Merge branch 'master' into jacob/ollama

docs/docs/how_to/functions.ipynb

@@ -99,8 +99,6 @@
     "\n",
     "prompt = ChatPromptTemplate.from_template(\"what is {a} + {b}\")\n",
     "\n",
-    "chain1 = prompt | model\n",
-    "\n",
     "chain = (\n",
     "    {\n",
     "        \"a\": itemgetter(\"foo\") | RunnableLambda(length_function),\n",

docs/docs/integrations/vectorstores/vearch.ipynb

@@ -156,6 +156,15 @@
     "    db_name=\"vearch_cluster_langchian\",\n",
     "    table_name=\"tobenumone\",\n",
     "    flag=1,\n",
+    ")\n",
+    "\n",
+    "# The vector data is usually already initialized, so we don’t need the document parameter and can directly create the object.\n",
+    "vearch_cluster_b = Vearch(\n",
+    "    embeddings,\n",
+    "    path_or_url=\"http://test-vearch-langchain-router.vectorbase.svc.ht1.n.jd.local\",\n",
+    "    db_name=\"vearch_cluster_langchian\",\n",
+    "    table_name=\"tobenumone\",\n",
+    "    flag=1,\n",
     ")"
    ]
   },
@@ -244,6 +253,7 @@
    ],
    "source": [
     "query = \"你知道凌波微步吗，你知道都有谁会凌波微步?\"\n",
+    "# The second parameter is the top-n to retrieve, and its default value is 4.\n",
     "vearch_standalone_res = vearch_standalone.similarity_search(query, 3)\n",
     "for idx, tmp in enumerate(vearch_standalone_res):\n",
     "    print(f\"{'#'*20}第{idx+1}段相关文档{'#'*20}\\n\\n{tmp.page_content}\\n\")\n",
@@ -261,6 +271,11 @@
     "for idx, tmp in enumerate(cluster_res):\n",
     "    print(f\"{'#'*20}第{idx+1}段相关文档{'#'*20}\\n\\n{tmp.page_content}\\n\")\n",
     "\n",
+    "# In practical applications, we usually limit the boundary value of similarity. The following method can set this value.\n",
+    "cluster_res_with_bound = vearch_cluster.similarity_search_with_score(\n",
+    "    query=query_c, k=3, min_score=0.5\n",
+    ")\n",
+    "\n",
     "# combine your local knowleadge and query\n",
     "context_c = \"\".join([tmp.page_content for tmp in cluster_res])\n",
     "new_query_c = f\"基于以下信息，尽可能准确的来回答用户的问题。背景信息:\\n {context_c} \\n 回答用户这个问题:{query_c}\\n\\n\"\n",

docs/docs/tutorials/classification.ipynb

@@ -154,7 +154,7 @@
    "id": "ff3cf30d",
    "metadata": {},
    "source": [
-    "If we want dictionary output, we can just call `.dict()`"
+    "If we want dictionary output, we can just call `.model_dump()`"
    ]
   },
   {
@@ -179,7 +179,7 @@
     "prompt = tagging_prompt.invoke({\"input\": inp})\n",
     "response = llm.invoke(prompt)\n",
     "\n",
-    "response.dict()"
+    "response.model_dump()"
    ]
   },
   {

libs/community/langchain_community/document_loaders/youtube.py

@@ -392,11 +392,11 @@ class GoogleApiYoutubeLoader(BaseLoader):
 
     @model_validator(mode="before")
     @classmethod
-    def validate_channel_or_videoIds_is_set(cls, values: Dict[str, Any]) -> Any:
+    def validate_channel_or_videoIds_is_set(cls, values: Any) -> Any:
         """Validate that either folder_id or document_ids is set, but not both."""
-        if not values.get("channel_name") and not values.get("video_ids"):
+        if not values.kwargs.get("channel_name") and not values.kwargs.get("video_ids"):
             raise ValueError("Must specify either channel_name or video_ids")
-        return values
+        return values.kwargs
 
     def _get_transcripe_for_video_id(self, video_id: str) -> str:
         from youtube_transcript_api import NoTranscriptFound, YouTubeTranscriptApi

libs/community/langchain_community/retrievers/pinecone_hybrid_search.py

@@ -31,6 +31,7 @@ def create_index(
     ids: Optional[List[str]] = None,
     metadatas: Optional[List[dict]] = None,
     namespace: Optional[str] = None,
+    text_key: str = "context",
 ) -> None:
     """Create an index from a list of contexts.
 
@@ -69,7 +70,7 @@ def create_index(
         )
         # add context passages as metadata
         meta = [
-            {"context": context, **metadata}
+            {text_key: context, **metadata}
             for context, metadata in zip(context_batch, metadata_batch)
         ]
 
@@ -114,7 +115,7 @@ class PineconeHybridSearchRetriever(BaseRetriever):
     """Alpha value for hybrid search."""
     namespace: Optional[str] = None
     """Namespace value for index partition."""
-
+    text_key: str = "context"
     model_config = ConfigDict(
         arbitrary_types_allowed=True,
         extra="forbid",
@@ -135,6 +136,7 @@ class PineconeHybridSearchRetriever(BaseRetriever):
             ids=ids,
             metadatas=metadatas,
             namespace=namespace,
+            text_key=self.text_key,
         )
 
     @pre_init
@@ -174,7 +176,7 @@ class PineconeHybridSearchRetriever(BaseRetriever):
         )
         final_result = []
         for res in result["matches"]:
-            context = res["metadata"].pop("context")
+            context = res["metadata"].pop(self.text_key)
             metadata = res["metadata"]
             if "score" not in metadata and "score" in res:
                 metadata["score"] = res["score"]