langchain[major]: breaks some chains to remove hidden defaults (#20759)

Breaks some chains in langchain to remove hidden chat model / llm instantiation.

libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py

@@ -2,7 +2,6 @@
 from typing import List
 
 from langchain_community.agent_toolkits.base import BaseToolkit
-from langchain_community.llms.openai import OpenAI
 from langchain_community.tools.vectorstore.tool import (
     VectorStoreQATool,
     VectorStoreQAWithSourcesTool,
@@ -31,7 +30,7 @@ class VectorStoreToolkit(BaseToolkit):
     """Toolkit for interacting with a Vector Store."""
 
     vectorstore_info: VectorStoreInfo = Field(exclude=True)
-    llm: BaseLanguageModel = Field(default_factory=lambda: OpenAI(temperature=0))
+    llm: BaseLanguageModel
 
     class Config:
         """Configuration for this pydantic object."""
@@ -65,7 +64,7 @@ class VectorStoreRouterToolkit(BaseToolkit):
     """Toolkit for routing between Vector Stores."""
 
     vectorstores: List[VectorStoreInfo] = Field(exclude=True)
-    llm: BaseLanguageModel = Field(default_factory=lambda: OpenAI(temperature=0))
+    llm: BaseLanguageModel
 
     class Config:
         """Configuration for this pydantic object."""

libs/langchain/langchain/chains/natbot/base.py

@@ -4,7 +4,6 @@ from __future__ import annotations
 import warnings
 from typing import Any, Dict, List, Optional
 
-from langchain_community.llms.openai import OpenAI
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.pydantic_v1 import Extra, root_validator
@@ -68,8 +67,11 @@ class NatBotChain(Chain):
     @classmethod
     def from_default(cls, objective: str, **kwargs: Any) -> NatBotChain:
         """Load with default LLMChain."""
-        llm = OpenAI(temperature=0.5, best_of=10, n=3, max_tokens=50)
-        return cls.from_llm(llm, objective, **kwargs)
+        raise NotImplementedError(
+            "This method is no longer implemented. Please use from_llm."
+            "llm = OpenAI(temperature=0.5, best_of=10, n=3, max_tokens=50)"
+            "For example, NatBotChain.from_llm(llm, objective)"
+        )
 
     @classmethod
     def from_llm(

libs/langchain/langchain/chains/openai_functions/openapi.py

@@ -6,7 +6,6 @@ from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
 import requests
-from langchain_community.chat_models import ChatOpenAI
 from langchain_community.utilities.openapi import OpenAPISpec
 from langchain_core.callbacks import CallbackManagerForChainRun
 from langchain_core.language_models import BaseLanguageModel
@@ -272,8 +271,11 @@ def get_openapi_chain(
         if isinstance(spec, str):
             raise ValueError(f"Unable to parse spec from source {spec}")
     openai_fns, call_api_fn = openapi_spec_to_openai_fn(spec)
-    llm = llm or ChatOpenAI(
-        model="gpt-3.5-turbo-0613",
+    if not llm:
+        raise ValueError(
+            "Must provide an LLM for this chain.For example,\n"
+            "from langchain_openai import ChatOpenAI\n"
+            "llm = ChatOpenAI()\n"
         )
     prompt = prompt or ChatPromptTemplate.from_template(
         "Use the provided API's to respond to this user query:\n\n{query}"

libs/langchain/langchain/chains/router/multi_retrieval_qa.py

@@ -3,7 +3,6 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Mapping, Optional
 
-from langchain_community.chat_models import ChatOpenAI
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import PromptTemplate
 from langchain_core.retrievers import BaseRetriever
@@ -42,6 +41,8 @@ class MultiRetrievalQAChain(MultiRouteChain):
         default_retriever: Optional[BaseRetriever] = None,
         default_prompt: Optional[PromptTemplate] = None,
         default_chain: Optional[Chain] = None,
+        *,
+        default_chain_llm: Optional[BaseLanguageModel] = None,
         **kwargs: Any,
     ) -> MultiRetrievalQAChain:
         if default_prompt and not default_retriever:
@@ -78,8 +79,20 @@ class MultiRetrievalQAChain(MultiRouteChain):
             prompt = PromptTemplate(
                 template=prompt_template, input_variables=["history", "query"]
             )
+            if default_chain_llm is None:
+                raise NotImplementedError(
+                    "conversation_llm must be provided if default_chain is not "
+                    "specified. This API has been changed to avoid instantiating "
+                    "default LLMs on behalf of users."
+                    "You can provide a conversation LLM like so:\n"
+                    "from langchain_openai import ChatOpenAI\n"
+                    "llm = ChatOpenAI()"
+                )
             _default_chain = ConversationChain(
-                llm=ChatOpenAI(), prompt=prompt, input_key="query", output_key="result"
+                llm=default_chain_llm,
+                prompt=prompt,
+                input_key="query",
+                output_key="result",
             )
         return cls(
             router_chain=router_chain,

