merge

Preview:
https://langchain-git-fork-cbornet-astra-self-retriever-docs-langchain.vercel.app/docs/integrations/retrievers/self_query/astradb

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -5,60 +5,84 @@ body:
   - type: markdown
     attributes:
       value: >
-        Thank you for taking the time to file a bug report. Before creating a new
-        issue, please make sure to take a few moments to check the issue tracker
-        for existing issues about the bug.
+        Thank you for taking the time to file a bug report. 
+        
+        Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
+        if there's another way to solve your problem:
+        
+        [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
+        [API Reference](https://api.python.langchain.com/en/stable/),
+        [GitHub search](https://github.com/langchain-ai/langchain),
+        [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
+        [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue)
+  - type: checkboxes
+    id: checks
+    attributes:
+      label: Checked other resources
+      description: Please confirm and check all the following options.
+      options:
+        - label: I added a very descriptive title to this issue.
+          required: true
+        - label: I searched the LangChain documentation with the integrated search.
+          required: true
+        - label: I used the GitHub search to find a similar question and didn't find it.
+          required: true
+  - type: textarea
+    id: reproduction
+    validations:
+      required: true
+    attributes:
+      label: Example Code
+      description: |
+        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
+        
+        If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
+        
+        If you're including an error message, please include the full stack trace not just the last error.
+        
+        **Important!** Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+        Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
 
+      placeholder: |
+        The following code: 
+        
+        ```python
+        from langchain_core.runnables import RunnableLambda
+
+        def bad_code(inputs) -> int:
+          raise NotImplementedError('For demo purpose')
+          
+          chain = RunnableLambda(bad_code)
+          chain.invoke('Hello!')
+        ```
+        
+        Include both the error and the full stack trace if reporting an exception!
+
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: |
+        What is the problem, question, or error?
+
+        Write a short description telling what you are doing, what you expect to happen, and what is currently happening.
+      placeholder: |
+        * I'm trying to use the `langchain` library to do X.
+        * I expect to see Y.
+        * Instead, it does Z.
+    validations:
+      required: true
   - type: textarea
     id: system-info
     attributes:
       label: System Info
       description: Please share your system info with us.
-      placeholder: LangChain version, platform, python version, ...
+      placeholder: |
+        "pip freeze | grep langchain"
+        platform
+        python version
     validations:
       required: true
-
-  - type: textarea
-    id: who-can-help
-    attributes:
-      label: Who can help?
-      description: |
-        Your issue will be replied to more quickly if you can figure out the right person to tag with @
-        If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
-
-        The core maintainers strive to read all issues, but tagging them will help them prioritize.
-
-        Please tag fewer than 3 people.
-
-        @hwchase17 - project lead
-
-        Tracing / Callbacks
-        - @agola11
-
-        Async
-        - @agola11
-
-        DataLoader Abstractions
-        - @eyurtsev
-
-        LLM/Chat Wrappers
-        - @hwchase17
-        - @agola11
-
-        Tools / Toolkits
-        - ...
-
-      placeholder: "@Username ..."
-
-  - type: checkboxes
-    id: information-scripts-examples
-    attributes:
-      label: Information
-      description: "The problem arises when using:"
-      options:
-        - label: "The official example notebooks/scripts"
-        - label: "My own modified scripts"
-
   - type: checkboxes
     id: related-components
     attributes:
@@ -77,30 +101,3 @@ body:
         - label: "Chains"
         - label: "Callbacks/Tracing"
         - label: "Async"
-
-  - type: textarea
-    id: reproduction
-    validations:
-      required: true
-    attributes:
-      label: Reproduction
-      description: |
-        Please provide a [code sample](https://stackoverflow.com/help/minimal-reproducible-example) that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        If you have code snippets, error messages, stack traces please provide them here as well.
-        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-        Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-
-      placeholder: |
-        Steps to reproduce the behavior:
-
-          1.
-          2.
-          3.
-
-  - type: textarea
-    id: expected-behavior
-    validations:
-      required: true
-    attributes:
-      label: Expected behavior
-      description: "A clear and concise description of what you would expect to happen."

.github/ISSUE_TEMPLATE/config.yml

@@ -1,6 +1,9 @@
 blank_issues_enabled: true
 version: 2.1
 contact_links:
+  - name: 🤔 Question or Problem
+    about: Ask a question or ask about a problem in GitHub Discussions.
+    url: https://github.com/langchain-ai/langchain/discussions
   - name: Discord
     url: https://discord.gg/6adMQxSpJS
     about: General community discussions

.github/ISSUE_TEMPLATE/other.yml

@@ -1,18 +0,0 @@
-name: Other Issue
-description: Raise an issue that wouldn't be covered by the other templates.
-title: "Issue: <Please write a comprehensive title after the 'Issue: ' prefix>"
-labels: [04 - Other]
-
-body:
-  - type: textarea
-    attributes:
-      label: "Issue you'd like to raise."
-      description: >
-        Please describe the issue you'd like to raise as clearly as possible.
-        Make sure to include any relevant links or references.
-
-  - type: textarea
-    attributes:
-      label: "Suggestion:"
-      description: >
-        Please outline a suggestion to improve the issue here.

.github/actions/poetry_setup/action.yml

@@ -28,6 +28,7 @@ runs:
   steps:
     - uses: actions/setup-python@v5
       name: Setup python ${{ inputs.python-version }}
+      id: setup-python
       with:
         python-version: ${{ inputs.python-version }}
 
@@ -74,7 +75,8 @@ runs:
       env:
         POETRY_VERSION: ${{ inputs.poetry-version }}
         PYTHON_VERSION: ${{ inputs.python-version }}
-      run: pipx install "poetry==$POETRY_VERSION" --python "python$PYTHON_VERSION" --verbose
+      # Install poetry using the python version installed by setup-python step.
+      run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose
 
     - name: Restore pip and poetry cached dependencies
       uses: actions/cache@v3

.github/workflows/_integration_test.yml

@@ -37,6 +37,12 @@ jobs:
         shell: bash
         run: poetry install --with test,test_integration
 
+      - name: 'Authenticate to Google Cloud'
+        id: 'auth'
+        uses: google-github-actions/auth@v2
+        with:
+          credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
+
       - name: Run integration tests
         shell: bash
         env:
@@ -44,6 +50,7 @@ jobs:
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         run: |
           make integration_tests
 

.github/workflows/_release.yml

@@ -1,5 +1,5 @@
 name: release
-
+run-name: Release ${{ inputs.working-directory }} by @${{ github.actor }}
 on:
   workflow_call:
     inputs:
@@ -117,11 +117,18 @@ jobs:
         #   are not found on test PyPI can be resolved and installed anyway.
         #   (https://test.pypi.org/simple). This will include the PKG_NAME==VERSION
         #   package because VERSION will not have been uploaded to regular PyPI yet.
-        #
+        # - attempt install again after 5 seconds if it fails because there is
+        #   sometimes a delay in availability on test pypi
         run: |
           poetry run pip install \
             --extra-index-url https://test.pypi.org/simple/ \
-            "$PKG_NAME==$VERSION"
+            "$PKG_NAME==$VERSION" || \
+          ( \
+            sleep 5 && \
+            poetry run pip install \
+              --extra-index-url https://test.pypi.org/simple/ \
+              "$PKG_NAME==$VERSION" \
+          )
 
           # Replace all dashes in the package name with underscores,
           # since that's how Python imports packages with dashes in the name.
@@ -149,6 +156,12 @@ jobs:
         run: make tests
         working-directory: ${{ inputs.working-directory }}
 
+      - name: 'Authenticate to Google Cloud'
+        id: 'auth'
+        uses: google-github-actions/auth@v2
+        with:
+          credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
+
       - name: Run integration tests
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         env:
@@ -156,6 +169,7 @@ jobs:
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 

README.md

@@ -49,7 +49,7 @@ The LangChain libraries themselves are made up of several different packages.
 - **[`langchain-community`](libs/community)**: Third party integrations.
 - **[`langchain`](libs/langchain)**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
 
-![LangChain Stack](docs/static/img/langchain_stack.png)
+![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/img/langchain_stack.png "LangChain Architecture Overview")
 
 ## 🧱 What can you build with LangChain?
 **❓ Retrieval augmented generation**

cookbook/LLaMA2_sql_chat.ipynb

@@ -149,7 +149,7 @@
    ],
    "source": [
     "# Prompt\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
     "# Update the template based on the type of SQL Database like MySQL, Microsoft SQL Server and so on\n",
     "template = \"\"\"Based on the table schema below, write a SQL query that would answer the user's question:\n",
@@ -278,7 +278,7 @@
    "source": [
     "# Prompt\n",
     "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "\n",
     "template = \"\"\"Given an input question, convert it to a SQL query. No pre-amble. Based on the table schema below, write a SQL query that would answer the user's question:\n",
     "{schema}\n",

cookbook/Multi_modal_RAG.ipynb

@@ -198,9 +198,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "\n",
     "# Generate summaries of text elements\n",
@@ -355,9 +355,9 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "\n",
     "def create_multi_vector_retriever(\n",

cookbook/Semi_Structured_RAG.ipynb

@@ -235,9 +235,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_core.output_parsers import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -320,9 +320,9 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",

cookbook/Semi_structured_and_multi_modal_RAG.ipynb

@@ -211,9 +211,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_core.output_parsers import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -375,9 +375,9 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -209,9 +209,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain_community.chat_models import ChatOllama\n",
-    "from langchain_core.output_parsers import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate"
    ]
   },
   {

