Migrate to langchain >=0.1.0,<0.3

Chroma_DB_with_Langchain.ipynb

@@ -103,7 +103,7 @@
         }
       ],
       "source": [
-        "from langchain.document_loaders import DirectoryLoader\n",
+        "from langchain_community.document_loaders import DirectoryLoader\n",
         "\n",
         "directory = '/content/pets'\n",
         "\n",
@@ -146,7 +146,7 @@
         }
       ],
       "source": [
-        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
         "\n",
         "def split_docs(documents,chunk_size=1000,chunk_overlap=20):\n",
         "  text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
@@ -168,7 +168,7 @@
         "# # import openai\n",
         "# from langchain.embeddings.openai import OpenAIEmbeddings\n",
         "# embeddings = OpenAIEmbeddings(model_name=\"ada\")\n",
-        "from langchain.embeddings import SentenceTransformerEmbeddings\n",
+        "from langchain_community.embeddings import SentenceTransformerEmbeddings\n",
         "embeddings = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
       ]
     },
@@ -180,7 +180,7 @@
       },
       "outputs": [],
       "source": [
-        "from langchain.vectorstores import Chroma\n",
+        "from langchain_community.vectorstores import Chroma\n",
         "db = Chroma.from_documents(docs, embeddings)"
       ]
     },
@@ -380,7 +380,7 @@
       },
       "outputs": [],
       "source": [
-        "from langchain.chat_models import ChatOpenAI\n",
+        "from langchain_openai import ChatOpenAI\n",
         "model_name = \"gpt-3.5-turbo\"\n",
         "llm = ChatOpenAI(model_name=model_name)"
       ]

LangChain_Expression_Language_(LCEL)_Tutorial.ipynb

@@ -210,9 +210,9 @@
     {
       "cell_type": "code",
       "source": [
-        "from langchain.chat_models import ChatOpenAI\n",
+        "from langchain_openai import ChatOpenAI\n",
         "from langchain.prompts import ChatPromptTemplate\n",
-        "from langchain.schema.output_parser import StrOutputParser"
+        "from langchain_core.output_parsers import StrOutputParser"
       ],
       "metadata": {
         "id": "ZQGsQtxlmUoO"
@@ -558,10 +558,10 @@
     {
       "cell_type": "code",
       "source": [
-        "from langchain.embeddings import OpenAIEmbeddings\n",
+        "from langchain_openai import OpenAIEmbeddings\n",
         "from langchain.prompts import ChatPromptTemplate\n",
-        "from langchain.schema.runnable import RunnableParallel, RunnablePassthrough\n",
-        "from langchain.vectorstores import Chroma"
+        "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
+        "from langchain_community.vectorstores import Chroma"
       ],
       "metadata": {
         "id": "WWa6ZZGc2GKs"

LangGraph FastAPI Integration/api/requirements.txt

@@ -1,4 +1,4 @@
-langchain
+langchain>=0.1
 langchain-openai
 langchain-core
 langchain_community
@@ -13,3 +13,4 @@ fastapi
 uvicorn
 pydantic
 python-dotenv
+langchain-text-splitters>=0.0.1

Langchain Chatbot/Langchain_Pinecone_Indexing_.ipynb

@@ -4892,7 +4892,7 @@
     {
       "cell_type": "code",
       "source": [
-        "from langchain.document_loaders import DirectoryLoader\n",
+        "from langchain_community.document_loaders import DirectoryLoader\n",
         "\n",
         "directory = '/content/data'\n",
         "\n",
@@ -4944,7 +4944,7 @@
     {
       "cell_type": "code",
       "source": [
-        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
         "\n",
         "def split_docs(documents,chunk_size=500,chunk_overlap=20):\n",
         "  text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
@@ -5015,7 +5015,7 @@
         "# import openai\n",
         "# from langchain.embeddings.openai import OpenAIEmbeddings\n",
         "# embeddings = OpenAIEmbeddings(model_name=\"ada\")\n",
-        "from langchain.embeddings import SentenceTransformerEmbeddings\n",
+        "from langchain_community.embeddings import SentenceTransformerEmbeddings\n",
         "embeddings = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
       ],
       "metadata": {
@@ -5448,7 +5448,7 @@
       "cell_type": "code",
       "source": [
         "import pinecone \n",
-        "from langchain.vectorstores import Pinecone\n",
+        "from langchain_community.vectorstores import Pinecone\n",
         "# initialize pinecone\n",
         "pinecone.init(\n",
         "    api_key=\"\",  # find at app.pinecone.io\n",

Langchain Chatbot/main.py

@@ -1,4 +1,4 @@
-from langchain.chat_models import ChatOpenAI
+from langchain_openai import ChatOpenAI
 from langchain.chains import ConversationChain
 from langchain.chains.conversation.memory import ConversationBufferWindowMemory
 from langchain.prompts import (

