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Merge pull request #1032 from ArindamRoy23/main

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Filter by ID during Query for Postgres VDB
This commit is contained in:
zrguo
2025-03-11 15:26:59 +08:00
committed by GitHub
parent 141de28015 92ae895713
commit 91f96f2a8b
4 changed files with 110 additions and 26 deletions
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2
README.md
View File

@@ -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."""
...
```

7
lightrag/base.py
View File

@@ -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

107
lightrag/kg/postgres_impl.py
View File

@@ -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,
@@ -1491,6 +1502,7 @@ 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 +1516,7 @@ 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 +1599,9 @@ 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 +1609,8 @@ 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 +1618,21 @@ 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 +1656,55 @@ 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
""",
}

20
lightrag/operate.py
View File

@@ -962,7 +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
@@ -1171,7 +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
@@ -1424,7 +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 "", "", ""
@@ -1673,7 +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"]