Merge branch 'master' into Integration-Needle

README.md

@@ -129,7 +129,7 @@ Please see [here](https://python.langchain.com) for full documentation, which in
 
 - [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
 - [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
-- [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploy LangChain runnables and chains as REST APIs.
+- [🦜🕸️ LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): Deploy LLM applications built with LangGraph into production.
 
 ## 💁 Contributing
 

docs/docs/how_to/self_query.ipynb

@@ -95,7 +95,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains.query_constructor.base import AttributeInfo\n",
+    "from langchain.chains.query_constructor.schema import AttributeInfo\n",
     "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",

docs/docs/integrations/retrievers/self_query/elasticsearch_self_query.ipynb

@@ -136,7 +136,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.chains.query_constructor.base import AttributeInfo\n",
+    "from langchain.chains.query_constructor.schema import AttributeInfo\n",
     "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
     "from langchain_openai import OpenAI\n",
     "\n",

docs/docs/introduction.mdx

@@ -33,7 +33,7 @@ Concretely, the framework consists of the following open-source libraries:
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
 - **`langchain-community`**: Third-party integrations that are community maintained.
 - **[LangGraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
-- **[LangServe](/docs/langserve)**: Deploy LangChain chains as REST APIs.
+- **[LangGraphPlatform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform)**: Deploy LLM applications built with LangGraph to production.
 - **[LangSmith](https://docs.smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor LLM applications.
 
 

docs/docs/tutorials/summarization.ipynb

@@ -156,7 +156,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet tiktoken langchain langgraph beautifulsoup4\n",
+    "%pip install --upgrade --quiet tiktoken langchain langgraph beautifulsoup4 langchain-community\n",
     "\n",
     "# Set env var OPENAI_API_KEY or load from a .env file\n",
     "# import dotenv\n",

docs/docs/versions/v0_3/index.mdx

@@ -132,7 +132,7 @@ should ensure that they are passing Pydantic 2 objects to these APIs rather than
 Pydantic 1 objects (created via the `pydantic.v1` namespace of pydantic 2).
 
 :::caution
-While `v1` objets may be accepted by some of these APIs, users are advised to
+While `v1` objects may be accepted by some of these APIs, users are advised to
 use Pydantic 2 objects to avoid future issues.
 :::
 

libs/community/langchain_community/vectorstores/opensearch_vector_search.py

@@ -401,7 +401,7 @@ def __init__(
         self.is_aoss = _is_aoss_enabled(http_auth=http_auth)
         self.client = _get_opensearch_client(opensearch_url, **kwargs)
         self.async_client = _get_async_opensearch_client(opensearch_url, **kwargs)
-        self.engine = kwargs.get("engine")
+        self.engine = kwargs.get("engine", "nmslib")
 
     @property
     def embeddings(self) -> Embeddings:
@@ -420,7 +420,7 @@ def __add(
         index_name = kwargs.get("index_name", self.index_name)
         text_field = kwargs.get("text_field", "text")
         dim = len(embeddings[0])
-        engine = kwargs.get("engine", "nmslib")
+        engine = kwargs.get("engine", self.engine)
         space_type = kwargs.get("space_type", "l2")
         ef_search = kwargs.get("ef_search", 512)
         ef_construction = kwargs.get("ef_construction", 512)
@@ -461,7 +461,7 @@ async def __aadd(
         index_name = kwargs.get("index_name", self.index_name)
         text_field = kwargs.get("text_field", "text")
         dim = len(embeddings[0])
-        engine = kwargs.get("engine", "nmslib")
+        engine = kwargs.get("engine", self.engine)
         space_type = kwargs.get("space_type", "l2")
         ef_search = kwargs.get("ef_search", 512)
         ef_construction = kwargs.get("ef_construction", 512)
@@ -530,7 +530,7 @@ def create_index(
             )
 
         if is_appx_search:
-            engine = kwargs.get("engine", "nmslib")
+            engine = kwargs.get("engine", self.engine)
             space_type = kwargs.get("space_type", "l2")
             ef_search = kwargs.get("ef_search", 512)
             ef_construction = kwargs.get("ef_construction", 512)