gardel/lightrag
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge branch 'main' into clear-text-before-insert

Browse Source
This commit is contained in:
yangdx
2025-02-22 10:06:46 +08:00
parent 3c866eec16 ff921b1274
commit 2fff9ae1ab
3 changed files with 16 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
examples/lightrag_zhipu_postgres_demo.py
View File

@@ -37,20 +37,22 @@ async def main():
llm_model_max_token_size=32768,
enable_llm_cache_for_entity_extract=True,
embedding_func=EmbeddingFunc(
embedding_dim=768,
embedding_dim=1024,
max_token_size=8192,
func=lambda texts: ollama_embedding(
texts, embed_model="nomic-embed-text", host="http://localhost:11434"
texts, embed_model="bge-m3", host="http://localhost:11434"
),
),
kv_storage="PGKVStorage",
doc_status_storage="PGDocStatusStorage",
graph_storage="PGGraphStorage",
vector_storage="PGVectorStorage",
auto_manage_storages_states=False,
)
# add embedding_func for graph database, it's deleted in commit 5661d76860436f7bf5aef2e50d9ee4a59660146c
rag.chunk_entity_relation_graph.embedding_func = rag.embedding_func
await rag.initialize_storages()
with open(f"{ROOT_DIR}/book.txt", "r", encoding="utf-8") as f:
await rag.ainsert(f.read())

2
lightrag/__init__.py
View File

@@ -1,5 +1,5 @@
from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
__version__ = "1.1.11"
__version__ = "1.2.1"
__author__ = "Zirui Guo"
__url__ = "https://github.com/HKUDS/LightRAG"

16
lightrag/kg/postgres_impl.py
View File

@@ -254,6 +254,8 @@ class PGKVStorage(BaseKVStorage):
db: PostgreSQLDB = field(default=None)
def __post_init__(self):
namespace_prefix = self.global_config.get("namespace_prefix")
self.base_namespace = self.namespace.replace(namespace_prefix, "")
self._max_batch_size = self.global_config["embedding_batch_num"]
async def initialize(self):
@@ -269,7 +271,7 @@ class PGKVStorage(BaseKVStorage):
async def get_by_id(self, id: str) -> dict[str, Any] | None:
"""Get doc_full data by id."""
sql = SQL_TEMPLATES["get_by_id_" + self.namespace]
sql = SQL_TEMPLATES["get_by_id_" + self.base_namespace]
params = {"workspace": self.db.workspace, "id": id}
if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
array_res = await self.db.query(sql, params, multirows=True)
@@ -283,7 +285,7 @@ class PGKVStorage(BaseKVStorage):
async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]:
"""Specifically for llm_response_cache."""
sql = SQL_TEMPLATES["get_by_mode_id_" + self.namespace]
sql = SQL_TEMPLATES["get_by_mode_id_" + self.base_namespace]
params = {"workspace": self.db.workspace, mode: mode, "id": id}
if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
array_res = await self.db.query(sql, params, multirows=True)
@@ -297,7 +299,7 @@ class PGKVStorage(BaseKVStorage):
# Query by id
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
"""Get doc_chunks data by id"""
sql = SQL_TEMPLATES["get_by_ids_" + self.namespace].format(
sql = SQL_TEMPLATES["get_by_ids_" + self.base_namespace].format(
ids=",".join([f"'{id}'" for id in ids])
)
params = {"workspace": self.db.workspace}
@@ -318,7 +320,7 @@ class PGKVStorage(BaseKVStorage):
async def get_by_status(self, status: str) -> Union[list[dict[str, Any]], None]:
"""Specifically for llm_response_cache."""
SQL = SQL_TEMPLATES["get_by_status_" + self.namespace]
SQL = SQL_TEMPLATES["get_by_status_" + self.base_namespace]
params = {"workspace": self.db.workspace, "status": status}
return await self.db.query(SQL, params, multirows=True)
@@ -391,6 +393,8 @@ class PGVectorStorage(BaseVectorStorage):
def __post_init__(self):
self._max_batch_size = self.global_config["embedding_batch_num"]
namespace_prefix = self.global_config.get("namespace_prefix")
self.base_namespace = self.namespace.replace(namespace_prefix, "")
config = self.global_config.get("vector_db_storage_cls_kwargs", {})
cosine_threshold = config.get("cosine_better_than_threshold")
if cosine_threshold is None:
@@ -493,7 +497,9 @@ class PGVectorStorage(BaseVectorStorage):
embedding = embeddings[0]
embedding_string = ",".join(map(str, embedding))
sql = SQL_TEMPLATES[self.namespace].format(embedding_string=embedding_string)
sql = SQL_TEMPLATES[self.base_namespace].format(
embedding_string=embedding_string
)
params = {
"workspace": self.db.workspace,
"better_than_threshold": self.cosine_better_than_threshold,