Replace remaining usage of basellm with baselangmodel (#3981)

langchain/agents/agent_toolkits/csv/base.py

@@ -3,11 +3,14 @@ from typing import Any, Optional
 
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
-from langchain.llms.base import BaseLLM
+from langchain.base_language import BaseLanguageModel
 
 
 def create_csv_agent(
-    llm: BaseLLM, path: str, pandas_kwargs: Optional[dict] = None, **kwargs: Any
+    llm: BaseLanguageModel,
+    path: str,
+    pandas_kwargs: Optional[dict] = None,
+    **kwargs: Any
 ) -> AgentExecutor:
     """Create csv agent by loading to a dataframe and using pandas agent."""
     import pandas as pd

langchain/agents/agent_toolkits/json/base.py

@@ -6,13 +6,13 @@ from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
 from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 
 
 def create_json_agent(
-    llm: BaseLLM,
+    llm: BaseLanguageModel,
     toolkit: JsonToolkit,
     callback_manager: Optional[BaseCallbackManager] = None,
     prefix: str = JSON_PREFIX,

langchain/agents/agent_toolkits/nla/tool.py

@@ -4,8 +4,8 @@
 from typing import Any, Optional
 
 from langchain.agents.tools import Tool
+from langchain.base_language import BaseLanguageModel
 from langchain.chains.api.openapi.chain import OpenAPIEndpointChain
-from langchain.llms.base import BaseLLM
 from langchain.requests import Requests
 from langchain.tools.openapi.utils.api_models import APIOperation
 from langchain.tools.openapi.utils.openapi_utils import OpenAPISpec
@@ -32,7 +32,7 @@ class NLATool(Tool):
     @classmethod
     def from_llm_and_method(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         path: str,
         method: str,
         spec: OpenAPISpec,

langchain/agents/agent_toolkits/nla/toolkit.py

@@ -7,7 +7,7 @@ from pydantic import Field
 
 from langchain.agents.agent_toolkits.base import BaseToolkit
 from langchain.agents.agent_toolkits.nla.tool import NLATool
-from langchain.llms.base import BaseLLM
+from langchain.base_language import BaseLanguageModel
 from langchain.requests import Requests
 from langchain.tools.base import BaseTool
 from langchain.tools.openapi.utils.openapi_utils import OpenAPISpec
@@ -26,7 +26,7 @@ class NLAToolkit(BaseToolkit):
 
     @staticmethod
     def _get_http_operation_tools(
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         spec: OpenAPISpec,
         requests: Optional[Requests] = None,
         verbose: bool = False,
@@ -53,7 +53,7 @@ class NLAToolkit(BaseToolkit):
     @classmethod
     def from_llm_and_spec(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         spec: OpenAPISpec,
         requests: Optional[Requests] = None,
         verbose: bool = False,
@@ -68,7 +68,7 @@ class NLAToolkit(BaseToolkit):
     @classmethod
     def from_llm_and_url(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         open_api_url: str,
         requests: Optional[Requests] = None,
         verbose: bool = False,
@@ -83,7 +83,7 @@ class NLAToolkit(BaseToolkit):
     @classmethod
     def from_llm_and_ai_plugin(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         ai_plugin: AIPlugin,
         requests: Optional[Requests] = None,
         verbose: bool = False,
@@ -103,7 +103,7 @@ class NLAToolkit(BaseToolkit):
     @classmethod
     def from_llm_and_ai_plugin_url(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         ai_plugin_url: str,
         requests: Optional[Requests] = None,
         verbose: bool = False,

langchain/agents/agent_toolkits/openapi/base.py

@@ -9,13 +9,13 @@ from langchain.agents.agent_toolkits.openapi.prompt import (
 from langchain.agents.agent_toolkits.openapi.toolkit import OpenAPIToolkit
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 
 
 def create_openapi_agent(
-    llm: BaseLLM,
+    llm: BaseLanguageModel,
     toolkit: OpenAPIToolkit,
     callback_manager: Optional[BaseCallbackManager] = None,
     prefix: str = OPENAPI_PREFIX,

