diff --git a/README.md b/README.md index 00da54fb..018a94e6 100644 --- a/README.md +++ b/README.md @@ -176,6 +176,8 @@ class QueryParam: """Maximum number of tokens allocated for relationship descriptions in global retrieval.""" max_token_for_local_context: int = 4000 """Maximum number of tokens allocated for entity descriptions in local retrieval.""" + ids: list[str] | None = None # ONLY SUPPORTED FOR PG VECTOR DBs + """List of ids to filter the RAG.""" ... ``` diff --git a/lightrag/base.py b/lightrag/base.py index 4b840b37..86566787 100644 --- a/lightrag/base.py +++ b/lightrag/base.py @@ -81,6 +81,9 @@ class QueryParam: history_turns: int = 3 """Number of complete conversation turns (user-assistant pairs) to consider in the response context.""" + ids: list[str] | None = None + """List of ids to filter the results.""" + @dataclass class StorageNameSpace(ABC): @@ -107,7 +110,9 @@ class BaseVectorStorage(StorageNameSpace, ABC): meta_fields: set[str] = field(default_factory=set) @abstractmethod - async def query(self, query: str, top_k: int) -> list[dict[str, Any]]: + async def query( + self, query: str, top_k: int, ids: list[str] | None = None + ) -> list[dict[str, Any]]: """Query the vector storage and retrieve top_k results.""" @abstractmethod @@ -122,6 +127,30 @@ class BaseVectorStorage(StorageNameSpace, ABC): async def delete_entity_relation(self, entity_name: str) -> None: """Delete relations for a given entity.""" + @abstractmethod + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + pass + + @abstractmethod + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + pass + @dataclass class BaseKVStorage(StorageNameSpace, ABC): diff --git a/lightrag/kg/chroma_impl.py b/lightrag/kg/chroma_impl.py index 6b521180..f668c87a 100644 --- a/lightrag/kg/chroma_impl.py +++ b/lightrag/kg/chroma_impl.py @@ -269,3 +269,67 @@ class ChromaVectorDBStorage(BaseVectorStorage): except Exception as e: logger.error(f"Error during prefix search in ChromaDB: {str(e)}") raise + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + try: + # Query the collection for a single vector by ID + result = self._collection.get( + ids=[id], include=["metadatas", "embeddings", "documents"] + ) + + if not result or not result["ids"] or len(result["ids"]) == 0: + return None + + # Format the result to match the expected structure + return { + "id": result["ids"][0], + "vector": result["embeddings"][0], + "content": result["documents"][0], + **result["metadatas"][0], + } + except Exception as e: + logger.error(f"Error retrieving vector data for ID {id}: {e}") + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + try: + # Query the collection for multiple vectors by IDs + result = self._collection.get( + ids=ids, include=["metadatas", "embeddings", "documents"] + ) + + if not result or not result["ids"] or len(result["ids"]) == 0: + return [] + + # Format the results to match the expected structure + return [ + { + "id": result["ids"][i], + "vector": result["embeddings"][i], + "content": result["documents"][i], + **result["metadatas"][i], + } + for i in range(len(result["ids"])) + ] + except Exception as e: + logger.error(f"Error retrieving vector data for IDs {ids}: {e}") + return [] diff --git a/lightrag/kg/faiss_impl.py b/lightrag/kg/faiss_impl.py index ab036e6f..a5716e9c 100644 --- a/lightrag/kg/faiss_impl.py +++ b/lightrag/kg/faiss_impl.py @@ -392,3 +392,46 @@ class FaissVectorDBStorage(BaseVectorStorage): logger.debug(f"Found {len(matching_records)} records with prefix '{prefix}'") return matching_records + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + # Find the Faiss internal ID for the custom ID + fid = self._find_faiss_id_by_custom_id(id) + if fid is None: + return None + + # Get the metadata for the found ID + metadata = self._id_to_meta.get(fid, {}) + if not metadata: + return None + + return {**metadata, "id": metadata.get("__id__")} + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + results = [] + for id in ids: + fid = self._find_faiss_id_by_custom_id(id) + if fid is not None: + metadata = self._id_to_meta.get(fid, {}) + if metadata: + results.append({**metadata, "id": metadata.get("__id__")}) + + return results diff --git a/lightrag/kg/milvus_impl.py b/lightrag/kg/milvus_impl.py index f3a6fcc4..4fb5f012 100644 --- a/lightrag/kg/milvus_impl.py +++ b/lightrag/kg/milvus_impl.py @@ -231,3 +231,57 @@ class MilvusVectorDBStorage(BaseVectorStorage): except Exception as e: logger.error(f"Error searching for records with prefix '{prefix}': {e}") return [] + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + try: + # Query Milvus for a specific ID + result = self._client.query( + collection_name=self.namespace, + filter=f'id == "{id}"', + output_fields=list(self.meta_fields) + ["id"], + ) + + if not result or len(result) == 0: + return None + + return result[0] + except Exception as e: + logger.error(f"Error retrieving vector data for ID {id}: {e}") + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + try: + # Prepare the ID filter expression + id_list = '", "'.join(ids) + filter_expr = f'id in ["{id_list}"]' + + # Query Milvus with the filter + result = self._client.query( + collection_name=self.namespace, + filter=filter_expr, + output_fields=list(self.meta_fields) + ["id"], + ) + + return result or [] + except Exception as e: + logger.error(f"Error retrieving vector data for IDs {ids}: {e}") + return [] diff --git a/lightrag/kg/mongo_impl.py b/lightrag/kg/mongo_impl.py index f2ab6ae0..da4dc32c 100644 --- a/lightrag/kg/mongo_impl.py +++ b/lightrag/kg/mongo_impl.py @@ -1071,6 +1071,59 @@ class MongoVectorDBStorage(BaseVectorStorage): logger.error(f"Error searching by prefix in {self.namespace}: {str(e)}") return [] + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + try: + # Search for the specific ID in MongoDB + result = await self._data.find_one({"_id": id}) + if result: + # Format the result to include id field expected by API + result_dict = dict(result) + if "_id" in result_dict and "id" not in result_dict: + result_dict["id"] = result_dict["_id"] + return result_dict + return None + except Exception as e: + logger.error(f"Error retrieving vector data for ID {id}: {e}") + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + try: + # Query MongoDB for multiple IDs + cursor = self._data.find({"_id": {"$in": ids}}) + results = await cursor.to_list(length=None) + + # Format results to include id field expected by API + formatted_results = [] + for result in results: + result_dict = dict(result) + if "_id" in result_dict and "id" not in result_dict: + result_dict["id"] = result_dict["_id"] + formatted_results.append(result_dict) + + return formatted_results + except Exception as e: + logger.error(f"Error retrieving vector data for IDs {ids}: {e}") + return [] + async def get_or_create_collection(db: AsyncIOMotorDatabase, collection_name: str): collection_names = await db.list_collection_names() diff --git a/lightrag/kg/nano_vector_db_impl.py b/lightrag/kg/nano_vector_db_impl.py index 07ccd566..ac010f16 100644 --- a/lightrag/kg/nano_vector_db_impl.py +++ b/lightrag/kg/nano_vector_db_impl.py @@ -256,3 +256,33 @@ class NanoVectorDBStorage(BaseVectorStorage): logger.debug(f"Found {len(matching_records)} records with prefix '{prefix}'") return matching_records + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + client = await self._get_client() + result = client.get([id]) + if result: + return result[0] + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + client = await self._get_client() + return client.get(ids) diff --git a/lightrag/kg/neo4j_impl.py b/lightrag/kg/neo4j_impl.py index 8d5a1a55..d0c6c779 100644 --- a/lightrag/kg/neo4j_impl.py +++ b/lightrag/kg/neo4j_impl.py @@ -176,23 +176,6 @@ class Neo4JStorage(BaseGraphStorage): # Noe4J handles persistence automatically pass - def _ensure_label(self, label: str) -> str: - """Ensure a label is valid - - Args: - label: The label to validate - - Returns: - str: The cleaned label - - Raises: - ValueError: If label is empty after cleaning - """ - clean_label = label.strip('"') - if not clean_label: - raise ValueError("Neo4j: Label cannot be empty") - return clean_label - async def has_node(self, node_id: str) -> bool: """ Check if a node with the given label exists in the database @@ -207,20 +190,17 @@ class Neo4JStorage(BaseGraphStorage): ValueError: If node_id is invalid Exception: If there is an error executing the query """ - entity_name_label = self._ensure_label(node_id) async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: try: - query = f"MATCH (n:`{entity_name_label}`) RETURN count(n) > 0 AS node_exists" - result = await session.run(query) + query = "MATCH (n:base {entity_id: $entity_id}) RETURN count(n) > 0 AS node_exists" + result = await session.run(query, entity_id=node_id) single_result = await result.single() await result.consume() # Ensure result is fully consumed return single_result["node_exists"] except Exception as e: - logger.error( - f"Error checking node existence for {entity_name_label}: {str(e)}" - ) + logger.error(f"Error checking node existence for {node_id}: {str(e)}") await result.consume() # Ensure results are consumed even on error raise @@ -239,24 +219,25 @@ class Neo4JStorage(BaseGraphStorage): ValueError: If either node_id is invalid Exception: If there is an error executing the query """ - entity_name_label_source = self._ensure_label(source_node_id) - entity_name_label_target = self._ensure_label(target_node_id) - async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: try: query = ( - f"MATCH (a:`{entity_name_label_source}`)-[r]-(b:`{entity_name_label_target}`) " + "MATCH (a:base {entity_id: $source_entity_id})-[r]-(b:base {entity_id: $target_entity_id}) " "RETURN COUNT(r) > 0 AS edgeExists" ) - result = await session.run(query) + result = await session.run( + query, + source_entity_id=source_node_id, + target_entity_id=target_node_id, + ) single_result = await result.single() await result.consume() # Ensure result is fully consumed return single_result["edgeExists"] except Exception as e: logger.error( - f"Error checking edge existence between {entity_name_label_source} and {entity_name_label_target}: {str(e)}" + f"Error checking edge existence between {source_node_id} and {target_node_id}: {str(e)}" ) await result.consume() # Ensure results are consumed even on error raise @@ -275,13 +256,12 @@ class Neo4JStorage(BaseGraphStorage): ValueError: If node_id is invalid Exception: If there is an error executing the query """ - entity_name_label = self._ensure_label(node_id) async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: try: - query = f"MATCH (n:`{entity_name_label}` {{entity_id: $entity_id}}) RETURN n" - result = await session.run(query, entity_id=entity_name_label) + query = "MATCH (n:base {entity_id: $entity_id}) RETURN n" + result = await session.run(query, entity_id=node_id) try: records = await result.fetch( 2 @@ -289,20 +269,25 @@ class Neo4JStorage(BaseGraphStorage): if