Update import statements for langchain_community packages

src/backend/langflow/components/agents/OpenAIConversationalAgent.py

@@ -3,7 +3,7 @@ from typing import List, Optional
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import _get_default_system_message
 from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
-from langchain.chat_models import ChatOpenAI
+from langchain_community.chat_models import ChatOpenAI
 from langchain.memory.token_buffer import ConversationTokenBufferMemory
 from langchain.prompts import SystemMessagePromptTemplate
 from langchain.prompts.chat import MessagesPlaceholder

src/backend/langflow/components/llms/AnthropicLLM.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from langchain.chat_models.anthropic import ChatAnthropic
+from langchain_community.chat_models.anthropic import ChatAnthropic
 from langchain.llms.base import BaseLanguageModel
 from pydantic.v1 import SecretStr
 

src/backend/langflow/components/llms/AzureChatOpenAI.py

@@ -1,7 +1,7 @@
 from typing import Optional
 from langflow import CustomComponent
 from langchain.llms.base import BaseLanguageModel
-from langchain.chat_models.azure_openai import AzureChatOpenAI
+from langchain_community.chat_models.azure_openai import AzureChatOpenAI
 
 
 class AzureChatOpenAIComponent(CustomComponent):

src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
+from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
 from langchain.llms.base import BaseLLM
 from pydantic.v1 import SecretStr
 

src/backend/langflow/components/toolkits/VectorStoreInfo.py

@@ -1,7 +1,9 @@
-from langflow import CustomComponent
-from langchain.vectorstores import VectorStore
-from typing import Union, Callable
+from typing import Callable, Union
+
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
+from langchain_community.vectorstores import VectorStore
+
+from langflow import CustomComponent
 
 
 class VectorStoreInfoComponent(CustomComponent):

src/backend/langflow/components/vectorstores/Chroma.py

@@ -3,9 +3,8 @@ from typing import List, Optional, Union
 import chromadb  # type: ignore
 from langchain.embeddings.base import Embeddings
 from langchain.schema import BaseRetriever, Document
-from langchain.vectorstores.chroma import Chroma
-from langchain.vectorstores.base import VectorStore
-
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.chroma import Chroma
 from langflow import CustomComponent
 
 

src/backend/langflow/components/vectorstores/FAISS.py

@@ -1,12 +1,10 @@
-from langflow import CustomComponent
-from langchain_community.vectorstores.faiss import FAISS
 from typing import List, Union
+
 from langchain.schema import BaseRetriever
-from langchain.vectorstores.base import VectorStore
-from langflow.field_typing import (
-    Document,
-    Embeddings,
-)
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.faiss import FAISS
+from langflow import CustomComponent
+from langflow.field_typing import Document, Embeddings
 
 
 class FAISSComponent(CustomComponent):

src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py

@@ -1,6 +1,8 @@
+from typing import List, Optional
+
+from langchain_community.vectorstores import MongoDBAtlasVectorSearch
+
 from langflow import CustomComponent
-from langchain.vectorstores import MongoDBAtlasVectorSearch
-from typing import Optional, List
 from langflow.field_typing import (
     Document,
     Embeddings,

src/backend/langflow/components/vectorstores/Pinecone.py

@@ -3,7 +3,7 @@ from typing import List, Optional, Union
 
 import pinecone  # type: ignore
 from langchain.schema import BaseRetriever
-from langchain.vectorstores.base import VectorStore
+from langchain_community.vectorstores import VectorStore
 from langchain_community.vectorstores.pinecone import Pinecone
 
 from langflow import CustomComponent

src/backend/langflow/components/vectorstores/Qdrant.py

@@ -1,9 +1,10 @@
-from langflow import CustomComponent
-from langchain_community.vectorstores.qdrant import Qdrant
-from typing import Optional, List, Union
-from langflow.field_typing import Document, Embeddings, NestedDict
+from typing import List, Optional, Union
+
 from langchain.schema import BaseRetriever
-from langchain.vectorstores.base import VectorStore
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.qdrant import Qdrant
+from langflow import CustomComponent
+from langflow.field_typing import Document, Embeddings, NestedDict
 
 
 class QdrantComponent(CustomComponent):

src/backend/langflow/components/vectorstores/Redis.py

@@ -1,10 +1,10 @@
 from typing import Optional
-from langflow import CustomComponent
 