langchain/agents/agent_toolkits/openapi/toolkit.py

@@ -9,7 +9,7 @@ from langchain.agents.agent_toolkits.json.base import create_json_agent
 from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
 from langchain.agents.agent_toolkits.openapi.prompt import DESCRIPTION
 from langchain.agents.tools import Tool
-from langchain.llms.base import BaseLLM
+from langchain.base_language import BaseLanguageModel
 from langchain.requests import TextRequestsWrapper
 from langchain.tools import BaseTool
 from langchain.tools.json.tool import JsonSpec
@@ -57,7 +57,7 @@ class OpenAPIToolkit(BaseToolkit):
     @classmethod
     def from_llm(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         json_spec: JsonSpec,
         requests_wrapper: TextRequestsWrapper,
         **kwargs: Any,

langchain/agents/agent_toolkits/pandas/base.py

@@ -4,14 +4,14 @@ from typing import Any, Dict, List, Optional
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.pandas.prompt import PREFIX, SUFFIX
 from langchain.agents.mrkl.base import ZeroShotAgent
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 from langchain.tools.python.tool import PythonAstREPLTool
 
 
 def create_pandas_dataframe_agent(
-    llm: BaseLLM,
+    llm: BaseLanguageModel,
     df: Any,
     callback_manager: Optional[BaseCallbackManager] = None,
     prefix: str = PREFIX,

langchain/agents/agent_toolkits/powerbi/base.py

@@ -9,14 +9,14 @@ from langchain.agents.agent_toolkits.powerbi.prompt import (
 from langchain.agents.agent_toolkits.powerbi.toolkit import PowerBIToolkit
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 from langchain.utilities.powerbi import PowerBIDataset
 
 
 def create_pbi_agent(
-    llm: BaseLLM,
+    llm: BaseLanguageModel,
     toolkit: Optional[PowerBIToolkit],
     powerbi: Optional[PowerBIDataset] = None,
     callback_manager: Optional[BaseCallbackManager] = None,

langchain/agents/agent_toolkits/python/base.py

@@ -5,14 +5,14 @@ from typing import Any, Dict, Optional
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.python.prompt import PREFIX
 from langchain.agents.mrkl.base import ZeroShotAgent
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 from langchain.tools.python.tool import PythonREPLTool
 
 
 def create_python_agent(
-    llm: BaseLLM,
+    llm: BaseLanguageModel,
     tool: PythonREPLTool,
     callback_manager: Optional[BaseCallbackManager] = None,
     verbose: bool = False,

langchain/agents/agent_toolkits/sql/base.py

@@ -6,13 +6,13 @@ from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
 from langchain.agents.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 
 
 def create_sql_agent(
-    llm: BaseLLM,
+    llm: BaseLanguageModel,
     toolkit: SQLDatabaseToolkit,
     callback_manager: Optional[BaseCallbackManager] = None,
     prefix: str = SQL_PREFIX,

langchain/agents/agent_toolkits/vectorstore/base.py

@@ -8,13 +8,13 @@ from langchain.agents.agent_toolkits.vectorstore.toolkit import (
     VectorStoreToolkit,
 )
 from langchain.agents.mrkl.base import ZeroShotAgent
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 
 
 def create_vectorstore_agent(
-    llm: BaseLLM,
+    llm: BaseLanguageModel,
     toolkit: VectorStoreToolkit,
     callback_manager: Optional[BaseCallbackManager] = None,
     prefix: str = PREFIX,
@@ -42,7 +42,7 @@ def create_vectorstore_agent(
 
 
 def create_vectorstore_router_agent(
-    llm: BaseLLM,
+    llm: BaseLanguageModel,
     toolkit: VectorStoreRouterToolkit,
     callback_manager: Optional[BaseCallbackManager] = None,
     prefix: str = ROUTER_PREFIX,

langchain/agents/load_tools.py

@@ -5,12 +5,12 @@ from typing import Any, Dict, List, Optional, Callable, Tuple
 from mypy_extensions import Arg, KwArg
 
 from langchain.agents.tools import Tool
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.api import news_docs, open_meteo_docs, podcast_docs, tmdb_docs
 from langchain.chains.api.base import APIChain
 from langchain.chains.llm_math.base import LLMMathChain
 from langchain.chains.pal.base import PALChain
-from langchain.llms.base import BaseLLM
 from langchain.requests import TextRequestsWrapper
 from langchain.tools.arxiv.tool import ArxivQueryRun
 from langchain.tools.base import BaseTool
@@ -32,8 +32,6 @@ from langchain.tools.shell.tool import ShellTool
 from langchain.tools.wikipedia.tool import WikipediaQueryRun
 from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
 from langchain.utilities import ArxivAPIWrapper
-from langchain.utilities.apify import ApifyWrapper
-from langchain.utilities.bash import BashProcess
 from langchain.utilities.bing_search import BingSearchAPIWrapper
 from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
 from langchain.utilities.google_search import GoogleSearchAPIWrapper
@@ -85,7 +83,7 @@ _BASE_TOOLS: Dict[str, Callable[[], BaseTool]] = {
 }
 
