用的解析后端 | +| `_version_name` | `string` | `"2.6.4"` | MinerU 版本标识 | +| `pdf_info` | `array` | 含 1 个元素 | 按页组织的解析结果 | + +#### 3.3.2 页级结构（实测） + +```json +{ + "page_idx": 0, + "page_size": [595, 841], + "preproc_blocks": [ ... ], + "para_blocks": [ ... ], + "discarded_blocks": [] +} +``` + +| 字段 | 类型 | 实测值 | 说明 | +|------|------|--------|------| +| `page_idx` | `int` | `0` | 页码（0-indexed） | +| `page_size` | `[int, int]` | `[595, 841]` | 页面尺寸 `[宽, 高]`（PDF pt 单位，A4≈595×841） | +| `preproc_blocks` | `array` | 10 个块 | 预处理阶段的内容块 | +| `para_blocks` | `array` | 10 个块 | 段落分段后的内容块 | +| `discarded_blocks` | `array` | `[]` | 被过滤的内容（页眉/页脚等） | + +> **与官方文档差异：** 实测页级结构 **仅包含 3 个数组**（`preproc_blocks`、`para_blocks`、`discarded_blocks`），**不含** 官方文档提到的 `images`、`tables`、`interline_equations` 独立数组。表格和图片直接嵌入在 `preproc_blocks` / `para_blocks` 中。 + +#### 3.3.3 内容块层级结构（Block → Line → Span，实测验证） + +**文本/标题块（实测）：** + +```json +{ + "type": "title", + "bbox": [84, 79, 512, 127], + "lines": [ + { + "bbox": [80, 77, 515, 106], + "spans": [ + { + "bbox": [80, 77, 515, 106], + "score": 1.0, + "content": "GraphRAG: Knowledge Graph Enhanced", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0.5 +} +``` + +**Block 字段（实测）：** + +| 字段 | 类型 | 说明 | +|------|------|------| +| `type` | `string` | 块类型：实测出现 `title`, `text`, `table` | +| `bbox` | `[int, int, int, int]` | 边界框（原始 PDF pt 坐标） | +| `lines` | `array` | 行数组（文本/标题块） | +| `blocks` | `array` | 子块数组（仅 `table` 类型容器块） | +| `index` | `int \| float` | 排序索引（可为小数，如 `0.5`） | + +**Line 字段（实测）：** + +| 字段 | 类型 | 说明 | +|------|------|------| +| `bbox` | `[int, int, int, int]` | 行边界框 | +| `spans` | `array` | Span 数组 | +| `index` | `int` | 行内排序索引 | + +**Span 字段（实测）：** + +| 字段 | 类型 | 说明 | +|------|------|------| +| `bbox` | `[int, int, int, int]` | Span 边界框 | +| `type` | `string` | 实测出现：`text`, `table` | +| `content` | `string` | 文本内容（`type=text` 时） | +| `score` | `float` | 置信度（实测多为 `1.0`） | + +**表格容器块（实测）：** + +```json +{ + "type": "table", + "bbox": [69, 474, 525, 596], + "blocks": [ + { + "type": "table_body", + "bbox": [69, 474, 525, 596], + "group_id": 0, + "lines": [ ... ], + "index": 0, + "virtual_lines": [ ... ] + } + ], + "index": 7 +} +``` + +表格容器块内的子块额外包含： + +| 字段 | 类型 | 说明 | +|------|------|------| +| `group_id` | `int` | 分组 ID | +| `virtual_lines` | `array` | 虚拟行结构（表格布局专用） | + +**`para_blocks` 额外字段（实测）：** + +部分 `para_blocks` 中的文本块额外包含 `bbox_fs` 字段（疑似字体大小相关的边界框），如： + +```json +{ + "type": "text", + "bbox": [77, 198, 518, 259], + "lines": [...], + "index": 2, + "bbox_fs": [77, 198, 518, 259] +} +``` + +--- + +### 3.4 full.md Markdown 输出规范（实测验证） + +**实测产物：** 单个 `full.md` 文件（非官方文档描述的 `auto/auto.md` + `auto_nlp/auto_nlp.md` 双目录结构）。 + +**实测特征：** + +| 特征 | 实测行为 | +|------|---------| +| 标题 | 使用 `# ` 前缀，所有标题均为一级（`# `） | +| 段落 | 纯文本，段落间以空行分隔 | +| 表格 | 直接嵌入 HTML `
` 标签 | +| 公式 | 内联使用 `$...$` 定界符（如 `$7 2 . 0 \%$`） | +| 图片引用 | 本次未出现独立图片引用 | + +**实测输出示例（节选）：** + +```markdown +# GraphRAG: Knowledge Graph Enhanced RAG System + +# 1. Introduction + +GraphRAG is an advanced retrieval-augmented generation technique developed by... + +# 3. Performance Comparison + +The following table compares GraphRAG with traditional RAG approaches... + +
...
Method Comprehensiveness
+ +# 4. Conclusion + +...comprehensiveness $7 2 . 0 \%$ vs $3 2 . 4 \%$... +``` + +--- + +## 四、布局信息规范 + +### 4.1 坐标系定义（实测验证） + +| 坐标系 | 适用文件 | 实测范围 | 原点 | 说明 | +|--------|---------|---------|------|------| +| **归一化整数坐标** | `*content_list.json` | `0 – 1000` | 左上角 | 页面宽高均映射到 0~1000 | +| **原始 PDF 坐标** | `layout.json` | 实测 `[595, 841]`（A4 pt） | 左上角 | 与 PDF 页面尺寸一致 | + +**bbox 格式统一为 `[x0, y0, x1, y1]`：** + +``` +(x0, y0) ─────────────────── (x1, y0) + │ │ + │ 内容区域 │ + │ │ +(x0, y1) ─────────────────── (x1, y1) +``` + +**实测对照（标题块 "1. Introduction"）：** + +| 文件 | bbox | 坐标系 | +|------|------|--------| +| `content_list.json` | `[131, 200, 317, 222]` | 归一化 0-1000 | +| `layout.json` | `[78, 169, 189, 187]` | PDF pt（页面 595×841） | + +### 4.2 布局分类体系 + +#### Pipeline 后端（实测 + 官方文档合并） + +**layout.json 中的 `type` 值（实测出现标记 ✅）：** + +| type 值 | 说明 | 实测出现 | +|---------|------|---------| +| `title` | 标题 | ✅ | +| `text` | 正文段落 | ✅ | +| `table` | 表格容器 | ✅ | +| `table_body` | 表格主体（子块） | ✅ | +| `table_caption` | 表格标题 | — | +| `table_footnote` | 表格脚注 | — | +| `image_body` | 图片主体 | — | +| `image_caption` | 图片标题 | — | +| `image_footnote` | 图片脚注 | — | +| `interline_equation` | 行间公式 | — | +| `index` | 目录项 | — | +| `list` | 列表项 | — | + +#### VLM 后端（官方文档，未实测） + +VLM 后端额外支持：`code`, `code_caption`, `list`, `header`, `footer`, `page_number`, `aside_text`, `page_footnote`, `ref_text`, `algorithm`, `phonetic`。 + +### 4.3 内容层级与标题级别 + +`content_list.json` 中 `text_level` 字段标识文档结构层级： + +| text_level | 含义 | Markdown | 实测验证 | +|------------|------|----------|---------| +| **字段缺失** | 正文 | 无标记 | ✅ 实测正文块不含 `text_level` 字段 | +| `1` | 一级标题 | `# Heading` | ✅ 实测验证 | +| `2` | 二级标题 | `## Heading` | — | +| `3` | 三级标题 | `### Heading` | — | +| `4+` | 更深层标题 | `####+ Heading` | — | + +> **重要纠正：** 官方文档描述正文为 `text_level: null` 或 `0`，但实测正文块中 **该字段完全不存在**。正确判断方式： + +```python +# 正确写法 +is_heading = block.get("text_level") is not None + +# 错误写法（会 KeyError） +is_heading = block["text_level"] >= 1 +``` + +### 4.4 布局精度提取指南 + +#### 提取文档大纲 + +```python +headings = [ + {"level": b["text_level"], "text": b["text"].strip(), "page": b["page_idx"]} + for b in content_list + if b["type"] == "text" and b.get("text_level") is not None +] +``` + +#### 提取正文段落 + +```python +paragraphs = [ + b["text"].strip() + for b in content_list + if b["type"] == "text" and b.get("text_level") is None +] +``` + +#### 解析表格数值 + +```python +from bs4 import BeautifulSoup + +for b in content_list: + if b["type"] != "table": + continue + soup = BeautifulSoup(b["table_body"], "html.parser") + rows = [] + for tr in soup.find_all("tr"): + cells = [td.get_text(strip=True) for td in tr.find_all(["td", "th"])] + rows.append(cells) + # rows 即为二维表格数据 +``` + +#### 按页面位置过滤 + +```python +def is_upper_half(block): + """判断内容块是否在页面上半部分（归一化坐标 0-1000）""" + y_center = (block["bbox"][1] + block["bbox"][3]) / 2 + return y_center < 500 +``` + +--- + +## 五、云端 API 关键参数规范 + +### 5.1 认证配置 + +| 项目 | 值 | +|------|-----| +| 请求头 | `Authorization: Bearer {token}` | +| Token 获取 | [mineru.net/apiManage/token](https://mineru.net/apiManage/token) | +| .env 配置 | `MINERU_API_TOKEN=xxx` | + +所有接口均需携带 `Authorization` 头，`Content-Type: application/json`（上传文件 PUT 请求除外）。 + +--- + +### 5.2 本地文件上传流程 — file-urls/batch + +**用途：** 本地文件场景 — 获取预签名 URL → PUT 上传 → 自动触发解析 + +**接口：** `POST https://mineru.net/api/v4/file-urls/batch` + +#### 请求体 + +| 字段 | 类型 | 必填 | 默认值 | 说明 | +|------|------|------|--------|------| +| `files` | `array[object]` | **是** | — | 文件列表（最多 200 个） | +| `files[].name` | `string` | **是** | — | 文件名（须含正确扩展名） | +| `files[].data_id` | `string` | 否 | — | 业务标识（最长 128 字符，支持字母数字 `_` `-` `.`） | +| `files[].is_ocr` | `bool` | 否 | `false` | 是否强制 OCR | +| `files[].page_ranges` | `string` | 否 | — | 页码范围（如 `"2,4-6"` 或 `"2--2"` 表示到倒数第二页） | +| `model_version` | `string` | 否 | `"pipeline"` | 模型版本：`pipeline` / `vlm` / `MinerU-HTML` | +| `enable_formula` | `bool` | 否 | `true` | 是否启用公式识别 | +| `enable_table` | `bool` | 否 | `true` | 是否启用表格识别 | +| `language` | `string` | 否 | `"ch"` | OCR 语言（PaddleOCR v3 语言代码） | +| `callback` | `string` | 否 | — | 回调通知 URL（HTTP/HTTPS POST） | +| `seed` | `string` | 否 | — | 回调签名种子（与 callback 配合，最长 64 字符） | +| `extra_formats` | `string[]` | 否 | — | 额外输出格式：`"docx"`, `"html"`, `"latex"` | + +#### 响应体（实测验证） + +```json +{ + "code": 0, + "msg": "ok", + "trace_id": "9ef836ce2a65f46c5f54389e55a14039", + "data": { + "batch_id": "6ce0e838-b324-4f1d-8b06-01ddc07e4cd4", + "file_urls": [ + "https://mineru.oss-cn-shanghai.aliyuncs.com/api-upload/extract/2026-03-04/{batch_id}/{file_uuid}.pdf?Expires=...&OSSAccessKeyId=...&Signature=..." + ] + } +} +``` + +| 响应字段 | 类型 | 说明 | +|---------|------|------| +| `code` | `int` | `0` 表示成功 | +| `msg` | `string` | 状态信息 | +| `trace_id` | `string` | 请求追踪 ID | +| `data.batch_id` | `string` | 批次 ID（后续查询结果使用） | +| `data.file_urls` | `string[]` | 预签名上传 URL 列表（与 `files` 一一对应） | + +#### 文件上传 + +``` +PUT {file_urls[i]} +Body: 文件二进制流 +``` + +> **不要传任何请求头**（包括 `Content-Type`），否则 OSS 签名校验失败。 + +--- + +### 5.3 URL 直传解析 — extract/task + +**用途：** 文件已有公网 URL 时直接提交解析 + +**接口：** `POST https://mineru.net/api/v4/extract/task` + +#### 请求体 + +| 字段 | 类型 | 必填 | 默认值 | 说明 | +|------|------|------|--------|------| +| `url` | `string` | **是** | — | 文件公网 URL | +| `model_version` | `string` | 否 | `"pipeline"` | 模型版本 | +| `is_ocr` | `bool` | 否 | `false` | 是否强制 OCR | +| `enable_formula` | `bool` | 否 | `true` | 是否启用公式识别 | +| `enable_table` | `bool` | 否 | `true` | 是否启用表格识别 | +| `language` | `string` | 否 | `"ch"` | OCR 语言 | +| `data_id` | `string` | 否 | — | 业务标识 | +| `callback` | `string` | 否 | — | 回调 URL | +| `seed` | `string` | 否 | — | 回调种子 | +| `extra_formats` | `string[]` | 否 | — | 额外输出格式 | +| `page_ranges` | `string` | 否 | — | 页码范围 | +| `no_cache` | `bool` | 否 | `false` | 跳过 URL 缓存 | +| `cache_tolerance` | `int` | 否 | `900` | 缓存容忍时间（秒） | + +#### 响应体 + +```json +{ + "code": 0, + "msg": "ok", + "trace_id": "string", + "data": { "task_id": "string" } +} +``` + +#### 查询结果 + +`GET https://mineru.net/api/v4/extract/task/{task_id}` + +```json +{ + "code": 0, + "data": { + "task_id": "string", + "data_id": "string", + "state": "done", + "full_zip_url": "https://cdn-mineru.openxlab.org.cn/...", + "err_msg": null, + "extract_progress": { + "extracted_pages": 1, + "total_pages": 1, + "start_time": "2026-03-04 12:00:00" + } + } +} +``` + +--- + +### 5.4 批量 URL 解析 — extract/task/batch + +**接口：** `POST https://mineru.net/api/v4/extract/task/batch` + +#### 请求体 + +```json +{ + "files": [ + {"url": "https://...", "data_id": "doc1", "is_ocr": false, "page_ranges": "1-5"} + ], + "model_version": "pipeline", + "enable_formula": true, + "enable_table": true, + "language": "ch", + "extra_formats": ["docx"], + "no_cache": false, + "cache_tolerance": 900 +} +``` + +#### 响应体 + +```json +{ + "code": 0, + "data": { "batch_id": "string" } +} +``` + +--- + +### 5.5 查询结果接口 + +#### 单任务查询 + +`GET https://mineru.net/api/v4/extract/task/{task_id}` + +#### 批量查询（实测验证） + +`GET https://mineru.net/api/v4/extract-results/batch/{batch_id}` + +**响应体（实测验证）：** + +```json +{ + "code": 0, + "msg": "ok", + "trace_id": "string", + "data": { + "batch_id": "3b1729e9-c833-44b4-b9c2-201164001ab0", + "extract_result": [ + { + "file_name": "test_sample.pdf", + "state": "done", + "full_zip_url": "https://cdn-mineru.openxlab.org.cn/pdf/2026-03-04/...", + "err_msg": null, + "data_id": "mvp_test", + "extract_progress": { + "extracted_pages": 1, + "total_pages": 1, + "start_time": "2026-03-04 ..." + } + } + ] + } +} +``` + +--- + +### 5.6 通用响应包装结构 + +所有 API 响应均遵循统一包装格式： + +```json +{ + "code": 0, // 0 = 成功，非 0 = 失败 + "msg": "ok", // 状态描述 + "trace_id": "...", // 请求追踪 ID + "data": { ... } // 业务数据 +} +``` + +--- + +### 5.7 任务状态枚举（实测验证） + +| state | 说明 | 实测出现 | +|-------|------|---------| +| `waiting-file` | 等待文件上传完成 | ✅ | +| `pending` | 排队等待解析 | ✅ | +| `running` | 正在解析 | — | +| `converting` | 格式转换中 | — | +| `done` | 解析完成 | ✅ | +| `failed` | 解析失败 | — | + +> **实测状态流转：** `waiting-file` → `pending` → `done`（小文件跳过 `running`） + +--- + +### 5.8 错误码速查 + +| 错误码 | 含义 | +|--------|------| +| `A0202` | Token 无效 | +| `A0211` | Token 过期 | +| `-60005` | 文件超过 200MB | +| `-60006` | 页数超过 600 页 | +| `-60018` | 当日解析额度用尽 | diff --git a/docs/mineru_specification.md b/docs/mineru_specification.md new file mode 100644 index 0000000..e58a484 --- /dev/null +++ b/docs/mineru_specification.md @@ -0,0 +1,680 @@ +# MinerU 文档解析规范文档 + +> 基于 [opendatalab/MinerU](https://github.com/opendatalab/MinerU) 官方文档及云端 API 调研 +> 版本基线：2026-03-04 + +--- + +## 目录 + +- [一、支持的原始输入文件格式](#一支持的原始输入文件格式) + - [1.1 支持格式清单](#11-支持格式清单) + - [1.2 输入限制](#12-输入限制) + - [1.3 OCR 语言支持](#13-ocr-语言支持) +- [二、云端 API 输出格式规范](#二云端-api-输出格式规范) + - [2.1 输出文件总览](#21-输出文件总览) + - [2.2 content_list.json 字段规范](#22-content_listjson-字段规范) + - [2.3 middle.json 字段规范](#23-middlejson-字段规范) + - [2.4 Markdown 输出规范](#24-markdown-输出规范) + - [2.5 调试与可视化文件](#25-调试与可视化文件) +- [三、布局信息规范](#三布局信息规范) + - [3.1 坐标系定义](#31-坐标系定义) + - [3.2 布局分类体系（Pipeline 后端）](#32-布局分类体系pipeline-后端) + - [3.3 布局分类体系（VLM 后端）](#33-布局分类体系vlm-后端) + - [3.4 内容层级与标题级别](#34-内容层级与标题级别) + - [3.5 布局精度提取指南](#35-布局精度提取指南) +- [四、云端 API MVP 必要字段](#四云端-api-mvp-必要字段) + - [4.1 认证配置](#41-认证配置) + - [4.2 创建解析任务 — 请求规范](#42-创建解析任务--请求规范) + - [4.3 查询任务结果 — 响应规范](#43-查询任务结果--响应规范) + - [4.4 批量任务接口](#44-批量任务接口) + - [4.5 MVP 最小可用请求示例](#45-mvp-最小可用请求示例) + +--- + +## 一、支持的原始输入文件格式 + +### 1.1 支持格式清单 + +| 格式 | 扩展名 | 说明 | +|------|--------|------| +| **PDF** | `.pdf` | 核心能力 — 文本型 / 扫描型 / 混合型均支持 | +| **Word** | `.doc`, `.docx` | 旧版和新版 Word 文档 | +| **PowerPoint** | `.ppt`, `.pptx` | 旧版和新版演示文稿 | +| **图片** | `.png`, `.jpg`, `.jpeg` | 单页图片文档，支持 EXIF 方向自动校正 | +| **HTML** | `.html` | 需指定 `MinerU-HTML` 模型版本 | + +### 1.2 输入限制 + +| 约束项 | 限制值 | +|--------|--------| +| 单文件最大体积 | **200 MB** | +| 单文件最大页数 | **600 页** | +| 云端 API 每日免费额度 | **2,000 页**（最高优先级），超出部分降低优先级 | + +### 1.3 OCR 语言支持 + +MinerU 内置 OCR 引擎支持 **109 种语言**，可通过 `language` 参数指定文档主语言（默认 `zh` 中文）。常用语言代码： + +| 代码 | 语言 | 代码 | 语言 | +|------|------|------|------| +| `zh` | 中文 | `en` | 英文 | +| `ja` | 日文 | `ko` | 韩文 | +| `fr` | 法文 | `de` | 德文 | + +--- + +## 二、云端 API 输出格式规范 + +### 2.1 输出文件总览 + +云端 API 任务完成后，返回一个 ZIP 压缩包（通过 `full_zip_url` 获取），解压后包含以下文件： + +``` +output/ +├── auto/ +│ ├── auto.md # 多模态 Markdown（含图片引用） +│ └── images/ # 提取的图片资源 +│ ├── img_0_0.png +│ ├── table_0_1.png +│ └── ... +├── auto_nlp/ +│ └── auto_nlp.md # 纯文本 NLP Markdown（无图片） +├── middle.json # 富元数据中间格式（完整层级结构） +├── content_list.json # 扁平化内容块列表（按阅读顺序） +├── layout.pdf # 布局分析可视化（调试用） +├── span.pdf # Span 级别标注（Pipeline 后端，调试用） +└── model.json # 原始模型推理结果（调试用） +``` + +| 文件 | 用途 | 推荐场景 | +|------|------|---------| +| `content_list.json` | 扁平化内容块，按阅读顺序 | **推荐用于下游 NLP/KG 管道对接** | +| `middle.json` | 完整层级结构，含丰富元数据 | 需要精确布局信息或二次开发 | +| `auto/auto.md` | 多模态 Markdown | 人工阅读、LLM 直接消费 | +| `auto_nlp/auto_nlp.md` | 纯文本 Markdown | 纯文本 NLP 处理 | +| `layout.pdf` | 布局可视化 | 调试、验证解析质量 | + +--- + +### 2.2 content_list.json 字段规范 + +`content_list.json` 是一个 **JSON 数组**，每个元素是一个内容块，按文档阅读顺序排列。 + +#### 2.2.1 公共字段（所有类型共有） + +| 字段 | 类型 | 说明 | +|------|------|------| +| `type` | `string` | 内容类型：`text` / `image` / `table` / `equation` / `code` / `list` | +| `page_idx` | `int` | 所在页码（**0-indexed**） | +| `bbox` | `[x0, y0, x1, y1]` | 边界框坐标，归一化到 **0–1000** 范围 | + +#### 2.2.2 文本块（type: "text"） + +```json +{ + "type": "text", + "text": "段落正文内容...", + "text_level": 0, + "page_idx": 0, + "bbox": [72, 120, 540, 145] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `text` | `string` | 文本内容 | +| `text_level` | `int \| null` | 标题级别：`null` 或 `0` = 正文，`1` = 一级标题，`2` = 二级标题，依此类推 | + +#### 2.2.3 图片块（type: "image"） + +```json +{ + "type": "image", + "img_path": "images/img_0_0.png", + "image_caption": ["Figure 1: System architecture"], + "image_footnote": ["Source: internal report"], + "page_idx": 1, + "bbox": [100, 200, 500, 600] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `img_path` | `string` | 图片文件相对路径 | +| `image_caption` | `string[]` | 图片标题列表 | +| `image_footnote` | `string[]` | 图片脚注列表 | + +#### 2.2.4 表格块（type: "table"） + +```json +{ + "type": "table", + "img_path": "images/table_0_1.png", + "table_body": "
