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yangdx 2025-10-10 12:31:47 +08:00
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63
lightrag/api/README.md
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@ -543,6 +543,69 @@ You can test the API endpoints using the provided curl commands or through the S
4. Query the system using the query endpoints
5. Trigger document scan if new files are put into the inputs directory
### Graph Manipulation Endpoints
LightRAG provides REST API endpoints for direct knowledge graph manipulation:
#### Create Entity
Create a new entity in the knowledge graph:
```bash
curl -X POST "http://localhost:9621/graph/entity/create" \
-H "Content-Type: application/json" \
-d '{
"entity_name": "Tesla",
"entity_data": {
"description": "Electric vehicle manufacturer",
"entity_type": "ORGANIZATION"
}
}'
```
#### Create Relationship
Create a new relationship between two existing entities:
```bash
curl -X POST "http://localhost:9621/graph/relation/create" \
-H "Content-Type: application/json" \
-d '{
"source_entity": "Elon Musk",
"target_entity": "Tesla",
"relation_data": {
"description": "Elon Musk is the CEO of Tesla",
"keywords": "CEO, founder",
"weight": 1.0
}
}'
```
#### Merge Entities
Consolidate duplicate or misspelled entities while preserving all relationships:
```bash
curl -X POST "http://localhost:9621/graph/entities/merge" \
-H "Content-Type: application/json" \
-d '{
"entities_to_change": ["Elon Msk", "Ellon Musk"],
"entity_to_change_into": "Elon Musk"
}'
```
**What the merge operation does:**
- Deletes the specified source entities
- Transfers all relationships from source entities to the target entity
- Intelligently merges duplicate relationships
- Updates vector embeddings for accurate retrieval
- Preserves the entire graph structure
This is particularly useful for:
- Fixing spelling errors in entity names
- Consolidating duplicate entities discovered after document processing
- Cleaning up the knowledge graph for better query performance
## Asynchronous Document Indexing with Progress Tracking
LightRAG implements asynchronous document indexing to enable frontend monitoring and querying of document processing progress. Upon uploading files or inserting text through designated endpoints, a unique Track ID is returned to facilitate real-time progress monitoring.

