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Merge branch 'HKUDS:main' into main

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This commit is contained in:
Saifeddine ALOUI
2025-02-18 10:25:31 +01:00
committed by GitHub
parent 1fe47e5ef1 00704aa76a
commit 2bbbfe50c6
5 changed files with 24 additions and 60 deletions
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4
lightrag/base.py
View File

@@ -135,10 +135,6 @@ class BaseKVStorage(StorageNameSpace, ABC):
async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
"""Upsert data"""
@abstractmethod
async def drop(self) -> None:
"""Drop the storage"""
@dataclass
class BaseGraphStorage(StorageNameSpace, ABC):

3
lightrag/kg/json_kv_impl.py
View File

@@ -50,6 +50,3 @@ class JsonKVStorage(BaseKVStorage):
for doc_id in ids:
self._data.pop(doc_id, None)
await self.index_done_callback()
async def drop(self) -> None:
self._data = {}

8
lightrag/kg/mongo_impl.py
View File

@@ -117,10 +117,6 @@ class MongoKVStorage(BaseKVStorage):
# Mongo handles persistence automatically
pass
async def drop(self) -> None:
"""Drop the collection"""
await self._data.drop()
@final
@dataclass
@@ -202,10 +198,6 @@ class MongoDocStatusStorage(DocStatusStorage):
# Mongo handles persistence automatically
pass
async def drop(self) -> None:
"""Drop the collection"""
await self._data.drop()
@final
@dataclass

5
lightrag/kg/redis_impl.py
View File

@@ -61,8 +61,3 @@ class RedisKVStorage(BaseKVStorage):
async def index_done_callback(self) -> None:
# Redis handles persistence automatically
pass
async def drop(self) -> None:
keys = await self._redis.keys(f"{self.namespace}:*")
if keys:
await self._redis.delete(*keys)

40
lightrag/operate.py
View File

@@ -640,15 +640,13 @@ async def kg_query(
)
query_param.mode = "local"
ll_keywords = ", ".join(ll_keywords) if ll_keywords else ""
hl_keywords = ", ".join(hl_keywords) if hl_keywords else ""
logger.info("Using %s mode for query processing", query_param.mode)
ll_keywords_str = ", ".join(ll_keywords) if ll_keywords else ""
hl_keywords_str = ", ".join(hl_keywords) if hl_keywords else ""
# Build context
keywords = [ll_keywords, hl_keywords]
context = await _build_query_context(
keywords,
ll_keywords_str,
hl_keywords_str,
knowledge_graph_inst,
entities_vdb,
relationships_vdb,
@@ -873,7 +871,8 @@ async def mix_kg_vector_query(
# Build knowledge graph context
context = await _build_query_context(
[ll_keywords_str, hl_keywords_str],
ll_keywords_str,
hl_keywords_str,
knowledge_graph_inst,
entities_vdb,
relationships_vdb,
@@ -1013,18 +1012,14 @@ async def mix_kg_vector_query(
async def _build_query_context(
query: list,
ll_keywords: str,
hl_keywords: str,
knowledge_graph_inst: BaseGraphStorage,
entities_vdb: BaseVectorStorage,
relationships_vdb: BaseVectorStorage,
text_chunks_db: BaseKVStorage,
query_param: QueryParam,
):
# ll_entities_context, ll_relations_context, ll_text_units_context = "", "", ""
# hl_entities_context, hl_relations_context, hl_text_units_context = "", "", ""
ll_keywords, hl_keywords = query[0], query[1]
if query_param.mode == "local":
entities_context, relations_context, text_units_context = await _get_node_data(
ll_keywords,
@@ -1093,20 +1088,12 @@ async def _build_query_context(
```csv
{text_units_context}
```
"""
contex_tokens = len(encode_string_by_tiktoken(result))
entities_tokens = len(encode_string_by_tiktoken(entities_context))
relations_tokens = len(encode_string_by_tiktoken(relations_context))
text_units_tokens = len(encode_string_by_tiktoken(text_units_context))
logger.debug(
f"Context Tokens - Total: {contex_tokens}, Entities: {entities_tokens}, Relations: {relations_tokens}, Chunks: {text_units_tokens}"
)
""".strip()
return result
async def _get_node_data(
query,
query: str,
knowledge_graph_inst: BaseGraphStorage,
entities_vdb: BaseVectorStorage,
text_chunks_db: BaseKVStorage,
@@ -1760,15 +1747,12 @@ async def kg_query_with_keywords(
ll_keywords_str = ", ".join(ll_keywords_flat) if ll_keywords_flat else ""
hl_keywords_str = ", ".join(hl_keywords_flat) if hl_keywords_flat else ""
keywords = [ll_keywords_str, hl_keywords_str]
logger.info("Using %s mode for query processing", query_param.mode)
# ---------------------------
# 3) BUILD CONTEXT
# ---------------------------
context = await _build_query_context(
keywords,
ll_keywords_str,
hl_keywords_str,
knowledge_graph_inst,
entities_vdb,
relationships_vdb,