 
-def _get_pal_math(llm: BaseLLM) -> BaseTool:
+def _get_pal_math(llm: BaseLanguageModel) -> BaseTool:
     return Tool(
         name="PAL-MATH",
         description="A language model that is really good at solving complex word math problems. Input should be a fully worded hard word math problem.",
@@ -93,7 +91,7 @@ def _get_pal_math(llm: BaseLLM) -> BaseTool:
     )
 
 
-def _get_pal_colored_objects(llm: BaseLLM) -> BaseTool:
+def _get_pal_colored_objects(llm: BaseLanguageModel) -> BaseTool:
     return Tool(
         name="PAL-COLOR-OBJ",
         description="A language model that is really good at reasoning about position and the color attributes of objects. Input should be a fully worded hard reasoning problem. Make sure to include all information about the objects AND the final question you want to answer.",
@@ -101,7 +99,7 @@ def _get_pal_colored_objects(llm: BaseLLM) -> BaseTool:
     )
 
 
-def _get_llm_math(llm: BaseLLM) -> BaseTool:
+def _get_llm_math(llm: BaseLanguageModel) -> BaseTool:
     return Tool(
         name="Calculator",
         description="Useful for when you need to answer questions about math.",
@@ -110,7 +108,7 @@ def _get_llm_math(llm: BaseLLM) -> BaseTool:
     )
 
 
-def _get_open_meteo_api(llm: BaseLLM) -> BaseTool:
+def _get_open_meteo_api(llm: BaseLanguageModel) -> BaseTool:
     chain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS)
     return Tool(
         name="Open Meteo API",
@@ -119,7 +117,7 @@ def _get_open_meteo_api(llm: BaseLLM) -> BaseTool:
     )
 
 
-_LLM_TOOLS: Dict[str, Callable[[BaseLLM], BaseTool]] = {
+_LLM_TOOLS: Dict[str, Callable[[BaseLanguageModel], BaseTool]] = {
     "pal-math": _get_pal_math,
     "pal-colored-objects": _get_pal_colored_objects,
     "llm-math": _get_llm_math,
@@ -127,7 +125,7 @@ _LLM_TOOLS: Dict[str, Callable[[BaseLLM], BaseTool]] = {
 }
 
 
-def _get_news_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
+def _get_news_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
     news_api_key = kwargs["news_api_key"]
     chain = APIChain.from_llm_and_api_docs(
         llm, news_docs.NEWS_DOCS, headers={"X-Api-Key": news_api_key}
@@ -139,7 +137,7 @@ def _get_news_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
     )
 
 
-def _get_tmdb_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
+def _get_tmdb_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
     tmdb_bearer_token = kwargs["tmdb_bearer_token"]
     chain = APIChain.from_llm_and_api_docs(
         llm,
@@ -153,7 +151,7 @@ def _get_tmdb_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
     )
 
 
-def _get_podcast_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
+def _get_podcast_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
     listen_api_key = kwargs["listen_api_key"]
     chain = APIChain.from_llm_and_api_docs(
         llm,
@@ -238,7 +236,8 @@ def _get_scenexplain(**kwargs: Any) -> BaseTool:
 
