cherry-pick 6015e8bc
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Raphaël MANSUY
parent
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commit
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1 changed files with 29 additions and 42 deletions
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@ -299,6 +299,7 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
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entity_data (dict): Entity properties including:
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- description (str): Textual description of the entity
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- entity_type (str): Category/type of the entity (e.g., PERSON, ORGANIZATION, LOCATION)
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- source_id (str): Related chunk_id from which the description originates
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- Additional custom properties as needed
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Response Schema:
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@ -309,6 +310,7 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
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"entity_name": "Tesla",
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"description": "Electric vehicle manufacturer",
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"entity_type": "ORGANIZATION",
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"source_id": "chunk-123<SEP>chunk-456"
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... (other entity properties)
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}
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}
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@ -329,24 +331,20 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
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}
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"""
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try:
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# Check if entity already exists
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exists = await rag.chunk_entity_relation_graph.has_node(request.entity_name)
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if exists:
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raise ValueError(f"Entity '{request.entity_name}' already exists")
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# Prepare entity data
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entity_data = request.entity_data.copy()
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entity_data["entity_id"] = request.entity_name
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# Create the entity
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await rag.chunk_entity_relation_graph.upsert_node(
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request.entity_name, entity_data
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# Use the proper acreate_entity method which handles:
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# - Graph lock for concurrency
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# - Vector embedding creation in entities_vdb
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# - Metadata population and defaults
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# - Index consistency via _edit_entity_done
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result = await rag.acreate_entity(
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entity_name=request.entity_name,
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entity_data=request.entity_data,
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)
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return {
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"status": "success",
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"message": f"Entity '{request.entity_name}' created successfully",
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"data": entity_data,
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"data": result,
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}
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except ValueError as ve:
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logger.error(
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@ -365,10 +363,11 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
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"""
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Create a new relationship between two entities in the knowledge graph
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This endpoint establishes a directed relationship between two existing entities.
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Both the source and target entities must already exist in the knowledge graph.
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The system automatically generates vector embeddings for the relationship to
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enable semantic search and graph traversal.
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This endpoint establishes an undirected relationship between two existing entities.
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The provided source/target order is accepted for convenience, but the backend
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stored edge is undirected and may be returned with the entities swapped.
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Both entities must already exist in the knowledge graph. The system automatically
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generates vector embeddings for the relationship to enable semantic search and graph traversal.
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Prerequisites:
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- Both source_entity and target_entity must exist in the knowledge graph
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@ -380,6 +379,7 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
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relation_data (dict): Relationship properties including:
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- description (str): Textual description of the relationship
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- keywords (str): Comma-separated keywords describing the relationship type
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- source_id (str): Related chunk_id from which the description originates
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- weight (float): Relationship strength/importance (default: 1.0)
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- Additional custom properties as needed
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@ -392,6 +392,7 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
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"tgt_id": "Tesla",
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"description": "Elon Musk is the CEO of Tesla",
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"keywords": "CEO, founder",
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"source_id": "chunk-123<SEP>chunk-456"
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"weight": 1.0,
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... (other relationship properties)
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}
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@ -415,36 +416,22 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
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}
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"""
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try:
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# Check if both entities exist
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source_exists = await rag.chunk_entity_relation_graph.has_node(
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request.source_entity
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)
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target_exists = await rag.chunk_entity_relation_graph.has_node(
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request.target_entity
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)
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if not source_exists:
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raise ValueError(
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f"Source entity '{request.source_entity}' does not exist"
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)
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if not target_exists:
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raise ValueError(
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f"Target entity '{request.target_entity}' does not exist"
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)
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# Create the relationship
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await rag.chunk_entity_relation_graph.upsert_edge(
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request.source_entity, request.target_entity, request.relation_data
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# Use the proper acreate_relation method which handles:
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# - Graph lock for concurrency
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# - Entity existence validation
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# - Duplicate relation checks
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# - Vector embedding creation in relationships_vdb
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# - Index consistency via _edit_relation_done
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result = await rag.acreate_relation(
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source_entity=request.source_entity,
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target_entity=request.target_entity,
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relation_data=request.relation_data,
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)
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return {
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"status": "success",
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"message": f"Relation created successfully between '{request.source_entity}' and '{request.target_entity}'",
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"data": {
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"source": request.source_entity,
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"target": request.target_entity,
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**request.relation_data,
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},
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"data": result,
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}
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except ValueError as ve:
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logger.error(
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