len(records) > 1: logger.warning( - f"Multiple nodes found with label '{entity_name_label}'. Using first node." + f"Multiple nodes found with label '{node_id}'. Using first node." ) if records: node = records[0]["n"] node_dict = dict(node) - logger.debug( - f"{inspect.currentframe().f_code.co_name}: query: {query}, result: {node_dict}" - ) + # Remove base label from labels list if it exists + if "labels" in node_dict: + node_dict["labels"] = [ + label + for label in node_dict["labels"] + if label != "base" + ] + logger.debug(f"Neo4j query node {query} return: {node_dict}") return node_dict return None finally: await result.consume() # Ensure result is fully consumed except Exception as e: - logger.error(f"Error getting node for {entity_name_label}: {str(e)}") + logger.error(f"Error getting node for {node_id}: {str(e)}") raise async def node_degree(self, node_id: str) -> int: @@ -320,43 +305,32 @@ class Neo4JStorage(BaseGraphStorage): ValueError: If node_id is invalid Exception: If there is an error executing the query """ - entity_name_label = self._ensure_label(node_id) - async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: try: - query = f""" - MATCH (n:`{entity_name_label}`) + query = """ + MATCH (n:base {entity_id: $entity_id}) OPTIONAL MATCH (n)-[r]-() - RETURN n, COUNT(r) AS degree + RETURN COUNT(r) AS degree """ - result = await session.run(query) + result = await session.run(query, entity_id=node_id) try: - records = await result.fetch(100) + record = await result.single() - if not records: - logger.warning( - f"No node found with label '{entity_name_label}'" - ) + if not record: + logger.warning(f"No node found with label '{node_id}'") return 0 - if len(records) > 1: - logger.warning( - f"Multiple nodes ({len(records)}) found with label '{entity_name_label}', using first node's degree" - ) - - degree = records[0]["degree"] + degree = record["degree"] logger.debug( - f"{inspect.currentframe().f_code.co_name}:query:{query}:result:{degree}" + "Neo4j query node degree for {node_id} return: {degree}" ) return degree finally: await result.consume() # Ensure result is fully consumed except Exception as e: - logger.error( - f"Error getting node degree for {entity_name_label}: {str(e)}" - ) + logger.error(f"Error getting node degree for {node_id}: {str(e)}") raise async def edge_degree(self, src_id: str, tgt_id: str) -> int: @@ -369,11 +343,8 @@ class Neo4JStorage(BaseGraphStorage): Returns: int: Sum of the degrees of both nodes """ - entity_name_label_source = self._ensure_label(src_id) - entity_name_label_target = self._ensure_label(tgt_id) - - src_degree = await self.node_degree(entity_name_label_source) - trg_degree = await self.node_degree(entity_name_label_target) + src_degree = await self.node_degree(src_id) + trg_degree = await self.node_degree(tgt_id) # Convert None to 0 for addition src_degree = 0 if src_degree is None else src_degree @@ -399,24 +370,24 @@ class Neo4JStorage(BaseGraphStorage): Exception: If there is an error executing the query """ try: - entity_name_label_source = self._ensure_label(source_node_id) - entity_name_label_target = self._ensure_label(target_node_id) - async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: - query = f""" - MATCH (start:`{entity_name_label_source}`)-[r]-(end:`{entity_name_label_target}`) + query = """ + MATCH (start:base {entity_id: $source_entity_id})-[r]-(end:base {entity_id: $target_entity_id}) RETURN properties(r) as edge_properties """ - - result = await session.run(query) + result = await session.run( + query, + source_entity_id=source_node_id, + target_entity_id=target_node_id, + ) try: records = await result.fetch(2) if len(records) > 1: logger.warning( - f"Multiple edges found between '{entity_name_label_source}' and '{entity_name_label_target}'. Using first edge." + f"Multiple edges found between '{source_node_id}' and '{target_node_id}'. Using first edge." ) if records: try: @@ -433,7 +404,7 @@ class Neo4JStorage(BaseGraphStorage): if key not in edge_result: edge_result[key] = default_value logger.warning( - f"Edge between {entity_name_label_source} and {entity_name_label_target} " + f"Edge between {source_node_id} and {target_node_id} " f"missing {key}, using default: {default_value}" ) @@ -443,8 +414,8 @@ class Neo4JStorage(BaseGraphStorage): return edge_result except (KeyError, TypeError, ValueError) as e: logger.error( - f"Error processing edge properties between {entity_name_label_source} " - f"and {entity_name_label_target}: {str(e)}" + f"Error processing edge properties between {source_node_id} " + f"and {target_node_id}: {str(e)}" ) # Return default edge properties on error return { @@ -455,7 +426,7 @@ class Neo4JStorage(BaseGraphStorage): } logger.debug( - f"{inspect.currentframe().f_code.co_name}: No edge found between {entity_name_label_source} and {entity_name_label_target}" + f"{inspect.currentframe().f_code.co_name}: No edge found between {source_node_id} and {target_node_id}" ) # Return default edge properties when no edge found return { @@ -488,29 +459,33 @@ class Neo4JStorage(BaseGraphStorage): Exception: If there is an error executing the query """ try: - node_label = self._ensure_label(source_node_id) - - query = f"""MATCH (n:`{node_label}`) - OPTIONAL MATCH (n)-[r]-(connected) - RETURN n, r, connected""" - async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: try: - results = await session.run(query) - edges = [] + query = """MATCH (n:base {entity_id: $entity_id}) + OPTIONAL MATCH (n)-[r]-(connected:base) + WHERE connected.entity_id IS NOT NULL + RETURN n, r, connected""" + results = await session.run(query, entity_id=source_node_id) + edges = [] async for record in results: source_node = record["n"] connected_node = record["connected"] + # Skip if either node is None + if not source_node or not connected_node: + continue + source_label = ( - list(source_node.labels)[0] if source_node.labels else None + source_node.get("entity_id") + if source_node.get("entity_id") + else None ) target_label = ( - list(connected_node.labels)[0] - if connected_node and connected_node.labels + connected_node.get("entity_id") + if connected_node.get("entity_id") else None ) @@ -520,7 +495,9 @@ class Neo4JStorage(BaseGraphStorage): await results.consume() # Ensure results are consumed return edges except Exception as e: - logger.error(f"Error getting edges for node {node_label}: {str(e)}") + logger.error( + f"Error getting edges for node {source_node_id}: {str(e)}" + ) await results.consume() # Ensure results are consumed even on error raise except Exception as e: @@ -547,8 +524,9 @@ class Neo4JStorage(BaseGraphStorage): node_id: The unique identifier for the node (used as label) node_data: Dictionary of node properties """ - label = self._ensure_label(node_id) properties = node_data + entity_type = properties["entity_type"] + entity_id = properties["entity_id"] if "entity_id" not in properties: raise ValueError("Neo4j: node properties must contain an 'entity_id' field") @@ -556,13 +534,17 @@ class Neo4JStorage(BaseGraphStorage): async with self._driver.session(database=self._DATABASE) as session: async def execute_upsert(tx: AsyncManagedTransaction): - query = f""" - MERGE (n:`{label}` {{entity_id: $properties.entity_id}}) + query = ( + """ + MERGE (n:base {entity_id: $properties.entity_id}) SET n += $properties + SET n:`%s` """ + % entity_type + ) result = await tx.run(query, properties=properties) logger.debug( - f"Upserted node with label '{label}' and properties: {properties}" + f"Upserted node with entity_id '{entity_id}' and properties: {properties}" ) await result.consume() # Ensure result is fully consumed @@ -583,52 +565,6 @@ class Neo4JStorage(BaseGraphStorage): ) ), ) - async def _get_unique_node_entity_id(self, node_label: str) -> str: - """ - Get the entity_id of a node with the given label, ensuring the node is unique. - - Args: - node_label (str): Label of the node to check - - Returns: - str: The entity_id of the unique node - - Raises: - ValueError: If no node with the given label exists or if multiple nodes have the same label - """ - async with self._driver.session( - database=self._DATABASE, default_access_mode="READ" - ) as session: - query = f""" - MATCH (n:`{node_label}`) - RETURN n, count(n) as node_count - """ - result = await session.run(query) - try: - records = await result.fetch( - 2 - ) # We only need to know if there are 0, 1, or >1 nodes - - if not records or records[0]["node_count"] == 0: - raise ValueError( - f"Neo4j: node with label '{node_label}' does not exist" - ) - - if records[0]["node_count"] > 1: - raise ValueError( - f"Neo4j: multiple nodes found with label '{node_label}', cannot determine unique node" - ) - - node = records[0]["n"] - if "entity_id" not in node: - raise ValueError( - f"Neo4j: node with label '{node_label}' does not have an entity_id property" - ) - - return node["entity_id"] - finally: - await result.consume() # Ensure result is fully consumed - @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10), @@ -657,38 +593,30 @@ class Neo4JStorage(BaseGraphStorage): Raises: ValueError: If either source or target node does not exist or is not unique """ - source_label = self._ensure_label(source_node_id) - target_label = self._ensure_label(target_node_id) - edge_properties = edge_data - - # Get entity_ids for source and target nodes, ensuring they are unique - source_entity_id = await self._get_unique_node_entity_id(source_label) - target_entity_id = await self._get_unique_node_entity_id(target_label) - try: + edge_properties = edge_data async with self._driver.session(database=self._DATABASE) as session: async def execute_upsert(tx: AsyncManagedTransaction): - query = f""" - MATCH (source:`{source_label}` {{entity_id: $source_entity_id}}) + query = """ + MATCH (source:base {entity_id: $source_entity_id}) WITH source - MATCH (target:`{target_label}` {{entity_id: $target_entity_id}}) + MATCH (target:base {entity_id: $target_entity_id}) MERGE (source)-[r:DIRECTED]-(target) SET r += $properties RETURN r, source, target """ result = await tx.run( query, - source_entity_id=source_entity_id, - target_entity_id=target_entity_id, + source_entity_id=source_node_id, + target_entity_id=target_node_id, properties=edge_properties, ) try: - records = await result.fetch(100) + records = await result.fetch(2) if records: logger.debug( - f"Upserted edge from '{source_label}' (entity_id: {source_entity_id}) " - f"to '{target_label}' (entity_id: {target_entity_id}) " + f"Upserted edge from '{source_node_id}' to '{target_node_id}'" f"with properties: {edge_properties}" ) finally: @@ -726,7 +654,6 @@ class Neo4JStorage(BaseGraphStorage): Returns: KnowledgeGraph: Complete connected subgraph for specified node """ - label = node_label.strip('"') result = KnowledgeGraph() seen_nodes = set() seen_edges = set() @@ -735,7 +662,7 @@ class Neo4JStorage(BaseGraphStorage): database=self._DATABASE, default_access_mode="READ" ) as session: try: - if label == "*": + if node_label == "*": main_query = """ MATCH (n) OPTIONAL MATCH (n)-[r]-() @@ -760,12 +687,11 @@ class Neo4JStorage(BaseGraphStorage): # Main query uses partial matching main_query = """ MATCH (start) - WHERE any(label IN labels(start) WHERE + WHERE CASE - WHEN $inclusive THEN label CONTAINS $label - ELSE label = $label + WHEN $inclusive THEN start.entity_id CONTAINS $entity_id + ELSE start.entity_id = $entity_id END - ) WITH start CALL apoc.path.subgraphAll(start, { relationshipFilter: '', @@ -799,7 +725,7 @@ class Neo4JStorage(BaseGraphStorage): main_query, { "max_nodes": MAX_GRAPH_NODES, - "label": label, + "entity_id": node_label, "inclusive": inclusive, "max_depth": max_depth, "min_degree": min_degree, @@ -818,7 +744,11 @@ class Neo4JStorage(BaseGraphStorage): result.nodes.append( KnowledgeGraphNode( id=f"{node_id}", - labels=list(node.labels), + labels=[ + label + for label in node.labels + if label != "base" + ], properties=dict(node), ) ) @@ -849,7 +779,7 @@ class Neo4JStorage(BaseGraphStorage): except neo4jExceptions.ClientError as e: logger.warning(f"APOC plugin error: {str(e)}") - if label != "*": + if node_label != "*": logger.warning( "Neo4j: falling back to basic Cypher recursive search..." ) @@ -857,12 +787,14 @@ class Neo4JStorage(BaseGraphStorage): logger.warning( "Neo4j: inclusive search mode is not supported in recursive query, using exact matching" ) - return await self._robust_fallback(label, max_depth, min_degree) + return await self._robust_fallback( + node_label, max_depth, min_degree + ) return result async def _robust_fallback( - self, label: str, max_depth: int, min_degree: int = 0 + self, node_label: str, max_depth: int, min_degree: int = 0 ) -> KnowledgeGraph: """ Fallback implementation when APOC plugin is not available or incompatible. @@ -895,12 +827,11 @@ class Neo4JStorage(BaseGraphStorage): database=self._DATABASE, default_access_mode="READ" ) as session: query = """ - MATCH (a)-[r]-(b) - WHERE id(a) = toInteger($node_id) + MATCH (a:base {entity_id: $entity_id})-[r]-(b) WITH r, b, id(r) as edge_id, id(b) as target_id RETURN r, b, edge_id, target_id """ - results = await session.run(query, {"node_id": node.id}) + results = await session.run(query, entity_id=node.id) # Get all records and release database connection records = await results.fetch( @@ -928,14 +859,16 @@ class Neo4JStorage(BaseGraphStorage): edge_id = str(record["edge_id"]) if edge_id not in visited_edges: b_node = record["b"] - target_id = str(record["target_id"]) + target_id = b_node.get("entity_id") - if b_node.labels: # Only process if target node has labels + if target_id: # Only process if target node has entity_id # Create KnowledgeGraphNode for target target_node = KnowledgeGraphNode( id=f"{target_id}", - labels=list(b_node.labels), - properties=dict(b_node), + labels=[ + label for label in b_node.labels if label != "base" + ], + properties=dict(b_node.properties), ) # Create KnowledgeGraphEdge @@ -961,11 +894,11 @@ class Neo4JStorage(BaseGraphStorage): async with self._driver.session( database=self._DATABASE, default_access_mode="READ" ) as session: - query = f""" - MATCH (n:`{label}`) + query = """ + MATCH (n:base {entity_id: $entity_id}) RETURN id(n) as node_id, n """ - node_result = await session.run(query) + node_result = await session.run(query, entity_id=node_label) try: node_record = await node_result.single() if not node_record: @@ -973,9 +906,11 @@ class Neo4JStorage(BaseGraphStorage): # Create initial KnowledgeGraphNode start_node = KnowledgeGraphNode( - id=f"{node_record['node_id']}", - labels=list(node_record["n"].labels), - properties=dict(node_record["n"]), + id=f"{node_record['n'].get('entity_id')}", + labels=[ + label for label in node_record["n"].labels if label != "base" + ], + properties=dict(node_record["n"].properties), ) finally: await node_result.consume() # Ensure results are consumed @@ -999,11 +934,10 @@ class Neo4JStorage(BaseGraphStorage): # Method 2: Query compatible with older versions query = """ - MATCH (n) - WITH DISTINCT labels(n) AS node_labels - UNWIND node_labels AS label - RETURN DISTINCT label - ORDER BY label + MATCH (n) + WHERE n.entity_id IS NOT NULL + RETURN DISTINCT n.entity_id AS label + ORDER BY label """ result = await session.run(query) labels = [] @@ -1034,15 +968,14 @@ class Neo4JStorage(BaseGraphStorage): Args: node_id: The label of the node to delete """ - label = self._ensure_label(node_id) async def _do_delete(tx: AsyncManagedTransaction): - query = f""" - MATCH (n:`{label}`) + query = """ + MATCH (n:base {entity_id: $entity_id}) DETACH DELETE n """ - result = await tx.run(query) - logger.debug(f"Deleted node with label '{label}'") + result = await tx.run(query, entity_id=node_id) + logger.debug(f"Deleted node with label '{node_id}'") await result.consume() # Ensure result is fully consumed try: @@ -1092,16 +1025,16 @@ class Neo4JStorage(BaseGraphStorage): edges: List of edges to be deleted, each edge is a (source, target) tuple """ for source, target in edges: - source_label = self._ensure_label(source) - target_label = self._ensure_label(target) async def _do_delete_edge(tx: AsyncManagedTransaction): - query = f""" - MATCH (source:`{source_label}`)-[r]-(target:`{target_label}`) + query = """ + MATCH (source:base {entity_id: $source_entity_id})-[r]-(target:base {entity_id: $target_entity_id}) DELETE r """ - result = await tx.run(query) - logger.debug(f"Deleted edge from '{source_label}' to '{target_label}'") + result = await tx.run( + query, source_entity_id=source, target_entity_id=target + ) + logger.debug(f"Deleted edge from '{source}' to '{target}'") await result.consume() # Ensure result is fully consumed try: diff --git a/lightrag/kg/oracle_impl.py b/lightrag/kg/oracle_impl.py index eda3ca63..32790f4f 100644 --- a/lightrag/kg/oracle_impl.py +++ b/lightrag/kg/oracle_impl.py @@ -529,6 +529,80 @@ class OracleVectorDBStorage(BaseVectorStorage): logger.error(f"Error searching records with prefix '{prefix}': {e}") return [] + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + try: + # Determine the table name based on namespace + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error(f"Unknown namespace for ID lookup: {self.namespace}") + return None + + # Create the appropriate ID field name based on namespace + id_field = "entity_id" if "NODES" in table_name else "relation_id" + if "CHUNKS" in table_name: + id_field = "chunk_id" + + # Prepare and execute the query + query = f""" + SELECT * FROM {table_name} + WHERE {id_field} = :id AND workspace = :workspace + """ + params = {"id": id, "workspace": self.db.workspace} + + result = await self.db.query(query, params) + return result + except Exception as e: + logger.error(f"Error retrieving vector data for ID {id}: {e}") + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + try: + # Determine the table name based on namespace + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error(f"Unknown namespace for IDs lookup: {self.namespace}") + return [] + + # Create the appropriate ID field name based on namespace + id_field = "entity_id" if "NODES" in table_name else "relation_id" + if "CHUNKS" in table_name: + id_field = "chunk_id" + + # Format the list of IDs for SQL IN clause + ids_list = ", ".join([f"'{id}'" for id in ids]) + + # Prepare and execute the query + query = f""" + SELECT * FROM {table_name} + WHERE {id_field} IN ({ids_list}) AND workspace = :workspace + """ + params = {"workspace": self.db.workspace} + + results = await self.db.query(query, params, multirows=True) + return results or [] + except Exception as e: + logger.error(f"Error retrieving vector data for IDs {ids}: {e}") + return [] + @final @dataclass diff --git a/lightrag/kg/postgres_impl.py b/lightrag/kg/postgres_impl.py index 3a636e6a..49d462f6 100644 --- a/lightrag/kg/postgres_impl.py +++ b/lightrag/kg/postgres_impl.py @@ -438,6 +438,8 @@ class PGVectorStorage(BaseVectorStorage): "entity_name": item["entity_name"], "content": item["content"], "content_vector": json.dumps(item["__vector__"].tolist()), + "chunk_id": item["source_id"], + # TODO: add document_id } return upsert_sql, data @@ -450,6 +452,8 @@ class PGVectorStorage(BaseVectorStorage): "target_id": item["tgt_id"], "content": item["content"], "content_vector": json.dumps(item["__vector__"].tolist()), + "chunk_id": item["source_id"], + # TODO: add document_id } return upsert_sql, data @@ -492,13 +496,20 @@ class PGVectorStorage(BaseVectorStorage): await self.db.execute(upsert_sql, data) #################### query method ############### - async def query(self, query: str, top_k: int) -> list[dict[str, Any]]: + async def query( + self, query: str, top_k: int, ids: list[str] | None = None + ) -> list[dict[str, Any]]: embeddings = await self.embedding_func([query]) embedding = embeddings[0] embedding_string = ",".join(map(str, embedding)) + if ids: + formatted_ids = ",".join(f"'{id}'" for id in ids) + else: + formatted_ids = "NULL" + sql = SQL_TEMPLATES[self.base_namespace].format( - embedding_string=embedding_string + embedding_string=embedding_string, doc_ids=formatted_ids ) params = { "workspace": self.db.workspace, @@ -610,6 +621,60 @@ class PGVectorStorage(BaseVectorStorage): logger.error(f"Error during prefix search for '{prefix}': {e}") return [] + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error(f"Unknown namespace for ID lookup: {self.namespace}") + return None + + query = f"SELECT * FROM {table_name} WHERE workspace=$1 AND id=$2" + params = {"workspace": self.db.workspace, "id": id} + + try: + result = await self.db.query(query, params) + if result: + return dict(result) + return None + except Exception as e: + logger.error(f"Error retrieving vector data for ID {id}: {e}") + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error(f"Unknown namespace for IDs lookup: {self.namespace}") + return [] + + ids_str = ",".join([f"'{id}'" for id in ids]) + query = f"SELECT * FROM {table_name} WHERE workspace=$1 AND id IN ({ids_str})" + params = {"workspace": self.db.workspace} + + try: + results = await self.db.query(query, params, multirows=True) + return [dict(record) for record in results] + except Exception as e: + logger.error(f"Error retrieving vector data for IDs {ids}: {e}") + return [] + @final @dataclass @@ -1491,6 +1556,7 @@ TABLES = { content_vector VECTOR, create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP, update_time TIMESTAMP, + chunk_id VARCHAR(255) NULL, CONSTRAINT LIGHTRAG_VDB_ENTITY_PK PRIMARY KEY (workspace, id) )""" }, @@ -1504,6 +1570,7 @@ TABLES = { content_vector VECTOR, create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP, update_time TIMESTAMP, + chunk_id VARCHAR(255) NULL, CONSTRAINT LIGHTRAG_VDB_RELATION_PK PRIMARY KEY (workspace, id) )""" }, @@ -1586,8 +1653,9 @@ SQL_TEMPLATES = { content_vector=EXCLUDED.content_vector, update_time = CURRENT_TIMESTAMP """, - "upsert_entity": """INSERT INTO LIGHTRAG_VDB_ENTITY (workspace, id, entity_name, content, content_vector) - VALUES ($1, $2, $3, $4, $5) + "upsert_entity": """INSERT INTO LIGHTRAG_VDB_ENTITY (workspace, id, entity_name, content, + content_vector, chunk_id) + VALUES ($1, $2, $3, $4, $5, $6) ON CONFLICT (workspace,id) DO UPDATE SET entity_name=EXCLUDED.entity_name, content=EXCLUDED.content, @@ -1595,8 +1663,8 @@ SQL_TEMPLATES = { update_time=CURRENT_TIMESTAMP """, "upsert_relationship": """INSERT INTO LIGHTRAG_VDB_RELATION (workspace, id, source_id, - target_id, content, content_vector) - VALUES ($1, $2, $3, $4, $5, $6) + target_id, content, content_vector, chunk_id) + VALUES ($1, $2, $3, $4, $5, $6, $7) ON CONFLICT (workspace,id) DO UPDATE SET source_id=EXCLUDED.source_id, target_id=EXCLUDED.target_id, @@ -1604,21 +1672,21 @@ SQL_TEMPLATES = { content_vector=EXCLUDED.content_vector, update_time = CURRENT_TIMESTAMP """, # SQL for VectorStorage - "entities": """SELECT entity_name FROM - (SELECT id, entity_name, 1 - (content_vector <=> '[{embedding_string}]'::vector) as distance - FROM LIGHTRAG_VDB_ENTITY where workspace=$1) - WHERE distance>$2 ORDER BY distance DESC LIMIT $3 - """, - "relationships": """SELECT source_id as src_id, target_id as tgt_id FROM - (SELECT id, source_id,target_id, 1 - (content_vector <=> '[{embedding_string}]'::vector) as distance - FROM LIGHTRAG_VDB_RELATION where workspace=$1) - WHERE distance>$2 ORDER BY distance DESC LIMIT $3 - """, - "chunks": """SELECT id FROM - (SELECT id, 1 - (content_vector <=> '[{embedding_string}]'::vector) as distance - FROM LIGHTRAG_DOC_CHUNKS where workspace=$1) - WHERE distance>$2 ORDER BY distance DESC LIMIT $3 - """, + # "entities": """SELECT entity_name FROM + # (SELECT id, entity_name, 1 - (content_vector <=> '[{embedding_string}]'::vector) as distance + # FROM LIGHTRAG_VDB_ENTITY where workspace=$1) + # WHERE distance>$2 ORDER BY distance DESC LIMIT $3 + # """, + # "relationships": """SELECT source_id as src_id, target_id as tgt_id FROM + # (SELECT id, source_id,target_id, 1 - (content_vector <=> '[{embedding_string}]'::vector) as distance + # FROM LIGHTRAG_VDB_RELATION where workspace=$1) + # WHERE distance>$2 ORDER BY distance DESC LIMIT $3 + # """, + # "chunks": """SELECT id FROM + # (SELECT id, 1 - (content_vector <=> '[{embedding_string}]'::vector) as distance + # FROM LIGHTRAG_DOC_CHUNKS where workspace=$1) + # WHERE distance>$2 ORDER BY distance DESC LIMIT $3 + # """, # DROP tables "drop_all": """ DROP TABLE IF EXISTS LIGHTRAG_DOC_FULL CASCADE; @@ -1642,4 +1710,55 @@ SQL_TEMPLATES = { "drop_vdb_relation": """ DROP TABLE IF EXISTS LIGHTRAG_VDB_RELATION CASCADE; """, + "relationships": """ + WITH relevant_chunks AS ( + SELECT id as chunk_id + FROM LIGHTRAG_DOC_CHUNKS + WHERE {doc_ids} IS NULL OR full_doc_id = ANY(ARRAY[{doc_ids}]) + ) + SELECT source_id as src_id, target_id as tgt_id + FROM ( + SELECT r.id, r.source_id, r.target_id, 1 - (r.content_vector <=> '[{embedding_string}]'::vector) as distance + FROM LIGHTRAG_VDB_RELATION r + WHERE r.workspace=$1 + AND r.chunk_id IN (SELECT chunk_id FROM relevant_chunks) + ) filtered + WHERE distance>$2 + ORDER BY distance DESC + LIMIT $3 + """, + "entities": """ + WITH relevant_chunks AS ( + SELECT id as chunk_id + FROM LIGHTRAG_DOC_CHUNKS + WHERE {doc_ids} IS NULL OR full_doc_id = ANY(ARRAY[{doc_ids}]) + ) + SELECT entity_name FROM + ( + SELECT id, entity_name, 1 - (content_vector <=> '[{embedding_string}]'::vector) as distance + FROM LIGHTRAG_VDB_ENTITY + where workspace=$1 + AND chunk_id IN (SELECT chunk_id FROM relevant_chunks) + ) + WHERE distance>$2 + ORDER BY distance DESC + LIMIT $3 + """, + "chunks": """ + WITH relevant_chunks AS ( + SELECT id as chunk_id + FROM LIGHTRAG_DOC_CHUNKS + WHERE {doc_ids} IS NULL OR full_doc_id = ANY(ARRAY[{doc_ids}]) + ) + SELECT id FROM + ( + SELECT id, 1 - (content_vector <=> '[{embedding_string}]'::vector) as distance + FROM LIGHTRAG_DOC_CHUNKS + where workspace=$1 + AND id IN (SELECT chunk_id FROM relevant_chunks) + ) + WHERE distance>$2 + ORDER BY distance DESC + LIMIT $3 + """, } diff --git a/lightrag/kg/tidb_impl.py b/lightrag/kg/tidb_impl.py index 7af9b48a..c4485df6 100644 --- a/lightrag/kg/tidb_impl.py +++ b/lightrag/kg/tidb_impl.py @@ -463,6 +463,100 @@ class TiDBVectorDBStorage(BaseVectorStorage): logger.error(f"Error searching records with prefix '{prefix}': {e}") return [] + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + try: + # Determine which table to query based on namespace + if self.namespace == NameSpace.VECTOR_STORE_ENTITIES: + sql_template = """ + SELECT entity_id as id, name as entity_name, entity_type, description, content + FROM LIGHTRAG_GRAPH_NODES + WHERE entity_id = :entity_id AND workspace = :workspace + """ + params = {"entity_id": id, "workspace": self.db.workspace} + elif self.namespace == NameSpace.VECTOR_STORE_RELATIONSHIPS: + sql_template = """ + SELECT relation_id as id, source_name as src_id, target_name as tgt_id, + keywords, description, content + FROM LIGHTRAG_GRAPH_EDGES + WHERE relation_id = :relation_id AND workspace = :workspace + """ + params = {"relation_id": id, "workspace": self.db.workspace} + elif self.namespace == NameSpace.VECTOR_STORE_CHUNKS: + sql_template = """ + SELECT chunk_id as id, content, tokens, chunk_order_index, full_doc_id + FROM LIGHTRAG_DOC_CHUNKS + WHERE chunk_id = :chunk_id AND workspace = :workspace + """ + params = {"chunk_id": id, "workspace": self.db.workspace} + else: + logger.warning( + f"Namespace {self.namespace} not supported for get_by_id" + ) + return None + + result = await self.db.query(sql_template, params=params) + return result + except Exception as e: + logger.error(f"Error retrieving vector data for ID {id}: {e}") + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + try: + # Format IDs for SQL IN clause + ids_str = ", ".join([f"'{id}'" for id in ids]) + + # Determine which table to query based on namespace + if self.namespace == NameSpace.VECTOR_STORE_ENTITIES: + sql_template = f""" + SELECT entity_id as id, name as entity_name, entity_type, description, content + FROM LIGHTRAG_GRAPH_NODES + WHERE entity_id IN ({ids_str}) AND workspace = :workspace + """ + elif self.namespace == NameSpace.VECTOR_STORE_RELATIONSHIPS: + sql_template = f""" + SELECT relation_id as id, source_name as src_id, target_name as tgt_id, + keywords, description, content + FROM LIGHTRAG_GRAPH_EDGES + WHERE relation_id IN ({ids_str}) AND workspace = :workspace + """ + elif self.namespace == NameSpace.VECTOR_STORE_CHUNKS: + sql_template = f""" + SELECT chunk_id as id, content, tokens, chunk_order_index, full_doc_id + FROM LIGHTRAG_DOC_CHUNKS + WHERE chunk_id IN ({ids_str}) AND workspace = :workspace + """ + else: + logger.warning( + f"Namespace {self.namespace} not supported for get_by_ids" + ) + return [] + + params = {"workspace": self.db.workspace} + results = await self.db.query(sql_template, params=params, multirows=True) + return results if results else [] + except Exception as e: + logger.error(f"Error retrieving vector data for IDs {ids}: {e}") + return [] + @final @dataclass diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py index 3a7d340a..3a5e4e84 100644 --- a/lightrag/lightrag.py +++ b/lightrag/lightrag.py @@ -30,11 +30,10 @@ from .namespace import NameSpace, make_namespace from .operate import ( chunking_by_token_size, extract_entities, - extract_keywords_only, kg_query, - kg_query_with_keywords, mix_kg_vector_query, naive_query, + query_with_keywords, ) from .prompt import GRAPH_FIELD_SEP, PROMPTS from .utils import ( @@ -45,6 +44,9 @@ from .utils import ( encode_string_by_tiktoken, lazy_external_import, limit_async_func_call, + get_content_summary, + clean_text, + check_storage_env_vars, logger, ) from .types import KnowledgeGraph @@ -309,7 +311,7 @@ class LightRAG: # Verify storage implementation compatibility verify_storage_implementation(storage_type, storage_name) # Check environment variables - # self.check_storage_env_vars(storage_name) + check_storage_env_vars(storage_name) # Ensure vector_db_storage_cls_kwargs has required fields self.vector_db_storage_cls_kwargs = { @@ -536,11 +538,6 @@ class LightRAG: storage_class = lazy_external_import(import_path, storage_name) return storage_class - @staticmethod - def clean_text(text: str) -> str: - """Clean text by removing null bytes (0x00) and whitespace""" - return text.strip().replace("\x00", "") - def insert( self, input: str | list[str], @@ -602,8 +599,8 @@ class LightRAG: update_storage = False try: # Clean input texts - full_text = self.clean_text(full_text) - text_chunks = [self.clean_text(chunk) for chunk in text_chunks] + full_text = clean_text(full_text) + text_chunks = [clean_text(chunk) for chunk in text_chunks] # Process cleaned texts if doc_id is None: @@ -682,7 +679,7 @@ class LightRAG: contents = {id_: doc for id_, doc in zip(ids, input)} else: # Clean input text and remove duplicates - input = list(set(self.clean_text(doc) for doc in input)) + input = list(set(clean_text(doc) for doc in input)) # Generate contents dict of MD5 hash IDs and documents contents = {compute_mdhash_id(doc, prefix="doc-"): doc for doc in input} @@ -698,7 +695,7 @@ class LightRAG: new_docs: dict[str, Any] = { id_: { "content": content, - "content_summary": self._get_content_summary(content), + "content_summary": get_content_summary(content), "content_length": len(content), "status": DocStatus.PENDING, "created_at": datetime.now().isoformat(), @@ -1063,7 +1060,7 @@ class LightRAG: all_chunks_data: dict[str, dict[str, str]] = {} chunk_to_source_map: dict[str, str] = {} for chunk_data in custom_kg.get("chunks", []): - chunk_content = self.clean_text(chunk_data["content"]) + chunk_content = clean_text(chunk_data["content"]) source_id = chunk_data["source_id"] tokens = len( encode_string_by_tiktoken( @@ -1296,8 +1293,17 @@ class LightRAG: self, query: str, prompt: str, param: QueryParam = QueryParam() ): """ - 1. Extract keywords from the 'query' using new function in operate.py. - 2. Then run the standard aquery() flow with the final prompt (formatted_question). + Query with separate keyword extraction step. + + This method extracts keywords from the query first, then uses them for the query. + + Args: + query: User query + prompt: Additional prompt for the query + param: Query parameters + + Returns: + Query response """ loop = always_get_an_event_loop() return loop.run_until_complete( @@ -1308,66 +1314,29 @@ class LightRAG: self, query: str, prompt: str, param: QueryParam = QueryParam() ) -> str | AsyncIterator[str]: """ - 1. Calls extract_keywords_only to get HL/LL keywords from 'query'. - 2. Then calls kg_query(...) or naive_query(...), etc. as the main query, while also injecting the newly extracted keywords if needed. + Async version of query_with_separate_keyword_extraction. + + Args: + query: User query + prompt: Additional prompt for the query + param: Query parameters + + Returns: + Query response or async iterator """ - # --------------------- - # STEP 1: Keyword Extraction - # --------------------- - hl_keywords, ll_keywords = await