cookbook/advanced_rag_eval.ipynb

@@ -132,8 +132,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "baseline = Chroma.from_texts(\n",
     "    texts=all_splits_pypdf_texts,\n",
@@ -160,9 +160,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "# Prompt\n",
     "prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text for retrieval. \\\n",

cookbook/agent_vectorstore.ipynb

@@ -29,9 +29,8 @@
    "source": [
     "from langchain.chains import RetrievalQA\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain_community.llms import OpenAI\n",
     "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import OpenAI, OpenAIEmbeddings\n",
     "\n",
     "llm = OpenAI(temperature=0)"
    ]
@@ -161,7 +160,7 @@
    "source": [
     "# Import things that are needed generically\n",
     "from langchain.agents import AgentType, Tool, initialize_agent\n",
-    "from langchain_community.llms import OpenAI"
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/analyze_document.ipynb

@@ -29,7 +29,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import AnalyzeDocumentChain\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)"
    ]

cookbook/autogpt/autogpt.ipynb

@@ -62,8 +62,8 @@
    "outputs": [],
    "source": [
     "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
-    "from langchain_community.vectorstores import FAISS"
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import OpenAIEmbeddings"
    ]
   },
   {
@@ -100,8 +100,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_experimental.autonomous_agents import AutoGPT"
+    "from langchain_experimental.autonomous_agents import AutoGPT\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/autogpt/marathon_times.ipynb

@@ -41,8 +41,8 @@
     "import pandas as pd\n",
     "from langchain.docstore.document import Document\n",
     "from langchain_community.agent_toolkits.pandas.base import create_pandas_dataframe_agent\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_experimental.autonomous_agents import AutoGPT\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "# Needed synce jupyter runs an async eventloop\n",
     "nest_asyncio.apply()"
@@ -311,8 +311,8 @@
     "# Memory\n",
     "import faiss\n",
     "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings_model = OpenAIEmbeddings()\n",
     "embedding_size = 1536\n",

cookbook/baby_agi.ipynb

@@ -31,9 +31,8 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
-    "from langchain_community.llms import OpenAI\n",
-    "from langchain_experimental.autonomous_agents import BabyAGI"
+    "from langchain_experimental.autonomous_agents import BabyAGI\n",
+    "from langchain_openai import OpenAI, OpenAIEmbeddings"
    ]
   },
   {

cookbook/baby_agi_with_agent.ipynb

@@ -29,9 +29,8 @@
     "\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
-    "from langchain_community.llms import OpenAI\n",
-    "from langchain_experimental.autonomous_agents import BabyAGI"
+    "from langchain_experimental.autonomous_agents import BabyAGI\n",
+    "from langchain_openai import OpenAI, OpenAIEmbeddings"
    ]
   },
   {
@@ -108,8 +107,8 @@
    "source": [
     "from langchain.agents import AgentExecutor, Tool, ZeroShotAgent\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms import OpenAI\n",
     "from langchain_community.utilities import SerpAPIWrapper\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "todo_prompt = PromptTemplate.from_template(\n",
     "    \"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\"\n",

cookbook/camel_role_playing.ipynb

@@ -46,7 +46,7 @@
     "    HumanMessage,\n",
     "    SystemMessage,\n",
     ")\n",
-    "from langchain_community.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/causal_program_aided_language_model.ipynb

@@ -47,9 +47,9 @@
    "outputs": [],
    "source": [
     "from IPython.display import SVG\n",
-    "from langchain_community.llms import OpenAI\n",
     "from langchain_experimental.cpal.base import CPALChain\n",
     "from langchain_experimental.pal_chain import PALChain\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0, max_tokens=512)\n",
     "cpal_chain = CPALChain.from_univariate_prompt(llm=llm, verbose=True)\n",

cookbook/code-analysis-deeplake.ipynb

@@ -657,7 +657,7 @@
     }
    ],
    "source": [
-    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings = OpenAIEmbeddings()\n",
     "embeddings"
@@ -834,7 +834,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import ConversationalRetrievalChain\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(\n",
     "    model_name=\"gpt-3.5-turbo-0613\"\n",

cookbook/custom_agent_with_plugin_retrieval.ipynb

@@ -44,8 +44,8 @@
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain_community.agent_toolkits import NLAToolkit\n",
-    "from langchain_community.llms import OpenAI\n",
-    "from langchain_community.tools.plugin import AIPlugin"
+    "from langchain_community.tools.plugin import AIPlugin\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {
@@ -115,8 +115,8 @@
    "outputs": [],
    "source": [
     "from langchain.schema import Document\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
-    "from langchain_community.vectorstores import FAISS"
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import OpenAIEmbeddings"
    ]
   },
   {

cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb

@@ -69,8 +69,8 @@
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain_community.agent_toolkits import NLAToolkit\n",
-    "from langchain_community.llms import OpenAI\n",
-    "from langchain_community.tools.plugin import AIPlugin"
+    "from langchain_community.tools.plugin import AIPlugin\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {
@@ -139,8 +139,8 @@
    "outputs": [],
    "source": [
     "from langchain.schema import Document\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
-    "from langchain_community.vectorstores import FAISS"
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import OpenAIEmbeddings"
    ]
   },
   {

cookbook/custom_agent_with_tool_retrieval.ipynb

@@ -41,8 +41,8 @@
     "from langchain.chains import LLMChain\n",
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
-    "from langchain_community.llms import OpenAI\n",
-    "from langchain_community.utilities import SerpAPIWrapper"
+    "from langchain_community.utilities import SerpAPIWrapper\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {
@@ -104,8 +104,8 @@
    "outputs": [],
    "source": [
     "from langchain.schema import Document\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
-    "from langchain_community.vectorstores import FAISS"
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import OpenAIEmbeddings"
    ]
   },
   {

cookbook/databricks_sql_db.ipynb

@@ -93,7 +93,7 @@
    "outputs": [],
    "source": [
     "# Creating a OpenAI Chat LLM wrapper\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
    ]

cookbook/deeplake_semantic_search_over_chat.ipynb

@@ -56,9 +56,8 @@
     "    CharacterTextSplitter,\n",
     "    RecursiveCharacterTextSplitter,\n",
     ")\n",
-    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain_community.llms import OpenAI\n",
     "from langchain_community.vectorstores import DeepLake\n",
+    "from langchain_openai import OpenAI, OpenAIEmbeddings\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
     "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",

cookbook/docugami_xml_kg_rag.ipynb

@@ -475,8 +475,8 @@
     "    HumanMessagePromptTemplate,\n",
     "    SystemMessagePromptTemplate,\n",
     ")\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_core.output_parsers import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -547,9 +547,9 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores.chroma import Chroma\n",
     "from langchain_core.documents import Document\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "\n",
     "def build_retriever(text_elements, tables, table_summaries):\n",

cookbook/elasticsearch_db_qa.ipynb

@@ -39,7 +39,7 @@
    "source": [
     "from elasticsearch import Elasticsearch\n",
     "from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain\n",
-    "from langchain_community.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/extraction_openai_tools.ipynb

@@ -22,8 +22,8 @@
     "from typing import List, Optional\n",
     "\n",
     "from langchain.chains.openai_tools import create_extraction_chain_pydantic\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_core.pydantic_v1 import BaseModel"
+    "from langchain_core.pydantic_v1 import BaseModel\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -153,7 +153,7 @@
     "from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
     "from langchain_core.runnables import Runnable\n",
     "from langchain_core.pydantic_v1 import BaseModel\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.messages import SystemMessage\n",
     "from langchain_core.language_models import BaseLanguageModel\n",
     "\n",

cookbook/forward_looking_retrieval_augmented_generation.ipynb

@@ -74,9 +74,8 @@
     "    CallbackManagerForRetrieverRun,\n",
     ")\n",
     "from langchain.schema import BaseRetriever, Document\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_community.llms import OpenAI\n",
-    "from langchain_community.utilities import GoogleSerperAPIWrapper"
+    "from langchain_community.utilities import GoogleSerperAPIWrapper\n",
+    "from langchain_openai import ChatOpenAI, OpenAI"
    ]
   },
   {

cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb

@@ -49,9 +49,8 @@
     "\n",
     "from langchain.docstore import InMemoryDocstore\n",
     "from langchain.retrievers import TimeWeightedVectorStoreRetriever\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "from termcolor import colored"
    ]
   },

cookbook/hugginggpt.ipynb

@@ -75,8 +75,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.llms import OpenAI\n",
     "from langchain_experimental.autonomous_agents import HuggingGPT\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "# %env OPENAI_API_BASE=http://localhost:8000/v1"
    ]

cookbook/human_approval.ipynb

@@ -159,7 +159,7 @@
    "outputs": [],
    "source": [
     "from langchain.agents import AgentType, initialize_agent, load_tools\n",
-    "from langchain_community.llms import OpenAI"
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/hypothetical_document_embeddings.ipynb