-from langchain.vectorstores.redis import Redis
-from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
 from langchain.embeddings.base import Embeddings
+from langchain.schema import Document
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.redis import Redis
+from langflow import CustomComponent
 
 
 class RedisComponent(CustomComponent):

src/backend/langflow/components/vectorstores/SupabaseVectorStore.py

@@ -1,13 +1,10 @@
-from langflow import CustomComponent
 from typing import List, Union
-from langchain_community.vectorstores.supabase import SupabaseVectorStore
-from langflow.field_typing import (
-    Document,
-    Embeddings,
-    NestedDict,
-)
+
 from langchain.schema import BaseRetriever
-from langchain.vectorstores.base import VectorStore
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.supabase import SupabaseVectorStore
+from langflow import CustomComponent
+from langflow.field_typing import Document, Embeddings, NestedDict
 from supabase.client import Client, create_client
 
 

src/backend/langflow/components/vectorstores/Vectara.py

@@ -1,14 +1,13 @@
-from typing import Optional, Union, List
-from langflow import CustomComponent
 import tempfile
-import urllib.request
 import urllib
+import urllib.request
+from typing import List, Optional, Union
 
-from langchain.vectorstores import Vectara
-from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
-from langchain.schema import BaseRetriever
 from langchain.embeddings import FakeEmbeddings
+from langchain.schema import BaseRetriever, Document
+from langchain_community.vectorstores import Vectara, VectorStore
+
+from langflow import CustomComponent
 
 
 class VectaraComponent(CustomComponent):

src/backend/langflow/components/vectorstores/Weaviate.py

@@ -3,8 +3,7 @@ from typing import Optional, Union
 import weaviate  # type: ignore
 from langchain.embeddings.base import Embeddings
 from langchain.schema import BaseRetriever, Document
-from langchain.vectorstores import Weaviate
-from langchain.vectorstores.base import VectorStore
+from langchain_community.vectorstores import VectorStore, Weaviate
 
 from langflow import CustomComponent
 

src/backend/langflow/components/vectorstores/pgvector.py

@@ -1,10 +1,10 @@
-from typing import Optional, List
-from langflow import CustomComponent
+from typing import List, Optional
 
-from langchain.vectorstores.pgvector import PGVector
-from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
 from langchain.embeddings.base import Embeddings
+from langchain.schema import Document
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.pgvector import PGVector
+from langflow import CustomComponent
 
 
 class PostgresqlVectorComponent(CustomComponent):

src/backend/langflow/field_typing/constants.py

@@ -12,7 +12,7 @@ from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.memory import BaseMemory
 from langchain.text_splitter import TextSplitter
 from langchain.tools import Tool
-from langchain.vectorstores.base import VectorStore
+from langchain_community.vectorstores import VectorStore
 
 # Type alias for more complex dicts
 NestedDict = Dict[str, Union[str, Dict]]

src/backend/langflow/interface/agents/custom.py

@@ -2,14 +2,10 @@ from typing import Any, Optional
 
 from langchain.agents import AgentExecutor, ZeroShotAgent
 from langchain.agents.agent_toolkits import (
-    SQLDatabaseToolkit,
     VectorStoreInfo,
     VectorStoreRouterToolkit,
     VectorStoreToolkit,
 )
-from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
-from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
-from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
 from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX as VECTORSTORE_PREFIX
 from langchain.agents.agent_toolkits.vectorstore.prompt import ROUTER_PREFIX as VECTORSTORE_ROUTER_PREFIX
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
@@ -17,9 +13,14 @@ from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
 from langchain.sql_database import SQLDatabase
 from langchain.tools.sql_database.prompt import QUERY_CHECKER
+from langchain_community.agent_toolkits import SQLDatabaseToolkit
+from langchain_community.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
+from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
+from langchain_community.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
 from langchain_experimental.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX
 from langchain_experimental.agents.agent_toolkits.pandas.prompt import SUFFIX_WITH_DF as PANDAS_SUFFIX
 from langchain_experimental.tools.python.tool import PythonAstREPLTool
+
 from langflow.interface.base import CustomAgentExecutor
 
 

src/backend/langflow/interface/custom_lists.py

@@ -1,9 +1,10 @@
 import inspect
 from typing import Any
 
-from langchain import document_loaders, embeddings, llms, memory, requests, text_splitter
-from langchain.agents import agent_toolkits
-from langchain.chat_models import AzureChatOpenAI, ChatAnthropic, ChatOpenAI, ChatVertexAI
+from langchain import llms, memory, requests, text_splitter
+from langchain_community.chat_models import AzureChatOpenAI, ChatAnthropic, ChatOpenAI, ChatVertexAI
+from langchain_community import agent_toolkits, document_loaders, embeddings
+
 from langflow.interface.agents.custom import CUSTOM_AGENTS
 from langflow.interface.chains.custom import CUSTOM_CHAINS
 from langflow.interface.importing.utils import import_class
@@ -24,14 +25,14 @@ llm_type_to_cls_dict["vertexai-chat"] = ChatVertexAI  # type: ignore
 