...
", + "table_caption": ["Table 1: Performance comparison"], + "table_footnote": ["* p < 0.05"], + "page_idx": 2, + "bbox": [50, 300, 950, 700] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `img_path` | `string` | 表格截图相对路径 | +| `table_body` | `string` | 表格 HTML 表示（`` 标签） | +| `table_caption` | `string[]` | 表格标题列表 | +| `table_footnote` | `string[]` | 表格脚注列表 | + +#### 2.2.5 公式块（type: "equation"） + +```json +{ + "type": "equation", + "text": "E = mc^2", + "text_format": "latex", + "img_path": "images/eq_0_0.png", + "page_idx": 3, + "bbox": [200, 400, 800, 450] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `text` | `string` | 公式的 LaTeX 表示 | +| `text_format` | `string` | 固定值 `"latex"` | +| `img_path` | `string` | 公式截图相对路径 | + +#### 2.2.6 代码块（type: "code"）— VLM 后端 + +```json +{ + "type": "code", + "sub_type": "code", + "code_body": "def hello():\n print('hello')", + "code_caption": ["Listing 1: Example function"], + "page_idx": 4, + "bbox": [80, 100, 920, 300] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `sub_type` | `string` | `"code"` 或 `"algorithm"` | +| `code_body` | `string` | 代码文本内容 | +| `code_caption` | `string[]` | 代码块标题（可选） | + +#### 2.2.7 列表块（type: "list"）— VLM 后端 + +```json +{ + "type": "list", + "sub_type": "text", + "list_items": ["第一项", "第二项", "第三项"], + "page_idx": 5, + "bbox": [72, 200, 540, 350] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `sub_type` | `string` | `"text"` 或 `"ref_text"`（参考文献列表） | +| `list_items` | `string[]` | 列表项内容 | + +--- + +### 2.3 middle.json 字段规范 + +`middle.json` 是 MinerU 的富元数据中间格式，保留完整的文档层级结构。 + +#### 2.3.1 顶层结构 + +```json +{ + "_backend": "pipeline | vlm | hybrid", + "_version_name": "2.7.4", + "pdf_info": [ ... ] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `_backend` | `string` | 使用的解析后端 | +| `_version_name` | `string` | MinerU 版本标识 | +| `pdf_info` | `array` | 按页组织的解析结果数组 | + +#### 2.3.2 页级结构（pdf_info 数组元素） + +```json +{ + "page_idx": 0, + "page_size": [595.0, 842.0], + "preproc_blocks": [ ... ], + "para_blocks": [ ... ], + "images": [ ... ], + "tables": [ ... ], + "interline_equations": [ ... ], + "discarded_blocks": [ ... ] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `page_idx` | `int` | 页码（0-indexed） | +| `page_size` | `[float, float]` | 页面尺寸 `[宽, 高]`（原始 PDF 坐标系，单位 pt） | +| `preproc_blocks` | `array` | 未分段的预处理块 | +| `para_blocks` | `array` | **已分段的内容块**（主输出） | +| `images` | `array` | 提取的图片块 | +| `tables` | `array` | 提取的表格块 | +| `interline_equations` | `array` | 行间公式块 | +| `discarded_blocks` | `array` | 被过滤的内容（页眉、页脚、页码等） | + +#### 2.3.3 内容块层级结构 + +内容块采用三级层级：**Block → Line → Span** + +**一级块（Level 1）— 容器块：** + +```json +{ + "type": "table", + "bbox": [x0, y0, x1, y1], + "blocks": [ ... ] +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `type` | `string` | `"table"` 或 `"image"` | +| `bbox` | `[x0, y0, x1, y1]` | 边界框坐标（原始 PDF 坐标系） | +| `blocks` | `array` | 包含的二级块 | + +**二级块（Level 2）— 语义块：** + +```json +{ + "type": "text", + "bbox": [x0, y0, x1, y1], + "lines": [ ... ] +} +``` + +| `type` 值 | 说明 | +|-----------|------| +| `text` | 正文段落 | +| `title` | 标题 | +| `image_body` | 图片主体 | +| `image_caption` | 图片标题 | +| `image_footnote` | 图片脚注 | +| `table_body` | 表格主体 | +| `table_caption` | 表格标题 | +| `table_footnote` | 表格脚注 | +| `interline_equation` | 行间公式 | +| `index` | 目录项 | +| `list` | 列表项 | + +**行结构（Line）：** + +```json +{ + "bbox": [x0, y0, x1, y1], + "spans": [ ... ] +} +``` + +**Span 结构（最小粒度）：** + +```json +{ + "bbox": [x0, y0, x1, y1], + "type": "text", + "content": "具体文本内容", + "score": 0.95 +} +``` + +| 字段 | 类型 | 说明 | +|------|------|------| +| `bbox` | `[x0, y0, x1, y1]` | 边界框坐标 | +| `type` | `string` | `text` / `image` / `table` / `inline_equation` / `interline_equation` | +| `content` | `string` | 文本内容（text 类型）| +| `img_path` | `string` | 图片路径（image/table 类型）| +| `score` | `float` | 模型置信度（0.0~1.0） | + +--- + +### 2.4 Markdown 输出规范 + +| 文件 | 特点 | +|------|------| +| `auto/auto.md` | 图片以 `![](images/img_x_x.png)` 引用；表格保留为 Markdown 表格或 HTML；公式使用 `$...$` 和 `$$...$$` 定界符 | +| `auto_nlp/auto_nlp.md` | 纯文本，图片/表格替换为占位文本描述；适合直接送入 NLP 管道 | + +--- + +### 2.5 调试与可视化文件 + +| 文件 | 格式 | 说明 | +|------|------|------| +| `layout.pdf` | PDF | 每页叠加带编号的检测框，不同颜色区分内容类型，验证布局分析准确性和阅读顺序 | +| `span.pdf` | PDF | 用不同颜色线框标注页面内容的 span 类型（仅 Pipeline 后端），排查文本丢失和公式识别问题 | +| `model.json` | JSON | 原始模型推理结果，包含 `category_id`、`poly`（四边形坐标）、`score`（置信度） | + +--- + +## 三、布局信息规范 + +### 3.1 坐标系定义 + +MinerU 使用两套坐标系，取决于输出文件： + +| 坐标系 | 适用文件 | 范围 | 原点 | 说明 | +|--------|---------|------|------|------| +| **归一化坐标** | `content_list.json` | `0 – 1000` | 左上角 | 页面宽高均映射到 0~1000 | +| **原始 PDF 坐标** | `middle.json` | 实际 pt 值 | 左上角 | 与 PDF 页面尺寸一致（如 A4 = 595×842） | +| **归一化比例坐标** | `model.json`（VLM） | `0.0 – 1.0` | 左上角 | 宽高均映射到 0~1 | + +**bbox 格式统一为：`[x0, y0, x1, y1]`** + +``` +(x0, y0) ─────────────────── (x1, y0) + │ │ + │ 内容区域 │ + │ │ +(x0, y1) ─────────────────── (x1, y1) +``` + +- `x0, y0`：左上角坐标 +- `x1, y1`：右下角坐标 + +### 3.2 布局分类体系（Pipeline 后端） + +`model.json` 中的 `category_id` 枚举： + +| category_id | 类型 | 说明 | +|-------------|------|------| +| 0 | `title` | 标题 | +| 1 | `plain_text` | 正文文本 | +| 2 | `abandon` | 丢弃区域（页眉/页脚/页码等） | +| 3 | `figure` | 图片 | +| 4 | `figure_caption` | 图片标题 | +| 5 | `table` | 表格 | +| 6 | `table_caption` | 表格标题 | +| 7 | `table_footnote` | 表格脚注 | +| 8 | `isolate_formula` | 独立行间公式 | +| 9 | `formula_caption` | 公式标题 | +| 13 | `embedding` | 嵌入内容 | +| 14 | `isolated` | 隔离内容 | +| 15 | `OCR_text` | OCR 识别文本 | + +### 3.3 布局分类体系（VLM 后端） + +VLM 后端使用字符串类型标识，分类更细： + +| type 值 | 说明 | +|---------|------| +| `text` | 正文 | +| `title` | 标题 | +| `equation` | 公式 | +| `image` | 图片 | +| `image_caption` | 图片标题 | +| `image_footnote` | 图片脚注 | +| `table` | 表格 | +| `table_caption` | 表格标题 | +| `table_footnote` | 表格脚注 | +| `code` | 代码块 | +| `code_caption` | 代码标题 | +| `list` | 列表 | +| `header` | 页眉（discarded） | +| `footer` | 页脚（discarded） | +| `page_number` | 页码（discarded） | +| `aside_text` | 边栏文字（discarded） | +| `page_footnote` | 页面脚注（discarded） | +| `ref_text` | 参考文献 | +| `algorithm` | 算法伪代码 | +| `phonetic` | 注音 | + +### 3.4 内容层级与标题级别 + +`content_list.json` 中的 `text_level` 字段标识文档结构层级： + +| text_level | 含义 | 对应 Markdown | +|------------|------|--------------| +| `null` 或 `0` | 正文 | 无标记 | +| `1` | 一级标题 | `# Heading` | +| `2` | 二级标题 | `## Heading` | +| `3` | 三级标题 | `### Heading` | +| `4` | 四级标题 | `#### Heading` | +| `5+` | 更深层标题 | `#####+ Heading` | + +### 3.5 布局精度提取指南 + +针对不同数据类型的精确提取建议： + +#### 文本提取 + +```python +# 从 content_list.json 提取所有正文文本 +texts = [ + block for block in content_list + if block["type"] == "text" +] +# 按页过滤 +page_0_texts = [b for b in texts if b["page_idx"] == 0] +``` + +#### 标题层级提取 + +```python +# 提取文档大纲结构 +headings = [ + {"level": block["text_level"], "text": block["text"], "page": block["page_idx"]} + for block in content_list + if block["type"] == "text" and block.get("text_level") and block["text_level"] >= 1 +] +``` + +#### 表格数值提取 + +```python +# 表格以 HTML 形式存储在 table_body 中，可用 BeautifulSoup 解析 +from bs4 import BeautifulSoup + +tables = [b for b in content_list if b["type"] == "table"] +for table in tables: + soup = BeautifulSoup(table["table_body"], "html.parser") + rows = [] + for tr in soup.find_all("tr"): + cells = [td.get_text(strip=True) for td in tr.find_all(["td", "th"])] + rows.append(cells) +``` + +#### 空间位置定位 + +```python +# 利用 bbox 判断内容在页面中的位置 +def get_position(bbox, threshold=500): + """判断内容在页面的上半部分还是下半部分（归一化坐标 0-1000）""" + y_center = (bbox[1] + bbox[3]) / 2 + return "upper" if y_center < threshold else "lower" + +# 判断两个块是否水平相邻（同一行） +def is_same_row(block_a, block_b, tolerance=20): + return abs(block_a["bbox"][1] - block_b["bbox"][1]) < tolerance +``` + +--- + +## 四、云端 API MVP 必要字段 + +### 4.1 认证配置 + +| 配置项 | 值 | 获取方式 | +|--------|-----|---------| +| Token | Bearer Token 字符串 | [mineru.net/apiManage/token](https://mineru.net/apiManage/token) 注册后获取 | + +**请求头格式（所有接口通用）：** + +``` +Authorization: Bearer {your_token} +Content-Type: application/json +``` + +--- + +### 4.2 创建解析任务 — 请求规范 + +**接口：** `POST https://mineru.net/api/v4/extract/task` + +#### 请求体字段 + +| 字段 | 类型 | 必填 | 默认值 | 说明 | +|------|------|------|--------|------| +| `url` | `string` | **是** | — | 待解析文件的公网可访问 URL | +| `is_ocr` | `bool` | 否 | `false` | 是否强制启用 OCR（扫描件建议开启） | +| `enable_formula` | `bool` | 否 | `true` | 是否启用公式识别 | +| `enable_table` | `bool` | 否 | `true` | 是否启用表格识别 | +| `language` | `string` | 否 | `"zh"` | 文档主语言代码 | +| `model` | `string` | 否 | 自动选择 | 模型版本：`pipeline` / `vlm` / `MinerU-HTML` | +| `data_id` | `string` | 否 | — | 自定义业务标识（用于关联追踪） | +| `callback_url` | `string` | 否 | — | 任务完成后的回调通知 URL | + +#### MVP 最小必填字段 + +```json +{ + "url": "https://example.com/document.pdf" +} +``` + +> 仅 `url` 为必填，其余参数均有合理默认值。 + +--- + +### 4.3 查询任务结果 — 响应规范 + +**接口：** `GET https://mineru.net/api/v4/extract/task/{task_id}` + +#### 响应体字段 + +| 字段 | 类型 | 说明 | +|------|------|------| +| `task_id` | `string` | 任务唯一标识 | +| `state` | `string` | 任务状态（见下方枚举） | +| `err_msg` | `string \| null` | 错误信息（失败时） | +| `full_zip_url` | `string \| null` | 完整输出 ZIP 下载地址（成功时） | +| `file_name` | `string` | 原始文件名 | +| `batch_id` | `string \| null` | 批量任务 ID（如有） | + +#### 任务状态枚举 + +| state | 说明 | +|-------|------| +| `pending` | 排队等待中 | +| `processing` | 正在解析 | +| `done` | 解析完成 | +| `failed` | 解析失败（查看 `err_msg`） | + +--- + +### 4.4 批量任务接口 + +#### 4.4.1 批量获取上传 URL + +**接口：** `POST https://mineru.net/api/v4/file-urls/batch` + +用于获取文件上传的预签名 URL（适合本地文件上传场景）。 + +#### 4.4.2 批量创建任务 + +**接口：** `POST https://mineru.net/api/v4/extract/task/batch` + +请求体中 `files` 数组包含多个文件的解析参数。 + +#### 4.4.3 批量查询结果 + +**接口：** `GET https://mineru.net/api/v4/extract-results/batch/{batch_id}` + +--- + +### 4.5 MVP 最小可用请求示例 + +#### Python 实现 + +```python +import os +import time +import requests + +MINERU_API_TOKEN = os.getenv("MINERU_API_TOKEN") +BASE_URL = "https://mineru.net/api/v4" +HEADERS = { + "Authorization": f"Bearer {MINERU_API_TOKEN}", + "Content-Type": "application/json", +} + +# ① 创建解析任务（仅需 url 一个必填字段） +resp = requests.post( + f"{BASE_URL}/extract/task", + headers=HEADERS, + json={ + "url": "https://example.com/sample.pdf", # 必填：文件公网 URL + # "is_ocr": False, # 可选：默认 false + # "enable_formula": True, # 可选：默认 true + # "enable_table": True, # 可选：默认 true + # "language": "zh", # 可选：默认中文 + }, +) +task_id = resp.json()["task_id"] +print(f"Task created: {task_id}") + +# ② 轮询查询结果 +while True: + result = requests.get( + f"{BASE_URL}/extract/task/{task_id}", + headers=HEADERS, + ).json() + + state = result["state"] + print(f"State: {state}") + + if state == "done": + zip_url = result["full_zip_url"] + print(f"Download: {zip_url}") + break + elif state == "failed": + print(f"Error: {result['err_msg']}") + break + + time.sleep(5) + +# ③ 下载并解压结果 +import zipfile, io + +zip_data = requests.get(zip_url).content +with zipfile.ZipFile(io.BytesIO(zip_data)) as zf: + zf.extractall("./mineru_output/") + print("Files:", zf.namelist()) +``` + +#### cURL 实现 + +```bash +# 创建任务 +curl -X POST https://mineru.net/api/v4/extract/task \ + -H "Authorization: Bearer YOUR_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{"url": "https://example.com/sample.pdf"}' + +# 查询结果 +curl https://mineru.net/api/v4/extract/task/{task_id} \ + -H "Authorization: Bearer YOUR_TOKEN" +``` + +#### MVP 检查清单 + +- [ ] 已在 [mineru.net](https://mineru.net/) 注册账号 +- [ ] 已在 [Token 管理页](https://mineru.net/apiManage/token) 获取 API Token +- [ ] 已将 Token 配置到 `.env` 文件：`MINERU_API_TOKEN=xxx` +- [ ] 准备了公网可访问的测试文件 URL（PDF/DOCX/PPT/图片） +- [ ] 安装了 `requests` 库：`pip install requests` diff --git a/docs/product_requirements_document-v1.0.md b/docs/product_requirements_document-v1.0.md new file mode 100644 index 0000000..7672422 --- /dev/null +++ b/docs/product_requirements_document-v1.0.md @@ -0,0 +1,1442 @@ +# GraphRAG Studio — 产品需求文档（PRD）v1.0 + +> 文档类型：Product Requirements Document +> 产品名称：GraphRAG Studio +> 版本：v1.0 +> 日期：2026-03-05 +> 作者：产品设计团队 +> 状态：草稿（Draft） + +--- + +## 目录 + +- [一、产品概述](#一产品概述) + - [1.1 产品背景](#11-产品背景) + - [1.2 核心目标](#12-核心目标) + - [1.3 业务痛点](#13-业务痛点) + - [1.4 核心场景](#14-核心场景) + - [1.5 用户群体](#15-用户群体) + - [1.6 产品边界](#16-产品边界) +- [二、产品流程](#二产品流程) + - [2.1 整体用户旅程](#21-整体用户旅程) + - [2.2 核心流程一：文件上传与知识图谱构建](#22-核心流程一文件上传与知识图谱构建) + - [2.3 核心流程二：知识图谱探索](#23-核心流程二知识图谱探索) + - [2.4 核心流程三：Agentic 知识问答](#24-核心流程三agentic-知识问答) + - [2.5 核心流程四：多模式实体搜索](#25-核心流程四多模式实体搜索) + - [2.6 错误与异常处理流程](#26-错误与异常处理流程) +- [三、页面与模块清单](#三页面与模块清单) + - [3.1 全局框架模块](#31-全局框架模块) + - [3.2 Page 1 — Dashboard（系统总览）](#32-page-1--dashboard系统总览) + - [3.3 Page 2 — Document Manager（文档管理）](#33-page-2--document-manager文档管理) + - [3.4 Page 3 — KG Explorer（知识图谱）](#34-page-3--kg-explorer知识图谱) + - [3.5 Page 4 — QA Chat（智能问答）](#35-page-4--qa-chat智能问答) + - [3.6 Page 5 — Search（搜索）](#36-page-5--search搜索) +- [四、核心功能交互逻辑](#四核心功能交互逻辑) + - [4.1 文件上传与索引任务管理](#41-文件上传与索引任务管理) + - [4.2 知识图谱可视化交互](#42-知识图谱可视化交互) + - [4.3 多轮对话与推理链展示](#43-多轮对话与推理链展示) + - [4.4 跨页面联动导航](#44-跨页面联动导航) + - [4.5 全局通知与反馈机制](#45-全局通知与反馈机制) +- [五、UI 设计规范](#五ui-设计规范) + - [5.1 整体布局结构](#51-整体布局结构) + - [5.2 配色系统](#52-配色系统) + - [5.3 字体与排版](#53-字体与排版) + - [5.4 组件样式规范](#54-组件样式规范) + - [5.5 响应式断点规范](#55-响应式断点规范) +- [六、后端接口依赖](#六后端接口依赖) +- [七、非功能性需求](#七非功能性需求) +- [八、验收标准](#八验收标准) + +--- + +## 一、产品概述 + +### 1.1 产品背景 + +在学术研究、企业知识管理、技术调研等场景中，研究人员需要从大量非结构化文档（PDF 论文、DOCX 报告、PPT 演示文稿、图片等）中快速提炼关键知识，理解概念之间的语义关联。 + +传统的 RAG（检索增强生成）系统采用向量检索方式，其结果呈现为零散的文本片段，缺乏实体间的结构化关联。用户无法直观感知"某个概念与哪些技术相关""两个实体之间的路径是什么"等图谱层面的知识。 + +**GraphRAG Studio** 基于知识图谱增强的多模态 RAG 技术，将文档自动解析为结构化知识图谱，并提供图谱可视化、多轮问答、实体搜索等完整工具链，将非结构化文档转化为可交互的知识网络。 + +### 1.2 核心目标 + +| 目标 | 说明 | +|------|------| +| **知识结构化** | 将多格式文档（PDF/DOCX/PPTX/PNG 等）自动解析并提取实体与关系，构建知识图谱 | +| **知识可视化** | 提供交互式力导向图，直观呈现实体及其语义关联，支持筛选、搜索、节点详情 | +| **知识问答** | 基于 Agentic-RAG（ReAct 框架），对自然语言问题进行多步工具调用推理，给出有据可查的答案 | +| **知识探索** | 提供实体搜索、路径发现、子图搜索三种探索模式，支持从不同维度切入知识网络 | +| **工程化管理** | 提供完整的文档上传、索引任务管理、任务进度监控、失败重试等工程化能力 | + +### 1.3 业务痛点 + +**P1 — 高优先级痛点（必须解决）** + +| # | 痛点描述 | 影响 | +|---|---------|------| +| P1-1 | 文档中知识密度高，人工阅读耗时长，无法快速定位关键概念 | 研究效率低下 | +| P1-2 | 概念之间的关联关系隐含在文字中，难以可视化梳理 | 知识理解片面 | +| P1-3 | 传统 RAG 只能返回文本片段，缺乏结构化推理能力 | 问答质量不稳定 | +| P1-4 | 多份文档的知识无法统一管理和关联查询 | 知识孤岛问题 | + +**P2 — 中优先级痛点（应当解决）** + +| # | 痛点描述 | 影响 | +|---|---------|------| +| P2-1 | 索引任务耗时长（MinerU 解析 + LLM 抽取），用户无法感知进度 | 操作体验差 | +| P2-2 | LLM 推理过程黑箱，用户不知道答案从何而来 | 可信度低 | +| P2-3 | 搜索只能通过全文检索，无法按实体类型或图谱路径搜索 | 搜索精度不足 | + +**P3 — 低优先级痛点（后续解决）** + +| # | 痛点描述 | +|---|---------| +| P3-1 | 多人协作时知识图谱共享困难 | +| P3-2 | 知识图谱版本管理缺失 | + +### 1.4 核心场景 + +**场景一：论文精读与知识提炼** + +> 研究人员上传一篇 GraphRAG 相关的 PDF 论文，系统自动提取论文中的技术概念（TECHNOLOGY）、研究方法（CONCEPT）、研究机构（ORGANIZATION）及其关联关系，生成知识图谱。研究人员通过图谱可视化快速理解论文核心知识结构，并通过 QA Chat 就具体问题进行深度追问。 + +**场景二：多文档跨文件知识整合** + +> 用户批量上传多份研究报告，系统对每份文档独立索引后，将所有实体和关系合并至全局知识图谱。用户可在图谱中筛选特定文档的节点，发现跨文档的共同实体，通过路径搜索追溯概念间的传播关系。 + +**场景三：快速技术调研问答** + +> 产品经理或技术负责人上传若干竞品白皮书，通过 QA Chat 提问"这些文档中提到了哪些核心技术？它们之间的关系是什么？"。系统通过 Agentic-RAG 进行多步工具调用（实体搜索 → 邻居查询 → 描述生成），返回结构化答案并展示推理步骤，Cited Nodes 直接链接到图谱中对应的节点。 + +**场景四：知识图谱演示与汇报** + +> 教师或讲师上传课程材料，通过 KG Explorer 的全屏图谱视图向学生展示知识体系结构；通过 Demo 模式加载预置数据集，无需实际索引即可演示产品能力。 + +### 1.5 用户群体 + +| 用户角色 | 特征描述 | 核心诉求 | 使用频次 | +|---------|---------|---------|---------| +| **研究员 / 学者** | 熟悉 AI/NLP 领域，有文献精读需求 | 快速提取论文概念关联，深度问答 | 高频（每日） | +| **产品经理 / 分析师** | 非技术背景，有竞品调研需求 | 直观图谱展示，简单问答 | 中频（每周） | +| **技术工程师** | 熟悉 API，有集成需求 | 完整 API 文档，调试便利 | 中频（按需） | +| **教师 / 讲师** | 有知识可视化展示需求 | 图谱演示，无需深度操作 | 低频（按需） | +| **系统管理员** | 负责部署和维护 | 系统健康监控，日志排查 | 低频（运维） | + +**主要设计对象：** 研究员 / 学者（核心用户）、产品经理 / 分析师（次核心用户） + +### 1.6 产品边界 + +**In Scope（本版本包含）：** +- 多格式文档上传与管理（PDF/DOCX/PPTX/DOC/PPT/PNG/JPG/HTML） +- MinerU 文档解析 + LangExtract 实体抽取自动索引链路 +- 全局知识图谱的可视化浏览与交互 +- 基于 Agentic-RAG（DeepSeek + LangChain）的多轮问答 +- 实体搜索、路径搜索、子图搜索 +- 系统健康监控与任务状态看板 + +**Out of Scope（本版本不包含）：** +- 用户账号体系与权限管理 +- 多人协作与知识图谱共享 +- 知识图谱版本历史与回滚 +- 流式（Stream）问答输出 +- 自定义实体类型与关系类型配置 + +--- + +## 二、产品流程 + +### 2.1 整体用户旅程 + +``` +进入系统 + │ + ▼ +[Dashboard] ─── 查看系统概览 & 健康状态 + │ + ├─── 无文档 → [Documents] 上传文件 → 触发索引 → 等待完成 + │ │ + │ 索引完成 (done) + │ │ + ├─── 有 KG ──────────────────────────────────────────┤ + │ │ + │ ┌──────────────────────────────────────────────┘ + │ │ + ├────▼─── [KG Explorer] 图谱可视化浏览 + │ └─── 点击节点 → 查看详情 → [Ask AI] → Chat + │ + ├────────── [QA Chat] 自然语言问答 + │ └─── Cited Nodes → KG Explorer 节点定位 + │ + └────────── [Search] 实体/路径/子图搜索 + └─── View in KG → KG Explorer + └─── Chat → Chat 预填问题 +``` + +### 2.2 核心流程一：文件上传与知识图谱构建 + +这是系统最核心的数据入口流程，共分为 6 个阶段，涵盖客户端校验、上传、解析、实体抽取、图谱构建、结果呈现。 + +``` +┌──────────────────────────────────────────────────────────────────┐ +│ 文件上传与索引完整流程 │ +└──────────────────────────────────────────────────────────────────┘ + +用户操作：拖拽文件 / 点击"Browse Files" + │ + ▼ +[Stage 0 — 客户端预校验] + 检查文件扩展名（PDF/DOCX/DOC/PPTX/PPT/PNG/JPG/JPEG/HTML） + 检查文件大小（≤ 200MB） + ─── 校验失败 → 文件名标红 + 行内错误说明（不上传） + ─── 校验通过 → 进入 Stage 1 + │ + ▼ +[Stage 1 — 文件上传] + POST /api/v1/documents/upload (multipart/form-data) + ─── 成功 → 返回 doc_id，文档行项目添加到列表（status: uploaded） + ─── 失败 → Toast.error + 终止流程 + │ + ▼ +[Stage 1.5 — 用户确认] + 弹出二次确认： + "Start indexing now? [Yes, Start Indexing] [Later]" + ─── Later → 保持 uploaded 状态，显示 [▶ Index] 按钮，流程结束 + ─── Yes → 进入 Stage 2 + │ + ▼ +[Stage 2 — 启动索引任务] + POST /api/v1/index/start { doc_id } + ─── 成功 → 返回 job_id，status 更新为 indexing，显示进度条 + ─── 失败 → Toast.error（如"文档已在索引中"） + │ + ▼ +[Stage 3 — MinerU 文档解析（parsing）] + 轮询 GET /api/v1/index/status/{job_id}（每 3 秒） + stage = "parsing" + 进度显示："Stage: Parsing document pages (2/4)..." + progress = parsed_pages / total_pages × 33% + │ + ▼ +[Stage 4 — LangExtract 实体抽取（extracting）] + stage = "extracting" + 进度显示："Stage: Extracting entities (18 found so far)..." + progress = 33% + extracted_entities / estimated_total × 33% + │ + ▼ +[Stage 5 — 知识图谱构建（indexing）] + stage = "indexing" + 进度显示："Stage: Building knowledge graph..." + progress ≈ 90% + │ + ▼ +[Stage 6 — 完成（done / failed）] + ─── done: + GET /api/v1/index/result/{job_id} → 获取结果统计 + 更新状态标记为 ● indexed + 展开结果摘要：40 nodes · 780 edges · 4 pages · 45 extractions · 42.1s + Toast.success("paper.pdf indexed: 40 nodes, 780 edges") + AppState.kg.loaded = false（触发 KG Explorer 下次进入时重新加载） + ─── failed: + 行内展示错误信息（如 "MinerU failed: timeout after 600s"） + 显示 [⟳ Retry] 按钮 + Toast.error("Indexing failed: paper.pdf") +``` + +**多文件串行处理规则：** +- 同时拖入多个文件时，依次串行执行上述流程（上传 → 确认 → 索引） +- 每个文件独立显示进度，互不影响 +- 任一文件失败不阻塞其他文件的处理 + +### 2.3 核心流程二：知识图谱探索 + +``` +进入 KG Explorer (#/graph) + │ + ▼ +[初始化加载] + 优先读取 AppState.kg 缓存（避免重复请求） + 若无缓存 → GET /api/v1/kg/nodes?page_size=200 + GET /api/v1/kg/edges?page_size=500 + ─── KG 为空（code 3002）→ 显示空状态引导页面 + ─── 加载成功 → D3 力导向图渲染 + │ + ▼ +[URL 参数处理] + 解析 hash query： + ?doc_id=xxx → 自动勾选对应文档筛选，其余节点淡化 + ?node=xxx → 自动选中节点 + 高亮 + 展开 Detail Panel + 平移居中 + │ + ▼ +[用户交互分支] + │ + ├─ 节点悬停 → Tooltip（name / type / page / confidence / degree） + │ + ├─ 节点单击 + │ GET /api/v1/kg/nodes/{node_id}（获取详情） + │ GET /api/v1/kg/nodes/{node_id}/neighbors?hops=1（获取邻居） + │ → 右侧 Detail Panel 展开 + │ → 点击节点高亮，其余节点 opacity 0.1，相连边 opacity 0.8 + │ + ├─ 节点拖拽 → pin 到固定位置（fx/fy 设置） + │ + ├─ 点击空白处 → 取消选中，Detail Panel 收起，恢复透明度 + │ + ├─ Filter Panel 操作 + │ 勾选/取消实体类型 → 实时过滤可见节点 + │ 选择 Source Doc → 筛选对应文档节点 + │ 勾选/取消 Confidence 等级 → 实时过滤 + │ + ├─ 工具栏操作 + │ [+ -] → 缩放 + │ [⊡ Fit] → 自适应全部节点 + │ [Search] → GET /api/v1/search/entities?q=...（闪烁高亮 + 居中） + │ [PNG] → SVG → PNG 下载 + │ [JSON] → GET /api/v1/kg/export → 文件下载 + │ + └─ Detail Panel 操作 + [💬 Ask AI] → #/chat?q=Tell+me+about+{name} + 邻居节点点击 → 切换选中节点 + [All neighbors →] → 展开完整邻居列表 +``` + +### 2.4 核心流程三：Agentic 知识问答 + +``` +进入 Chat (#/chat) + │ + ▼ +[初始化] + GET /api/v1/query/history?page_size=20 → 渲染左侧历史列表 + 若无历史 → 显示欢迎页 + Suggested Prompts + 若有 URL 参数 ?q=... → 预填输入框并自动聚焦 + │ + ▼ +[用户输入问题] + 输入框（多行）+ Enter 或 点击 [▶] 按钮 + │ + ▼ +[发送请求] + 构建 payload: + { question: "...", history: [ {role:"human",...}, {role:"ai",...} ] } + POST /api/v1/query + 立即显示： + - 用户消息气泡（右对齐，蓝色背景） + - "thinking..." 三点跳动动画（左对齐） + 输入框禁用（发送中） + │ + ▼ +[等待 API 响应（超时 60s）] + ─── 超时 → Toast.warning + 三点动画消失 + 输入框恢复 + ─── 网络错误 → Toast.error("Network error") + [Retry] + ─── API 成功 → 进入渲染阶段 + │ + ▼ +[渲染 AI 回答] + 消除 thinking 动画 + 渲染 AI 消息气泡（左对齐，卡片样式）： + │ + ├─ answer 文本（marked.js 渲染 Markdown） + │ + ├─ Tool Calls 折叠面板（默认收起） + │ 显示 "▶ Tool Calls (N steps)" + │ 展开后逐步显示每个工具调用： + │ Step 1: search_entities / Input / Output + │ Step 2: get_neighbors / Input / Output + │ ...（等宽字体，代码块样式） + │ + ├─ Cited Nodes 标签组 + │ [◉ GraphRAG] [◉ LLMs] [◉ knowledge graphs] ... + │ 点击任一标签 → #/graph?node={node_id} + │ hover → 显示 Tooltip（type + page） + │ + └─ 耗时信息（⏱ 8.4s） + │ + ▼ +[更新状态] + AppState.chatHistory.push(result)（维护多轮 history） + 输入框清空 + 恢复可用 + 左侧历史列表：新会话置顶 +``` + +### 2.5 核心流程四：多模式实体搜索 + +``` +进入 Search (#/search) + │ + ▼ +[初始化] + 解析 URL 参数 ?q=...&type=...&tab=... + 预填搜索框，自动触发搜索 + │ + ┌──────────────────────────────────────────────────────────────┐ + │ 三种搜索模式 │ + └──────────────────────────────────────────────────────────────┘ + │ + ├── [Tab 1: Entity Search] + │ 输入实体名称 + 可选类型过滤（All / TECHNOLOGY / CONCEPT / ...） + │ 点击 [Search] 或 Enter + │ GET /api/v1/search/entities?q=...&type=... + │ ─── 无结果 → 空状态提示 + "Try KG Explorer" + │ ─── 有结果 → 左侧结果列表（实体卡片） + │ 点击结果行 → 右侧 Preview Graph 渲染（1-hop 邻居子图） + │ GET /api/v1/kg/nodes/{id}/neighbors?hops=1 + │ [View KG] → #/graph?node={node_id} + │ [Chat] → #/chat?q=What+is+{name} + │ + ├── [Tab 2: Path Search] + │ 选择起点节点（搜索选择器） + │ 选择终点节点（搜索选择器） + │ 设置最大跳数（1-5） + │ 点击 [Find Path] + │ GET /api/v1/search/path?from={id}&to={id}&max_hops={n} + │ ─── 无路径 → "No path found between these entities" + │ ─── 有路径 → 路径可视化（线性节点链）+ 文字描述 + │ + └── [Tab 3: Graph Search] + 输入关键词 + 可选"Include Neighbors"开关 + 点击 [Search] + GET /api/v1/search/graph?q=...&include_neighbors=true + ─── 渲染子图（D3 可视化，仅展示匹配节点及其邻边） + ─── 列表显示匹配节点摘要（name / type / page） +``` + +### 2.6 错误与异常处理流程 + +``` +API 调用失败（code ≠ 0） + │ + ├─ code 1001/1002/1003（参数/格式/大小校验） + │ → 行内错误提示（不弹 Toast） + │ + ├─ code 2001（job 不存在） + │ → Toast.error + 刷新任务列表 + │ + ├─ code 3002（KG 为空） + │ → 不弹 Toast，页面内空状态引导：[Upload & Index →] + │ + ├─ code 4001（QA 失败） + │ → Toast.error(msg) + 输入框恢复 + │ + ├─ code 5000（服务器内部错误） + │ → Toast.error("Server error, check API logs") + │ + └─ 网络不可达（fetch 异常） + → Toast.error("Network error. Is API server running on :8000?") + → 相关操作显示 [Retry] 按钮 + → Header 右侧健康指示器变红点 +``` + +--- + +## 三、页面与模块清单 + +### 3.1 全局框架模块 + +系统采用 SPA（单页应用）架构，Hash 路由（`#/dashboard`、`#/documents` 等），全局框架由以下四个固定区域构成： + +#### 3.1.1 Header（顶部导航栏） + +- **高度：** 56px，`position: sticky; top: 0; z-index: 100` +- **左区：** 汉堡折叠按钮（`[≡]`）+ 产品 Logo 文字 "GraphRAG Studio" +- **中区：** 全局搜索框（max-width 400px） + - 输入 3+ 字符 → 实时调用实体搜索接口（debounce 300ms） + - 下拉展示最多 5 条建议（名称 + 类型 Badge） + - 按 Enter → 跳转 `#/search?q={input}` + - 点击建议项 → 跳转 `#/graph?node={node_id}` +- **右区：** 系统健康指示器（绿点/红点）+ "API: localhost:8000" + +#### 3.1.2 Sidebar（左侧导航栏） + +- **宽度：** 220px（默认），折叠后 72px（仅图标） +- **导航项列表：** + +| 图标 | 标签 | 路由 | 右侧 Badge | +|------|------|------|-----------| +| ◈ | Dashboard | `#/dashboard` | — | +| ▤ | Documents | `#/documents` | 文档总数 | +| ◉ | KG Explorer | `#/graph` | — | +| ◇ | Chat | `#/chat` | 历史查询数 | +| ⊕ | Search | `#/search` | — | +| ☰ | System | — | — | + +- **激活状态：** 左侧 2px 蓝色竖线 + 背景 `rgba(88,166,255,0.1)` + 文字变蓝 + +#### 3.1.3 Status Bar（底部状态栏） + +- **高度：** 32px，常驻底部 +- **左区：** 当前活跃 Job 进度（如 "Indexing paper.pdf... 65%"），无 Job 时隐藏 +- **右区：** API 版本号（v1.0.0）+ 健康状态点（常驻） + +#### 3.1.4 全局状态管理（AppState） + +前端维护内存级全局状态，页面间共享： + +| 字段 | 类型 | 说明 | +|------|------|------| +| `currentPage` | string | 当前激活页面 | +| `kg.nodes` | array | 全量 KG 节点缓存（首次加载后复用） | +| `kg.edges` | array | 全量 KG 边缓存 | +| `kg.loaded` | boolean | 索引完成后置 false，触发重新加载 | +| `documents` | array | 文档列表缓存 | +| `activeJobs` | object | `job_id → polling timer`（轮询管理） | +| `chatHistory` | array | 当前会话的多轮对话历史 | +| `health` | object | 最近一次健康检查结果 | + +--- + +### 3.2 Page 1 — Dashboard（系统总览） + +**路由：** `#/dashboard` +**目的：** 系统全局状态一览，快速导航到各功能，最近活动监控。 + +#### 模块组成 + +**A. Overview 指标卡（4 个）** + +| 指标 | 数据来源 | 颜色 | +|------|---------|------| +| KG Nodes（知识节点数） | `GET /api/v1/system/stats` | 蓝色 `#58a6ff` | +| KG Edges（知识关系数） | `GET /api/v1/system/stats` | 紫色 `#8957e5` | +| Documents（文档总数） | `GET /api/v1/system/stats` | 绿色 `#3fb950` | +| Queries（问答次数） | `GET /api/v1/system/stats` | 黄色 `#d29922` | + +每 10 秒自动轮询刷新，数值变化时平滑过渡动画。 + +**B. System Health 面板** + +显示 4 个依赖服务的健康状态，数据来源 `GET /api/v1/health`： + +| 服务 | 状态指示 | +|------|---------| +| MinerU venv | ● ok / ● error | +| LangExtract venv | ● ok / ● error | +| DeepSeek API | ● ok / ● error | +| Storage | ● ok / ● error | + +**C. Recent Documents 列表** + +显示最近 5 条文档记录，数据来源 `GET /api/v1/documents?page=1&page_size=5`： +- 文件名、格式、页数、状态 Badge、日期 +- indexed 状态：显示 [KG] 跳转按钮 +- indexing 状态：显示行内进度条 + [✕] 取消按钮 +- uploaded 状态：显示 [▶ Index] 按钮 +- 右上角 [View All →] 跳转 `#/documents` + +**D. Quick Actions 快捷按钮组** + +| 按钮 | 操作 | +|------|------| +| [◉ Explore KG] | 跳转 `#/graph` | +| [◇ Start Chat] | 跳转 `#/chat` | +| [⊕ Search] | 跳转 `#/search` | +| [⚡ Demo] | 调用 `GET /api/v1/system/demo`，加载演示数据，跳转 `#/graph` | + +**E. Upload & Index 入口** + +右上角 `[+ Upload & Index]` 主操作按钮，点击打开 Upload Modal（复用 Documents 页上传区），完成后跳转 `#/documents`。 + +--- + +### 3.3 Page 2 — Document Manager（文档管理） + +**路由：** `#/documents` +**目的：** 文件上传、列表管理、触发/监控索引任务、查看索引结果。 + +#### 模块组成 + +**A. 拖拽上传区** + +- 虚线边框卡片，居中显示上传图标和说明文字 +- 拖拽悬停时：边框变蓝（`#58a6ff`）+ 背景 `rgba(88,166,255,0.05)` +- 点击 [Browse Files] 触发文件选择器 +- 支持格式说明：`PDF · DOCX · DOC · PPTX · PPT · PNG · JPG · HTML` +- 文件大小限制：`Max 200MB per file` + +**B. 文档列表工具栏** + +- 格式筛选下拉（All / PDF / DOCX / PPTX / PNG / JPG / HTML） +- 状态筛选下拉（All / indexed / indexing / uploaded / failed） +- 关键词搜索框（客户端过滤，实时） + +**C. 文档列表（表格）** + +每行字段：文件名、格式、页数、状态 Badge、上传日期、操作按钮组 + +| 状态 | 显示内容 | 操作按钮 | +|------|---------|---------| +| `uploaded` | ● uploaded（灰） | [▶ Index] [🗑 Delete] | +| `indexing` | ● indexing（黄，动画点） + 进度条 + stage 说明 | [✕ Cancel] | +| `indexed` | ● indexed（绿） | [◉ View KG] [🗑 Delete] | +| `failed` | ● failed（红） + 错误摘要 | [⟳ Retry] [🗑 Delete] | + +**D. 索引结果展开行**（仅 indexed 状态） + +点击展开： +``` +40 nodes · 780 edges · 4 pages · 45 extractions · 42.1s +TECHNOLOGY(4) CONCEPT(36) +[◉ View in KG] [≡ Show Extractions] +``` + +展开 [Show Extractions] 显示抽取记录表：text / type / alignment / page（最多 50 条，可滚动） + +--- + +### 3.4 Page 3 — KG Explorer（知识图谱） + +**路由：** `#/graph` +**目的：** 全屏交互式知识图谱可视化，节点筛选、详情查看、图谱导出。 + +#### 模块组成 + +**A. Filter Panel（左侧筛选栏，280px）** + +- Source Docs 下拉：按文档筛选图谱节点 +- Entity Types 多选：TECHNOLOGY / CONCEPT / PERSON / ORGANIZATION / LOCATION（每类显示数量） +- Confidence 等级多选：exact / greater / lesser / fuzzy +- 图谱导出按钮：[📷 Export PNG] [⬇ Export JSON] + +**B. D3 力导向图（中央主区，flex: 1）** + +- SVG 全屏渲染，支持 `d3.zoom`（scaleExtent 0.1 ～ 8） +- 工具栏（浮于图谱左上角）：[+] [-] [⊡ Fit All] [🔍 Search input] +- 图例（浮于图谱左下角）：五种颜色对应五种实体类型 +- 空状态（KG 无数据）：居中提示 + [Upload & Index →] 按钮 + +**C. Detail Panel（右侧详情栏，300px，点击节点后出现）** + +- 节点名称（H1 样式） +- 实体类型 Badge +- 属性信息：Page / Confidence / Degree / Centrality +- 邻居节点列表（最多展示 5 个，[All N →] 查看全部） +- [💬 Ask AI] 按钮 → 跳转 Chat 预填问题 +- [× Close] 关闭 Detail Panel + +--- + +### 3.5 Page 4 — QA Chat（智能问答） + +**路由：** `#/chat` +**目的：** 多轮知识图谱问答，展示 Agentic-RAG 推理过程，Cited Node 联动跳转。 + +#### 模块组成 + +**A. History Sidebar（左侧历史栏，240px）** + +- [+ New Chat] 新建会话按钮 +- 历史记录列表（按时间分组：Today / Yesterday / Earlier） + - 每条显示问题前 30 字符 + - 点击切换历史会话（前端记录，当前版本不持久化多会话） +- 数据来源：`GET /api/v1/query/history?page_size=20` + +**B. Chat Area（右侧对话区）** + +- 欢迎界面（无历史时）：产品介绍 + Suggested Prompts（4 个常用问题） +- 消息时间分隔线 +- 用户消息气泡（右对齐，蓝色背景） +- AI 消息气泡（左对齐，卡片样式）： + - Markdown 渲染的答案文本 + - Tool Calls 折叠面板（默认收起） + - Cited Nodes 标签组（可点击跳转） + - 耗时信息（⏱ N.Ns） +- Thinking 动画（三点跳动） + +**C. 输入区** + +- 多行文本输入框（placeholder: "Ask about the knowledge graph..."） +- [▶ Send] 发送按钮 +- Enter 发送，Shift+Enter 换行 +- 发送中：输入框禁用，按钮 loading 状态 + +--- + +### 3.6 Page 5 — Search（搜索） + +**路由：** `#/search` +**目的：** 多模式实体搜索，支持关键词、路径、子图三种搜索范式。 + +#### 模块组成 + +**A. 搜索头区** + +- 主搜索框（全宽） +- 类型筛选下拉（All Types / TECHNOLOGY / CONCEPT / PERSON / ORG / LOC） +- [Search] 搜索按钮 +- 搜索状态与 URL 双向同步：`#/search?q=...&type=...&tab=...` + +**B. Tab 切换栏** + +3 个 Tab：Entity Search（默认） / Path Search / Graph Search + +**C. Entity Search Tab** + +- 左栏：结果列表（卡片式） + - 实体名称 + 类型 Badge + 页码 + Degree + Confidence + - [View KG] + [Chat] 操作按钮 +- 右栏：Preview Graph（D3 迷你图，展示选中实体的 1-hop 邻居子图） + +**D. Path Search Tab** + +- From 节点选择器（搜索 + 下拉选择） +- To 节点选择器 +- Max Hops 下拉（1-5） +- [Find Path] 按钮 +- 路径结果：线性节点链可视化 + 文字描述 + +**E. Graph Search Tab** + +- 关键词输入框 +- [Include Neighbors] 开关 +- D3 子图可视化（全宽） +- 匹配节点列表 + +--- + +## 四、核心功能交互逻辑 + +### 4.1 文件上传与索引任务管理 + +#### 拖拽上传区状态机 + +``` +初始状态（idle） + │ + ├── dragenter → hover 状态（边框蓝，背景浅蓝） + │ └── dragleave → 回到 idle + │ + └── drop / browse → uploading 状态 + │ + ├── 校验失败 → idle（行内错误） + └── 校验通过 → 文件加入上传队列 +``` + +#### 进度条渲染规则 + +| 阶段 | 进度计算 | 展示文字 | +|------|---------|---------| +| parsing | `parsed_pages / total_pages × 33%` | "Parsing document... (2/4 pages)" | +| extracting | `33% + extracted_entities / estimated × 33%` | "Extracting entities... (18 found)" | +| indexing | 固定 80% ～ 95%（indeterminate） | "Building knowledge graph..." | +| done | 100% | 渐隐，展开结果行 | + +#### 索引取消逻辑 + +- 点击 [✕ Cancel] → 弹出确认弹窗 +- 确认后 → `DELETE /api/v1/index/jobs/{job_id}` +- 停止对应 job_id 的轮询 timer +- 文档状态退回 "uploaded"，显示 [▶ Index] 按钮 + +#### 文档删除确认弹窗规范 + +删除 indexed 状态的文档时，弹窗需展示关联数据影响范围： + +``` +Delete "paper.pdf"? + +This document and all associated KG data will be permanently deleted. + · 40 nodes removed from knowledge graph + · 780 edges removed from knowledge graph + +This action cannot be undone. + + [Cancel] [Delete →] +``` + +- [Delete] 为红色危险按钮 +- 操作完成后 Toast.success("paper.pdf deleted") + 刷新列表 + +### 4.2 知识图谱可视化交互 + +#### 节点视觉映射规则 + +| 视觉属性 | 规则 | +|---------|------| +| 颜色 | 实体类型 → 5 色方案（蓝/紫/绿/红/橙） | +| 半径 | `r = Math.max(4, Math.log(degree + 1) × 4)`（连接越多越大） | +| 描边 | 正常 1.5px / hover 2.5px 白色 | +| 透明度 | 正常 0.9；高亮模式下非焦点节点 0.1 | + +#### 节点高亮逻辑（聚焦模式） + +``` +点击节点 N： + 所有节点 → opacity 0.1（淡化） + 节点 N 及其直连邻居 → opacity 0.9（保持） + 节点 N → 半径 × 1.5（放大） + 节点 N 相连的边 → opacity 0.8（清晰） + 其余边 → opacity 0.05（几乎隐藏） + +点击空白区域： + 全部节点恢复 opacity 0.9 + 全部边恢复 opacity 0.25 + Detail Panel 收起 +``` + +#### D3 力参数配置 + +| 参数 | 值 | 说明 | +|------|---|------| +| `forceLink distance` | 60 | 边长 | +| `forceLink strength` | 0.3 | 弹力 | +| `forceManyBody strength` | -120 | 节点斥力 | +| `forceCollide radius` | `d.r + 4` | 防重叠 | +| `alphaDecay` | 0.02 | 稳定速度 | + +#### Tooltip 内容规范 + +鼠标悬停节点时，在鼠标右下方 8px 处显示： + +``` +┌─────────────────────────┐ +│ GraphRAG [TECH] │ +│ Page: 0 │ +│ Confidence: match_exact │ +│ Degree: 39 │ +└─────────────────────────┘ +``` + +### 4.3 多轮对话与推理链展示 + +#### 多轮 History 维护规则 + +```javascript +// 每次发送携带完整历史（前端维护） +const payload = { + question: inputText, + history: AppState.chatHistory.flatMap(msg => [ + { role: 'human', content: msg.question }, + { role: 'ai', content: msg.answer } + ]) +}; +``` + +- 每次问答后将 QAResult 追加到 `AppState.chatHistory` +- 历史条数上限：20 轮（超出后丢弃最早的对话） + +#### Tool Call 折叠面板展示规范 + +- 默认收起（▶ Tool Calls (N steps)） +- 点击展开 → 逐步展示每个 tool step +- 样式：等宽字体，暗色背景（`--bg-s3`），黄色 tool 名称 +- 输入（Input）/ 输出（Output）用分隔线区分 + +#### Suggested Prompts 展示逻辑 + +| 条件 | Prompts 显示 | +|------|-------------| +| KG 非空 + 无对话历史 | 展示 4 个预设问题 | +| KG 为空 | 提示"先上传文档构建 KG" | +| 已有对话历史 | 不显示 Prompts | + +预设问题： +1. "Give me an overview of the knowledge graph" +2. "List all TECHNOLOGY entities" +3. "How does GraphRAG relate to knowledge graphs?" +4. "What is retrieval-augmented generation?" + +### 4.4 跨页面联动导航 + +系统各页面通过 Hash 路由参数实现深度联动： + +| 触发位置 | 触发动作 | 目标页面 | URL 参数 | +|---------|---------|---------|---------| +| Dashboard [KG] 按钮 | 点击 | KG Explorer | `#/graph?doc_id={doc_id}` | +| Dashboard [Explore KG] | 点击 | KG Explorer | `#/graph` | +| Dashboard [Demo] | 点击 | KG Explorer | 加载 demo → `#/graph` | +| Dashboard [Start Chat] | 点击 | QA Chat | `#/chat` | +| Documents [◉ View KG] | 点击 | KG Explorer | `#/graph?doc_id={doc_id}` | +| Chat Cited Node 标签 | 点击 | KG Explorer | `#/graph?node={node_id}` | +| KG Detail Panel [💬 Ask AI] | 点击 | QA Chat | `#/chat?q=Tell+me+about+{name}` | +| Search [View KG] | 点击 | KG Explorer | `#/graph?node={node_id}` | +| Search [Chat] | 点击 | QA Chat | `#/chat?q=What+is+{name}` | +| Header 全局搜索框 Enter | 提交 | Search | `#/search?q={input}` | +| Header 全局搜索框建议项 | 点击 | KG Explorer | `#/graph?node={node_id}` | + +**URL 参数接收规则（KG Explorer）：** + +```javascript +// KG Explorer 初始化时解析 +const params = new URLSearchParams(window.location.hash.split('?')[1]); +const docFilter = params.get('doc_id'); // 按文档筛选 +const nodeHighlight = params.get('node'); // 聚焦并定位节点 + +if (docFilter) { + // 自动勾选 Filter Panel 中对应文档，其余节点淡化 +} +if (nodeHighlight) { + // 找到节点 → 模拟点击（触发 Detail Panel）→ 平移居中 → 高亮 +} +``` + +### 4.5 全局通知与反馈机制 + +#### Toast 通知系统 + +**位置：** 右上角 `position: fixed; top: 72px; right: 24px` +**宽度：** 320px +**堆叠规则：** 最多同时显示 3 条，从上向下排列，间距 8px + +| 类型 | 背景 | 左边框 | 图标 | 使用场景 | +|------|------|-------|------|---------| +| Success | `#1a3a22` | 3px `#3fb950` | ✓ | 索引完成、删除成功 | +| Warning | `#2d2a16` | 3px `#d29922` | ! | 请求超时、格式警告 | +| Error | `#3b1a1a` | 3px `#f85149` | ✗ | 上传失败、网络错误 | +| Info | `#161f2e` | 3px `#58a6ff` | i | 操作提示、加载状态 | + +**生命周期：** +- 出现：从右侧 slide-in（200ms ease-out） +- 停留：4000ms（hover 时暂停计时器） +- 消失：fade-out（300ms）→ 从 DOM 移除 + +#### Loading 状态层级 + +| 层级 | 样式 | 触发场景 | +|------|------|---------| +| Header 进度条 | 2px 高，顶部蓝色横条 | 所有 API 请求期间 | +| Skeleton Loader | 灰色矩形 shimmer 动画 | 列表首次加载 | +| 图谱 Loading | SVG 中央文字 + 三点动画 | KG 数据加载 | +| Chat Thinking | 三点跳动动画（气泡内） | 等待 QA 响应 | + +#### 空状态引导设计 + +| 页面 | 空状态触发条件 | 引导内容 | +|------|--------------|---------| +| KG Explorer | KG 无节点数据 | 图标 + "No knowledge graph yet" + [Upload & Index →] | +| Chat | 无历史对话 | 欢迎语 + Suggested Prompts（4 个） | +| Search | 搜索结果为空 | "No entities found for '{query}'" + [Explore KG] | +| Documents | 无文档记录 | 上传区更突出，拖拽提示放大 | + +--- + +## 五、UI 设计规范 + +### 5.1 整体布局结构 + +系统采用 CSS Grid 四区布局，高度占满视口（`height: 100vh`）： + +``` +┌──────────────────────────────────────────────────────────────────┐ +│ HEADER 56px (sticky, z-index: 100) │ +├──────────────┬───────────────────────────────────────────────────┤ +│ │ │ +│ SIDEBAR │ MAIN CONTENT AREA │ +│ 220px │ (overflow-y: auto, padding: 24px) │ +│ (fixed) │ │ +│ │ KG Explorer 例外：padding: 0，内部自行布局 │ +│ │ │ +├──────────────┴───────────────────────────────────────────────────┤ +│ STATUS BAR 32px │ +└──────────────────────────────────────────────────────────────────┘ +``` + +**CSS Grid 骨架代码：** + +```css +.app { + display: grid; + grid-template-areas: "header header" "sidebar main" "footer footer"; + grid-template-columns: var(--sidebar-w, 220px) 1fr; + grid-template-rows: 56px 1fr 32px; + height: 100vh; + overflow: hidden; +} +``` + +**KG Explorer 三栏内部布局：** + +``` +┌─────────────┬────────────────────────────────────┬─────────────┐ +│ Filter Panel│ D3 Graph（flex: 1） │ Detail Panel│ +│ 280px │ │ 300px │ +│ │ 全屏 SVG，支持缩放/拖拽/pin │ 点击节点后 │ +│ │ │ 滑入出现 │ +└─────────────┴────────────────────────────────────┴─────────────┘ +``` + +**Chat 双栏内部布局：** + +``` +┌──────────────┬──────────────────────────────────────────────────┐ +│ History │ Chat Area │ +│ 240px │ (display: flex; flex-direction: column) │ +│ │ 消息列表（flex: 1, overflow-y: auto） │ +│ │ 输入区（固定底部） │ +└──────────────┴──────────────────────────────────────────────────┘ +``` + +### 5.2 配色系统 + +采用 GitHub 深色主题（Dark）配色体系，在现有 `index.html` 调色板基础上扩展： + +#### 背景层级 + +| 变量 | 色值 | 用途 | +|------|------|------| +| `--bg-base` | `#0f1117` | 页面底色（body background） | +| `--bg-s1` | `#161b22` | 主要表面（sidebar / header / card） | +| `--bg-s2` | `#21262d` | 次级表面（hover 态 / input / tag） | +| `--bg-s3` | `#1c2128` | 浮层表面（tooltip / popover / 代码块） | + +#### 边框 + +| 变量 | 色值 | 用途 | +|------|------|------| +| `--border` | `#30363d` | 主边框 | +| `--border-muted` | `#21262d` | 次级分隔线 | + +#### 文字层级 + +| 变量 | 色值 | 用途 | +|------|------|------| +| `--text-1` | `#f0f6fc` | 主标题、强调文字 | +| `--text-2` | `#c9d1d9` | 正文内容 | +| `--text-3` | `#8b949e` | 辅助信息、Label | +| `--text-4` | `#484f58` | Placeholder、极弱文字 | + +#### 功能色 + +| 变量 | 色值 | 用途 | +|------|------|------| +| `--blue` | `#58a6ff` | 链接 / 激活态 / 聚焦 / 进度条 | +| `--green` | `#3fb950` | 成功状态 / indexed | +| `--green-btn` | `#238636` | 主操作按钮背景 | +| `--green-hover` | `#2ea043` | 主操作按钮 hover | +| `--red` | `#f85149` | 错误 / 危险操作 / failed | +| `--yellow` | `#d29922` | 警告 / indexing 进行中 | +| `--purple` | `#8957e5` | 边数指标 / 特殊强调 | + +#### 实体类型颜色（与 D3 图谱一致） + +| 实体类型 | 颜色变量 | 色值 | 节点颜色 | +|---------|---------|------|---------| +| TECHNOLOGY | `--type-tech` | `#58a6ff` | 蓝色 | +| CONCEPT | `--type-concept` | `#bc8cff` | 紫色 | +| PERSON | `--type-person` | `#3fb950` | 绿色 | +| ORGANIZATION | `--type-org` | `#ff7b72` | 红色 | +| LOCATION | `--type-loc` | `#ffa657` | 橙色 | + +#### 状态 Badge 配色 + +| 状态 | 背景 | 文字 | +|------|------|------| +| indexed | `#1a3a22` | `#3fb950` | +| indexing | `#2d2a16` | `#d29922` | +| uploaded | `#1c2128` | `#8b949e` | +| failed | `#3b1a1a` | `#f85149` | + +### 5.3 字体与排版 + +**字体族：** + +```css +--font-sans: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; +--font-mono: 'SFMono-Regular', Consolas, 'Liberation Mono', monospace; +``` + +**字体层级规范：** + +| 层级 | 大小 | 粗细 | 颜色 | 用途 | +|------|------|------|------|------| +| H1（页面标题） | 20px | 600 | `--text-1` | 页面大标题 | +| H2（区块标题） | 16px | 600 | `--text-1` | 区块标题 | +| H3（面板子标题） | 13px | 600 uppercase | `--text-3` | 面板分类标题（字母间距 0.5px） | +| Body（正文） | 14px | 400 | `--text-2` | 列表内容、说明文字 | +| Small（辅助） | 12px | 400 | `--text-3` | 日期、辅助信息、Tooltip | +| Badge（标签） | 11px | 600 | — | 状态标记、类型标签 | +| Mono（代码） | 13px | 400 | `--text-2` | Tool Call 输入输出、文件路径 | + +**行间距：** 正文 `1.6`，代码块 `1.5` + +### 5.4 组件样式规范 + +#### 按钮（4 种变体） + +| 变体 | class | 背景 | 边框 | 文字颜色 | 用途 | +|------|-------|------|------|---------|------| +| Primary | `.btn-primary` | `--green-btn` | `--green-btn` | `#ffffff` | 主操作（Upload / Send / Index） | +| Secondary | `.btn-secondary` | `--bg-s2` | `--border` | `--text-2` | 次要操作（Cancel / Filter） | +| Ghost | `.btn-ghost` | transparent | none | `--text-3` | 图标按钮（内联操作） | +| Danger | `.btn-danger` | `--bg-s2` | `--border` | `--red` | 危险操作（Delete） | + +```css +.btn { + padding: 6px 14px; + border-radius: 6px; /* --r-md */ + font-size: 13px; + font-weight: 500; + cursor: pointer; + transition: all 150ms ease; + display: inline-flex; + align-items: center; + gap: 6px; + border: 1px solid transparent; +} +.btn-sm { padding: 4px 10px; font-size: 12px; } +``` + +#### 卡片（Card） + +```css +.card { + background: var(--bg-s1); + border: 1px solid var(--border); + border-radius: 8px; /* --r-lg */ + padding: 16px; +} +``` + +#### 输入框（Input） + +```css +.input { + background: var(--bg-s2); + border: 1px solid var(--border); + border-radius: 6px; + color: var(--text-1); + padding: 8px 12px; + font-size: 14px; + width: 100%; + transition: border-color 150ms ease; +} +.input:focus { + outline: none; + border-color: var(--blue); + box-shadow: 0 0 0 3px rgba(88,166,255,0.15); +} +``` + +#### 进度条 + +```css +.progress-bar { + height: 4px; + background: var(--bg-s2); + border-radius: 2px; + overflow: hidden; +} +.progress-fill { + height: 100%; + background: var(--blue); + border-radius: 2px; + transition: width 300ms ease; +} +``` + +#### Modal（确认弹窗） + +```css +.modal-overlay { + position: fixed; inset: 0; + background: rgba(0,0,0,0.6); + z-index: 1000; + display: flex; align-items: center; justify-content: center; +} +.modal { + background: var(--bg-s1); + border: 1px solid var(--border); + border-radius: 12px; /* --r-xl */ + padding: 24px; + width: 360px; + box-shadow: 0 8px 32px rgba(0,0,0,0.6); +} +``` + +#### Skeleton Loader（骨架屏） + +```css +.skeleton { + background: linear-gradient(90deg, var(--bg-s2) 25%, var(--bg-s1) 50%, var(--bg-s2) 75%); + background-size: 200% 100%; + animation: shimmer 1.5s infinite; + border-radius: 4px; +} +@keyframes shimmer { + 0% { background-position: 200% 0; } + 100% { background-position: -200% 0; } +} +``` + +#### 阴影规范 + +```css +--shadow-sm: 0 1px 3px rgba(0,0,0,0.4); /* card 轻阴影 */ +--shadow-md: 0 4px 16px rgba(0,0,0,0.5); /* dropdown / tooltip */ +--shadow-lg: 0 8px 32px rgba(0,0,0,0.6); /* modal / drawer */ +``` + +#### 圆角规范 + +```css +--r-sm: 4px; /* badge / tag / 小按钮 */ +--r-md: 6px; /* 按钮 / 输入框 / 小卡片 */ +--r-lg: 8px; /* 卡片 / 面板 */ +--r-xl: 12px; /* Modal / 大弹窗 */ +``` + +### 5.5 响应式断点规范 + +系统支持 4 个断点，覆盖从 4K 显示器到移动端的使用场景： + +| 断点 | 宽度范围 | 目标设备 | +|------|---------|---------| +| Desktop | > 1280px | 台式机、宽屏笔记本 | +| Laptop | 1024 ～ 1280px | 普通 13/14 寸笔记本 | +| Tablet | 768 ～ 1024px | iPad、竖屏笔记本 | +| Mobile | < 768px | 手机 | + +#### 各断点布局变化 + +**Desktop（> 1280px）：** +- 完整三列布局（Sidebar 220px + Main + Detail Panel） +- 所有功能面板默认展开 + +**Laptop（1024 ～ 1280px）：** +- Sidebar 折叠为图标模式（72px），仅显示图标 +- 导航文字 Label、Badge、Logo 文字隐藏 +- 内容区相应扩展 + +**Tablet（768 ～ 1024px）：** +- Sidebar 完全隐藏（`grid-template-columns: 0 1fr`） +- 汉堡菜单按钮（`[≡]`）展开/收起 Sidebar Drawer +- Drawer 覆盖内容区（`position: fixed; z-index: 500`）+ 半透明遮罩 +- KG Explorer：Filter Panel → 浮动侧边 Drawer（FAB 触发） +- Chat：History Sidebar → 顶部 Drawer + +**Mobile（< 768px）：** +- 底部 Tab Bar 替代左侧 Sidebar（高度 56px，5 个图标导航） +- Header 精简（隐藏全局搜索框，仅保留 Logo + 健康状态） +- Dashboard 指标卡：4 列 → 2 列网格 +- Documents 列表：表格 → 卡片堆叠（隐藏 Pages、Date 列） +- KG Explorer：三栏 → 图谱全屏，Detail Panel → 底部 Sheet（滑入，高度 60vh） +- Search：左右双栏 → 上下堆叠（Preview Graph 缩小到 200px） + +#### CSS 媒体查询关键规则 + +```css +/* Laptop */ +@media (max-width: 1280px) { + .app { --sidebar-w: 