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GraphRAG Studio — initial commit: multimodal RAG system with KG visualization

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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 <noreply@anthropic.com>
This commit is contained in:
plf
2026-06-07 17:30:04 +08:00
commit b02d3378fc
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"""
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