@@ -22,8 +22,7 @@
    "source": [
     "from langchain.chains import HypotheticalDocumentEmbedder, LLMChain\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
-    "from langchain_community.llms import OpenAI"
+    "from langchain_openai import OpenAI, OpenAIEmbeddings"
    ]
   },
   {

cookbook/learned_prompt_optimization.ipynb

@@ -49,7 +49,7 @@
    "source": [
     "# pick and configure the LLM of your choice\n",
     "\n",
-    "from langchain_community.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(model=\"gpt-3.5-turbo-instruct\")"
    ]

cookbook/llm_bash.ipynb

@@ -43,8 +43,8 @@
     }
    ],
    "source": [
-    "from langchain_community.llms import OpenAI\n",
     "from langchain_experimental.llm_bash.base import LLMBashChain\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "\n",

cookbook/llm_checker.ipynb

@@ -42,7 +42,7 @@
    ],
    "source": [
     "from langchain.chains import LLMCheckerChain\n",
-    "from langchain_community.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0.7)\n",
     "\n",

cookbook/llm_math.ipynb

@@ -46,7 +46,7 @@
    ],
    "source": [
     "from langchain.chains import LLMMathChain\n",
-    "from langchain_community.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "llm_math = LLMMathChain.from_llm(llm, verbose=True)\n",

cookbook/llm_summarization_checker.ipynb

@@ -331,7 +331,7 @@
    ],
    "source": [
     "from langchain.chains import LLMSummarizationCheckerChain\n",
-    "from langchain_community.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=2)\n",
@@ -822,7 +822,7 @@
    ],
    "source": [
     "from langchain.chains import LLMSummarizationCheckerChain\n",
-    "from langchain_community.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=3)\n",
@@ -1096,7 +1096,7 @@
    ],
    "source": [
     "from langchain.chains import LLMSummarizationCheckerChain\n",
-    "from langchain_community.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, max_checks=3, verbose=True)\n",

cookbook/llm_symbolic_math.ipynb

@@ -14,8 +14,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.llms import OpenAI\n",
     "from langchain_experimental.llm_symbolic_math.base import LLMSymbolicMathChain\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "llm_symbolic_math = LLMSymbolicMathChain.from_llm(llm)"

cookbook/meta_prompt.ipynb

@@ -59,7 +59,7 @@
     "from langchain.chains import LLMChain\n",
     "from langchain.memory import ConversationBufferWindowMemory\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.llms import OpenAI"
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/multi_modal_QA.ipynb

@@ -91,8 +91,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage"
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/multi_modal_RAG_chroma.ipynb

@@ -315,10 +315,10 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "\n",
     "def prompt_func(data_dict):\n",

cookbook/multi_modal_output_agent.ipynb

@@ -44,7 +44,7 @@
    "source": [
     "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain.tools import SteamshipImageGenerationTool\n",
-    "from langchain_community.llms import OpenAI"
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/multi_player_dnd.ipynb

@@ -32,7 +32,7 @@
     "    HumanMessage,\n",
     "    SystemMessage,\n",
     ")\n",
-    "from langchain_community.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/multiagent_authoritarian.ipynb

@@ -41,7 +41,7 @@
     "    HumanMessage,\n",
     "    SystemMessage,\n",
     ")\n",
-    "from langchain_community.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/multiagent_bidding.ipynb

@@ -33,7 +33,7 @@
     "    HumanMessage,\n",
     "    SystemMessage,\n",
     ")\n",
-    "from langchain_community.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/myscale_vector_sql.ipynb

@@ -32,9 +32,9 @@
     "\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.llms import OpenAI\n",
     "from langchain_community.utilities import SQLDatabase\n",
     "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
+    "from langchain_openai import OpenAI\n",
     "from sqlalchemy import MetaData, create_engine\n",
     "\n",
     "MYSCALE_HOST = \"msc-4a9e710a.us-east-1.aws.staging.myscale.cloud\"\n",
@@ -75,10 +75,10 @@
    "outputs": [],
    "source": [
     "from langchain.callbacks import StdOutCallbackHandler\n",
-    "from langchain_community.llms import OpenAI\n",
     "from langchain_community.utilities.sql_database import SQLDatabase\n",
     "from langchain_experimental.sql.prompt import MYSCALE_PROMPT\n",
     "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "chain = VectorSQLDatabaseChain(\n",
     "    llm_chain=LLMChain(\n",
@@ -117,7 +117,6 @@
    "outputs": [],
    "source": [
     "from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_experimental.retrievers.vector_sql_database import (\n",
     "    VectorSQLDatabaseChainRetriever,\n",
     ")\n",
@@ -126,6 +125,7 @@
     "    VectorSQLDatabaseChain,\n",
     "    VectorSQLRetrieveAllOutputParser,\n",
     ")\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "output_parser_retrieve_all = VectorSQLRetrieveAllOutputParser.from_embeddings(\n",
     "    output_parser.model\n",

cookbook/openai_functions_retrieval_qa.ipynb

@@ -22,8 +22,8 @@
     "from langchain.chains import RetrievalQA\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain_community.document_loaders import TextLoader\n",
-    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain_community.vectorstores import Chroma"
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import OpenAIEmbeddings"
    ]
   },
   {
@@ -53,7 +53,7 @@
     "from langchain.chains import create_qa_with_sources_chain\n",
     "from langchain.chains.combine_documents.stuff import StuffDocumentsChain\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/openai_v1_cookbook.ipynb

