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fix: AnalyticdbVector retrieval scores (#8803)

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8bitpd 2024-09-27 12:05:21 +08:00 committed by GitHub
parent d6b9587a97
commit 4c1063e1c5
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1 changed files with 6 additions and 13 deletions
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19
api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py
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@ -40,19 +40,8 @@ class AnalyticdbConfig(BaseModel):
class AnalyticdbVector(BaseVector): class AnalyticdbVector(BaseVector):
_instance = None
_init = False
def __new__(cls, *args, **kwargs):
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self, collection_name: str, config: AnalyticdbConfig): def __init__(self, collection_name: str, config: AnalyticdbConfig):
# collection_name must be updated every time
self._collection_name = collection_name.lower() self._collection_name = collection_name.lower()
if AnalyticdbVector._init:
return
try: try:
from alibabacloud_gpdb20160503.client import Client from alibabacloud_gpdb20160503.client import Client
from alibabacloud_tea_openapi import models as open_api_models from alibabacloud_tea_openapi import models as open_api_models
@ -62,7 +51,6 @@ class AnalyticdbVector(BaseVector):
self._client_config = open_api_models.Config(user_agent="dify", **config.to_analyticdb_client_params()) self._client_config = open_api_models.Config(user_agent="dify", **config.to_analyticdb_client_params())
self._client = Client(self._client_config) self._client = Client(self._client_config)
self._initialize() self._initialize()
AnalyticdbVector._init = True
def _initialize(self) -> None: def _initialize(self) -> None:
cache_key = f"vector_indexing_{self.config.instance_id}" cache_key = f"vector_indexing_{self.config.instance_id}"
@ -257,11 +245,14 @@ class AnalyticdbVector(BaseVector):
documents = [] documents = []
for match in response.body.matches.match: for match in response.body.matches.match:
if match.score > score_threshold: if match.score > score_threshold:
metadata = json.loads(match.metadata.get("metadata_"))
metadata["score"] = match.score
doc = Document( doc = Document(
page_content=match.metadata.get("page_content"), page_content=match.metadata.get("page_content"),
metadata=json.loads(match.metadata.get("metadata_")), metadata=metadata,
) )
documents.append(doc) documents.append(doc)
documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True)
return documents return documents
def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]: def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
@ -286,12 +277,14 @@ class AnalyticdbVector(BaseVector):
for match in response.body.matches.match: for match in response.body.matches.match:
if match.score > score_threshold: if match.score > score_threshold:
metadata = json.loads(match.metadata.get("metadata_")) metadata = json.loads(match.metadata.get("metadata_"))
metadata["score"] = match.score
doc = Document( doc = Document(
page_content=match.metadata.get("page_content"), page_content=match.metadata.get("page_content"),
vector=match.metadata.get("vector"), vector=match.metadata.get("vector"),
metadata=metadata, metadata=metadata,
) )
documents.append(doc) documents.append(doc)
documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True)
return documents return documents
def delete(self) -> None: def delete(self) -> None: