refactor: Migrate from BaseLanguageModel to field_typing.LanguageModel

src/backend/base/langflow/components/agents/CSVAgent.py

@@ -1,7 +1,7 @@
 from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import AgentExecutor, BaseLanguageModel
+from langflow.field_typing import AgentExecutor, LanguageModel
 
 
 class CSVAgentComponent(CustomComponent):
@@ -11,7 +11,7 @@ class CSVAgentComponent(CustomComponent):
 
     def build_config(self):
         return {
-            "llm": {"display_name": "LLM", "type": BaseLanguageModel},
+            "llm": {"display_name": "LLM", "type": LanguageModel},
             "path": {"display_name": "Path", "field_type": "file", "suffixes": [".csv"], "file_types": [".csv"]},
             "handle_parsing_errors": {"display_name": "Handle Parse Errors", "advanced": True},
             "agent_type": {
@@ -22,7 +22,7 @@ class CSVAgentComponent(CustomComponent):
         }
 
     def build(
-        self, llm: BaseLanguageModel, path: str, handle_parsing_errors: bool = True, agent_type: str = "openai-tools"
+        self, llm: LanguageModel, path: str, handle_parsing_errors: bool = True, agent_type: str = "openai-tools"
     ) -> AgentExecutor:
         # Instantiate and return the CSV agent class with the provided llm and path
         return create_csv_agent(

src/backend/base/langflow/components/agents/JsonAgent.py

@@ -3,7 +3,7 @@ from langchain_community.agent_toolkits import create_json_agent
 from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class JsonAgentComponent(CustomComponent):
@@ -18,7 +18,7 @@ class JsonAgentComponent(CustomComponent):
 
     def build(
         self,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         toolkit: JsonToolkit,
     ) -> AgentExecutor:
         return create_json_agent(llm=llm, toolkit=toolkit)

src/backend/base/langflow/components/agents/SQLAgent.py

@@ -6,7 +6,7 @@ from langchain_community.agent_toolkits.sql.base import create_sql_agent
 from langchain_community.utilities import SQLDatabase
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class SQLAgentComponent(CustomComponent):
@@ -22,7 +22,7 @@ class SQLAgentComponent(CustomComponent):
 
     def build(
         self,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         database_uri: str,
         verbose: bool = False,
     ) -> Union[AgentExecutor, Callable]:

src/backend/base/langflow/components/agents/ToolCallingAgent.py

@@ -4,7 +4,7 @@ from langchain.agents.tool_calling_agent.base import create_tool_calling_agent
 from langchain_core.prompts import ChatPromptTemplate
 
 from langflow.base.agents.agent import LCAgentComponent
-from langflow.field_typing import BaseLanguageModel, Text, Tool
+from langflow.field_typing import LanguageModel, Text, Tool
 from langflow.schema import Data
 
 
@@ -39,7 +39,7 @@ class ToolCallingAgentComponent(LCAgentComponent):
     async def build(
         self,
         input_value: str,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         tools: List[Tool],
         user_prompt: str = "{input}",
         message_history: Optional[List[Data]] = None,

src/backend/base/langflow/components/agents/VectorStoreAgent.py

@@ -4,7 +4,7 @@ from langchain.agents import AgentExecutor, create_vectorstore_agent
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class VectorStoreAgentComponent(CustomComponent):
@@ -19,7 +19,7 @@ class VectorStoreAgentComponent(CustomComponent):
 
     def build(
         self,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         vector_store_toolkit: VectorStoreToolkit,
     ) -> Union[AgentExecutor, Callable]:
         return create_vectorstore_agent(llm=llm, toolkit=vector_store_toolkit)

src/backend/base/langflow/components/agents/VectorStoreRouterAgent.py

@@ -2,7 +2,7 @@ from typing import Callable
 
 from langchain.agents import create_vectorstore_router_agent
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit
-from langchain_core.language_models.base import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 from langflow.custom import CustomComponent
 
@@ -17,5 +17,5 @@ class VectorStoreRouterAgentComponent(CustomComponent):
             "vectorstoreroutertoolkit": {"display_name": "Vector Store Router Toolkit"},
         }
 
-    def build(self, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:
+    def build(self, llm: LanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:
         return create_vectorstore_router_agent(llm=llm, toolkit=vectorstoreroutertoolkit)

src/backend/base/langflow/components/agents/XMLAgent.py

@@ -4,7 +4,7 @@ from langchain.agents import create_xml_agent
 from langchain_core.prompts import ChatPromptTemplate
 
 from langflow.base.agents.agent import LCAgentComponent
-from langflow.field_typing import BaseLanguageModel, Text, Tool
+from langflow.field_typing import LanguageModel, Text, Tool
 from langflow.schema import Data
 
 
@@ -72,7 +72,7 @@ class XMLAgentComponent(LCAgentComponent):
     async def build(
         self,
         input_value: str,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         tools: List[Tool],
         user_prompt: str = "{input}",
         system_message: str = "You are a helpful assistant",

src/backend/base/langflow/components/chains/ConversationChain.py

@@ -3,7 +3,7 @@ from typing import Optional
 from langchain.chains import ConversationChain
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, Text
+from langflow.field_typing import BaseMemory, LanguageModel, Text
 
 
 class ConversationChainComponent(CustomComponent):
@@ -27,7 +27,7 @@ class ConversationChainComponent(CustomComponent):
     def build(
         self,
         input_value: Text,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         memory: Optional[BaseMemory] = None,
     ) -> Text:
         if memory is None:

src/backend/base/langflow/components/chains/LLMChain.py

@@ -4,7 +4,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.prompts import PromptTemplate
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, Text
+from langflow.field_typing import BaseMemory, LanguageModel, Text
 
 
 class LLMChainComponent(CustomComponent):
@@ -21,7 +21,7 @@ class LLMChainComponent(CustomComponent):
     def build(
         self,
         template: Text,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         memory: Optional[BaseMemory] = None,
     ) -> Text:
         prompt = PromptTemplate.from_template(template)

src/backend/base/langflow/components/chains/LLMCheckerChain.py

@@ -1,7 +1,7 @@
 from langchain.chains import LLMCheckerChain
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, Text
+from langflow.field_typing import LanguageModel, Text
 
 
 class LLMCheckerChainComponent(CustomComponent):
@@ -21,7 +21,7 @@ class LLMCheckerChainComponent(CustomComponent):
     def build(
         self,
         input_value: Text,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
     ) -> Text:
         chain = LLMCheckerChain.from_llm(llm=llm)
         response = chain.invoke({chain.input_key: input_value})

src/backend/base/langflow/components/chains/LLMMathChain.py

@@ -3,7 +3,7 @@ from typing import Optional
 from langchain.chains import LLMChain, LLMMathChain
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, Text
+from langflow.field_typing import BaseMemory, LanguageModel, Text
 
 
 class LLMMathChainComponent(CustomComponent):
@@ -27,7 +27,7 @@ class LLMMathChainComponent(CustomComponent):
     def build(
         self,
         input_value: Text,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         llm_chain: LLMChain,
         input_key: str = "question",
         output_key: str = "answer",

src/backend/base/langflow/components/chains/RetrievalQA.py

@@ -4,7 +4,7 @@ from langchain.chains.retrieval_qa.base import RetrievalQA
 from langchain_core.documents import Document
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text
+from langflow.field_typing import BaseMemory, BaseRetriever, LanguageModel, Text
 from langflow.schema import Data
 
 
@@ -29,7 +29,7 @@ class RetrievalQAComponent(CustomComponent):
 
     def build(
         self,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         chain_type: str,
         retriever: BaseRetriever,
         input_value: str = "",

src/backend/base/langflow/components/chains/RetrievalQAWithSourcesChain.py

@@ -4,7 +4,7 @@ from langchain.chains import RetrievalQAWithSourcesChain
 from langchain_core.documents import Document
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text
+from langflow.field_typing import BaseMemory, BaseRetriever, LanguageModel, Text
 
 
 class RetrievalQAWithSourcesChainComponent(CustomComponent):
@@ -32,7 +32,7 @@ class RetrievalQAWithSourcesChainComponent(CustomComponent):
         self,
         input_value: Text,
         retriever: BaseRetriever,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         chain_type: str,
         memory: Optional[BaseMemory] = None,
         return_source_documents: Optional[bool] = True,

src/backend/base/langflow/components/chains/SQLGenerator.py

@@ -6,7 +6,7 @@ from langchain_core.prompts import PromptTemplate
 from langchain_core.runnables import Runnable
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, Text
+from langflow.field_typing import LanguageModel, Text
 
 
 class SQLGeneratorComponent(CustomComponent):
@@ -35,7 +35,7 @@ class SQLGeneratorComponent(CustomComponent):
         self,
         input_value: Text,
         db: SQLDatabase,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         top_k: int = 5,
         prompt: Optional[Text] = None,
     ) -> Text:

src/backend/base/langflow/components/experimental/AgentComponent.py

@@ -5,7 +5,7 @@ from langchain_core.prompts.chat import HumanMessagePromptTemplate, SystemMessag
 
 from langflow.base.agents.agent import LCAgentComponent
 from langflow.base.agents.utils import AGENTS, AgentSpec, get_agents_list
-from langflow.field_typing import BaseLanguageModel, Text, Tool
+from langflow.field_typing import LanguageModel, Text, Tool
 from langflow.schema import Data
 from langflow.schema.dotdict import dotdict
 
