update

.scripts/community_split/script_integrations.sh

@@ -130,7 +130,6 @@ git grep -l 'from langchain.tools.base' | xargs sed -i '' 's/from langchain.tool
 git grep -l 'from langchain_community.llms.openai' | xargs sed -i '' 's/from langchain_community.llms.openai/from langchain_openai.llm/g'
 git grep -l 'from langchain_community.chat_models.openai' | xargs sed -i '' 's/from langchain_community.chat_models.openai/from langchain_openai.chat_model/g'
 git grep -l 'from langchain_community.embeddings.openai' | xargs sed -i '' 's/from langchain_community.embeddings.openai/from langchain_openai.embedding/g'
-git grep -l 'from langchain.utils.json_schema' | xargs sed -i '' 's/from langchain.utils.json_schema/from langchain_core.utils.json_schema/g'
 
 cd ..
 
@@ -152,11 +151,10 @@ mv community/langchain_community/embeddings/azure_openai.py partners/openai/lang
 cp langchain/langchain/utils/openai.py partners/openai/langchain_openai/utils.py
 cp langchain/langchain/utils/openai_functions.py partners/openai/langchain_openai/functions.py
 
+git add partners core
 
 git grep -l 'from langchain.utils.json_schema' | xargs sed -i '' 's/from langchain.utils.json_schema/from langchain_core.utils.json_schema/g'
 
-git add partners core
-
 rm community/langchain_community/chat_models/base.py
 rm community/langchain_community/llms/base.py
 rm community/langchain_community/tools/base.py

libs/community/langchain_community/adapters/openai.py

@@ -25,6 +25,7 @@
     SystemMessage,
     ToolMessage,
 )
+from langchain_core.pydantic_v1 import BaseModel
 from typing_extensions import Literal
 
 
@@ -38,6 +39,29 @@ async def aenumerate(
         i += 1
 
 
+class IndexableBaseModel(BaseModel):
+    """Allows a BaseModel to return its fields by string variable indexing"""
+
+    def __getitem__(self, item: str) -> Any:
+        return getattr(self, item)
+
+
+class Choice(IndexableBaseModel):
+    message: dict
+
+
+class ChatCompletions(IndexableBaseModel):
+    choices: List[Choice]
+
+
+class ChoiceChunk(IndexableBaseModel):
+    delta: dict
+
+
+class ChatCompletionChunk(IndexableBaseModel):
+    choices: List[ChoiceChunk]
+
+
 def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
     """Convert a dictionary to a LangChain message.
 
@@ -129,7 +153,7 @@ def convert_openai_messages(messages: Sequence[Dict[str, Any]]) -> List[BaseMess
     return [convert_dict_to_message(m) for m in messages]
 
 
-def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str, Any]:
+def _convert_message_chunk(chunk: BaseMessageChunk, i: int) -> dict:
     _dict: Dict[str, Any] = {}
     if isinstance(chunk, AIMessageChunk):
         if i == 0:
@@ -148,6 +172,11 @@ def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str
     # This only happens at the end of streams, and OpenAI returns as empty dict
     if _dict == {"content": ""}:
         _dict = {}
+    return _dict
+
+
+def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str, Any]:
+    _dict = _convert_message_chunk(chunk, i)
     return {"choices": [{"delta": _dict}]}
 