72px; } + .nav-label, .nav-badge, .sidebar-logo-text { display: none; } +} + +/* Tablet */ +@media (max-width: 1024px) { + .app { grid-template-columns: 0 1fr; } + .sidebar { + position: fixed; left: 0; top: 0; bottom: 0; + width: 220px; z-index: 500; + transform: translateX(-220px); + transition: transform 0.2s ease; + } + .sidebar.open { transform: translateX(0); } +} + +/* Mobile */ +@media (max-width: 768px) { + .app { grid-template-rows: 56px 1fr 56px; } + .sidebar { display: none; } + .bottom-nav { display: flex; } + .metrics-grid { grid-template-columns: repeat(2, 1fr); } +} +``` + +--- + +## 六、后端接口依赖 + +GraphRAG Studio 前端依赖以下 25 个 FastAPI 后端接口，服务地址 `http://localhost:8000/api/v1`，统一响应格式 `{code, msg, request_id, data}`： + +### A 组：文档管理 + +| 接口 | 方法 | 路径 | 触发页面 | +|------|------|------|---------| +| 上传文档 | POST | `/documents/upload` | Dashboard Modal, Documents | +| 文档详情 | GET | `/documents/{doc_id}` | Documents | +| 文档列表 | GET | `/documents` | Dashboard（最近5条），Documents（全部） | +| 删除文档 | DELETE | `/documents/{doc_id}` | Documents | + +### B 组：Indexing Pipeline + +| 接口 | 方法 | 路径 | 触发页面 | +|------|------|------|---------| +| 启动索引 | POST | `/index/start` | Dashboard, Documents | +| 查询任务状态 | GET | `/index/status/{job_id}` | Dashboard（轮询），Documents（轮询） | +| 获取索引结果 | GET | `/index/result/{job_id}` | Documents（完成后） | +| 取消任务 | DELETE | `/index/jobs/{job_id}` | Dashboard, Documents | + +### C 组：知识图谱 + +| 接口 | 方法 | 路径 | 触发页面 | +|------|------|------|---------| +| 节点列表 | GET | `/kg/nodes` | KG Explorer（初始化） | +| 边列表 | GET | `/kg/edges` | KG Explorer（初始化） | +| 节点详情 | GET | `/kg/nodes/{node_id}` | KG Explorer（点击），Chat（hover），Search | +| 节点邻居 | GET | `/kg/nodes/{node_id}/neighbors` | KG Explorer（Detail Panel），Search（Preview） | +| 图谱统计 | GET | `/kg/stats` | KG Explorer | +| 导出图谱 | GET | `/kg/export` | KG Explorer（工具栏） | + +### D 组：QA 问答 + +| 接口 | 方法 | 路径 | 触发页面 | +|------|------|------|---------| +| 同步问答 | POST | `/query` | Chat（发送消息） | +| 批量问答 | POST | `/query/batch` | — | +| 批量状态 | GET | `/query/batch/{batch_id}` | — | +| 历史查询 | GET | `/query/history` | Chat（历史列表） | + +### E 组：搜索 + +| 接口 | 方法 | 路径 | 触发页面 | +|------|------|------|---------| +| 实体搜索 | GET | `/search/entities` | Header（全局搜索），KG Explorer（工具栏），Search Tab1 | +| 路径搜索 | GET | `/search/path` | Search Tab2 | +| 子图搜索 | GET | `/search/graph` | Search Tab3 | + +### F 组：系统 + +| 接口 | 方法 | 路径 | 触发页面 | +|------|------|------|---------| +| 健康检查 | GET | `/health` | Dashboard（Health 面板），Header（健康状态） | +| 系统统计 | GET | `/system/stats` | Dashboard（指标卡） | +| 支持格式 | GET | `/system/formats` | Documents（格式说明） | +| Demo 数据 | GET | `/system/demo` | Dashboard（Demo 按钮） | + +### 错误码处理规范 + +| code | 含义 | 前端处理 | +|------|------|---------| +| 0 | 成功 | 正常渲染数据 | +| 1001 | 参数校验失败 | 行内表单错误提示 | +| 1002 | 文件格式不支持 | 文件名标红 + 行内说明 | +| 1003 | 文件超过大小限制 | 同上 | +| 1004 | 文件不存在 | Toast.error | +| 2001 | Job 不存在 | Toast.error + 刷新列表 | +| 2002 | Job 仍在运行 | Toast.warning（防重复启动） | +| 2003 | Job 已完成 | Toast.info | +| 2004 | Job 取消失败 | Toast.error | +| 3001 | 节点不存在 | Toast.error | +| 3002 | KG 为空 | 页面空状态引导（不弹 Toast） | +| 4001 | QA 执行失败 | Toast.error + 输入框恢复 | +| 5000 | 服务器内部错误 | Toast.error("Server error") | + +--- + +## 七、非功能性需求 + +### 7.1 性能要求 + +| 指标 | 要求 | +|------|------| +| 页面切换（SPA 路由） | < 100ms | +| D3 图谱初始渲染（40 节点，780 边） | < 500ms | +| API 请求超时设置 | 普通接口 30s，QA 接口 60s | +| KG 节点轮询最小间隔 | 3s | +| Dashboard 刷新间隔 | 10s | +| 全局搜索建议 debounce | 300ms | +| D3 图谱性能阈值 | ≤ 500 节点使用 SVG；> 500 节点切换 Canvas 渲染 | + +### 7.2 兼容性要求 + +| 维度 | 要求 | +|------|------| +| 浏览器 | Chrome 90+ / Edge 90+ / Firefox 88+ / Safari 14+ | +| 屏幕分辨率 | 最低 1024×768，推荐 1440×900 以上 | +| 屏幕像素比 | 支持 1x / 2x（Retina） | +| JavaScript | 无构建工具，原生 ES2020+（使用 CDN） | + +### 7.3 可访问性（Accessibility） + +- 所有交互元素支持键盘导航（Tab / Enter / Esc） +- 图标按钮提供 `aria-label` 说明 +- 颜色对比度符合 WCAG 2.1 AA 标准（文字：≥ 4.5:1） +- Toast 通知区域添加 `aria-live="polite"` + +### 7.4 安全性 + +- API 请求不包含敏感数据（API Key 存于后端 `.env`，不暴露至前端） +- 上传文件客户端预校验格式与大小（后端二次校验） +- XSS 防护：Chat 页 AI 答案通过 marked.js 的 sanitize 选项过滤 + +--- + +## 八、验收标准 + +### 8.1 功能验收 + +| 编号 | 验收项 | 验收标准 | +|------|-------|---------| +| F-01 | 文件上传 | 支持 9 种格式，200MB 以内；拖拽和点击均可触发；校验失败行内提示 | +| F-02 | 索引任务 | 启动后每 3s 更新进度；stage 文字正确反映当前阶段；完成后展示结果摘要 | +| F-03 | 任务取消 | 点击 [✕] 确认后任务终止，状态回退到 uploaded | +| F-04 | KG 图谱渲染 | 节点颜色、大小按规则渲染；边透明度 0.25；力模拟稳定 | +| F-05 | 节点高亮 | 点击节点后非焦点节点 opacity 变 0.1，相连边 opacity 0.8 | +| F-06 | Detail Panel | 点击节点后出现，显示属性+邻居；点击空白处收起 | +| F-07 | QA 问答 | 发送问题后 thinking 动画出现；响应后 Markdown 渲染正确 | +| F-08 | Tool Call 展示 | 默认收起；展开后每步 tool name / input / output 完整显示 | +| F-09 | Cited Nodes | 显示为可点击标签；点击跳转 `#/graph?node=xxx` | +| F-10 | 实体搜索 | 输入关键词后返回结果；点击结果行渲染 Preview Graph | +| F-11 | 路径搜索 | 选择两个节点后返回路径；路径可视化正确 | +| F-12 | 跨页联动 | 所有 11 条跨页导航路径携带正确 URL 参数且被接收页正确处理 | +| F-13 | Toast 通知 | 4 种类型正确显示；4s 后自动消失；最多同时显示 3 条 | +| F-14 | 空状态引导 | KG 无数据时显示引导 UI；文档列表为空时上传区突出 | +| F-15 | 响应式布局 | 在 1024px 以上显示完整布局；768px 以下切换底部 Tab Bar | + +### 8.2 性能验收 + +- 页面切换动画流畅（60fps） +- D3 图谱 40 节点 + 780 边在 500ms 内完成初始渲染 +- 轮询机制不导致内存泄漏（离开页面时清除所有 timer） + +### 8.3 API 覆盖验收 + +所有 25 个后端 API 端点在前端代码中均有明确的调用时机，且均通过 `api.js` 封装层调用，错误统一由 `APIError` 捕获处理。 + +--- + +## 附录 + +### A. 技术栈汇总 + +| 层次 | 技术 | 版本 | 引入方式 | +|------|------|------|---------| +| 前端框架 | 原生 HTML/CSS/JS | ES2020+ | — | +| 图形渲染 | D3.js | v7 | CDN | +| Markdown 渲染 | marked.js | v9 | CDN | +| 后端框架 | FastAPI | 0.104+ | Python venv | +| ASGI 服务器 | Uvicorn | 0.24+ | Python venv | +| PDF 解析 | MinerU | 最新稳定版 | subprocess | +| 实体抽取 | LangExtract + DeepSeek | — | Direct import | +| 知识问答 | LangChain + DeepSeek | 0.2+ | Direct import | +| 图计算 | NetworkX | 3.x | Python venv | + +### B. 文件交付清单 + +| 类型 | 路径 | 说明 | +|------|------|------| +| 规范文档 | `docs/backend_service_specification-v1.0.md` | FastAPI 后端 25 个接口规范 | +| 规范文档 | `docs/frontend_design_specification-v1.0.md` | 前端 SPA 设计规范（5 页面） | +| 规范文档 | `docs/product_requirements_document-v1.0.md` | 本文档（产品需求文档） | +| 前端入口 | `graphrag_pipeline/static/app/index.html` | SPA 主入口（待实现） | +| 前端样式 | `graphrag_pipeline/static/app/css/` | 3 个 CSS 文件（待实现） | +| 前端逻辑 | `graphrag_pipeline/static/app/js/` | 7 个 JS 文件（待实现） | +| 后端服务 | `graphrag_pipeline/api_server.py` | FastAPI 主入口（待实现） | + +### C. 词汇表 + +| 术语 | 说明 | +|------|------| +| GraphRAG | 知识图谱增强的 RAG（检索增强生成）系统 | +| KG | Knowledge Graph，知识图谱 | +| Entity | 实体，知识图谱中的节点，包含名称、类型、页码、置信度等属性 | +| Relation | 关系，知识图谱中的边，当前版本固定为 `CO_OCCURS_IN`（共现关系） | +| MinerU | 文档解析工具，将 PDF/DOCX 等格式解析为结构化文本 | +| LangExtract | 基于 LLM 的实体抽取框架 | +| Agentic-RAG | 基于 ReAct 框架的智能问答，LLM 通过多步工具调用推理 | +| ReAct | Reasoning + Acting，推理与行动交替的 Agent 模式 | +| Job | 索引任务，一个文档的完整 parsing → extracting → indexing 流程 | +| SPA | Single Page Application，单页应用 | +| Hash 路由 | 通过 URL 的 `#` 部分控制页面切换，无需服务端路由 | +| Cited Nodes | QA 答案中引用的知识图谱节点，可点击跳转至图谱 | +| Tool Call | Agentic-RAG 中 LLM 调用的工具函数（如 search_entities） | diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000..d931370 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,2 @@ + +**/.git_embedded_backup/ diff --git a/frontend/ATTRIBUTIONS.md b/frontend/ATTRIBUTIONS.md new file mode 100644 index 0000000..5df5c40 --- /dev/null +++ b/frontend/ATTRIBUTIONS.md @@ -0,0 +1,3 @@ +This Figma Make file includes components from [shadcn/ui](https://ui.shadcn.com/) used under [MIT license](https://github.com/shadcn-ui/ui/blob/main/LICENSE.md). + +This Figma Make file includes photos from [Unsplash](https://unsplash.com) used under [license](https://unsplash.com/license). diff --git a/frontend/CLAUDE.md b/frontend/CLAUDE.md new file mode 100644 index 0000000..a7bb4bf --- /dev/null +++ b/frontend/CLAUDE.md @@ -0,0 +1,33 @@ +# Frontend — 开发说明 + +## 路径 + +``` +F:\GraphRAGAgent\frontend\ +``` + +## 启动开发服务器 + +```bash +cd F:/GraphRAGAgent/frontend +pnpm dev +``` + +启动后访问：http://localhost:5173 + +## 依赖安装 + +```bash +cd F:/GraphRAGAgent/frontend +pnpm install +pnpm rebuild @tailwindcss/oxide esbuild +``` + +> 注意：首次安装后需执行 `pnpm rebuild @tailwindcss/oxide esbuild`，否则 Vite 构建会因原生包未编译而失败。 + +## 构建生产包 + +```bash +cd F:/GraphRAGAgent/frontend +pnpm build +``` diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000..4a89e4b --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,11 @@ + + # 构建产品原型 + + This is a code bundle for 构建产品原型. The original project is available at https://www.figma.com/design/Tt95Sj8nC3HvirV1Vw4cA7/%E6%9E%84%E5%BB%BA%E4%BA%A7%E5%93%81%E5%8E%9F%E5%9E%8B. + + ## Running the code + + Run `npm i` to install the dependencies. + + Run `npm run dev` to start the development server. + \ No newline at end of file diff --git a/frontend/guidelines/Guidelines.md b/frontend/guidelines/Guidelines.md new file mode 100644 index 0000000..110f117 --- /dev/null +++ b/frontend/guidelines/Guidelines.md @@ -0,0 +1,61 @@ +**Add your own guidelines here** + diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..1f4095b --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,15 @@ + + + + + + + 构建产品原型 + + + +
+ + + + \ No newline at end of file diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000..8f24795 --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,6034 @@ +{ + "name": "@figma/my-make-file", + "version": "0.0.1", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "@figma/my-make-file", + "version": "0.0.1", + "dependencies": { + "@emotion/react": "11.14.0", + "@emotion/styled": "11.14.1", + "@mui/icons-material": "7.3.5", + "@mui/material": "7.3.5", + "@popperjs/core": "2.11.8", + "@radix-ui/react-accordion": "1.2.3", + "@radix-ui/react-alert-dialog": "1.1.6", + "@radix-ui/react-aspect-ratio": "1.1.2", + "@radix-ui/react-avatar": "1.1.3", + "@radix-ui/react-checkbox": "1.1.4", + "@radix-ui/react-collapsible": "1.1.3", + "@radix-ui/react-context-menu": "2.2.6", + "@radix-ui/react-dialog": "1.1.6", + "@radix-ui/react-dropdown-menu": "2.1.6", + "@radix-ui/react-hover-card": "1.1.6", + 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dependencies: + loose-envify: 1.4.0 + + semver@6.3.1: {} + + set-cookie-parser@2.7.2: {} + + sonner@2.0.3(react-dom@18.3.1(react@18.3.1))(react@18.3.1): + dependencies: + react: 18.3.1 + react-dom: 18.3.1(react@18.3.1) + + source-map-js@1.2.1: {} + + source-map@0.5.7: {} + + string-convert@0.2.1: {} + + stylis@4.2.0: {} + + supports-preserve-symlinks-flag@1.0.0: {} + + tailwind-merge@3.2.0: {} + + tailwindcss@4.1.12: {} + + tapable@2.3.0: {} + + tar@7.5.10: + dependencies: + '@isaacs/fs-minipass': 4.0.1 + chownr: 3.0.0 + minipass: 7.1.3 + minizlib: 3.1.0 + yallist: 5.0.0 + + tiny-invariant@1.3.3: {} + + tinyglobby@0.2.15: + dependencies: + fdir: 6.5.0(picomatch@4.0.3) + picomatch: 4.0.3 + + tslib@2.8.1: {} + + tw-animate-css@1.3.8: {} + + update-browserslist-db@1.2.3(browserslist@4.28.1): + dependencies: + browserslist: 4.28.1 + escalade: 3.2.0 + picocolors: 1.1.1 + + use-callback-ref@1.3.3(@types/react@19.2.14)(react@18.3.1): + dependencies: + react: 18.3.1 + tslib: 2.8.1 + optionalDependencies: + '@types/react': 19.2.14 + + use-sidecar@1.1.3(@types/react@19.2.14)(react@18.3.1): + dependencies: + detect-node-es: 1.1.0 + react: 18.3.1 + tslib: 2.8.1 + optionalDependencies: + '@types/react': 19.2.14 + + vaul@1.1.2(@types/react@19.2.14)(react-dom@18.3.1(react@18.3.1))(react@18.3.1): + dependencies: + '@radix-ui/react-dialog': 1.1.6(@types/react@19.2.14)(react-dom@18.3.1(react@18.3.1))(react@18.3.1) + react: 18.3.1 + react-dom: 18.3.1(react@18.3.1) + transitivePeerDependencies: + - '@types/react' + - '@types/react-dom' + + victory-vendor@36.9.2: + dependencies: + '@types/d3-array': 3.2.2 + '@types/d3-ease': 3.0.2 + '@types/d3-interpolate': 3.0.4 + '@types/d3-scale': 4.0.9 + '@types/d3-shape': 3.1.8 + '@types/d3-time': 3.0.4 + '@types/d3-timer': 3.0.2 + d3-array: 3.2.4 + d3-ease: 3.0.1 + d3-interpolate: 3.0.1 + d3-scale: 4.0.2 + d3-shape: 3.2.0 + d3-time: 3.1.0 + d3-timer: 3.0.1 + + vite@6.3.5(jiti@2.6.1)(lightningcss@1.30.1): + dependencies: + esbuild: 0.25.12 + fdir: 6.5.0(picomatch@4.0.3) + picomatch: 4.0.3 + postcss: 8.5.8 + rollup: 4.59.0 + tinyglobby: 0.2.15 + optionalDependencies: + fsevents: 2.3.3 + jiti: 2.6.1 + lightningcss: 1.30.1 + + warning@4.0.3: + dependencies: + loose-envify: 1.4.0 + + yallist@3.1.1: {} + + yallist@5.0.0: {} + + yaml@1.10.2: {} diff --git a/frontend/postcss.config.mjs b/frontend/postcss.config.mjs new file mode 100644 index 0000000..531dbec --- /dev/null +++ b/frontend/postcss.config.mjs @@ -0,0 +1,15 @@ +/** + * PostCSS Configuration + * + * Tailwind CSS v4 (via @tailwindcss/vite) automatically sets up all required + * PostCSS plugins — you do NOT need to include `tailwindcss` or `autoprefixer` here. + * + * This file only exists for adding additional PostCSS plugins, if needed. + * For example: + * + * import postcssNested from 'postcss-nested' + * export default { plugins: [postcssNested()] } + * + * Otherwise, you can leave this file empty. + */ +export default {} diff --git a/frontend/src/app/App.tsx b/frontend/src/app/App.tsx new file mode 100644 index 0000000..0eac69b --- /dev/null +++ b/frontend/src/app/App.tsx @@ -0,0 +1,7 @@ +import { RouterProvider } from 'react-router'; +import { router } from './routes'; +import '../styles/app.css'; + +export default function App() { + return ; +} \ No newline at end of file diff --git a/frontend/src/app/api.ts b/frontend/src/app/api.ts new file mode 100644 index 0000000..1d71402 --- /dev/null +++ b/frontend/src/app/api.ts @@ -0,0 +1,279 @@ +/** + * GraphRAG Studio — Backend API Client + * Base: http://localhost:8000/api/v1 + * All functions return the `data` field; throw ApiError on code !== 0 + */ + +const BASE = 'http://localhost:8000/api/v1'; + +export class ApiError extends Error { + code: number; + constructor(code: number, msg: string) { + super(msg); + this.code = code; + } +} + +async function request( + method: string, + path: string, + options: { + body?: unknown; + formData?: FormData; + params?: Record; + } = {} +): Promise { + let url = BASE + path; + + if (options.params) { + const parts = Object.entries(options.params) + .filter(([, v]) => v !== undefined && v !== null && v !== '') + .map(([k, v]) => `${encodeURIComponent(k)}=${encodeURIComponent(String(v))}`); + if (parts.length) url += '?' + parts.join('&'); + } + + const init: RequestInit = { method }; + if (options.formData) { + init.body = options.formData; + } else if (options.body !