@@ -145,7 +145,7 @@ class AgentComponent(LCAgentComponent):
         self,
         agent_name: str,
         input_value: str,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         tools: List[Tool],
         system_message: str = "You are a helpful assistant. Help the user answer any questions.",
         user_prompt: str = "{input}",

src/backend/base/langflow/components/helpers/ShouldRunNext.py

@@ -2,14 +2,14 @@ from langchain_core.messages import BaseMessage
 from langchain_core.prompts import PromptTemplate
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, Text
+from langflow.field_typing import LanguageModel, Text
 
 
 class ShouldRunNextComponent(CustomComponent):
     display_name = "Should Run Next"
     description = "Determines if a vertex is runnable."
 
-    def build(self, llm: BaseLanguageModel, question: str, context: str, retries: int = 3) -> Text:
+    def build(self, llm: LanguageModel, question: str, context: str, retries: int = 3) -> Text:
         template = "Given the following question and the context below, answer with a yes or no.\n\n{error_message}\n\nQuestion: {question}\n\nContext: {context}\n\nAnswer:"
 
         prompt = PromptTemplate.from_template(template)

src/backend/base/langflow/components/model_specs/AmazonBedrockSpecs.py

@@ -3,7 +3,7 @@ from typing import Optional
 from langchain_community.llms.bedrock import Bedrock
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class AmazonBedrockComponent(CustomComponent):
@@ -46,7 +46,7 @@ class AmazonBedrockComponent(CustomComponent):
         endpoint_url: Optional[str] = None,
         streaming: bool = False,
         cache: Optional[bool] = None,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         try:
             output = Bedrock(
                 credentials_profile_name=credentials_profile_name,

src/backend/base/langflow/components/model_specs/AnthropicLLMSpecs.py

@@ -1,10 +1,10 @@
 from typing import Optional
 
 from langchain_anthropic import ChatAnthropic
-from langchain_core.language_models import BaseLanguageModel
 from pydantic.v1 import SecretStr
 
 from langflow.custom import CustomComponent
+from langflow.field_typing import LanguageModel
 
 
 class ChatAntropicSpecsComponent(CustomComponent):
@@ -57,7 +57,7 @@ class ChatAntropicSpecsComponent(CustomComponent):
         max_tokens: Optional[int] = 1000,
         temperature: Optional[float] = None,
         api_endpoint: Optional[str] = None,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         # Set default API endpoint if not provided
         if not api_endpoint:
             api_endpoint = "https://api.anthropic.com"

src/backend/base/langflow/components/model_specs/AzureChatOpenAISpecs.py

@@ -1,10 +1,10 @@
 from typing import Optional
 
-from langchain_core.language_models import BaseLanguageModel
 from langchain_openai import AzureChatOpenAI
 from pydantic.v1 import SecretStr
 
 from langflow.custom import CustomComponent
+from langflow.field_typing import LanguageModel
 
 
 class AzureChatOpenAISpecsComponent(CustomComponent):
@@ -81,7 +81,7 @@ class AzureChatOpenAISpecsComponent(CustomComponent):
         api_version: str,
         temperature: float = 0.7,
         max_tokens: Optional[int] = 1000,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         if api_key:
             azure_api_key = SecretStr(api_key)
         else:

src/backend/base/langflow/components/model_specs/BaiduQianfanChatEndpointsSpecs.py

@@ -1,11 +1,10 @@
 from typing import Optional
 
 from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
-
 from pydantic.v1 import SecretStr
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class QianfanChatEndpointComponent(CustomComponent):
@@ -80,7 +79,7 @@ class QianfanChatEndpointComponent(CustomComponent):
         temperature: Optional[float] = None,
         penalty_score: Optional[float] = None,
         endpoint: Optional[str] = None,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         try:
             output = QianfanChatEndpoint(  # type: ignore
                 model=model,