169
lightrag/api/routers/graph_routes.py
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@ -25,6 +25,22 @@ class RelationUpdateRequest(BaseModel):
updated_data: Dict[str, Any]
class EntityMergeRequest(BaseModel):
entities_to_change: list[str]
entity_to_change_into: str
class EntityCreateRequest(BaseModel):
entity_name: str
entity_data: Dict[str, Any]
class RelationCreateRequest(BaseModel):
source_entity: str
target_entity: str
relation_data: Dict[str, Any]
def create_graph_routes(rag, api_key: Optional[str] = None):
combined_auth = get_combined_auth_dependency(api_key)
@ -225,4 +241,157 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
status_code=500, detail=f"Error updating relation: {str(e)}"
)
@router.post("/graph/entity/create", dependencies=[Depends(combined_auth)])
async def create_entity(request: EntityCreateRequest):
"""
Create a new entity in the knowledge graph
Args:
request (EntityCreateRequest): Request containing:
- entity_name: Name of the entity
- entity_data: Dictionary of entity properties (e.g., description, entity_type)
Returns:
Dict: Created entity information
Example:
{
"entity_name": "Tesla",
"entity_data": {
"description": "Electric vehicle manufacturer",
"entity_type": "ORGANIZATION"
}
}
"""
try:
# Use the proper acreate_entity method which handles:
# - Graph lock for concurrency
# - Vector embedding creation in entities_vdb
# - Metadata population and defaults
# - Index consistency via _edit_entity_done
result = await rag.acreate_entity(
entity_name=request.entity_name,
entity_data=request.entity_data,
)
return {
"status": "success",
"message": f"Entity '{request.entity_name}' created successfully",
"data": result,
}
except ValueError as ve:
logger.error(
f"Validation error creating entity '{request.entity_name}': {str(ve)}"
)
raise HTTPException(status_code=400, detail=str(ve))
except Exception as e:
logger.error(f"Error creating entity '{request.entity_name}': {str(e)}")
logger.error(traceback.format_exc())
raise HTTPException(
status_code=500, detail=f"Error creating entity: {str(e)}"
)
@router.post("/graph/relation/create", dependencies=[Depends(combined_auth)])
async def create_relation(request: RelationCreateRequest):
"""
Create a new relationship between two entities in the knowledge graph
Args:
request (RelationCreateRequest): Request containing:
- source_entity: Source entity name
- target_entity: Target entity name
- relation_data: Dictionary of relation properties (e.g., description, keywords, weight)
Returns:
Dict: Created relation information
Example:
{
"source_entity": "Elon Musk",
"target_entity": "Tesla",
"relation_data": {
"description": "Elon Musk is the CEO of Tesla",
"keywords": "CEO, founder",
"weight": 1.0
}
}
"""
try:
# Use the proper acreate_relation method which handles:
# - Graph lock for concurrency
# - Entity existence validation
# - Duplicate relation checks
# - Vector embedding creation in relationships_vdb
# - Index consistency via _edit_relation_done
result = await rag.acreate_relation(
source_entity=request.source_entity,
target_entity=request.target_entity,
relation_data=request.relation_data,
)
return {
"status": "success",
"message": f"Relation created successfully between '{request.source_entity}' and '{request.target_entity}'",
"data": result,
}
except ValueError as ve:
logger.error(
f"Validation error creating relation between '{request.source_entity}' and '{request.target_entity}': {str(ve)}"
)
raise HTTPException(status_code=400, detail=str(ve))
except Exception as e:
logger.error(
f"Error creating relation between '{request.source_entity}' and '{request.target_entity}': {str(e)}"
)
logger.error(traceback.format_exc())
raise HTTPException(
status_code=500, detail=f"Error creating relation: {str(e)}"
)
@router.post("/graph/entities/merge", dependencies=[Depends(combined_auth)])
async def merge_entities(request: EntityMergeRequest):
"""
Merge multiple entities into a single entity, preserving all relationships.
This endpoint is useful for consolidating duplicate or misspelled entities.
All relationships from the source entities will be transferred to the target entity.
Args:
request (EntityMergeRequest): Request containing:
- entities_to_change: List of entity names to be removed
- entity_to_change_into: Name of the target entity to merge into
Returns:
Dict: Result of the merge operation with merged entity information
Example:
{
"entities_to_change": ["Elon Msk", "Ellon Musk"],
"entity_to_change_into": "Elon Musk"
}
"""
try:
result = await rag.amerge_entities(
source_entities=request.entities_to_change,
target_entity=request.entity_to_change_into,
)
return {
"status": "success",
"message": f"Successfully merged {len(request.entities_to_change)} entities into '{request.entity_to_change_into}'",
"data": result,
}
except ValueError as ve:
logger.error(
f"Validation error merging entities {request.entities_to_change} into '{request.entity_to_change_into}': {str(ve)}"
)
raise HTTPException(status_code=400, detail=str(ve))
except Exception as e:
logger.error(
f"Error merging entities {request.entities_to_change} into '{request.entity_to_change_into}': {str(e)}"
)
logger.error(traceback.format_exc())
raise HTTPException(
status_code=500, detail=f"Error merging entities: {str(e)}"
)
return router

4
lightrag/operate.py
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@ -401,7 +401,7 @@ async def _handle_single_relationship_extraction(
): # treat "relationship" and "relation" interchangeable
if len(record_attributes) > 1 and "relation" in record_attributes[0]:
logger.warning(
f"{chunk_key}: LLM output format error; found {len(record_attributes)}/5 fields on REALTION `{record_attributes[1]}`~`{record_attributes[2] if len(record_attributes) >2 else 'N/A'}`"
f"{chunk_key}: LLM output format error; found {len(record_attributes)}/5 fields on REALTION `{record_attributes[1]}`~`{record_attributes[2] if len(record_attributes) > 2 else 'N/A'}`"
)
logger.debug(record_attributes)
return None
@ -2225,7 +2225,7 @@ async def extract_entities(
await asyncio.wait(pending)
# Add progress prefix to the exception message
progress_prefix = f"C[{processed_chunks+1}/{total_chunks}]"
progress_prefix = f"C[{processed_chunks + 1}/{total_chunks}]"
# Re-raise the original exception with a prefix
prefixed_exception = create_prefixed_exception(first_exception, progress_prefix)

3
lightrag/utils.py
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@ -1472,8 +1472,7 @@ async def aexport_data(
else:
raise ValueError(
f"Unsupported file format: {file_format}. "
f"Choose from: csv, excel, md, txt"
f"Unsupported file format: {file_format}. Choose from: csv, excel, md, txt"
)
if file_format is not None:
print(f"Data exported to: {output_path} with format: {file_format}")

2
reproduce/batch_eval.py
View file

@ -73,7 +73,7 @@ def batch_eval(query_file, result1_file, result2_file, output_file_path):
"""
request_data = {
"custom_id": f"request-{i+1}",
"custom_id": f"request-{i + 1}",
"method": "POST",
"url": "/v1/chat/completions",
"body": {