 
 _EXTRA_LLM_TOOLS: Dict[
-    str, Tuple[Callable[[Arg(BaseLLM, "llm"), KwArg(Any)], BaseTool], List[str]]
+    str,
+    Tuple[Callable[[Arg(BaseLanguageModel, "llm"), KwArg(Any)], BaseTool], List[str]],
 ] = {
     "news-api": (_get_news_api, ["news_api_key"]),
     "tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
@@ -277,7 +276,7 @@ _EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[st
 
 def load_tools(
     tool_names: List[str],
-    llm: Optional[BaseLLM] = None,
+    llm: Optional[BaseLanguageModel] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
     **kwargs: Any,
 ) -> List[BaseTool]:

langchain/agents/loading.py

@@ -8,15 +8,15 @@ import yaml
 from langchain.agents.agent import BaseSingleActionAgent
 from langchain.agents.tools import Tool
 from langchain.agents.types import AGENT_TO_CLASS
+from langchain.base_language import BaseLanguageModel
 from langchain.chains.loading import load_chain, load_chain_from_config
-from langchain.llms.base import BaseLLM
 from langchain.utilities.loading import try_load_from_hub
 
 URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/agents/"
 
 
 def _load_agent_from_tools(
-    config: dict, llm: BaseLLM, tools: List[Tool], **kwargs: Any
+    config: dict, llm: BaseLanguageModel, tools: List[Tool], **kwargs: Any
 ) -> BaseSingleActionAgent:
     config_type = config.pop("_type")
     if config_type not in AGENT_TO_CLASS:
@@ -29,7 +29,7 @@ def _load_agent_from_tools(
 
 def load_agent_from_config(
     config: dict,
-    llm: Optional[BaseLLM] = None,
+    llm: Optional[BaseLanguageModel] = None,
     tools: Optional[List[Tool]] = None,
     **kwargs: Any,
 ) -> BaseSingleActionAgent:

langchain/agents/react/base.py

@@ -10,9 +10,9 @@ from langchain.agents.react.textworld_prompt import TEXTWORLD_PROMPT
 from langchain.agents.react.wiki_prompt import WIKI_PROMPT
 from langchain.agents.tools import Tool
 from langchain.agents.utils import validate_tools_single_input
+from langchain.base_language import BaseLanguageModel
 from langchain.docstore.base import Docstore
 from langchain.docstore.document import Document
-from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 from langchain.tools.base import BaseTool
 
@@ -141,7 +141,7 @@ class ReActChain(AgentExecutor):
             react = ReAct(llm=OpenAI())
     """
 
-    def __init__(self, llm: BaseLLM, docstore: Docstore, **kwargs: Any):
+    def __init__(self, llm: BaseLanguageModel, docstore: Docstore, **kwargs: Any):
         """Initialize with the LLM and a docstore."""
         docstore_explorer = DocstoreExplorer(docstore)
         tools = [

langchain/agents/self_ask_with_search/base.py

@@ -9,7 +9,7 @@ from langchain.agents.self_ask_with_search.output_parser import SelfAskOutputPar
 from langchain.agents.self_ask_with_search.prompt import PROMPT
 from langchain.agents.tools import Tool
 from langchain.agents.utils import validate_tools_single_input
-from langchain.llms.base import BaseLLM
+from langchain.base_language import BaseLanguageModel
 from langchain.prompts.base import BasePromptTemplate
 from langchain.tools.base import BaseTool
 from langchain.utilities.google_serper import GoogleSerperAPIWrapper
@@ -71,7 +71,7 @@ class SelfAskWithSearchChain(AgentExecutor):
 
     def __init__(
         self,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         search_chain: Union[GoogleSerperAPIWrapper, SerpAPIWrapper],
         **kwargs: Any,
     ):

langchain/chains/api/openapi/chain.py

@@ -7,12 +7,12 @@ from typing import Any, Dict, List, NamedTuple, Optional, cast
 from pydantic import BaseModel, Field
 from requests import Response
 
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
 from langchain.chains.api.openapi.requests_chain import APIRequesterChain
 from langchain.chains.api.openapi.response_chain import APIResponderChain
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 from langchain.requests import Requests
 from langchain.tools.openapi.utils.api_models import APIOperation
 
@@ -168,7 +168,7 @@ class OpenAPIEndpointChain(Chain, BaseModel):
         spec_url: str,
         path: str,
         method: str,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         requests: Optional[Requests] = None,
         return_intermediate_steps: bool = False,
         **kwargs: Any
@@ -188,7 +188,7 @@ class OpenAPIEndpointChain(Chain, BaseModel):
     def from_api_operation(
         cls,
         operation: APIOperation,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         requests: Optional[Requests] = None,
         verbose: bool = False,
         return_intermediate_steps: bool = False,

langchain/chains/api/openapi/requests_chain.py

@@ -4,9 +4,9 @@ import json
 import re
 from typing import Any
 
+from langchain.base_language import BaseLanguageModel
 from langchain.chains.api.openapi.prompts import REQUEST_TEMPLATE
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import BaseOutputParser
 
