update SelfQueryRetriever

libs/core/langchain_core/load/mapping.py

@@ -157,6 +157,12 @@ SERIALIZABLE_MAPPING: Dict[Tuple[str, ...], Tuple[str, ...]] = {
         "base",
         "Document",
     ),
+    ("langchain", "schema", "document_search_hit", "DocumentSearchHit"): (
+        "langchain_core",
+        "documents",
+        "base",
+        "DocumentSearchHit",
+    ),
     ("langchain", "output_parsers", "fix", "OutputFixingParser"): (
         "langchain",
         "output_parsers",
@@ -666,6 +672,12 @@ OLD_CORE_NAMESPACES_MAPPING: Dict[Tuple[str, ...], Tuple[str, ...]] = {
         "base",
         "Document",
     ),
+    ("langchain_core", "documents", "base", "DocumentSearchHit"): (
+        "langchain_core",
+        "documents",
+        "base",
+        "DocumentSearchHit",
+    ),
     ("langchain_core", "prompts", "chat", "AIMessagePromptTemplate"): (
         "langchain_core",
         "prompts",

libs/langchain/langchain/retrievers/self_query/base.py

@@ -33,7 +33,7 @@ from langchain_core.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
 )
-from langchain_core.documents import Document
+from langchain_core.documents import Document, DocumentSearchHit
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.retrievers import BaseRetriever
@@ -192,19 +192,43 @@ class SelfQueryRetriever(BaseRetriever):
         return new_query, search_kwargs
 
     def _get_docs_with_query(
-        self, query: str, search_kwargs: Dict[str, Any]
+        self, query: str, search_kwargs: Dict[str, Any], include_score: bool = False
     ) -> List[Document]:
-        docs = self.vectorstore.search(query, self.search_type, **search_kwargs)
+        if include_score:
+            docs_and_scores = self.vectorstore.similarity_search_with_score(
+                query, **search_kwargs
+            )
+            return [
+                DocumentSearchHit(page_content=doc.page_content, score=score)
+                for doc, score in docs_and_scores
+            ]
+        else:
+            docs = self.vectorstore.search(query, self.search_type, **search_kwargs)
         return docs
 
     async def _aget_docs_with_query(
-        self, query: str, search_kwargs: Dict[str, Any]
+        self, query: str, search_kwargs: Dict[str, Any], include_score: bool = False
     ) -> List[Document]:
-        docs = await self.vectorstore.asearch(query, self.search_type, **search_kwargs)
+        if include_score:
+            docs_and_scores = await self.vectorstore.asimilarity_search_with_score(
+                query, **search_kwargs
+            )
+            return [
+                DocumentSearchHit(page_content=doc.page_content, score=score)
+                for doc, score in docs_and_scores
+            ]
+        else:
+            docs = await self.vectorstore.asearch(
+                query, self.search_type, **search_kwargs
+            )
         return docs
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: CallbackManagerForRetrieverRun,
+        include_score: bool = False,
     ) -> List[Document]:
         """Get documents relevant for a query.
 
@@ -220,11 +244,17 @@ class SelfQueryRetriever(BaseRetriever):
         if self.verbose:
             logger.info(f"Generated Query: {structured_query}")
         new_query, search_kwargs = self._prepare_query(query, structured_query)
-        docs = self._get_docs_with_query(new_query, search_kwargs)
+        docs = self._get_docs_with_query(
+            new_query, search_kwargs, include_score=include_score
+        )
         return docs
 
     async def _aget_relevant_documents(
-        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+        include_score: bool = False,
     ) -> List[Document]:
         """Get documents relevant for a query.
 
@@ -240,7 +270,9 @@ class SelfQueryRetriever(BaseRetriever):
         if self.verbose:
             logger.info(f"Generated Query: {structured_query}")
         new_query, search_kwargs = self._prepare_query(query, structured_query)
-        docs = await self._aget_docs_with_query(new_query, search_kwargs)
+        docs = await self._aget_docs_with_query(
+            new_query, search_kwargs, include_score=include_score
+        )
         return docs
 
     @classmethod