libs/langchain/langchain/indexes/vectorstore.py

@@ -1,8 +1,6 @@
 from typing import Any, Dict, List, Optional, Type
 
 from langchain_community.document_loaders.base import BaseLoader
-from langchain_community.embeddings.openai import OpenAIEmbeddings
-from langchain_community.llms.openai import OpenAI
 from langchain_community.vectorstores.inmemory import InMemoryVectorStore
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
@@ -38,7 +36,14 @@ class VectorStoreIndexWrapper(BaseModel):
         **kwargs: Any,
     ) -> str:
         """Query the vectorstore."""
-        llm = llm or OpenAI(temperature=0)
+        if llm is None:
+            raise NotImplementedError(
+                "This API has been changed to require an LLM. "
+                "Please provide an llm to use for querying the vectorstore.\n"
+                "For example,\n"
+                "from langchain_openai import OpenAI\n"
+                "llm = OpenAI(temperature=0)"
+            )
         retriever_kwargs = retriever_kwargs or {}
         chain = RetrievalQA.from_chain_type(
             llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs
@@ -53,7 +58,14 @@ class VectorStoreIndexWrapper(BaseModel):
         **kwargs: Any,
     ) -> str:
         """Query the vectorstore."""
-        llm = llm or OpenAI(temperature=0)
+        if llm is None:
+            raise NotImplementedError(
+                "This API has been changed to require an LLM. "
+                "Please provide an llm to use for querying the vectorstore.\n"
+                "For example,\n"
+                "from langchain_openai import OpenAI\n"
+                "llm = OpenAI(temperature=0)"
+            )
         retriever_kwargs = retriever_kwargs or {}
         chain = RetrievalQA.from_chain_type(
             llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs
@@ -68,7 +80,14 @@ class VectorStoreIndexWrapper(BaseModel):
         **kwargs: Any,
     ) -> dict:
         """Query the vectorstore and get back sources."""
-        llm = llm or OpenAI(temperature=0)
+        if llm is None:
+            raise NotImplementedError(
+                "This API has been changed to require an LLM. "
+                "Please provide an llm to use for querying the vectorstore.\n"
+                "For example,\n"
+                "from langchain_openai import OpenAI\n"
+                "llm = OpenAI(temperature=0)"
+            )
         retriever_kwargs = retriever_kwargs or {}
         chain = RetrievalQAWithSourcesChain.from_chain_type(
             llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs
@@ -83,7 +102,14 @@ class VectorStoreIndexWrapper(BaseModel):
         **kwargs: Any,
     ) -> dict:
         """Query the vectorstore and get back sources."""
-        llm = llm or OpenAI(temperature=0)
+        if llm is None:
+            raise NotImplementedError(
+                "This API has been changed to require an LLM. "
+                "Please provide an llm to use for querying the vectorstore.\n"
+                "For example,\n"
+                "from langchain_openai import OpenAI\n"
+                "llm = OpenAI(temperature=0)"
+            )
         retriever_kwargs = retriever_kwargs or {}
         chain = RetrievalQAWithSourcesChain.from_chain_type(
             llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs
@@ -95,7 +121,7 @@ class VectorstoreIndexCreator(BaseModel):
     """Logic for creating indexes."""
 
     vectorstore_cls: Type[VectorStore] = InMemoryVectorStore
-    embedding: Embeddings = Field(default_factory=OpenAIEmbeddings)
+    embedding: Embeddings
     text_splitter: TextSplitter = Field(default_factory=_get_default_text_splitter)
     vectorstore_kwargs: dict = Field(default_factory=dict)