Add message to documents (#12552)

This adds the response message as a document to the rag retriever so
users can choose to use this. Also drops document limit.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/langchain/langchain/chat_models/cohere.py

@@ -166,6 +166,16 @@ class ChatCohere(BaseChatModel, BaseCohere):
                 if run_manager:
                     await run_manager.on_llm_new_token(delta)
 
+    def _get_generation_info(self, response: Any) -> Dict[str, Any]:
+        """Get the generation info from cohere API response."""
+        return {
+            "documents": response.documents,
+            "citations": response.citations,
+            "search_results": response.search_results,
+            "search_queries": response.search_queries,
+            "token_count": response.token_count,
+        }
+
     def _generate(
         self,
         messages: List[BaseMessage],
@@ -185,7 +195,7 @@ class ChatCohere(BaseChatModel, BaseCohere):
         message = AIMessage(content=response.text)
         generation_info = None
         if hasattr(response, "documents"):
-            generation_info = {"documents": response.documents}
+            generation_info = self._get_generation_info(response)
         return ChatResult(
             generations=[
                 ChatGeneration(message=message, generation_info=generation_info)
@@ -211,7 +221,7 @@ class ChatCohere(BaseChatModel, BaseCohere):
         message = AIMessage(content=response.text)
         generation_info = None
         if hasattr(response, "documents"):
-            generation_info = {"documents": response.documents}
+            generation_info = self._get_generation_info(response)
         return ChatResult(
             generations=[
                 ChatGeneration(message=message, generation_info=generation_info)

libs/langchain/langchain/retrievers/cohere_rag_retriever.py

@@ -15,17 +15,27 @@ if TYPE_CHECKING:
 
 
 def _get_docs(response: Any) -> List[Document]:
-    return [
+    docs = [
         Document(page_content=doc["snippet"], metadata=doc)
         for doc in response.generation_info["documents"]
     ]
+    docs.append(
+        Document(
+            page_content=response.message.content,
+            metadata={
+                "type": "model_response",
+                "citations": response.generation_info["citations"],
+                "search_results": response.generation_info["search_results"],
+                "search_queries": response.generation_info["search_queries"],
+                "token_count": response.generation_info["token_count"],
+            },
+        )
+    )
+    return docs
 
 
 class CohereRagRetriever(BaseRetriever):
-    """`ChatGPT plugin` retriever."""
-
-    top_k: int = 3
-    """Number of documents to return."""
+    """Cohere Chat API with RAG."""
 
     connectors: List[Dict] = Field(default_factory=lambda: [{"id": "web-search"}])
     """
@@ -55,7 +65,7 @@ class CohereRagRetriever(BaseRetriever):
             callbacks=run_manager.get_child(),
             **kwargs,
         ).generations[0][0]
-        return _get_docs(res)[: self.top_k]
+        return _get_docs(res)
 
     async def _aget_relevant_documents(
         self,
@@ -73,4 +83,4 @@ class CohereRagRetriever(BaseRetriever):
                 **kwargs,
             )
         ).generations[0][0]
-        return _get_docs(res)[: self.top_k]
+        return _get_docs(res)