@@ -28,8 +28,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage"
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -414,7 +414,7 @@
     "BREAKING CHANGES:\n",
     "- To use Azure embeddings with OpenAI V1, you'll need to use the new `AzureOpenAIEmbeddings` instead of the existing `OpenAIEmbeddings`. `OpenAIEmbeddings` continue to work when using Azure with `openai<1`.\n",
     "```python\n",
-    "from langchain_community.embeddings import AzureOpenAIEmbeddings\n",
+    "from langchain_openai import AzureOpenAIEmbeddings\n",
     "```\n",
     "\n",
     "\n",
@@ -456,8 +456,8 @@
     "from typing import Literal\n",
     "\n",
     "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",

cookbook/petting_zoo.ipynb

@@ -52,7 +52,7 @@
     "    HumanMessage,\n",
     "    SystemMessage,\n",
     ")\n",
-    "from langchain_community.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/plan_and_execute_agent.ipynb

@@ -30,15 +30,14 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMMathChain\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_community.llms import OpenAI\n",
     "from langchain_community.utilities import DuckDuckGoSearchAPIWrapper\n",
     "from langchain_core.tools import Tool\n",
     "from langchain_experimental.plan_and_execute import (\n",
     "    PlanAndExecute,\n",
     "    load_agent_executor,\n",
     "    load_chat_planner,\n",
-    ")"
+    ")\n",
+    "from langchain_openai import ChatOpenAI, OpenAI"
    ]
   },
   {

cookbook/press_releases.ipynb

@@ -82,7 +82,7 @@
    "source": [
     "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain.retrievers import KayAiRetriever\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
     "retriever = KayAiRetriever.create(\n",

cookbook/program_aided_language_model.ipynb

@@ -17,8 +17,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.llms import OpenAI\n",
-    "from langchain_experimental.pal_chain import PALChain"
+    "from langchain_experimental.pal_chain import PALChain\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/qa_citations.ipynb

@@ -27,7 +27,7 @@
    ],
    "source": [
     "from langchain.chains import create_citation_fuzzy_match_chain\n",
-    "from langchain_community.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/rag_fusion.ipynb

@@ -30,8 +30,8 @@
    "outputs": [],
    "source": [
     "import pinecone\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import Pinecone\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "pinecone.init(api_key=\"...\", environment=\"...\")"
    ]
@@ -86,8 +86,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_core.output_parsers import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/retrieval_in_sql.ipynb

@@ -43,7 +43,7 @@
    "outputs": [],
    "source": [
     "from langchain.sql_database import SQLDatabase\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "CONNECTION_STRING = \"postgresql+psycopg2://postgres:test@localhost:5432/vectordb\"  # Replace with your own\n",
     "db = SQLDatabase.from_uri(CONNECTION_STRING)"
@@ -88,7 +88,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings_model = OpenAIEmbeddings()"
    ]
@@ -219,7 +219,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
     "template = \"\"\"You are a Postgres expert. Given an input question, first create a syntactically correct Postgres query to run, then look at the results of the query and return the answer to the input question.\n",
     "Unless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per Postgres. You can order the results to return the most informative data in the database.\n",
@@ -267,9 +267,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "db = SQLDatabase.from_uri(\n",
     "    CONNECTION_STRING\n",

cookbook/rewrite.ipynb

@@ -31,11 +31,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_community.utilities import DuckDuckGoSearchAPIWrapper\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.runnables import RunnablePassthrough"
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/sales_agent_with_context.ipynb

@@ -53,10 +53,9 @@
     "from langchain.prompts.base import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain_community.llms import BaseLLM, OpenAI\n",
+    "from langchain_community.llms import BaseLLM\n",
     "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import ChatOpenAI, OpenAI, OpenAIEmbeddings\n",
     "from pydantic import BaseModel, Field"
    ]
   },

cookbook/selecting_llms_based_on_context_length.ipynb

@@ -18,9 +18,9 @@
    "outputs": [],
    "source": [
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompt_values import PromptValue"
+    "from langchain_core.prompt_values import PromptValue\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/self_query_hotel_search.ipynb

@@ -255,7 +255,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(model=\"gpt-4\")\n",
     "res = model.predict(\n",
@@ -1083,8 +1083,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import ElasticsearchStore\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings = OpenAIEmbeddings()"
    ]

cookbook/sharedmemory_for_tools.ipynb

@@ -26,8 +26,8 @@
     "from langchain.chains import LLMChain\n",
     "from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.llms import OpenAI\n",
-    "from langchain_community.utilities import GoogleSearchAPIWrapper"
+    "from langchain_community.utilities import GoogleSearchAPIWrapper\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/smart_llm.ipynb