 
@@ -262,3 +291,109 @@ def convert_messages_for_finetuning(
         for session in sessions
         if _has_assistant_message(session)
     ]
+
+
+class Completions:
+    """Completion."""
+
+    @overload
+    @staticmethod
+    def create(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: Literal[False] = False,
+        **kwargs: Any,
+    ) -> ChatCompletions:
+        ...
+
+    @overload
+    @staticmethod
+    def create(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: Literal[True],
+        **kwargs: Any,
+    ) -> Iterable:
+        ...
+
+    @staticmethod
+    def create(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: bool = False,
+        **kwargs: Any,
+    ) -> Union[ChatCompletions, Iterable]:
+        models = importlib.import_module("langchain.chat_models")
+        model_cls = getattr(models, provider)
+        model_config = model_cls(**kwargs)
+        converted_messages = convert_openai_messages(messages)
+        if not stream:
+            result = model_config.invoke(converted_messages)
+            return ChatCompletions(
+                choices=[Choice(message=convert_message_to_dict(result))]
+            )
+        else:
+            return (
+                ChatCompletionChunk(
+                    choices=[ChoiceChunk(delta=_convert_message_chunk(c, i))]
+                )
+                for i, c in enumerate(model_config.stream(converted_messages))
+            )
+
+    @overload
+    @staticmethod
+    async def acreate(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: Literal[False] = False,
+        **kwargs: Any,
+    ) -> ChatCompletions:
+        ...
+
+    @overload
+    @staticmethod
+    async def acreate(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: Literal[True],
+        **kwargs: Any,
+    ) -> AsyncIterator:
+        ...
+
+    @staticmethod
+    async def acreate(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: bool = False,
+        **kwargs: Any,
+    ) -> Union[ChatCompletions, AsyncIterator]:
+        models = importlib.import_module("langchain.chat_models")
+        model_cls = getattr(models, provider)
+        model_config = model_cls(**kwargs)
+        converted_messages = convert_openai_messages(messages)
+        if not stream:
+            result = await model_config.ainvoke(converted_messages)
+            return ChatCompletions(
+                choices=[Choice(message=convert_message_to_dict(result))]
+            )
+        else:
+            return (
+                ChatCompletionChunk(
+                    choices=[ChoiceChunk(delta=_convert_message_chunk(c, i))]
+                )
+                async for i, c in aenumerate(model_config.astream(converted_messages))
+            )
+
+
+class Chat:
+    def __init__(self) -> None:
+        self.completions = Completions()
+
+
+chat = Chat()

libs/community/langchain_community/agent_toolkits/__init__.py

@@ -34,6 +34,7 @@
 from langchain_community.agent_toolkits.json.base import create_json_agent
 from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
 from langchain_community.agent_toolkits.multion.toolkit import MultionToolkit
+from langchain_community.agent_toolkits.nasa.toolkit import NasaToolkit
 from langchain_community.agent_toolkits.nla.toolkit import NLAToolkit
 from langchain_community.agent_toolkits.office365.toolkit import O365Toolkit
 from langchain_community.agent_toolkits.openapi.base import create_openapi_agent
@@ -49,6 +50,7 @@
 from langchain_community.agent_toolkits.spark_sql.toolkit import SparkSQLToolkit
 from langchain_community.agent_toolkits.sql.base import create_sql_agent
 from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
+from langchain_community.agent_toolkits.steam.toolkit import SteamToolkit
 from langchain_community.agent_toolkits.vectorstore.base import (
     create_vectorstore_agent,
     create_vectorstore_router_agent,
@@ -94,12 +96,14 @@ def __getattr__(name: str) -> Any:
     "JiraToolkit",
     "JsonToolkit",
     "MultionToolkit",
+    "NasaToolkit",
     "NLAToolkit",
     "O365Toolkit",
     "OpenAPIToolkit",
     "PlayWrightBrowserToolkit",
     "PowerBIToolkit",
     "SlackToolkit",
+    "SteamToolkit",
     "SQLDatabaseToolkit",
     "SparkSQLToolkit",
     "VectorStoreInfo",

libs/community/langchain_community/agent_toolkits/conversational_retrieval/openai_functions.py

@@ -5,7 +5,6 @@
 from langchain_core.messages import SystemMessage
 from langchain_core.prompts.chat import MessagesPlaceholder
 from langchain_core.tools import BaseTool
-from langchain_openai.chat_model import ChatOpenAI
 
 from langchain_community.agents.agent import AgentExecutor
 from langchain_community.agents.openai_functions_agent.agent_token_buffer_memory import (
@@ -57,8 +56,6 @@ def create_conversational_retrieval_agent(
         An agent executor initialized appropriately
     """
 
-    if not isinstance(llm, ChatOpenAI):
-        raise ValueError("Only supported with ChatOpenAI models.")
     if remember_intermediate_steps:
         memory: BaseMemory = AgentTokenBufferMemory(
             memory_key=memory_key, llm=llm, max_token_limit=max_token_limit