== undefined) { + init.headers = { 'Content-Type': 'application/json' }; + init.body = JSON.stringify(options.body); + } + + const res = await fetch(url, init); + const json = await res.json(); + if (json.code !== 0) throw new ApiError(json.code, json.msg ?? 'Unknown error'); + return json.data as T; +} + +const get = (path: string, params?: Record) => + request('GET', path, { params }); +const post = (path: string, body?: unknown) => request('POST', path, { body }); +const postForm = (path: string, fd: FormData) => request('POST', path, { formData: fd }); +const del = (path: string) => request('DELETE', path); + +// ─── Response Types ─────────────────────────────────────────────────────────── + +export interface ApiDoc { + doc_id: string; + filename: string; + format: string; + pages: number | null; + status: 'uploaded' | 'indexing' | 'indexed' | 'failed'; + upload_date: string; + job_id?: string | null; + file_size?: number; + error_msg?: string | null; +} + +export interface ApiJobStatus { + job_id: string; + doc_id: string; + status: 'submitted' | 'queued' | 'parsing' | 'extracting' | 'indexing' | 'done' | 'failed' | 'cancelled'; + stage: string; + progress: number; // 0.0–1.0 + started_at?: string; + updated_at?: string; + error_msg?: string | null; +} + +export interface ApiIndexResult { + job_id: string; + doc_id: string; + status: string; + nodes_added: number; + edges_added: number; + total_nodes: number; + total_edges: number; + pages_processed: number; + extractions_count: number; + duration_seconds: number; +} + +export interface ApiKGNode { + id: string; + name: string; + type: string; + page: number; + confidence: string; + degree: number; + source_doc: string; + // Only present in detail endpoint: + degree_centrality?: number; + neighbor_count?: number; +} + +export interface ApiKGEdge { + id: string; + source: string; + target: string; + relation: string; + doc_id: string; + page: number; +} + +export interface ApiHealthData { + status: string; + version: string; + uptime_seconds: number; + components: { + mineru_venv: { status: string }; + langextract_venv: { status: string }; + deepseek_api: { status: string }; + storage: { status: string }; + }; +} + +export interface ApiStats { + total_documents: number; + indexed_documents: number; + failed_documents: number; + total_nodes: number; + total_edges: number; + total_queries: number; + active_jobs: number; + storage_used_mb: number; +} + +export interface ApiToolCall { + step: number; + tool_name: string; + tool_input: string; + tool_output: string; +} + +export interface ApiQueryResult { + id: string; + question: string; + answer: string; + tool_calls: ApiToolCall[]; + cited_nodes: string[]; // node IDs + duration_seconds: number; + timestamp: string; +} + +export interface ApiSearchResult { + query: string; + total: number; + items: ApiKGNode[]; +} + +export interface ApiPathResult { + from: { id: string; name: string; type: string }; + to: { id: string; name: string; type: string }; + max_hops: number; + total_paths: number; + paths: Array<{ + length: number; + nodes: Array<{ id: string; name: string; type: string }>; + edges?: Array<{ source: string; target: string; relation: string }>; + }>; +} + +export interface ApiGraphSearchResult { + query: string; + matched_nodes: ApiKGNode[]; + subgraph_edges: ApiKGEdge[]; + total_nodes: number; +} + +// ─── API Functions ──────────────────────────────────────────────────────────── + +export const api = { + // A: Documents + listDocuments: (page = 1, pageSize = 100) => + get<{ total: number; page: number; page_size: number; items: ApiDoc[] }>( + '/documents', { page, page_size: pageSize } + ), + + getDocument: (docId: string) => get(`/documents/${docId}`), + + uploadDocument: (file: File) => { + const fd = new FormData(); + fd.append('file', file); + return postForm<{ doc_id: string; filename: string; format: string; status: string }>( + '/documents/upload', fd + ); + }, + + deleteDocument: (docId: string) => + del<{ doc_id: string; removed_nodes: number; removed_edges: number }>(`/documents/${docId}`), + + // B: Indexing + startIndexing: (docId: string) => + post<{ job_id: string; doc_id: string; status: string }>('/index/start', { doc_id: docId }), + + getJobStatus: (jobId: string) => get(`/index/status/${jobId}`), + + getJobResult: (jobId: string) => get(`/index/result/${jobId}`), + + cancelJob: (jobId: string) => del<{ job_id: string }>(`/index/jobs/${jobId}`), + + // C: Knowledge Graph + getNodes: (params?: { page?: number; pageSize?: number; type?: string; docId?: string }) => + get<{ total: number; page: number; page_size: number; items: ApiKGNode[] }>('/kg/nodes', { + page: params?.page, + page_size: params?.pageSize ?? 500, + type: params?.type, + doc_id: params?.docId, + }), + + getEdges: (params?: { page?: number; pageSize?: number; docId?: string }) => + get<{ total: number; page: number; page_size: number; items: ApiKGEdge[] }>('/kg/edges', { + page: params?.page, + page_size: params?.pageSize ?? 2000, + doc_id: params?.docId, + }), + + getNodeDetail: (nodeId: string) => get(`/kg/nodes/${nodeId}`), + + getNodeNeighbors: (nodeId: string, hops = 1) => + get<{ + center: ApiKGNode; + hops: number; + neighbors_by_hop: Record; + total_neighbors: number; + }>(`/kg/nodes/${nodeId}/neighbors`, { hops }), + + getKGStats: () => + get<{ total_nodes: number; total_edges: number; type_distribution: Record }>('/kg/stats'), + + exportKG: () => get<{ nodes: ApiKGNode[]; edges: ApiKGEdge[] }>('/kg/export'), + + // D: QA Query + query: (question: string, history: { question: string; answer: string }[] = []) => { + // Transform {question, answer}[] to ChatMessage format expected by backend + const chatHistory = history.flatMap(h => [ + { role: 'human' as const, content: h.question }, + { role: 'ai' as const, content: h.answer }, + ]); + return post('/query', { question, history: chatHistory }); + }, + + getQueryHistory: (page = 1, pageSize = 50) => + get<{ total: number; page: number; page_size: number; items: ApiQueryResult[] }>( + '/query/history', { page, page_size: pageSize } + ), + + // E: Search + searchEntities: (q: string, type?: string, limit = 15) => + get('/search/entities', { + q, + type: type && type !== '全部类型' ? type : undefined, + limit, + }), + + searchPath: (fromId: string, toId: string, maxHops = 3) => + get('/search/path', { from: fromId, to: toId, max_hops: maxHops }), + + searchGraph: (q: string, includeNeighbors = false) => + get('/search/graph', { q, include_neighbors: includeNeighbors }), + + // F: System + getHealth: () => get('/health'), + + getSystemStats: () => get('/system/stats'), + + getDemoData: () => + get<{ nodes: ApiKGNode[]; edges: ApiKGEdge[]; stats: Record }>('/system/demo'), +}; diff --git a/frontend/src/app/components/figma/ImageWithFallback.tsx b/frontend/src/app/components/figma/ImageWithFallback.tsx new file mode 100644 index 0000000..0e26139 --- /dev/null +++ b/frontend/src/app/components/figma/ImageWithFallback.tsx @@ -0,0 +1,27 @@ +import React, { useState } from 'react' + +const ERROR_IMG_SRC = + 'data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iODgiIGhlaWdodD0iODgiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyIgc3Ryb2tlPSIjMDAwIiBzdHJva2UtbGluZWpvaW49InJvdW5kIiBvcGFjaXR5PSIuMyIgZmlsbD0ibm9uZSIgc3Ryb2tlLXdpZHRoPSIzLjciPjxyZWN0IHg9IjE2IiB5PSIxNiIgd2lkdGg9IjU2IiBoZWlnaHQ9IjU2IiByeD0iNiIvPjxwYXRoIGQ9Im0xNiA1OCAxNi0xOCAzMiAzMiIvPjxjaXJjbGUgY3g9IjUzIiBjeT0iMzUiIHI9IjciLz48L3N2Zz4KCg==' + +export function ImageWithFallback(props: React.ImgHTMLAttributes) { + const [didError, setDidError] = useState(false) + + const handleError = () => { + setDidError(true) + } + + const { src, alt, style, className, ...rest } = props + + return didError ? ( +
+
+ Error loading image +
+
+ ) : ( + {alt} + ) +} diff --git a/frontend/src/app/components/layout/AppLayout.tsx b/frontend/src/app/components/layout/AppLayout.tsx new file mode 100644 index 0000000..fcf7214 --- /dev/null +++ b/frontend/src/app/components/layout/AppLayout.tsx @@ -0,0 +1,47 @@ +import React from 'react'; +import { Outlet } from 'react-router'; +import { Toaster } from 'sonner'; +import { Header } from './Header'; +import { Sidebar } from './Sidebar'; +import { StatusBar } from './StatusBar'; +import { useAppState, AppProvider } from '../../store'; + +function AppLayoutInner() { + const { sidebarCollapsed } = useAppState(); + + return ( +
+
+ +
+ +
+ +
+ ); +} + +export function AppLayout() { + return ( + + + + + ); +} \ No newline at end of file diff --git a/frontend/src/app/components/layout/Header.tsx b/frontend/src/app/components/layout/Header.tsx new file mode 100644 index 0000000..8ba728f --- /dev/null +++ b/frontend/src/app/components/layout/Header.tsx @@ -0,0 +1,145 @@ +import React, { useState, useRef, useEffect } from 'react'; +import { useNavigate } from 'react-router'; +import { Menu, Search, X } from 'lucide-react'; +import { useAppState, type KGNode } from '../../store'; +import { api } from '../../api'; +import { TYPE_COLORS } from '../../mock-data'; + +export function Header() { + const { sidebarCollapsed, setSidebarCollapsed, health } = useAppState(); + const [query, setQuery] = useState(''); + const [showSuggestions, setShowSuggestions] = useState(false); + const [suggestions, setSuggestions] = useState([]); + const navigate = useNavigate(); + const inputRef = useRef(null); + const timerRef = useRef>(); + + useEffect(() => { + if (query.length >= 2) { + clearTimeout(timerRef.current); + timerRef.current = setTimeout(async () => { + try { + const res = await api.searchEntities(query, undefined, 5); + setSuggestions(res.items.map(n => ({ + id: n.id, name: n.name, type: n.type as KGNode['type'], + page: n.page, confidence: n.confidence as KGNode['confidence'], + degree: n.degree, centrality: 0, doc_id: n.doc_id, + }))); + setShowSuggestions(true); + } catch { + setSuggestions([]); + } + }, 300); + } else { + setSuggestions([]); + setShowSuggestions(false); + } + return () => clearTimeout(timerRef.current); + }, [query]); + + const handleSubmit = (e: React.FormEvent) => { + e.preventDefault(); + if (query.trim()) { + setShowSuggestions(false); + navigate(`/search?q=${encodeURIComponent(query)}`); + } + }; + + const allOk = Object.values(health).every(v => v === 'ok'); + + return ( +
+ {/* Left */} + + + GraphRAG Studio + + + {/* Center - Search */} +
+
+ + setQuery(e.target.value)} + onFocus={() => query.length >= 3 && setShowSuggestions(true)} + onBlur={() => setTimeout(() => setShowSuggestions(false), 200)} + placeholder="搜索实体..." + className="w-full pl-9 pr-8 py-1.5 rounded-md outline-none" + style={{ + background: 'var(--bg-s2)', + border: '1px solid var(--border-main)', + color: 'var(--text-1)', + fontSize: 13, + }} + /> + {query && ( + + )} +
+ {showSuggestions && suggestions.length > 0 && ( +
+ {suggestions.map(s => ( + + ))} +
+ )} + + + {/* Right */} +
+ + API: localhost:8000 +
+
+ ); +} \ No newline at end of file diff --git a/frontend/src/app/components/layout/Sidebar.tsx b/frontend/src/app/components/layout/Sidebar.tsx new file mode 100644 index 0000000..9b19de6 --- /dev/null +++ b/frontend/src/app/components/layout/Sidebar.tsx @@ -0,0 +1,110 @@ +import React from 'react'; +import { useNavigate, useLocation } from 'react-router'; +import { LayoutDashboard, FileText, Share2, MessageSquare, Search, Settings } from 'lucide-react'; +import { useAppState } from '../../store'; + +const navItems = [ + { icon: LayoutDashboard, label: '仪表盘', path: '/dashboard', badge: null }, + { icon: FileText, label: '文档管理', path: '/documents', badgeKey: 'documents' as const }, + { icon: Share2, label: '知识图谱', path: '/graph', badge: null }, + { icon: MessageSquare, label: '智能问答', path: '/chat', badgeKey: 'queries' as const }, + { icon: Search, label: '搜索', path: '/search', badge: null }, +]; + +export function Sidebar() { + const { sidebarCollapsed, stats } = useAppState(); + const navigate = useNavigate(); + const location = useLocation(); + + const width = sidebarCollapsed ? 72 : 220; + + return ( + + ); +} \ No newline at end of file diff --git a/frontend/src/app/components/layout/StatusBar.tsx b/frontend/src/app/components/layout/StatusBar.tsx new file mode 100644 index 0000000..473deb2 --- /dev/null +++ b/frontend/src/app/components/layout/StatusBar.tsx @@ -0,0 +1,34 @@ +import React from 'react'; +import { useAppState } from '../../store'; + +export function StatusBar() { + const { documents, health } = useAppState(); + const indexingDoc = documents.find(d => d.status === 'indexing'); + const allOk = Object.values(health).every(v => v === 'ok'); + + return ( +
+
+ {indexingDoc && ( + + 正在索引 {indexingDoc.filename}... {indexingDoc.progress ?? 0}% + + )} +
+
+ v1.0.0 + +
+
+ ); +} \ No newline at end of file diff --git a/frontend/src/app/components/pages/Dashboard.tsx b/frontend/src/app/components/pages/Dashboard.tsx new file mode 100644 index 0000000..e618c92 --- /dev/null +++ b/frontend/src/app/components/pages/Dashboard.tsx @@ -0,0 +1,210 @@ +import React from 'react'; +import { useNavigate } from 'react-router'; +import { Share2, MessageSquare, Search, Zap, Upload, FileText, ExternalLink } from 'lucide-react'; +import { useAppState } from '../../store'; + +const statCards = [ + { key: 'kg_nodes', label: '图谱节点', color: '#58a6ff', icon: '◈' }, + { key: 'kg_edges', label: '图谱边', color: '#8957e5', icon: '◇' }, + { key: 'documents', label: '文档数', color: '#3fb950', icon: '▤' }, + { key: 'queries', label: '查询次数', color: '#d29922', icon: '◆' }, +] as const; + +const statusStyles: Record = { + indexed: { bg: '#1a3a22', color: '#3fb950' }, + indexing: { bg: '#2d2a16', color: '#d29922' }, + uploaded: { bg: '#1c2128', color: '#8b949e' }, + failed: { bg: '#3b1a1a', color: '#f85149' }, +}; + +export function Dashboard() { + const { stats, health, documents } = useAppState(); + const navigate = useNavigate(); + const recentDocs = documents.slice(0, 5); + + return ( +
+ {/* Page Title + Upload Button */} +
+