extract_keywords_only( - text=query, + response = await query_with_keywords( + query=query, + prompt=prompt, param=param, + knowledge_graph_inst=self.chunk_entity_relation_graph, + entities_vdb=self.entities_vdb, + relationships_vdb=self.relationships_vdb, + chunks_vdb=self.chunks_vdb, + text_chunks_db=self.text_chunks, global_config=asdict(self), - hashing_kv=self.llm_response_cache, # Directly use llm_response_cache + hashing_kv=self.llm_response_cache, ) - param.hl_keywords = hl_keywords - param.ll_keywords = ll_keywords - - # --------------------- - # STEP 2: Final Query Logic - # --------------------- - - # Create a new string with the prompt and the keywords - ll_keywords_str = ", ".join(ll_keywords) - hl_keywords_str = ", ".join(hl_keywords) - formatted_question = f"{prompt}\n\n### Keywords:\nHigh-level: {hl_keywords_str}\nLow-level: {ll_keywords_str}\n\n### Query:\n{query}" - - if param.mode in ["local", "global", "hybrid"]: - response = await kg_query_with_keywords( - formatted_question, - self.chunk_entity_relation_graph, - self.entities_vdb, - self.relationships_vdb, - self.text_chunks, - param, - asdict(self), - hashing_kv=self.llm_response_cache, # Directly use llm_response_cache - ) - elif param.mode == "naive": - response = await naive_query( - formatted_question, - self.chunks_vdb, - self.text_chunks, - param, - asdict(self), - hashing_kv=self.llm_response_cache, # Directly use llm_response_cache - ) - elif param.mode == "mix": - response = await mix_kg_vector_query( - formatted_question, - self.chunk_entity_relation_graph, - self.entities_vdb, - self.relationships_vdb, - self.chunks_vdb, - self.text_chunks, - param, - asdict(self), - hashing_kv=self.llm_response_cache, # Directly use llm_response_cache - ) - else: - raise ValueError(f"Unknown mode {param.mode}") - await self._query_done() return response @@ -1465,21 +1434,6 @@ class LightRAG: ] ) - def _get_content_summary(self, content: str, max_length: int = 100) -> str: - """Get summary of document content - - Args: - content: Original document content - max_length: Maximum length of summary - - Returns: - Truncated content with ellipsis if needed - """ - content = content.strip() - if len(content) <= max_length: - return content - return content[:max_length] + "..." - async def get_processing_status(self) -> dict[str, int]: """Get current document processing status counts @@ -1756,19 +1710,7 @@ class LightRAG: async def get_entity_info( self, entity_name: str, include_vector_data: bool = False ) -> dict[str, str | None | dict[str, str]]: - """Get detailed information of an entity - - Args: - entity_name: Entity name (no need for quotes) - include_vector_data: Whether to include data from the vector database - - Returns: - dict: A dictionary containing entity information, including: - - entity_name: Entity name - - source_id: Source document ID - - graph_data: Complete node data from the graph database - - vector_data: (optional) Data from the vector database - """ + """Get detailed information of an entity""" # Get information from the graph node_data = await self.chunk_entity_relation_graph.get_node(entity_name) @@ -1783,29 +1725,15 @@ class LightRAG: # Optional: Get vector database information if include_vector_data: entity_id = compute_mdhash_id(entity_name, prefix="ent-") - vector_data = self.entities_vdb._client.get([entity_id]) - result["vector_data"] = vector_data[0] if vector_data else None + vector_data = await self.entities_vdb.get_by_id(entity_id) + result["vector_data"] = vector_data return result async def get_relation_info( self, src_entity: str, tgt_entity: str, include_vector_data: bool = False ) -> dict[str, str | None | dict[str, str]]: - """Get detailed information of a relationship - - Args: - src_entity: Source entity name (no need for quotes) - tgt_entity: Target entity name (no need for quotes) - include_vector_data: Whether to include data from the vector database - - Returns: - dict: A dictionary containing relationship information, including: - - src_entity: Source entity name - - tgt_entity: Target entity name - - source_id: Source document ID - - graph_data: Complete edge data from the graph database - - vector_data: (optional) Data from the vector database - """ + """Get detailed information of a relationship""" # Get information from the graph edge_data = await self.chunk_entity_relation_graph.get_edge( @@ -1823,8 +1751,8 @@ class LightRAG: # Optional: Get vector database information if include_vector_data: rel_id = compute_mdhash_id(src_entity + tgt_entity, prefix="rel-") - vector_data = self.relationships_vdb._client.get([rel_id]) - result["vector_data"] = vector_data[0] if vector_data else None + vector_data = await self.relationships_vdb.get_by_id(rel_id) + result["vector_data"] = vector_data return result @@ -2622,6 +2550,12 @@ class LightRAG: # 9. Delete source entities for entity_name in source_entities: + if entity_name == target_entity: + logger.info( + f"Skipping deletion of '{entity_name}' as it's also the target entity" + ) + continue + # Delete entity node from knowledge graph await self.chunk_entity_relation_graph.delete_node(entity_name) diff --git a/lightrag/llm/azure_openai.py b/lightrag/llm/azure_openai.py index 84e45cfb..3405d29e 100644 --- a/lightrag/llm/azure_openai.py +++ b/lightrag/llm/azure_openai.py @@ -55,6 +55,7 @@ async def azure_openai_complete_if_cache( openai_async_client = AsyncAzureOpenAI( azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), + azure_deployment=model, api_key=os.getenv("AZURE_OPENAI_API_KEY"), api_version=os.getenv("AZURE_OPENAI_API_VERSION"), ) @@ -136,6 +137,7 @@ async def azure_openai_embed( openai_async_client = AsyncAzureOpenAI( azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), + azure_deployment=model, api_key=os.getenv("AZURE_OPENAI_API_KEY"), api_version=os.getenv("AZURE_OPENAI_API_VERSION"), ) diff --git a/lightrag/operate.py b/lightrag/operate.py index e352ff79..1815f308 100644 --- a/lightrag/operate.py +++ b/lightrag/operate.py @@ -141,18 +141,36 @@ async def _handle_single_entity_extraction( ): if len(record_attributes) < 4 or record_attributes[0] != '"entity"': return None - # add this record as a node in the G + + # Clean and validate entity name entity_name = clean_str(record_attributes[1]).strip('"') if not entity_name.strip(): + logger.warning( + f"Entity extraction error: empty entity name in: {record_attributes}" + ) return None + + # Clean and validate entity type entity_type = clean_str(record_attributes[2]).strip('"') + if not entity_type.strip() or entity_type.startswith('("'): + logger.warning( + f"Entity extraction error: invalid entity type in: {record_attributes}" + ) + return None + + # Clean and validate description entity_description = clean_str(record_attributes[3]).strip('"') - entity_source_id = chunk_key + if not entity_description.strip(): + logger.warning( + f"Entity extraction error: empty description for entity '{entity_name}' of type '{entity_type}'" + ) + return None + return dict( entity_name=entity_name, entity_type=entity_type, description=entity_description, - source_id=entity_source_id, + source_id=chunk_key, metadata={"created_at": time.time()}, ) @@ -438,47 +456,22 @@ async def extract_entities( else: return await use_llm_func(input_text) - async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]): - """ "Prpocess a single chunk + async def _process_extraction_result(result: str, chunk_key: str): + """Process a single extraction result (either initial or gleaning) Args: - chunk_key_dp (tuple[str, TextChunkSchema]): - ("chunck-xxxxxx", {"tokens": int, "content": str, "full_doc_id": str, "chunk_order_index": int}) + result (str): The extraction result to process + chunk_key (str): The chunk key for source tracking + Returns: + tuple: (nodes_dict, edges_dict) containing the extracted entities and relationships """ - nonlocal processed_chunks - chunk_key = chunk_key_dp[0] - chunk_dp = chunk_key_dp[1] - content = chunk_dp["content"] - # hint_prompt = entity_extract_prompt.format(**context_base, input_text=content) - hint_prompt = entity_extract_prompt.format( - **context_base, input_text="{input_text}" - ).format(**context_base, input_text=content) - - final_result = await _user_llm_func_with_cache(hint_prompt) - history = pack_user_ass_to_openai_messages(hint_prompt, final_result) - for now_glean_index in range(entity_extract_max_gleaning): - glean_result = await _user_llm_func_with_cache( - continue_prompt, history_messages=history - ) - - history += pack_user_ass_to_openai_messages(continue_prompt, glean_result) - final_result += glean_result - if now_glean_index == entity_extract_max_gleaning - 1: - break - - if_loop_result: str = await _user_llm_func_with_cache( - if_loop_prompt, history_messages=history - ) - if_loop_result = if_loop_result.strip().strip('"').strip("'").lower() - if if_loop_result != "yes": - break + maybe_nodes = defaultdict(list) + maybe_edges = defaultdict(list) records = split_string_by_multi_markers( - final_result, + result, [context_base["record_delimiter"], context_base["completion_delimiter"]], ) - maybe_nodes = defaultdict(list) - maybe_edges = defaultdict(list) for record in records: record = re.search(r"\((.*)\)", record) if record is None: @@ -487,6 +480,7 @@ async def extract_entities( record_attributes = split_string_by_multi_markers( record, [context_base["tuple_delimiter"]] ) + if_entities = await _handle_single_entity_extraction( record_attributes, chunk_key ) @@ -501,6 +495,62 @@ async def extract_entities( maybe_edges[(if_relation["src_id"], if_relation["tgt_id"])].append( if_relation ) + + return maybe_nodes, maybe_edges + + async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]): + """Process a single chunk + Args: + chunk_key_dp (tuple[str, TextChunkSchema]): + ("chunk-xxxxxx", {"tokens": int, "content": str, "full_doc_id": str, "chunk_order_index": int}) + """ + nonlocal processed_chunks + chunk_key = chunk_key_dp[0] + chunk_dp = chunk_key_dp[1] + content = chunk_dp["content"] + + # Get initial extraction + hint_prompt = entity_extract_prompt.format( + **context_base, input_text="{input_text}" + ).format(**context_base, input_text=content) + + final_result = await _user_llm_func_with_cache(hint_prompt) + history = pack_user_ass_to_openai_messages(hint_prompt, final_result) + + # Process initial extraction + maybe_nodes, maybe_edges = await _process_extraction_result( + final_result, chunk_key + ) + + # Process additional gleaning results + for now_glean_index in range(entity_extract_max_gleaning): + glean_result = await _user_llm_func_with_cache( + continue_prompt, history_messages=history + ) + + history += pack_user_ass_to_openai_messages(continue_prompt, glean_result) + + # Process gleaning result separately + glean_nodes, glean_edges = await _process_extraction_result( + glean_result, chunk_key + ) + + # Merge results + for entity_name, entities in glean_nodes.items(): + maybe_nodes[entity_name].extend(entities) + for edge_key, edges in