@@ -38,7 +38,7 @@ class APIRequesterChain(LLMChain):
     @classmethod
     def from_llm_and_typescript(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         typescript_definition: str,
         verbose: bool = True,
         **kwargs: Any,

langchain/chains/api/openapi/response_chain.py

@@ -4,9 +4,9 @@ import json
 import re
 from typing import Any
 
+from langchain.base_language import BaseLanguageModel
 from langchain.chains.api.openapi.prompts import RESPONSE_TEMPLATE
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import BaseOutputParser
 
@@ -36,7 +36,9 @@ class APIResponderChain(LLMChain):
     """Get the response parser."""
 
     @classmethod
-    def from_llm(cls, llm: BaseLLM, verbose: bool = True, **kwargs: Any) -> LLMChain:
+    def from_llm(
+        cls, llm: BaseLanguageModel, verbose: bool = True, **kwargs: Any
+    ) -> LLMChain:
         """Get the response parser."""
         output_parser = APIResponderOutputParser()
         prompt = PromptTemplate(

langchain/chains/graph_qa/base.py

@@ -5,12 +5,12 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import Field
 
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.graphs.networkx_graph import NetworkxEntityGraph, get_entities
-from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 
 
@@ -43,7 +43,7 @@ class GraphQAChain(Chain):
     @classmethod
     def from_llm(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         qa_prompt: BasePromptTemplate = PROMPT,
         entity_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT,
         **kwargs: Any,

langchain/chains/hyde/base.py

@@ -9,12 +9,12 @@ from typing import Any, Dict, List, Optional
 import numpy as np
 from pydantic import Extra
 
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.hyde.prompts import PROMPT_MAP
 from langchain.chains.llm import LLMChain
 from langchain.embeddings.base import Embeddings
-from langchain.llms.base import BaseLLM
 
 
 class HypotheticalDocumentEmbedder(Chain, Embeddings):
@@ -70,7 +70,7 @@ class HypotheticalDocumentEmbedder(Chain, Embeddings):
     @classmethod
     def from_llm(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         base_embeddings: Embeddings,
         prompt_key: str,
         **kwargs: Any,

langchain/chains/llm_checker/base.py

@@ -6,6 +6,7 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import Extra, root_validator
 
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
@@ -16,12 +17,11 @@ from langchain.chains.llm_checker.prompt import (
     REVISED_ANSWER_PROMPT,
 )
 from langchain.chains.sequential import SequentialChain
-from langchain.llms.base import BaseLLM
 from langchain.prompts import PromptTemplate
 
 
 def _load_question_to_checked_assertions_chain(
-    llm: BaseLLM,
+    llm: BaseLanguageModel,
     create_draft_answer_prompt: PromptTemplate,
     list_assertions_prompt: PromptTemplate,
     check_assertions_prompt: PromptTemplate,
@@ -75,7 +75,7 @@ class LLMCheckerChain(Chain):
 
     question_to_checked_assertions_chain: SequentialChain
 
-    llm: Optional[BaseLLM] = None
+    llm: Optional[BaseLanguageModel] = None
     """[Deprecated] LLM wrapper to use."""
     create_draft_answer_prompt: PromptTemplate = CREATE_DRAFT_ANSWER_PROMPT
     """[Deprecated]"""
@@ -158,7 +158,7 @@ class LLMCheckerChain(Chain):
     @classmethod
     def from_llm(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         create_draft_answer_prompt: PromptTemplate = CREATE_DRAFT_ANSWER_PROMPT,
         list_assertions_prompt: PromptTemplate = LIST_ASSERTIONS_PROMPT,
         check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT,

langchain/chains/llm_summarization_checker/base.py

@@ -8,11 +8,11 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import Extra, root_validator
 
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.sequential import SequentialChain
-from langchain.llms.base import BaseLLM
 from langchain.prompts.prompt import PromptTemplate
 