 # Toolkits
 toolkit_type_to_loader_dict: dict[str, Any] = {
-    toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}")
+    toolkit_name: import_class(f"langchain_community.agent_toolkits.{toolkit_name}")
     # if toolkit_name is lower case it is a loader
     for toolkit_name in agent_toolkits.__all__
     if toolkit_name.islower()
 }
 
 toolkit_type_to_cls_dict: dict[str, Any] = {
-    toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}")
+    toolkit_name: import_class(f"langchain_community.agent_toolkits.{toolkit_name}")
     # if toolkit_name is not lower case it is a class
     for toolkit_name in agent_toolkits.__all__
     if not toolkit_name.islower()
@@ -47,13 +48,14 @@ wrapper_type_to_cls_dict: dict[str, Any] = {wrapper.__name__: wrapper for wrappe
 
 # Embeddings
 embedding_type_to_cls_dict: dict[str, Any] = {
-    embedding_name: import_class(f"langchain.embeddings.{embedding_name}") for embedding_name in embeddings.__all__
+    embedding_name: import_class(f"langchain_community.embeddings.{embedding_name}")
+    for embedding_name in embeddings.__all__
 }
 
 
 # Document Loaders
 documentloaders_type_to_cls_dict: dict[str, Any] = {
-    documentloader_name: import_class(f"langchain.document_loaders.{documentloader_name}")
+    documentloader_name: import_class(f"langchain_community.document_loaders.{documentloader_name}")
     for documentloader_name in document_loaders.__all__
 }
 

src/backend/langflow/interface/importing/utils.py

@@ -6,9 +6,9 @@ from typing import Any, Type
 from langchain.agents import Agent
 from langchain.base_language import BaseLanguageModel
 from langchain.chains.base import Chain
-from langchain.chat_models.base import BaseChatModel
 from langchain.prompts import PromptTemplate
 from langchain.tools import BaseTool
+from langchain_core.language_models.chat_models import BaseChatModel
 from langflow.interface.custom.custom_component import CustomComponent
 from langflow.interface.wrappers.base import wrapper_creator
 from langflow.utils import validate
@@ -71,7 +71,7 @@ def import_output_parser(output_parser: str) -> Any:
 
 def import_chat_llm(llm: str) -> BaseChatModel:
     """Import chat llm from llm name"""
-    return import_class(f"langchain.chat_models.{llm}")
+    return import_class(f"langchain_community.chat_models.{llm}")
 
 
 def import_retriever(retriever: str) -> Any:
@@ -148,17 +148,17 @@ def import_chain(chain: str) -> Type[Chain]:
 
 def import_embedding(embedding: str) -> Any:
     """Import embedding from embedding name"""
-    return import_class(f"langchain.embeddings.{embedding}")
+    return import_class(f"langchain_community.embeddings.{embedding}")
 
 
 def import_vectorstore(vectorstore: str) -> Any:
     """Import vectorstore from vectorstore name"""
-    return import_class(f"langchain.vectorstores.{vectorstore}")
+    return import_class(f"langchain_community.vectorstores.{vectorstore}")
 
 
 def import_documentloader(documentloader: str) -> Any:
     """Import documentloader from documentloader name"""
-    return import_class(f"langchain.document_loaders.{documentloader}")
+    return import_class(f"langchain_community.document_loaders.{documentloader}")
 
 
 def import_textsplitter(textsplitter: str) -> Any:
@@ -169,8 +169,8 @@ def import_textsplitter(textsplitter: str) -> Any:
 def import_utility(utility: str) -> Any:
     """Import utility from utility name"""
     if utility == "SQLDatabase":
-        return import_class(f"langchain.sql_database.{utility}")
-    return import_class(f"langchain.utilities.{utility}")
+        return import_class(f"langchain_community.sql_database.{utility}")
+    return import_class(f"langchain_community.utilities.{utility}")
 
 
 def get_function(code):

src/backend/langflow/interface/initialize/loading.py

@@ -10,7 +10,7 @@ from langchain.agents.tools import BaseTool
 from langchain.chains.base import Chain
 from langchain.document_loaders.base import BaseLoader
 from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
+from langchain_community.vectorstores import VectorStore
 from loguru import logger
 from pydantic import ValidationError
 

src/backend/langflow/interface/initialize/vector_store.py

@@ -1,18 +1,18 @@
-from typing import Any, Callable, Dict, Type
-from langchain.vectorstores import (
-    Pinecone,
-    ElasticsearchStore,
-    Qdrant,
-    Chroma,
-    FAISS,
-    Weaviate,
-    SupabaseVectorStore,
-    MongoDBAtlasVectorSearch,
-)
-from langchain.schema import Document
 import os
+from typing import Any, Callable, Dict, Type
 