glean_edges.items(): + maybe_edges[edge_key].extend(edges) + + if now_glean_index == entity_extract_max_gleaning - 1: + break + + if_loop_result: str = await _user_llm_func_with_cache( + if_loop_prompt, history_messages=history + ) + if_loop_result = if_loop_result.strip().strip('"').strip("'").lower() + if if_loop_result != "yes": + break + processed_chunks += 1 entities_count = len(maybe_nodes) relations_count = len(maybe_edges) @@ -912,7 +962,10 @@ async def mix_kg_vector_query( try: # Reduce top_k for vector search in hybrid mode since we have structured information from KG mix_topk = min(10, query_param.top_k) - results = await chunks_vdb.query(augmented_query, top_k=mix_topk) + # TODO: add ids to the query + results = await chunks_vdb.query( + augmented_query, top_k=mix_topk, ids=query_param.ids + ) if not results: return None @@ -1121,7 +1174,11 @@ async def _get_node_data( logger.info( f"Query nodes: {query}, top_k: {query_param.top_k}, cosine: {entities_vdb.cosine_better_than_threshold}" ) - results = await entities_vdb.query(query, top_k=query_param.top_k) + + results = await entities_vdb.query( + query, top_k=query_param.top_k, ids=query_param.ids + ) + if not len(results): return "", "", "" # get entity information @@ -1374,7 +1431,10 @@ async def _get_edge_data( logger.info( f"Query edges: {keywords}, top_k: {query_param.top_k}, cosine: {relationships_vdb.cosine_better_than_threshold}" ) - results = await relationships_vdb.query(keywords, top_k=query_param.top_k) + + results = await relationships_vdb.query( + keywords, top_k=query_param.top_k, ids=query_param.ids + ) if not len(results): return "", "", "" @@ -1623,7 +1683,9 @@ async def naive_query( if cached_response is not None: return cached_response - results = await chunks_vdb.query(query, top_k=query_param.top_k) + results = await chunks_vdb.query( + query, top_k=query_param.top_k, ids=query_param.ids + ) if not len(results): return PROMPTS["fail_response"] @@ -1854,3 +1916,90 @@ async def kg_query_with_keywords( ) return response + + +async def query_with_keywords( + query: str, + prompt: str, + param: QueryParam, + knowledge_graph_inst: BaseGraphStorage, + entities_vdb: BaseVectorStorage, + relationships_vdb: BaseVectorStorage, + chunks_vdb: BaseVectorStorage, + text_chunks_db: BaseKVStorage, + global_config: dict[str, str], + hashing_kv: BaseKVStorage | None = None, +) -> str | AsyncIterator[str]: + """ + Extract keywords from the query and then use them for retrieving information. + + 1. Extracts high-level and low-level keywords from the query + 2. Formats the query with the extracted keywords and prompt + 3. Uses the appropriate query method based on param.mode + + Args: + query: The user's query + prompt: Additional prompt to prepend to the query + param: Query parameters + knowledge_graph_inst: Knowledge graph storage + entities_vdb: Entities vector database + relationships_vdb: Relationships vector database + chunks_vdb: Document chunks vector database + text_chunks_db: Text chunks storage + global_config: Global configuration + hashing_kv: Cache storage + + Returns: + Query response or async iterator + """ + # Extract keywords + hl_keywords, ll_keywords = await extract_keywords_only( + text=query, + param=param, + global_config=global_config, + hashing_kv=hashing_kv, + ) + + param.hl_keywords = hl_keywords + param.ll_keywords = ll_keywords + + # Create a new string with the prompt and the keywords + ll_keywords_str = ", ".join(ll_keywords) + hl_keywords_str = ", ".join(hl_keywords) + formatted_question = f"{prompt}\n\n### Keywords:\nHigh-level: {hl_keywords_str}\nLow-level: {ll_keywords_str}\n\n### Query:\n{query}" + + # Use appropriate query method based on mode + if param.mode in ["local", "global", "hybrid"]: + return await kg_query_with_keywords( + formatted_question, + knowledge_graph_inst, + entities_vdb, + relationships_vdb, + text_chunks_db, + param, + global_config, + hashing_kv=hashing_kv, + ) + elif param.mode == "naive": + return await naive_query( + formatted_question, + chunks_vdb, + text_chunks_db, + param, + global_config, + hashing_kv=hashing_kv, + ) + elif param.mode == "mix": + return await mix_kg_vector_query( + formatted_question, + knowledge_graph_inst, + entities_vdb, + relationships_vdb, + chunks_vdb, + text_chunks_db, + param, + global_config, + hashing_kv=hashing_kv, + ) + else: + raise ValueError(f"Unknown mode {param.mode}") diff --git a/lightrag/prompt.py b/lightrag/prompt.py index 1486ccf8..f81cd441 100644 --- a/lightrag/prompt.py +++ b/lightrag/prompt.py @@ -236,7 +236,7 @@ Given the query and conversation history, list both high-level and low-level key ---Instructions--- - Consider both the current query and relevant conversation history when extracting keywords -- Output the keywords in JSON format +- Output the keywords in JSON format, it will be parsed by a JSON parser, do not add any extra content in output - The JSON should have two keys: - "high_level_keywords" for overarching concepts or themes - "low_level_keywords" for specific entities or details diff --git a/lightrag/utils.py b/lightrag/utils.py index e8f79610..b8f00c5d 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -890,3 +890,52 @@ def lazy_external_import(module_name: str, class_name: str) -> Callable[..., Any return cls(*args, **kwargs) return import_class + + +def get_content_summary(content: str, max_length: int = 100) -> str: + """Get summary of document content + + Args: + content: Original document content + max_length: Maximum length of summary + + Returns: + Truncated content with ellipsis if needed + """ + content = content.strip() + if len(content) <= max_length: + return content + return content[:max_length] + "..." + + +def clean_text(text: str) -> str: + """Clean text by removing null bytes (0x00) and whitespace + + Args: + text: Input text to clean + + Returns: + Cleaned text + """ + return text.strip().replace("\x00", "") + + +def check_storage_env_vars(storage_name: str) -> None: + """Check if all required environment variables for storage implementation exist + + Args: + storage_name: Storage implementation name + + Raises: + ValueError: If required environment variables are missing + """ + from lightrag.kg import STORAGE_ENV_REQUIREMENTS + + required_vars = STORAGE_ENV_REQUIREMENTS.get(storage_name, []) + missing_vars = [var for var in required_vars if var not in os.environ] + + if missing_vars: + raise ValueError( + f"Storage implementation '{storage_name}' requires the following " + f"environment variables: {', '.join(missing_vars)}" + ) diff --git a/lightrag_webui/bun.lock b/lightrag_webui/bun.lock index 7435e125..a0fe0b89 100644 --- a/lightrag_webui/bun.lock +++ b/lightrag_webui/bun.lock @@ -34,11 +34,13 @@ "cmdk": "^1.0.4", "graphology": "^0.26.0", "graphology-generators": "^0.11.2", + "i18next": "^24.2.2", "lucide-react": "^0.475.0", "minisearch": "^7.1.2", "react": "^19.0.0", "react-dom": "^19.0.0", "react-dropzone": "^14.3.6", + "react-i18next": "^15.4.1", "react-markdown": "^9.1.0", "react-number-format": "^5.4.3", "react-router-dom": "^7.3.0", @@ -770,8 +772,12 @@ "hoist-non-react-statics": ["hoist-non-react-statics@3.3.2", "", { "dependencies": { "react-is": "^16.7.0" } }, "sha512-/gGivxi8JPKWNm/W0jSmzcMPpfpPLc3dY/6GxhX2hQ9iGj3aDfklV4ET7NjKpSinLpJ5vafa9iiGIEZg10SfBw=="], + "html-parse-stringify": ["html-parse-stringify@3.0.1", "", { "dependencies": { "void-elements": "3.1.0" } }, "sha512-KknJ50kTInJ7qIScF3jeaFRpMpE8/lfiTdzf/twXyPBLAGrLRTmkz3AdTnKeh40X8k9L2fdYwEp/42WGXIRGcg=="], + "html-url-attributes": ["html-url-attributes@3.0.1", "", {}, "sha512-ol6UPyBWqsrO6EJySPz2O7ZSr856WDrEzM5zMqp+FJJLGMW35cLYmmZnl0vztAZxRUoNZJFTCohfjuIJ8I4QBQ=="], + "i18next": ["i18next@24.2.2", "", { "dependencies": { "@babel/runtime": "^7.23.2" }, "peerDependencies": { "typescript": "^5" }, "optionalPeers": ["typescript"] }, "sha512-NE6i86lBCKRYZa5TaUDkU5S4HFgLIEJRLr3Whf2psgaxBleQ2LC1YW1Vc+SCgkAW7VEzndT6al6+CzegSUHcTQ=="], + "ignore": ["ignore@5.3.2", "", {}, "sha512-hsBTNUqQTDwkWtcdYI2i06Y/nUBEsNEDJKjWdigLvegy8kDuJAS8uRlpkkcQpyEXL0Z/pjDy5HBmMjRCJ2gq+g=="], "import-fresh": ["import-fresh@3.3.1", "", { "dependencies": { "parent-module": "^1.0.0", "resolve-from": "^4.0.0" } }, "sha512-TR3KfrTZTYLPB6jUjfx6MF9WcWrHL9su5TObK4ZkYgBdWKPOFoSoQIdEuTuR82pmtxH2spWG9h6etwfr1pLBqQ=="], @@ -1098,6 +1104,8 @@ "react-dropzone": ["react-dropzone@14.3.6", "", { "dependencies": { "attr-accept": "^2.2.4", "file-selector": "^2.1.0", "prop-types": "^15.8.1" }, "peerDependencies": { "react": ">= 16.8 || 18.0.0" } }, "sha512-U792j+x0rcwH/U/Slv/OBNU/LGFYbDLHKKiJoPhNaOianayZevCt4Y5S0CraPssH/6/wT6xhKDfzdXUgCBS0HQ=="], + "react-i18next": ["react-i18next@15.4.1", "", { "dependencies": { "@babel/runtime": "^7.25.0", "html-parse-stringify": "^3.0.1" }, "peerDependencies": { "i18next": ">= 23.2.3", "react": ">= 16.8.0" } }, "sha512-ahGab+IaSgZmNPYXdV1n+OYky95TGpFwnKRflX/16dY04DsYYKHtVLjeny7sBSCREEcoMbAgSkFiGLF5g5Oofw=="], + "react-is": ["react-is@16.13.1", "", {}, "sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ=="], "react-markdown": ["react-markdown@9.1.0", "", { "dependencies": { "@types/hast": "^3.0.0", "@types/mdast": "^4.0.0", "devlop": "^1.0.0", "hast-util-to-jsx-runtime": "^2.0.0", "html-url-attributes": "^3.0.0", "mdast-util-to-hast": "^13.0.0", "remark-parse": "^11.0.0", "remark-rehype": "^11.0.0", "unified": "^11.0.0", "unist-util-visit": "^5.0.0", "vfile": "^6.0.0" }, "peerDependencies": { "@types/react": ">=18", "react": ">=18" } }, "sha512-xaijuJB0kzGiUdG7nc2MOMDUDBWPyGAjZtUrow9XxUeua8IqeP+VlIfAZ3bphpcLTnSZXz6z9jcVC/TCwbfgdw=="], @@ -1284,6 +1292,8 @@ "vite": ["vite@6.1.1", "", { "dependencies": { "esbuild": "^0.24.2", "postcss": "^8.5.2", "rollup": "^4.30.1" }, "optionalDependencies": { "fsevents": "~2.3.3" }, "peerDependencies": { "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0", "jiti": ">=1.21.0", "less": "*", "lightningcss": "^1.21.0", "sass": "*", "sass-embedded": "*", "stylus": "*", "sugarss": "*", "terser": "^5.16.0", "tsx": "^4.8.1", "yaml": "^2.4.2" }, "optionalPeers": ["@types/node", "jiti", "less", "lightningcss", "sass", "sass-embedded", "stylus", "sugarss", "terser", "tsx", "yaml"], "bin": { "vite": "bin/vite.js" } }, "sha512-4GgM54XrwRfrOp297aIYspIti66k56v16ZnqHvrIM7mG+HjDlAwS7p+Srr7J6fGvEdOJ5JcQ/D9T7HhtdXDTzA=="], + "void-elements": ["void-elements@3.1.0", "", {}, "sha512-Dhxzh5HZuiHQhbvTW9AMetFfBHDMYpo23Uo9btPXgdYP+3T5S+p+jgNy7spra+veYhBP2dCSgxR/i2Y02h5/6w=="], + "which": ["which@2.0.2", "", { "dependencies": { "isexe": "^2.0.0" }, "bin": { "node-which": "./bin/node-which" } }, "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA=="], "which-boxed-primitive": ["which-boxed-primitive@1.1.1", "", { "dependencies": { "is-bigint": "^1.1.0", "is-boolean-object": "^1.2.1", "is-number-object": "^1.1.1", "is-string": "^1.1.1", "is-symbol": "^1.1.1" } }, "sha512-TbX3mj8n0odCBFVlY8AxkqcHASw3L60jIuF8jFP78az3C2YhmGvqbHBpAjTRH2/xqYunrJ9g1jSyjCjpoWzIAA=="], diff --git a/lightrag_webui/package.json