 PROMPTS_DIR = Path(__file__).parent / "prompts"
@@ -32,7 +32,7 @@ ARE_ALL_TRUE_PROMPT = PromptTemplate.from_file(
 
 
 def _load_sequential_chain(
-    llm: BaseLLM,
+    llm: BaseLanguageModel,
     create_assertions_prompt: PromptTemplate,
     check_assertions_prompt: PromptTemplate,
     revised_summary_prompt: PromptTemplate,
@@ -85,7 +85,7 @@ class LLMSummarizationCheckerChain(Chain):
     """
 
     sequential_chain: SequentialChain
-    llm: Optional[BaseLLM] = None
+    llm: Optional[BaseLanguageModel] = None
     """[Deprecated] LLM wrapper to use."""
 
     create_assertions_prompt: PromptTemplate = CREATE_ASSERTIONS_PROMPT
@@ -180,7 +180,7 @@ class LLMSummarizationCheckerChain(Chain):
     @classmethod
     def from_llm(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         create_assertions_prompt: PromptTemplate = CREATE_ASSERTIONS_PROMPT,
         check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT,
         revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT,

langchain/chains/mapreduce.py

@@ -9,6 +9,7 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import Extra
 
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
 from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
@@ -16,7 +17,6 @@ from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChai
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.docstore.document import Document
-from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 from langchain.text_splitter import TextSplitter
 
@@ -34,7 +34,7 @@ class MapReduceChain(Chain):
     @classmethod
     def from_params(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         prompt: BasePromptTemplate,
         text_splitter: TextSplitter,
         callbacks: Callbacks = None,

langchain/chains/natbot/base.py

@@ -6,11 +6,11 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import Extra, root_validator
 
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.natbot.prompt import PROMPT
-from langchain.llms.base import BaseLLM
 from langchain.llms.openai import OpenAI
 
 
@@ -27,7 +27,7 @@ class NatBotChain(Chain):
     llm_chain: LLMChain
     objective: str
     """Objective that NatBot is tasked with completing."""
-    llm: Optional[BaseLLM] = None
+    llm: Optional[BaseLanguageModel] = None
     """[Deprecated] LLM wrapper to use."""
     input_url_key: str = "url"  #: :meta private:
     input_browser_content_key: str = "browser_content"  #: :meta private:
@@ -59,7 +59,9 @@ class NatBotChain(Chain):
         return cls.from_llm(llm, objective, **kwargs)
 
     @classmethod
-    def from_llm(cls, llm: BaseLLM, objective: str, **kwargs: Any) -> NatBotChain:
+    def from_llm(
+        cls, llm: BaseLanguageModel, objective: str, **kwargs: Any
+    ) -> NatBotChain:
         """Load from LLM."""
         llm_chain = LLMChain(llm=llm, prompt=PROMPT)
         return cls(llm_chain=llm_chain, objective=objective, **kwargs)

langchain/evaluation/qa/eval_chain.py

@@ -4,9 +4,9 @@ from __future__ import annotations
 from typing import Any, List
 
 from langchain import PromptTemplate
+from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
 from langchain.evaluation.qa.eval_prompt import CONTEXT_PROMPT, COT_PROMPT, PROMPT
-from langchain.llms.base import BaseLLM
 
 
 class QAEvalChain(LLMChain):
@@ -14,12 +14,12 @@ class QAEvalChain(LLMChain):
 
     @classmethod
     def from_llm(
-        cls, llm: BaseLLM, prompt: PromptTemplate = PROMPT, **kwargs: Any
+        cls, llm: BaseLanguageModel, prompt: PromptTemplate = PROMPT, **kwargs: Any
     ) -> QAEvalChain:
         """Load QA Eval Chain from LLM.
 
         Args:
-            llm (BaseLLM): the base language model to use.
+            llm (BaseLanguageModel): the base language model to use.
 
             prompt (PromptTemplate): A prompt template containing the input_variables:
             'input', 'answer' and 'result' that will be used as the prompt
@@ -74,12 +74,15 @@ class ContextQAEvalChain(LLMChain):
 
     @classmethod
     def from_llm(
-        cls, llm: BaseLLM, prompt: PromptTemplate = CONTEXT_PROMPT, **kwargs: Any
+        cls,
+        llm: BaseLanguageModel,
+        prompt: PromptTemplate = CONTEXT_PROMPT,
+        **kwargs: Any,
     ) -> ContextQAEvalChain:
         """Load QA Eval Chain from LLM.
 