 import orjson
+from langchain.schema import Document
+from langchain_community.vectorstores import (
+    FAISS,
+    Chroma,
+    ElasticsearchStore,
+    MongoDBAtlasVectorSearch,
+    Pinecone,
+    Qdrant,
+    SupabaseVectorStore,
+    Weaviate,
+)
 
 
 def docs_in_params(params: dict) -> bool:
@@ -27,8 +27,8 @@ def initialize_mongodb(class_object: Type[MongoDBAtlasVectorSearch], params: dic
     MONGODB_ATLAS_CLUSTER_URI = params.pop("mongodb_atlas_cluster_uri")
     if not MONGODB_ATLAS_CLUSTER_URI:
         raise ValueError("Mongodb atlas cluster uri must be provided in the params")
-    from pymongo import MongoClient
     import certifi
+    from pymongo import MongoClient
 
     client: MongoClient = MongoClient(MONGODB_ATLAS_CLUSTER_URI, tlsCAFile=certifi.where())
     db_name = params.pop("db_name", None)

src/backend/langflow/interface/retrievers/base.py

@@ -1,14 +1,13 @@
 from typing import Any, ClassVar, Dict, List, Optional, Type
 
 from langchain import retrievers
+from loguru import logger
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class
 from langflow.services.deps import get_settings_service
-
 from langflow.template.frontend_node.retrievers import RetrieverFrontendNode
-from loguru import logger
-from langflow.utils.util import build_template_from_method, build_template_from_class
+from langflow.utils.util import build_template_from_class, build_template_from_method
 
 
 class RetrieverCreator(LangChainTypeCreator):
@@ -27,7 +26,7 @@ class RetrieverCreator(LangChainTypeCreator):
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:
             self.type_dict: dict[str, Any] = {
-                retriever_name: import_class(f"langchain.retrievers.{retriever_name}")
+                retriever_name: import_class(f"langchain_community.retrievers.{retriever_name}")
                 for retriever_name in retrievers.__all__
             }
         return self.type_dict

src/backend/langflow/interface/utilities/base.py

@@ -1,6 +1,6 @@
 from typing import Dict, List, Optional, Type
 
-from langchain import utilities
+from langchain_community import utilities
 from loguru import logger
 
 from langflow.custom.customs import get_custom_nodes
@@ -30,7 +30,7 @@ class UtilityCreator(LangChainTypeCreator):
             self.type_dict = {}
             for utility_name in utilities.__all__:
                 try:
-                    imported = import_class(f"langchain.utilities.{utility_name}")
+                    imported = import_class(f"langchain_community.utilities.{utility_name}")
                     self.type_dict[utility_name] = imported
                 except Exception:
                     pass

src/backend/langflow/interface/vector_store/base.py

@@ -1,13 +1,12 @@
 from typing import Any, Dict, List, Optional, Type
 
 from langchain import vectorstores
+from loguru import logger
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class
 from langflow.services.deps import get_settings_service
-
 from langflow.template.frontend_node.vectorstores import VectorStoreFrontendNode
-from loguru import logger
 from langflow.utils.util import build_template_from_method
 
 
@@ -22,7 +21,7 @@ class VectorstoreCreator(LangChainTypeCreator):
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:
             self.type_dict: dict[str, Any] = {
-                vectorstore_name: import_class(f"langchain.vectorstores.{vectorstore_name}")
+                vectorstore_name: import_class(f"langchain_community.vectorstores.{vectorstore_name}")
                 for vectorstore_name in vectorstores.__all__
             }
         return self.type_dict

src/backend/langflow/interface/wrappers/base.py

@@ -1,9 +1,9 @@
 from typing import ClassVar, Dict, List, Optional
 
-from langchain.utilities import requests, sql_database
+from langchain_community.utilities import requests, sql_database
+from loguru import logger
 
 from langflow.interface.base import LangChainTypeCreator
-from loguru import logger
 from langflow.utils.util import build_template_from_class, build_template_from_method
 
 

src/backend/langflow/processing/process.py

@@ -4,7 +4,7 @@ from typing import Any, Coroutine, Dict, List, Optional, Tuple, Union
 from langchain.agents import AgentExecutor
 from langchain.chains.base import Chain
 from langchain.schema import AgentAction, Document
-from langchain.vectorstores.base import VectorStore
+from langchain_community.vectorstores import VectorStore
 from langchain_core.messages import AIMessage
 from langchain_core.runnables.base import Runnable
 from langflow.graph.graph.base import Graph