仪表盘

+ +
+ + {/* Stat Cards */} +
+ {statCards.map(c => ( +
+
+ {c.label} + {c.icon} +
+
+ {stats[c.key].toLocaleString()} +
+
+ ))} +
+ +
+ {/* System Health */} +
+

系统健康

+
+ {[ + { name: 'MinerU venv', status: health.mineru }, + { name: 'LangExtract venv', status: health.langextract }, + { name: 'DeepSeek API', status: health.deepseek }, + { name: 'Storage', status: health.storage }, + ].map(s => ( +
+ {s.name} + + + {s.status} + +
+ ))} +
+ + {/* Quick Actions */} +

快捷操作

+
+ {[ + { icon: Share2, label: '浏览图谱', path: '/graph' }, + { icon: MessageSquare, label: '开始对话', path: '/chat' }, + { icon: Search, label: '搜索', path: '/search' }, + { icon: Zap, label: '演示', path: '/graph' }, + ].map(a => ( + + ))} +
+
+ + {/* Recent Documents */} +
+
+

最近文档

+ +
+ +
+ {/* Table header */} +
+ 文件名 + 格式 + 页数 + 状态 + 日期 + 操作 +
+ + {recentDocs.map(doc => { + const st = statusStyles[doc.status]; + return ( +
+ + + {doc.filename} + + {doc.format} + {doc.pages} + + + {doc.status} + + + + {new Date(doc.upload_date).toLocaleDateString('zh-CN', { month: 'short', day: 'numeric', year: 'numeric' })} + + + {doc.status === 'indexed' && ( + + )} + {doc.status === 'uploaded' && ( + + )} + {doc.status === 'indexing' && ( +
+
+
+
+ {doc.progress}% +
+ )} + {doc.status === 'failed' && ( + + )} + +
+ ); + })} +
+
+
+
+ ); +} \ No newline at end of file diff --git a/frontend/src/app/components/pages/Documents.tsx b/frontend/src/app/components/pages/Documents.tsx new file mode 100644 index 0000000..525988b --- /dev/null +++ b/frontend/src/app/components/pages/Documents.tsx @@ -0,0 +1,439 @@ +import React, { useState, useCallback, useRef } from 'react'; +import { useNavigate } from 'react-router'; +import { toast } from 'sonner'; +import { Upload, FileText, Trash2, Play, RotateCcw, X, ChevronDown, ChevronRight, Eye } from 'lucide-react'; +import { useAppState } from '../../store'; +import { api, ApiError } from '../../api'; + +const statusStyles: Record = { + indexed: { bg: '#1a3a22', color: '#3fb950' }, + indexing: { bg: '#2d2a16', color: '#d29922' }, + uploaded: { bg: '#1c2128', color: '#8b949e' }, + failed: { bg: '#3b1a1a', color: '#f85149' }, +}; + +export function Documents() { + const { documents, setDocuments, refreshDocuments, refreshKG } = useAppState(); + const navigate = useNavigate(); + const fileInputRef = useRef(null); + const [dragOver, setDragOver] = useState(false); + const [formatFilter, setFormatFilter] = useState('All'); + const [statusFilter, setStatusFilter] = useState('All'); + const [searchTerm, setSearchTerm] = useState(''); + const [expandedDoc, setExpandedDoc] = useState(null); + const [showDeleteModal, setShowDeleteModal] = useState(null); + const [uploading, setUploading] = useState(false); + + const filteredDocs = documents.filter(d => { + if (formatFilter !== 'All' && d.format !== formatFilter) return false; + if (statusFilter !== 'All' && d.status !== statusFilter) return false; + if (searchTerm && !d.filename.toLowerCase().includes(searchTerm.toLowerCase())) return false; + return true; + }); + + // ── Upload ────────────────────────────────────────────────────────────────── + + const handleFiles = useCallback(async (files: FileList | File[]) => { + const fileArr = Array.from(files); + if (fileArr.length === 0) return; + + setUploading(true); + for (const file of fileArr) { + try { + toast.loading(`上传 ${file.name}...`, { id: `upload-${file.name}` }); + + // 1. Upload + const uploaded = await api.uploadDocument(file); + const newDoc = { + id: uploaded.doc_id, + filename: uploaded.filename, + format: uploaded.format, + pages: 0, + status: 'uploaded' as const, + upload_date: new Date().toISOString(), + }; + setDocuments(prev => [newDoc, ...prev]); + toast.success(`${file.name} 上传成功`, { id: `upload-${file.name}` }); + + // 2. Auto-start indexing + try { + toast.loading(`开始索引 ${file.name}...`, { id: `index-${uploaded.doc_id}` }); + const job = await api.startIndexing(uploaded.doc_id); + setDocuments(prev => + prev.map(d => d.id === uploaded.doc_id + ? { ...d, status: 'indexing', job_id: job.job_id, progress: 0 } + : d + ) + ); + toast.success(`${file.name} 开始索引`, { id: `index-${uploaded.doc_id}` }); + } catch (err) { + const msg = err instanceof ApiError ? err.message : '启动索引失败'; + toast.error(msg, { id: `index-${uploaded.doc_id}` }); + } + } catch (err) { + const msg = err instanceof ApiError ? err.message : '上传失败'; + toast.error(`${file.name}: ${msg}`, { id: `upload-${file.name}` }); + } + } + setUploading(false); + }, [setDocuments]); + + const handleDragOver = useCallback((e: React.DragEvent) => { e.preventDefault(); setDragOver(true); }, []); + const handleDragLeave = useCallback(() => setDragOver(false), []); + const handleDrop = useCallback((e: React.DragEvent) => { + e.preventDefault(); + setDragOver(false); + handleFiles(e.dataTransfer.files); + }, [handleFiles]); + + const handleBrowse = () => fileInputRef.current?.click(); + const handleFileChange = (e: React.ChangeEvent) => { + if (e.target.files) handleFiles(e.target.files); + e.target.value = ''; + }; + + // ── Index / Retry ──────────────────────────────────────────────────────────── + + const handleStartIndex = useCallback(async (docId: string, filename: string) => { + try { + const job = await api.startIndexing(docId); + setDocuments(prev => + prev.map(d => d.id === docId + ? { ...d, status: 'indexing', job_id: job.job_id, progress: 0, error: undefined } + : d + ) + ); + toast.success(`${filename} 开始索引`); + } catch (err) { + const msg = err instanceof ApiError ? err.message : '启动索引失败'; + toast.error(msg); + } + }, [setDocuments]); + + // ── Cancel ─────────────────────────────────────────────────────────────────── + + const handleCancel = useCallback(async (docId: string, jobId: string) => { + try { + await api.cancelJob(jobId); + setDocuments(prev => + prev.map(d => d.id === docId + ? { ...d, status: 'uploaded', job_id: undefined, progress: undefined } + : d + ) + ); + toast.info('索引任务已取消'); + } catch (err) { + const msg = err instanceof ApiError ? err.message : '取消失败'; + toast.error(msg); + } + }, [setDocuments]); + + // ── Delete ─────────────────────────────────────────────────────────────────── + + const handleDelete = useCallback(async () => { + if (!showDeleteModal) return; + try { + await api.deleteDocument(showDeleteModal); + setDocuments(prev => prev.filter(d => d.id !== showDeleteModal)); + setShowDeleteModal(null); + toast.success('文档已删除'); + refreshKG(); + } catch (err) { + const msg = err instanceof ApiError ? err.message : '删除失败'; + toast.error(msg); + } + }, [showDeleteModal, setDocuments, refreshKG]); + + const deleteDoc = documents.find(d => d.id === showDeleteModal); + + return ( +
+