b/lightrag_webui/package.json index 9b85f01f..fff2d9d8 100644 --- a/lightrag_webui/package.json +++ b/lightrag_webui/package.json @@ -43,11 +43,13 @@ "cmdk": "^1.0.4", "graphology": "^0.26.0", "graphology-generators": "^0.11.2", + "i18next": "^24.2.2", "lucide-react": "^0.475.0", "minisearch": "^7.1.2", "react": "^19.0.0", "react-dom": "^19.0.0", "react-dropzone": "^14.3.6", + "react-i18next": "^15.4.1", "react-markdown": "^9.1.0", "react-number-format": "^5.4.3", "react-router-dom": "^7.3.0", diff --git a/lightrag_webui/src/components/ThemeToggle.tsx b/lightrag_webui/src/components/ThemeToggle.tsx index 8e92d862..ff333ff0 100644 --- a/lightrag_webui/src/components/ThemeToggle.tsx +++ b/lightrag_webui/src/components/ThemeToggle.tsx @@ -3,6 +3,7 @@ import useTheme from '@/hooks/useTheme' import { MoonIcon, SunIcon } from 'lucide-react' import { useCallback } from 'react' import { controlButtonVariant } from '@/lib/constants' +import { useTranslation } from 'react-i18next' /** * Component that toggles the theme between light and dark. @@ -11,13 +12,14 @@ export default function ThemeToggle() { const { theme, setTheme } = useTheme() const setLight = useCallback(() => setTheme('light'), [setTheme]) const setDark = useCallback(() => setTheme('dark'), [setTheme]) + const { t } = useTranslation() if (theme === 'dark') { return ( e.preventDefault()}> - Clear documents - Do you really want to clear all documents? + {t('documentPanel.clearDocuments.title')} + {t('documentPanel.clearDocuments.confirm')} diff --git a/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx b/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx index 7149eb28..7f17393c 100644 --- a/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx +++ b/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx @@ -14,8 +14,10 @@ import { errorMessage } from '@/lib/utils' import { uploadDocument } from '@/api/lightrag' import { UploadIcon } from 'lucide-react' +import { useTranslation } from 'react-i18next' export default function UploadDocumentsDialog() { + const { t } = useTranslation() const [open, setOpen] = useState(false) const [isUploading, setIsUploading] = useState(false) const [progresses, setProgresses] = useState>({}) @@ -29,24 +31,24 @@ export default function UploadDocumentsDialog() { filesToUpload.map(async (file) => { try { const result = await uploadDocument(file, (percentCompleted: number) => { - console.debug(`Uploading ${file.name}: ${percentCompleted}%`) + console.debug(t('documentPanel.uploadDocuments.uploading', { name: file.name, percent: percentCompleted })) setProgresses((pre) => ({ ...pre, [file.name]: percentCompleted })) }) if (result.status === 'success') { - toast.success(`Upload Success:\n${file.name} uploaded successfully`) + toast.success(t('documentPanel.uploadDocuments.success', { name: file.name })) } else { - toast.error(`Upload Failed:\n${file.name}\n${result.message}`) + toast.error(t('documentPanel.uploadDocuments.failed', { name: file.name, message: result.message })) } } catch (err) { - toast.error(`Upload Failed:\n${file.name}\n${errorMessage(err)}`) + toast.error(t('documentPanel.uploadDocuments.error', { name: file.name, error: errorMessage(err) })) } }) ) } catch (err) { - toast.error('Upload Failed\n' + errorMessage(err)) + toast.error(t('documentPanel.uploadDocuments.generalError', { error: errorMessage(err) })) } finally { setIsUploading(false) // setOpen(false) @@ -66,21 +68,21 @@ export default function UploadDocumentsDialog() { }} > - e.preventDefault()}> - Upload documents + {t('documentPanel.uploadDocuments.title')} - Drag and drop your documents here or click to browse. + {t('documentPanel.uploadDocuments.description')} { const { isFullScreen, toggle } = useFullScreen() + const { t } = useTranslation() return ( <> {isFullScreen ? ( - ) : ( - )} diff --git a/lightrag_webui/src/components/graph/GraphLabels.tsx b/lightrag_webui/src/components/graph/GraphLabels.tsx index a3849e1f..7bc26c88 100644 --- a/lightrag_webui/src/components/graph/GraphLabels.tsx +++ b/lightrag_webui/src/components/graph/GraphLabels.tsx @@ -5,6 +5,7 @@ import { useSettingsStore } from '@/stores/settings' import { useGraphStore } from '@/stores/graph' import { labelListLimit } from '@/lib/constants' import MiniSearch from 'minisearch' +import { useTranslation } from 'react-i18next' const lastGraph: any = { graph: null, @@ -13,6 +14,7 @@ const lastGraph: any = { } const GraphLabels = () => { + const { t } = useTranslation() const label = useSettingsStore.use.queryLabel() const graph = useGraphStore.use.sigmaGraph() @@ -69,7 +71,7 @@ const GraphLabels = () => { return result.length <= labelListLimit ? result - : [...result.slice(0, labelListLimit), `And ${result.length - labelListLimit} others`] + : [...result.slice(0, labelListLimit), t('graphLabels.andOthers', { count: result.length - labelListLimit })] }, [getSearchEngine] ) @@ -84,14 +86,14 @@ const GraphLabels = () => { className="ml-2" triggerClassName="max-h-8" searchInputClassName="max-h-8" - triggerTooltip="Select query label" + triggerTooltip={t('graphPanel.graphLabels.selectTooltip')} fetcher={fetchData} renderOption={(item) =>
{item}
} getOptionValue={(item) => item} getDisplayValue={(item) =>
{item}
} notFound={
No labels found
} - label="Label" - placeholder="Search labels..." + label={t('graphPanel.graphLabels.label')} + placeholder={t('graphPanel.graphLabels.placeholder')} value={label !== null ? label : ''} onChange={setQueryLabel} /> diff --git a/lightrag_webui/src/components/graph/GraphSearch.tsx b/lightrag_webui/src/components/graph/GraphSearch.tsx index 3edc3ede..bbb8cb5b 100644 --- a/lightrag_webui/src/components/graph/GraphSearch.tsx +++ b/lightrag_webui/src/components/graph/GraphSearch.tsx @@ -9,6 +9,7 @@ import { AsyncSearch } from '@/components/ui/AsyncSearch' import { searchResultLimit } from '@/lib/constants' import { useGraphStore } from '@/stores/graph' import MiniSearch from 'minisearch' +import { useTranslation } from 'react-i18next' interface OptionItem { id: string @@ -44,6 +45,7 @@ export const GraphSearchInput = ({ onFocus?: GraphSearchInputProps['onFocus'] value?: GraphSearchInputProps['value'] }) => { + const { t } = useTranslation() const graph = useGraphStore.use.sigmaGraph() const searchEngine = useMemo(() => { @@ -97,7 +99,7 @@ export const GraphSearchInput = ({ { type: 'message', id: messageId, - message: `And ${result.length - searchResultLimit} others` + message: t('graphPanel.search.message', { count: result.length - searchResultLimit }) } ] }, @@ -118,7 +120,7 @@ export const GraphSearchInput = ({ if (id !== messageId && onFocus) onFocus(id ? { id, type: 'nodes' } : null) }} label={'item'} - placeholder="Search nodes..." + placeholder={t('graphPanel.search.placeholder')} /> ) } diff --git a/lightrag_webui/src/components/graph/LayoutsControl.tsx b/lightrag_webui/src/components/graph/LayoutsControl.tsx index c57b371a..0ed97f2f 100644 --- a/lightrag_webui/src/components/graph/LayoutsControl.tsx +++ b/lightrag_webui/src/components/graph/LayoutsControl.tsx @@ -16,6 +16,7 @@ import { controlButtonVariant } from '@/lib/constants' import { useSettingsStore } from '@/stores/settings' import { GripIcon, PlayIcon, PauseIcon } from 'lucide-react' +import { useTranslation } from 'react-i18next' type LayoutName = | 'Circular' @@ -28,6 +29,7 @@ type LayoutName = const WorkerLayoutControl = ({ layout, autoRunFor }: WorkerLayoutControlProps) => { const sigma = useSigma() const { stop, start, isRunning } = layout + const { t } = useTranslation() /** * Init component when Sigma or component settings change. @@ -61,7 +63,7 @@ const WorkerLayoutControl = ({ layout, autoRunFor }: WorkerLayoutControlProps) = @@ -166,7 +169,7 @@ const LayoutsControl = () => { key={name} className="cursor-pointer text-xs" > - {name} + {t(`graphPanel.sideBar.layoutsControl.layouts.${name}`)} ))} diff --git a/lightrag_webui/src/components/graph/PropertiesView.tsx b/lightrag_webui/src/components/graph/PropertiesView.tsx index dec80460..4571b02b 100644 --- a/lightrag_webui/src/components/graph/PropertiesView.tsx +++ b/lightrag_webui/src/components/graph/PropertiesView.tsx @@ -2,6 +2,7 @@ import { useEffect, useState } from 'react' import { useGraphStore, RawNodeType, RawEdgeType } from '@/stores/graph' import Text from '@/components/ui/Text' import useLightragGraph from '@/hooks/useLightragGraph' +import { useTranslation } from 'react-i18next' /** * Component that view properties of elements in graph. @@ -147,21 +148,22 @@ const PropertyRow = ({ } const NodePropertiesView = ({ node }: { node: NodeType }) => { + const { t } = useTranslation() return (
- +
- + { useGraphStore.getState().setSelectedNode(node.id, true) }} /> - +
- +
{Object.keys(node.properties) .sort() @@ -172,7 +174,7 @@ const NodePropertiesView = ({ node }: { node: NodeType }) => { {node.relationships.length > 0 && ( <>
{node.relationships.map(({ type, id, label }) => { @@ -195,28 +197,29 @@ const NodePropertiesView = ({ node }: { node: NodeType }) => { } const EdgePropertiesView = ({ edge }: { edge: EdgeType }) => { + const { t } = useTranslation() return (
- +
- - {edge.type && } + + {edge.type && } { useGraphStore.getState().setSelectedNode(edge.source, true) }} /> { useGraphStore.getState().setSelectedNode(edge.target, true) }} />
- +
{Object.keys(edge.properties) .sort() diff --git a/lightrag_webui/src/components/graph/Settings.tsx b/lightrag_webui/src/components/graph/Settings.tsx index 67fb1ded..4a4b15a5 100644 --- a/lightrag_webui/src/components/graph/Settings.tsx +++ b/lightrag_webui/src/components/graph/Settings.tsx @@ -10,6 +10,7 @@ import { useSettingsStore } from '@/stores/settings' import { useBackendState } from '@/stores/state' import { SettingsIcon } from 'lucide-react' +import { useTranslation } from "react-i18next"; /** * Component that displays a checkbox with a label. @@ -204,10 +205,12 @@ export default function Settings() { [setTempApiKey] ) + const { t } = useTranslation(); + return ( - @@ -221,7 +224,7 @@ export default function Settings() { @@ -229,12 +232,12 @@ export default function Settings() { @@ -242,12 +245,12 @@ export default function Settings() { @@ -255,51 +258,50 @@ export default function Settings() { -
- +
e.preventDefault()}>
@@ -310,7 +312,7 @@ export default function Settings() { size="sm" className="max-h-full shrink-0" > - Save + {t("graphPanel.sideBar.settings.save")}
diff --git a/lightrag_webui/src/components/graph/StatusCard.tsx b/lightrag_webui/src/components/graph/StatusCard.tsx index 3084d103..e67cbd30 100644 --- a/lightrag_webui/src/components/graph/StatusCard.tsx +++ b/lightrag_webui/src/components/graph/StatusCard.tsx @@ -1,58 +1,60 @@ import { LightragStatus } from '@/api/lightrag' +import { useTranslation } from 'react-i18next' const StatusCard = ({ status }: { status: LightragStatus | null }) => { + const { t } = useTranslation() if (!status) { - return
Status information unavailable
+ return
{t('graphPanel.statusCard.unavailable')}
} return (
-