         Args:
-            llm (BaseLLM): the base language model to use.
+            llm (BaseLanguageModel): the base language model to use.
 
             prompt (PromptTemplate): A prompt template containing the input_variables:
             'query', 'context' and 'result' that will be used as the prompt
@@ -120,7 +123,7 @@ class CotQAEvalChain(ContextQAEvalChain):
 
     @classmethod
     def from_llm(
-        cls, llm: BaseLLM, prompt: PromptTemplate = COT_PROMPT, **kwargs: Any
+        cls, llm: BaseLanguageModel, prompt: PromptTemplate = COT_PROMPT, **kwargs: Any
     ) -> CotQAEvalChain:
         cls._validate_input_vars(prompt)
         return cls(llm=llm, prompt=prompt, **kwargs)

langchain/evaluation/qa/generate_chain.py

@@ -3,15 +3,15 @@ from __future__ import annotations
 
 from typing import Any
 
+from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
 from langchain.evaluation.qa.generate_prompt import PROMPT
-from langchain.llms.base import BaseLLM
 
 
 class QAGenerateChain(LLMChain):
     """LLM Chain specifically for generating examples for question answering."""
 
     @classmethod
-    def from_llm(cls, llm: BaseLLM, **kwargs: Any) -> QAGenerateChain:
+    def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> QAGenerateChain:
         """Load QA Generate Chain from LLM."""
         return cls(llm=llm, prompt=PROMPT, **kwargs)

langchain/example_generator.py

@@ -1,8 +1,8 @@
 """Utility functions for working with prompts."""
 from typing import List
 
+from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import BaseLLM
 from langchain.prompts.few_shot import FewShotPromptTemplate
 from langchain.prompts.prompt import PromptTemplate
 
@@ -10,7 +10,7 @@ TEST_GEN_TEMPLATE_SUFFIX = "Add another example."
 
 
 def generate_example(
-    examples: List[dict], llm: BaseLLM, prompt_template: PromptTemplate
+    examples: List[dict], llm: BaseLanguageModel, prompt_template: PromptTemplate
 ) -> str:
     """Return another example given a list of examples for a prompt."""
     prompt = FewShotPromptTemplate(

langchain/indexes/graph.py

@@ -3,18 +3,18 @@ from typing import Optional, Type
 
 from pydantic import BaseModel
 
+from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
 from langchain.graphs.networkx_graph import NetworkxEntityGraph, parse_triples
 from langchain.indexes.prompts.knowledge_triplet_extraction import (
     KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT,
 )
-from langchain.llms.base import BaseLLM
 
 
 class GraphIndexCreator(BaseModel):
     """Functionality to create graph index."""
 
-    llm: Optional[BaseLLM] = None
+    llm: Optional[BaseLanguageModel] = None
     graph_type: Type[NetworkxEntityGraph] = NetworkxEntityGraph
 
     def from_text(self, text: str) -> NetworkxEntityGraph:

langchain/indexes/vectorstore.py

@@ -2,12 +2,12 @@ from typing import Any, List, Optional, Type
 
 from pydantic import BaseModel, Extra, Field
 
+from langchain.base_language import BaseLanguageModel
 from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain
 from langchain.chains.retrieval_qa.base import RetrievalQA
 from langchain.document_loaders.base import BaseLoader
 from langchain.embeddings.base import Embeddings
 from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.llms.base import BaseLLM
 from langchain.llms.openai import OpenAI
 from langchain.schema import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
@@ -30,7 +30,9 @@ class VectorStoreIndexWrapper(BaseModel):
         extra = Extra.forbid
         arbitrary_types_allowed = True
 
-    def query(self, question: str, llm: Optional[BaseLLM] = None, **kwargs: Any) -> str:
+    def query(
+        self, question: str, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
+    ) -> str:
         """Query the vectorstore."""
         llm = llm or OpenAI(temperature=0)
         chain = RetrievalQA.from_chain_type(
@@ -39,7 +41,7 @@ class VectorStoreIndexWrapper(BaseModel):
         return chain.run(question)
 
     def query_with_sources(
-        self, question: str, llm: Optional[BaseLLM] = None, **kwargs: Any
+        self, question: str, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
     ) -> dict:
         """Query the vectorstore and get back sources."""
         llm = llm or OpenAI(temperature=0)