@@ -52,8 +52,8 @@
    "outputs": [],
    "source": [
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_experimental.smart_llm import SmartLLMChain"
+    "from langchain_experimental.smart_llm import SmartLLMChain\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/sql_db_qa.mdx

@@ -9,7 +9,7 @@ To set it up, follow the instructions on https://database.guide/2-sample-databas
 
 
 ```python
-from langchain_community.llms import OpenAI
+from langchain_openai import OpenAI
 from langchain_community.utilities import SQLDatabase
 from langchain_experimental.sql import SQLDatabaseChain
 ```
@@ -200,7 +200,7 @@ result["intermediate_steps"]
 How to add memory to a SQLDatabaseChain:
 
 ```python
-from langchain_community.llms import OpenAI
+from langchain_openai import OpenAI
 from langchain_community.utilities import SQLDatabase
 from langchain_experimental.sql import SQLDatabaseChain
 ```

cookbook/stepback-qa.ipynb

@@ -23,10 +23,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.runnables import RunnableLambda"
+    "from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate\n",
+    "from langchain_core.runnables import RunnableLambda\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/together_ai.ipynb

@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0fc0309d-4d49-4bb5-bec0-bd92c6fddb28",
+   "metadata": {},
+   "source": [
+    "## Together AI + RAG\n",
+    " \n",
+    "[Together AI](https://python.langchain.com/docs/integrations/llms/together) has a broad set of OSS LLMs via inference API.\n",
+    "\n",
+    "See [here](https://api.together.xyz/playground). We use `\"mistralai/Mixtral-8x7B-Instruct-v0.1` for RAG on the Mixtral paper.\n",
+    "\n",
+    "Download the paper:\n",
+    "https://arxiv.org/pdf/2401.04088.pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d12fb75a-f707-48d5-82a5-efe2d041813c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install --quiet pypdf chromadb tiktoken openai langchain-together"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ab49327-0532-4480-804c-d066c302a322",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load\n",
+    "from langchain_community.document_loaders import PyPDFLoader\n",
+    "\n",
+    "loader = PyPDFLoader(\"~/Desktop/mixtral.pdf\")\n",
+    "data = loader.load()\n",
+    "\n",
+    "# Split\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)\n",
+    "all_splits = text_splitter.split_documents(data)\n",
+    "\n",
+    "# Add to vectorDB\n",
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "\n",
+    "\"\"\"\n",
+    "from langchain_together.embeddings import TogetherEmbeddings\n",
+    "embeddings = TogetherEmbeddings(model=\"togethercomputer/m2-bert-80M-8k-retrieval\")\n",
+    "\"\"\"\n",
+    "vectorstore = Chroma.from_documents(\n",
+    "    documents=all_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=OpenAIEmbeddings(),\n",
+    ")\n",
+    "\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4efaddd9-3dbb-455c-ba54-0ad7f2d2ce0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel\n",
+    "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
+    "\n",
+    "# RAG prompt\n",
+    "template = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    "\n",
+    "Question: {question}\n",
+    "\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "\n",
+    "# LLM\n",
+    "from langchain_community.llms import Together\n",
+    "\n",
+    "llm = Together(\n",
+    "    model=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
+    "    temperature=0.0,\n",
+    "    max_tokens=2000,\n",
+    "    top_k=1,\n",
+    ")\n",
+    "\n",
+    "# RAG chain\n",
+    "chain = (\n",
+    "    RunnableParallel({\"context\": retriever, \"question\": RunnablePassthrough()})\n",
+    "    | prompt\n",
+    "    | llm\n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "88b1ee51-1b0f-4ebf-bb32-e50e843f0eeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nAnswer: The architectural details of Mixtral are as follows:\\n- Dimension (dim): 4096\\n- Number of layers (n\\\\_layers): 32\\n- Dimension of each head (head\\\\_dim): 128\\n- Hidden dimension (hidden\\\\_dim): 14336\\n- Number of heads (n\\\\_heads): 32\\n- Number of kv heads (n\\\\_kv\\\\_heads): 8\\n- Context length (context\\\\_len): 32768\\n- Vocabulary size (vocab\\\\_size): 32000\\n- Number of experts (num\\\\_experts): 8\\n- Number of top k experts (top\\\\_k\\\\_experts): 2\\n\\nMixtral is based on a transformer architecture and uses the same modifications as described in [18], with the notable exceptions that Mixtral supports a fully dense context length of 32k tokens, and the feedforward block picks from a set of 8 distinct groups of parameters. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. This technique increases the number of parameters of a model while controlling cost and latency, as the model only uses a fraction of the total set of parameters per token. Mixtral is pretrained with multilingual data using a context size of 32k tokens. It either matches or exceeds the performance of Llama 2 70B and GPT-3.5, over several benchmarks. In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke(\"What are the Architectural details of Mixtral?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "755cf871-26b7-4e30-8b91-9ffd698470f4",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/935fd642-06a6-4b42-98e3-6074f93115cd/r"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