Langchain Chatbot/requirements.txt

@@ -1,4 +1,8 @@
 streamlit
 streamlit_chat
-langchain
-sentence_transformers
+langchain>=0.1
+sentence_transformers
+langchain-community>=0.0.20
+langchain-openai>=0.0.5
+langchain-text-splitters>=0.0.1
+langchain-core>=0.1

Langchain RAG Course 2024/LangChain_Conversational_RAG_Crash_Course_From_Basics_to_Production_Part_1.ipynb

@@ -1062,7 +1062,7 @@
         }
       ],
       "source": [
-        "from langchain.schema.runnable import RunnablePassthrough\n",
+        "from langchain_core.runnables import RunnablePassthrough\n",
         "rag_chain = (\n",
         "    {\"context\": retriever, \"question\": RunnablePassthrough()} | prompt\n",
         ")\n",

Langchain RAG Course 2024/api/requirements.txt

@@ -1,9 +1,10 @@
-langchain
+langchain>=0.1
 langchain-openai
 langchain-core
 langchain_community
 docx2txt
 pypdf
 langchain_chroma
 python-multipart
-streamlit
+streamlit
+langchain-text-splitters>=0.0.1

Langchain_Agents_SQL_Database_Agent.ipynb

@@ -95,7 +95,7 @@
         "from langchain.agents import load_tools\n",
         "from langchain.agents import initialize_agent\n",
         "from langchain.agents import AgentType\n",
-        "from langchain.llms import OpenAI"
+        "from langchain_openai import OpenAI\n"
       ],
       "metadata": {
         "id": "66hNmNKMhd5n"
@@ -222,7 +222,7 @@
         "from langchain.agents import create_sql_agent\n",
         "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
         "from langchain.sql_database import SQLDatabase\n",
-        "from langchain.llms.openai import OpenAI\n",
+        "from langchain_openai import OpenAI\n",
         "from langchain.agents import AgentExecutor"
       ]
     },
@@ -254,7 +254,7 @@
       "cell_type": "code",
       "source": [
         "# llm=OpenAI(temperature=0)\n",
-        "from langchain.chat_models import ChatOpenAI\n",
+        "from langchain_openai import ChatOpenAI\n",
         "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
         "# chat = ChatOpenAI(model_name=\"gpt-4\")"
       ],

Langchain_Semnatic_Serach_Pinecone.ipynb

@@ -159,7 +159,7 @@
     {
       "cell_type": "code",
       "source": [
-        "from langchain.document_loaders import DirectoryLoader\n",
+        "from langchain_community.document_loaders import DirectoryLoader\n",
         "\n",
         "directory = '/content/data'\n",
         "\n",
@@ -204,7 +204,7 @@
     {
       "cell_type": "code",
       "source": [
-        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
         "\n",
         "def split_docs(documents,chunk_size=1000,chunk_overlap=20):\n",
         "  text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
@@ -304,7 +304,7 @@
       "cell_type": "code",
       "source": [
         "import openai\n",
-        "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+        "from langchain_openai import OpenAIEmbeddings\n",
         "\n",
         "embeddings = OpenAIEmbeddings(model_name=\"ada\")\n",
         "\n",
@@ -356,7 +356,7 @@
       "cell_type": "code",
       "source": [
         "import pinecone \n",
-        "from langchain.vectorstores import Pinecone\n",
+        "from langchain_community.vectorstores import Pinecone\n",
         "# initialize pinecone\n",
         "pinecone.init(\n",
         "    api_key=\"f7aa89af-ac99-4619-8914-8d08740f7b38\",  # find at app.pinecone.io\n",
@@ -419,7 +419,7 @@
     {
       "cell_type": "code",
       "source": [
-        "from langchain.llms import OpenAI\n",
+        "from langchain_openai import OpenAI\n",
         "\n",
         "# model_name = \"text-davinci-003\"\n",
         "# model_name = \"gpt-3.5-turbo\"\n",

LlamaIndex_Tutorial.ipynb

@@ -334,7 +334,7 @@
       "source": [
         "from llama_index import LLMPredictor, ServiceContext\n",
         "\n",
-        "from langchain.chat_models import ChatOpenAI\n",
+        "from langchain_openai import ChatOpenAI\n",
         "\n",
         "llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name=\"gpt-3.5-turbo\"))\n",
         "\n",
@@ -457,7 +457,7 @@
     {
       "cell_type": "code",
       "source": [
-        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
+        "from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings\n",
         "from llama_index import LangchainEmbedding, ServiceContext\n",
         "\n",
         "# load in HF embedding model from langchain\n",