Storage Info

+

{t('graphPanel.statusCard.storageInfo')}

- Working Directory: + {t('graphPanel.statusCard.workingDirectory')}: {status.working_directory} - Input Directory: + {t('graphPanel.statusCard.inputDirectory')}: {status.input_directory}
-

LLM Configuration

+

{t('graphPanel.statusCard.llmConfig')}

- LLM Binding: + {t('graphPanel.statusCard.llmBinding')}: {status.configuration.llm_binding} - LLM Binding Host: + {t('graphPanel.statusCard.llmBindingHost')}: {status.configuration.llm_binding_host} - LLM Model: + {t('graphPanel.statusCard.llmModel')}: {status.configuration.llm_model} - Max Tokens: + {t('graphPanel.statusCard.maxTokens')}: {status.configuration.max_tokens}
-

Embedding Configuration

+

{t('graphPanel.statusCard.embeddingConfig')}

- Embedding Binding: + {t('graphPanel.statusCard.embeddingBinding')}: {status.configuration.embedding_binding} - Embedding Binding Host: + {t('graphPanel.statusCard.embeddingBindingHost')}: {status.configuration.embedding_binding_host} - Embedding Model: + {t('graphPanel.statusCard.embeddingModel')}: {status.configuration.embedding_model}
-

Storage Configuration

+

{t('graphPanel.statusCard.storageConfig')}

- KV Storage: + {t('graphPanel.statusCard.kvStorage')}: {status.configuration.kv_storage} - Doc Status Storage: + {t('graphPanel.statusCard.docStatusStorage')}: {status.configuration.doc_status_storage} - Graph Storage: + {t('graphPanel.statusCard.graphStorage')}: {status.configuration.graph_storage} - Vector Storage: + {t('graphPanel.statusCard.vectorStorage')}: {status.configuration.vector_storage}
diff --git a/lightrag_webui/src/components/graph/StatusIndicator.tsx b/lightrag_webui/src/components/graph/StatusIndicator.tsx index 3272d9fa..d7a1831f 100644 --- a/lightrag_webui/src/components/graph/StatusIndicator.tsx +++ b/lightrag_webui/src/components/graph/StatusIndicator.tsx @@ -3,8 +3,10 @@ import { useBackendState } from '@/stores/state' import { useEffect, useState } from 'react' import { Popover, PopoverContent, PopoverTrigger } from '@/components/ui/Popover' import StatusCard from '@/components/graph/StatusCard' +import { useTranslation } from 'react-i18next' const StatusIndicator = () => { + const { t } = useTranslation() const health = useBackendState.use.health() const lastCheckTime = useBackendState.use.lastCheckTime() const status = useBackendState.use.status() @@ -33,7 +35,7 @@ const StatusIndicator = () => { )} /> - {health ? 'Connected' : 'Disconnected'} + {health ? t('graphPanel.statusIndicator.connected') : t('graphPanel.statusIndicator.disconnected')}
diff --git a/lightrag_webui/src/components/graph/ZoomControl.tsx b/lightrag_webui/src/components/graph/ZoomControl.tsx index 790b4423..0aa55416 100644 --- a/lightrag_webui/src/components/graph/ZoomControl.tsx +++ b/lightrag_webui/src/components/graph/ZoomControl.tsx @@ -3,12 +3,14 @@ import { useCallback } from 'react' import Button from '@/components/ui/Button' import { ZoomInIcon, ZoomOutIcon, FullscreenIcon } from 'lucide-react' import { controlButtonVariant } from '@/lib/constants' +import { useTranslation } from "react-i18next"; /** * Component that provides zoom controls for the graph viewer. */ const ZoomControl = () => { const { zoomIn, zoomOut, reset } = useCamera({ duration: 200, factor: 1.5 }) + const { t } = useTranslation(); const handleZoomIn = useCallback(() => zoomIn(), [zoomIn]) const handleZoomOut = useCallback(() => zoomOut(), [zoomOut]) @@ -16,16 +18,16 @@ const ZoomControl = () => { return ( <> - -
@@ -98,29 +100,29 @@ export default function DocumentManager() { - Uploaded documents - view the uploaded documents here + {t('documentPanel.documentManager.uploadedTitle')} + {t('documentPanel.documentManager.uploadedDescription')} {!docs && ( )} {docs && ( - ID - Summary - Status - Length - Chunks - Created - Updated - Metadata + {t('documentPanel.documentManager.columns.id')} + {t('documentPanel.documentManager.columns.summary')} + {t('documentPanel.documentManager.columns.status')} + {t('documentPanel.documentManager.columns.length')} + {t('documentPanel.documentManager.columns.chunks')} + {t('documentPanel.documentManager.columns.created')} + {t('documentPanel.documentManager.columns.updated')} + {t('documentPanel.documentManager.columns.metadata')} @@ -137,13 +139,13 @@ export default function DocumentManager() { {status === 'processed' && ( - Completed + {t('documentPanel.documentManager.status.completed')} )} {status === 'processing' && ( - Processing + {t('documentPanel.documentManager.status.processing')} )} - {status === 'pending' && Pending} - {status === 'failed' && Failed} + {status === 'pending' && {t('documentPanel.documentManager.status.pending')}} + {status === 'failed' && {t('documentPanel.documentManager.status.failed')}} {doc.error && ( ⚠️ diff --git a/lightrag_webui/src/features/RetrievalTesting.tsx b/lightrag_webui/src/features/RetrievalTesting.tsx index 340255a2..c7fdf2a9 100644 --- a/lightrag_webui/src/features/RetrievalTesting.tsx +++ b/lightrag_webui/src/features/RetrievalTesting.tsx @@ -8,8 +8,10 @@ import { useDebounce } from '@/hooks/useDebounce' import QuerySettings from '@/components/retrieval/QuerySettings' import { ChatMessage, MessageWithError } from '@/components/retrieval/ChatMessage' import { EraserIcon, SendIcon } from 'lucide-react' +import { useTranslation } from 'react-i18next' export default function RetrievalTesting() { + const { t } = useTranslation() const [messages, setMessages] = useState( () => useSettingsStore.getState().retrievalHistory || [] ) @@ -89,7 +91,7 @@ export default function RetrievalTesting() { } } catch (err) { // Handle error - updateAssistantMessage(`Error: Failed to get response\n${errorMessage(err)}`, true) + updateAssistantMessage(`${t('retrievePanel.retrieval.error')}\n${errorMessage(err)}`, true) } finally { // Clear loading and add messages to state setIsLoading(false) @@ -98,7 +100,7 @@ export default function RetrievalTesting() { .setRetrievalHistory([...prevMessages, userMessage, assistantMessage]) } }, - [inputValue, isLoading, messages, setMessages] + [inputValue, isLoading, messages, setMessages, t] ) const debouncedMessages = useDebounce(messages, 100) @@ -117,7 +119,7 @@ export default function RetrievalTesting() {
{messages.length === 0 ? (
- Start a retrieval by typing your query below + {t('retrievePanel.retrieval.startPrompt')}
) : ( messages.map((message, idx) => ( @@ -143,18 +145,18 @@ export default function RetrievalTesting() { size="sm" > - Clear + {t('retrievePanel.retrieval.clear')} setInputValue(e.target.value)} - placeholder="Type your query..." + placeholder={t('retrievePanel.retrieval.placeholder')} disabled={isLoading} />
diff --git a/lightrag_webui/src/features/SiteHeader.tsx b/lightrag_webui/src/features/SiteHeader.tsx index b92e260e..f85d6251 100644 --- a/lightrag_webui/src/features/SiteHeader.tsx +++ b/lightrag_webui/src/features/SiteHeader.tsx @@ -5,6 +5,7 @@ import { TabsList, TabsTrigger } from '@/components/ui/Tabs' import { useSettingsStore } from '@/stores/settings' import { useAuthStore } from '@/stores/state' import { cn } from '@/lib/utils' +import { useTranslation } from 'react-i18next' import { useNavigate } from 'react-router-dom' import { ZapIcon, GithubIcon, LogOutIcon } from 'lucide-react' @@ -31,21 +32,22 @@ function NavigationTab({ value, currentTab, children }: NavigationTabProps) { function TabsNavigation() { const currentTab = useSettingsStore.use.currentTab() + const { t } = useTranslation() return (
- Documents + {t('header.documents')} - Knowledge Graph + {t('header.knowledgeGraph')} - Retrieval + {t('header.retrieval')} - API + {t('header.api')}
@@ -53,6 +55,7 @@ function TabsNavigation() { } export default function SiteHeader() { + const { t } = useTranslation() const navigate = useNavigate() const { logout } = useAuthStore() @@ -74,7 +77,7 @@ export default function SiteHeader() {