文档管理

+ + {/* Hidden file input */} + + + {/* Upload Area */} +
+ +
+ {uploading ? '正在上传...' : ( + <>拖拽文件到此处，或{' '}浏览文件 + )} +
+
+ PDF · DOCX · DOC · PPTX · PPT · PNG · JPG · HTML  |  单文件最大 200MB +
+
+ + {/* Toolbar */} +
+ + + setSearchTerm(e.target.value)} + placeholder="搜索文档..." + className="px-3 py-1.5 rounded-md flex-1" + style={{ background: 'var(--bg-s2)', border: '1px solid var(--border-main)', color: 'var(--text-1)', fontSize: 13, outline: 'none' }} + /> +
+ + {/* Document Table */} +
+ {/* Header */} +
+ + 文件名 + 格式 + 页数 + 状态 + 上传日期 + 操作 +
+ + {/* Rows */} + {filteredDocs.length === 0 ? ( +
+ + + {documents.length === 0 ? '暂无文档，请上传文件' : '未找到匹配文档'} + +
+ ) : ( + filteredDocs.map(doc => { + const st = statusStyles[doc.status]; + const isExpanded = expandedDoc === doc.id; + return ( + +
+ + + + {doc.filename} + + {doc.format} + {doc.pages || '—'} + + + {doc.status === 'indexing' && ( + + )} + {doc.status} + + + + {new Date(doc.upload_date).toLocaleDateString('zh-CN', { month: 'short', day: 'numeric', year: 'numeric' })} + + + {doc.status === 'uploaded' && ( + + )} + {doc.status === 'indexing' && ( + <> +
+
+
+
+ {doc.progress ?? 0}% +
+ {doc.job_id && ( + + )} + + )} + {doc.status === 'indexed' && ( + + )} + {doc.status === 'failed' && ( + + )} + {doc.status !== 'indexing' && ( + + )} + +
+ + {/* Expanded Result Row */} + {isExpanded && doc.result && ( +
+
+ {doc.result.nodes} 个节点 + · + {doc.result.edges} 条边 + · + {doc.result.pages} 页 + · + {doc.result.extractions} 次提取 + · + {doc.result.duration.toFixed(1)}秒 +
+
+ + {/* 查看提取结果：后端暂未提供独立 API，功能未开发 */} + +
+
+ )} + + {/* Error message */} + {doc.status === 'failed' && doc.error && ( +
+ {doc.error} +
+ )} + + ); + }) + )} +
+ + {/* Delete Modal */} + {showDeleteModal && deleteDoc && ( +
setShowDeleteModal(null)} + > +
e.stopPropagation()} + > +

+ 确认删除 "{deleteDoc.filename}"？ +

+

+ 该文档及其关联的所有知识图谱数据将被永久删除，此操作不可撤销。 +

+
+ + +
+
+
+ )} +
+ ); +} diff --git a/frontend/src/app/components/pages/KGExplorer.tsx b/frontend/src/app/components/pages/KGExplorer.tsx new file mode 100644 index 0000000..c5b3e84 --- /dev/null +++ b/frontend/src/app/components/pages/KGExplorer.tsx @@ -0,0 +1,439 @@ +import React, { useEffect, useRef, useState } from 'react'; +import { useNavigate, useSearchParams } from 'react-router'; +import * as d3 from 'd3'; +import { ZoomIn, ZoomOut, Maximize2, Search, Download, Image, X, MessageSquare, Upload, Share2 } from 'lucide-react'; +import { useAppState, type KGNode } from '../../store'; +import { TYPE_COLORS } from '../../mock-data'; + +const ENTITY_TYPES = ['TECHNOLOGY', 'CONCEPT', 'PERSON', 'ORGANIZATION', 'LOCATION'] as const; +const CONFIDENCE_LEVELS = ['match_exact', 'match_greater', 'match_lesser', 'match_fuzzy'] as const; + +export function KGExplorer() { + const { nodes, edges, documents, selectedNode, setSelectedNode, getNeighbors } = useAppState(); + const navigate = useNavigate(); + const [searchParams] = useSearchParams(); + const svgRef = useRef(null); + const containerRef = useRef(null); + const simulationRef = useRef>(); + const zoomRef = useRef>(); + + const [filterTypes, setFilterTypes] = useState>(new Set(ENTITY_TYPES)); + const [filterConfidence, setFilterConfidence] = useState>(new Set(CONFIDENCE_LEVELS)); + const [filterDoc, setFilterDoc] = useState('all'); + const [searchQuery, setSearchQuery] = useState(''); + const [showFilter, setShowFilter] = useState(true); + const [tooltip, setTooltip] = useState<{ x: number; y: number; node: KGNode } | null>(null); + + const indexedDocs = documents.filter(d => d.status === 'indexed'); + + // Filtered nodes/edges + const visibleNodes = nodes.filter(n => { + if (!filterTypes.has(n.type)) return false; + if (!filterConfidence.has(n.confidence)) return false; + if (filterDoc !== 'all' && n.doc_id !== filterDoc) return false; + if (searchQuery && !n.name.toLowerCase().includes(searchQuery.toLowerCase())) return false; + return true; + }); + + const visibleNodeIds = new Set(visibleNodes.map(n => n.id)); + const visibleEdges = edges.filter(e => visibleNodeIds.has(e.source as string) && visibleNodeIds.has(e.target as string)); + + // Neighbors of selected + const neighborInfo = selectedNode ? getNeighbors(selectedNode.id) : null; + + // D3 rendering + useEffect(() => { + if (!svgRef.current || !containerRef.current) return; + const svg = d3.select(svgRef.current); + svg.selectAll('*').remove(); + if (visibleNodes.length === 0) return; + + const rect = containerRef.current.getBoundingClientRect(); + const width = rect.width; + const height = rect.height; + + svg.attr('width', width).attr('height', height); + + const g = svg.append('g'); + + const zoom = d3.zoom() + .scaleExtent([0.1, 8]) + .on('zoom', (event) => g.attr('transform', event.transform)); + zoomRef.current = zoom; + svg.call(zoom); + + // Create simulation data copies + const simNodes = visibleNodes.map(n => ({ ...n, x: width / 2 + (Math.random() - 0.5) * 200, y: height / 2 + (Math.random() - 0.5) * 200 })); + const simEdges = visibleEdges.map(e => ({ ...e, source: e.source, target: e.target })); + + const simulation = d3.forceSimulation(simNodes) + .force('link', d3.forceLink(simEdges).id((d: any) => d.id).distance(60).strength(0.3)) + .force('charge', d3.forceManyBody().strength(-120)) + .force('center', d3.forceCenter(width / 2, height / 2)) + .force('collide', d3.forceCollide().radius((d: any) => getRadius(d.degree) + 4)) + .alphaDecay(0.02); + + simulationRef.current = simulation; + + // Edges + const link = g.append('g') + .selectAll('line') + .data(simEdges) + .join('line') + .attr('stroke', '#30363d') + .attr('stroke-width', 1) + .attr('stroke-opacity', 0.25); + + // Nodes + const node = g.append('g') + .selectAll('circle') + .data(simNodes) + .join('circle') + .attr('r', (d: any) => getRadius(d.degree)) + .attr('fill', (d: any) => TYPE_COLORS[d.type] || '#8b949e') + .attr('stroke', '#0f1117') + .attr('stroke-width', 1.5) + .attr('opacity', 0.9) + .attr('cursor', 'pointer') + .on('mouseover', function(event, d: any) { + d3.select(this).attr('stroke', '#ffffff').attr('stroke-width', 2.5); + setTooltip({ x: event.clientX + 8, y: event.clientY + 8, node: d }); + }) + .on('mouseout', function() { + d3.select(this).attr('stroke', '#0f1117').attr('stroke-width', 1.5); + setTooltip(null); + }) + .on('click', (_, d: any) => { + setSelectedNode(d); + // Highlight logic + node.attr('opacity', (n: any) => { + if (n.id === d.id) return 0.9; + const isNeighbor = simEdges.some((e: any) => + (e.source.id === d.id && e.target.id === n.id) || + (e.target.id === d.id && e.source.id === n.id) + ); + return isNeighbor ? 0.9 : 0.1; + }); + d3.select(node.nodes()[simNodes.indexOf(d)]) + .attr('r', getRadius(d.degree) * 1.5); + link.attr('stroke-opacity', (e: any) => + e.source.id === d.id || e.target.id === d.id ? 0.8 : 0.05 + ); + }) + .call(d3.drag() + .on('start', (event, d: any) => { + if (!event.active) simulation.alphaTarget(0.3).restart(); + d.fx = d.x; d.fy = d.y; + }) + .on('drag', (event, d: any) => { d.fx = event.x; d.fy = event.y; }) + .on('end', (event, d: any) => { + if (!event.active) simulation.alphaTarget(0); + }) + ); + + // Labels for high-degree nodes + const label = g.append('g') + .selectAll('text') + .data(simNodes.filter(n => n.degree >= 12)) + .join('text') + .text((d: any) => d.name) + .attr('font-size', 10) + .attr('fill', 'var(--text-3)') + .attr('text-anchor', 'middle') + .attr('dy', (d: any) => -(getRadius(d.degree) + 6)) + .attr('pointer-events', 'none'); + + // Click blank to reset + svg.on('click', (event) => { + if (event.target === svgRef.current) { + setSelectedNode(null); + node.attr('opacity', 0.9).attr('r', (d: any) => getRadius(d.degree)); + link.attr('stroke-opacity', 0.25); + } + }); + + simulation.on('tick', () => { + link + .attr('x1', (d: any) => d.source.x) + .attr('y1', (d: any) => d.source.y) + .attr('x2', (d: any) => d.target.x) + .attr('y2', (d: any) => d.target.y); + node + .attr('cx', (d: any) => d.x) + .attr('cy', (d: any) => d.y); + label + .attr('x', (d: any) => d.x) + .attr('y', (d: any) => d.y); + }); + + // Handle URL params + const nodeParam = searchParams.get('node'); + if (nodeParam) { + const target = simNodes.find(n => n.id === nodeParam); + if (target) { + setTimeout(() => { + const nd = nodes.find(n => n.id === nodeParam); + if (nd) setSelectedNode(nd); + }, 500); + } + } + + const docParam = searchParams.get('doc_id'); + if (docParam) { + setFilterDoc(docParam); + } + + return () => { simulation.stop(); }; + }, [visibleNodes.length, visibleEdges.length, searchQuery, filterDoc]); + + const handleZoomIn = () => { + if (svgRef.current && zoomRef.current) { + d3.select(svgRef.current).transition().duration(300).call(zoomRef.current.scaleBy, 1.3); + } + }; + const handleZoomOut = () => { + if (svgRef.current && zoomRef.current) { + d3.select(svgRef.current).transition().duration(300).call(zoomRef.current.scaleBy, 0.7); + } + }; + const handleFitAll = () => { + if (svgRef.current && zoomRef.current) { + d3.select(svgRef.current).transition().duration(500).call(zoomRef.current.transform, d3.zoomIdentity); + } + }; + + const toggleType = (t: string) => { + const next = new Set(filterTypes); + if (next.has(t)) next.delete(t); else next.add(t); + setFilterTypes(next); + }; + + const toggleConfidence = (c: string) => { + const next = new Set(filterConfidence); + if (next.has(c)) next.delete(c); else next.add(c); + setFilterConfidence(next); + }; + + return ( +
+ {/* Filter Panel */} + {showFilter && ( +
+

来源文档

+ + +

实体类型

+
+ {ENTITY_TYPES.map(t => { + const count = nodes.filter(n => n.type === t).length; + return ( + + ); + })} +
+ +

置信度

+
+ {CONFIDENCE_LEVELS.map(c => ( + + ))} +
+ +
+ + +
+
+ )} + + {/* Graph Area */} +
+ {/* Toolbar */} +
+ + + +
+
+ + setSearchQuery(e.target.value)} + placeholder="搜索..." + className="pl-7 pr-2 py-1 rounded" + style={{ width: 120, background: 'var(--bg-s2)', border: '1px solid var(--border-main)', color: 'var(--text-1)', fontSize: 12, outline: 'none' }} + /> +
+
+ + {/* Legend */} +
+ {ENTITY_TYPES.map(t => ( +
+ + {t} +
+ ))} +
+ + {/* Stats */} +
+ {visibleNodes.length} 个节点 · {visibleEdges.length} 条边 +
+ + {visibleNodes.length === 0 ? ( +
+ + 暂无知识图谱 + +
+ ) : ( + + )} + + {/* Tooltip */} + {tooltip && ( +
+
+ {tooltip.node.name} + + {tooltip.node.type} + +
+
页码: {tooltip.node.page}
+
置信度: {tooltip.node.confidence}
+
度数: {tooltip.node.degree}
+
+ )} +
+ + {/* Detail Panel */} + {selectedNode && ( +
+
+

{selectedNode.name}

+ +
+ + + {selectedNode.type} + + + {selectedNode.description && ( +

+ {selectedNode.description} +

+ )} + +
+ {[ + { label: '页码', value: selectedNode.page }, + { label: '置信度', value: selectedNode.confidence.replace('match_', '') }, + { label: '度数', value: selectedNode.degree }, + { label: '中心性', value: selectedNode.centrality.toFixed(2) }, + ].map(p => ( +
+ {p.label} + {p.value} +
+ ))} +
+ +

+ 邻居节点 ({neighborInfo?.nodes.length ?? 0}) +

+
+ {neighborInfo?.nodes.slice(0, 5).map(n => ( + + ))} + {(neighborInfo?.nodes.length ?? 0) > 5 && ( + + 查看全部 {neighborInfo?.nodes.length} 个邻居 → + + )} +
+ + +
+ )} +
+ ); +} + +function getRadius(degree: number): number { + return Math.max(4, Math.log(degree + 1) * 4); +} \ No newline at end of file diff --git a/frontend/src/app/components/pages/QAChat.tsx b/frontend/src/app/components/pages/QAChat.tsx new file mode 100644 index 0000000..42f567d --- /dev/null +++ b/frontend/src/app/components/pages/QAChat.tsx @@ -0,0 +1,377 @@ +import React, { useState, useRef, useEffect } from 'react'; +import { useNavigate, useSearchParams } from 'react-router'; +import { Send, Plus, ChevronRight, Clock, ExternalLink, Info } from 'lucide-react'; +import { toast } from 'sonner'; +import { useAppState, type ChatMessage, type ToolCall } from '../../store'; +import { api, ApiError } from '../../api'; +import { TYPE_COLORS } from '../../mock-data'; + +export function QAChat() { + const { messages, setMessages, chatHistory, suggestedPrompts, nodes, refreshHistory } = useAppState(); + const navigate = useNavigate(); + const [searchParams] = useSearchParams(); + const [input, setInput] = useState(''); + const [isThinking, setIsThinking] = useState(false); + const [activeHistoryId, setActiveHistoryId] = useState(null); + const [conversationHistory, setConversationHistory] = useState<{ question: string; answer: string }[]>([]); + const messagesEndRef = useRef(null); + const inputRef = useRef(null); + + useEffect(() => { + const q = searchParams.get('q'); + if (q) { + setInput(q); + inputRef.current?.focus(); + } + }, [searchParams]); + + useEffect(() => { + messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' }); + }, [messages, isThinking]); + + // Build cited node objects from node IDs using local KG + function resolveCitedNodes(ids: string[]) { + return ids + .map(id => { + const n = nodes.find(n => n.id === id); + return n ? { id: n.id, name: n.name, type: n.type } : null; + }) + .filter(Boolean) as { id: string; name: string; type: string }[]; + } + + const handleSend = async () => { + if (!input.trim() || isThinking) return; + const question = input.trim(); + setInput(''); + setIsThinking(true); + + const userMsg: ChatMessage = { + id: `m${Date.now()}`, + role: 'human', + content: question, + timestamp: new Date().toISOString(), + }; + setMessages(prev => [...prev, userMsg]); + + try { + const result = await api.query(question, conversationHistory); + const aiMsg: ChatMessage = { + id: result.id ?? `m${Date.now() + 1}`, + role: 'ai', + content: result.answer, + timestamp: result.timestamp ?? new Date().toISOString(), + toolCalls: result.tool_calls.map((tc, i) => ({ + step: tc.step ?? i + 1, + tool: tc.tool_name, + input: tc.tool_input, + output: tc.tool_output, + })), + citedNodes: resolveCitedNodes(result.cited_nodes ?? []), + duration: result.duration_seconds, + }; + setMessages(prev => [...prev, aiMsg]); + setConversationHistory(prev => [...prev, { question, answer: result.answer }]); + // Refresh history sidebar + refreshHistory(); + } catch (err) { + const msg = err instanceof ApiError ? err.message : '问答服务异常'; + toast.error(msg); + setMessages(prev => [...prev, { + id: `err${Date.now()}`, + role: 'ai', + content: `⚠️ 请求失败：${msg}\n\n请确认：\n1. 后端服务已启动（localhost:8000）\n2. 知识图谱已有数据（请先上传并索引文档）\n3. DeepSeek API Key 已配置`, + timestamp: new Date().toISOString(), + }]); + } finally { + setIsThinking(false); + } + }; + + const handleKeyDown = (e: React.KeyboardEvent) => { + if (e.key === 'Enter' && !e.shiftKey) { + e.preventDefault(); + handleSend(); + } + }; + + const handleNewChat = () => { + setMessages([]); + setInput(''); + setActiveHistoryId(null); + setConversationHistory([]); + }; + + // Load a history item as a single Q&A session + const handleLoadHistory = (h: typeof chatHistory[0]) => { + setActiveHistoryId(h.id); + const msgs: ChatMessage[] = [ + { id: `${h.id}-q`, role: 'human', content: h.question, timestamp: h.timestamp }, + { + id: `${h.id}-a`, role: 'ai', content: h.answer, timestamp: h.timestamp, + toolCalls: h.toolCalls, + citedNodes: resolveCitedNodes(h.citedNodeIds ?? []), + duration: h.duration, + }, + ]; + setMessages(msgs); + setConversationHistory([{ question: h.question, answer: h.answer }]); + }; + + const groupedHistory = { + '今天': chatHistory.filter(h => h.group === '今天'), + '昨天': chatHistory.filter(h => h.group === '昨天'), + '更早': chatHistory.filter(h => h.group === '更早'), + }; + + return ( +
+ {/* History Sidebar */} +
+
+ +
+ + {/* 历史会话管理说明 */} +
+ + + 点击历史记录查看单条问答；多轮对话会话管理 + 未开发 + +
+ +
+ {Object.entries(groupedHistory).map(([group, items]) => items.length > 0 && ( +
+
+ {group} +
+ {items.map(h => ( + + ))} +
+ ))} + {chatHistory.length === 0 && ( +
暂无历史记录
+ )} +
+
+ + {/* Chat Area */} +
+ {/* Messages */} +
+ {messages.length === 0 ? ( +
+
+ GraphRAG{' '} + Studio +
+

+ 向知识图谱提问。我将使用多步推理从已索引的文档中为您找到准确答案。 +

+
+ {suggestedPrompts.map((p, i) => ( + + ))} +
+
+ ) : ( +
+ {messages.map(msg => ( +
+ {msg.role === 'human' ? ( +
+
+ {msg.content} +
+
+ ) : ( +
+
+
+ + {msg.toolCalls && msg.toolCalls.length > 0 && ( + + )} + + {msg.citedNodes && msg.citedNodes.length > 0 && ( +
+ {msg.citedNodes.map(cn => ( + + ))} +
+ )} + + {msg.duration !== undefined && ( +
+ {msg.duration.toFixed(1)}s +
+ )} +
+
+ )} +
+ ))} + + {isThinking && ( +
+
+ + + +
+
+ )} + +
+
+ )} +
+ + {/* Input Area */} +
+
+