cookbook/tree_of_thought.ipynb

@@ -24,7 +24,7 @@
     }
    ],
    "source": [
-    "from langchain_community.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=1, max_tokens=512, model=\"gpt-3.5-turbo-instruct\")"
    ]

cookbook/twitter-the-algorithm-analysis-deeplake.ipynb

@@ -37,8 +37,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import DeepLake\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
     "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",
@@ -3809,7 +3809,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import ConversationalRetrievalChain\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(model_name=\"gpt-3.5-turbo-0613\")  # switch to 'gpt-4'\n",
     "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"

cookbook/two_agent_debate_tools.ipynb

@@ -30,7 +30,7 @@
     "    HumanMessage,\n",
     "    SystemMessage,\n",
     ")\n",
-    "from langchain_community.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/two_player_dnd.ipynb

@@ -28,7 +28,7 @@
     "    HumanMessage,\n",
     "    SystemMessage,\n",
     ")\n",
-    "from langchain_community.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/wikibase_agent.ipynb

@@ -599,7 +599,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0)"
    ]

docs/.local_build.sh

@@ -20,4 +20,4 @@ wget https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O
 
 yarn
 
-quarto preview docs
+poetry run quarto preview docs

docs/docs/changelog/core.mdx

@@ -0,0 +1,27 @@
+# langchain-core
+
+## 0.1.7 (Jan 5, 2024)
+
+#### Deleted
+
+No deletions.
+
+#### Deprecated
+
+- `BaseChatModel` methods `__call__`, `call_as_llm`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.invoke` instead.
+- `BaseChatModel` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.ainvoke` instead.
+- `BaseLLM` methods `__call__, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseLLM.invoke` instead.
+- `BaseLLM` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseLLM.ainvoke` instead.
+
+#### Fixed
+
+- Restrict recursive URL scraping: [#15559](https://github.com/langchain-ai/langchain/pull/15559)
+
+#### Added
+
+No additions.
+
+#### Beta
+
+- Marked `langchain_core.load.load` and `langchain_core.load.loads` as beta.
+- Marked `langchain_core.beta.runnables.context.ContextGet` and `langchain_core.beta.runnables.context.ContextSet` as beta.

docs/docs/changelog/langchain.mdx

@@ -0,0 +1,36 @@
+# langchain
+
+## 0.1.0 (Jan 5, 2024)
+
+#### Deleted
+
+No deletions.
+
+#### Deprecated
+
+Deprecated classes and methods will be removed in 0.2.0
+
+| Deprecated                | Alternative                       | Reason                                         |
+|---------------------------------|-----------------------------------|------------------------------------------------|
+| ChatVectorDBChain               | ConversationalRetrievalChain      | More general to all retrievers                 |
+| create_ernie_fn_chain           | create_ernie_fn_runnable          | Use LCEL under the hood                        |
+| created_structured_output_chain | create_structured_output_runnable | Use LCEL under the hood                        |
+| NatBotChain                     |                                   | Not used                                       |
+| create_openai_fn_chain          | create_openai_fn_runnable         | Use LCEL under the hood                        |
+| create_structured_output_chain  | create_structured_output_runnable | Use LCEL under the hood                        |
+| load_query_constructor_chain    | load_query_constructor_runnable   | Use LCEL under the hood                        |
+| VectorDBQA                      | RetrievalQA                       | More general to all retrievers                 |
+| Sequential Chain                | LCEL                              | Obviated by LCEL                               |
+| SimpleSequentialChain           | LCEL                              | Obviated by LCEL                               |
+| TransformChain                  | LCEL/RunnableLambda               | Obviated by LCEL                               |
+| create_tagging_chain            | create_structured_output_runnable | Use LCEL under the hood                        |
+| ChatAgent                       | create_react_agent                | Use LCEL builder over a class                  |
+| ConversationalAgent             | create_react_agent                | Use LCEL builder over a class                  |
+| ConversationalChatAgent         | create_json_chat_agent            | Use LCEL builder over a class                  |
+| initialize_agent                | Individual create agent methods   | Individual create agent methods are more clear |
+| ZeroShotAgent                   | create_react_agent                | Use LCEL builder over a class                  |
+| OpenAIFunctionsAgent            | create_openai_functions_agent     | Use LCEL builder over a class                  |
+| OpenAIMultiFunctionsAgent       | create_openai_tools_agent         | Use LCEL builder over a class                  |
+| SelfAskWithSearchAgent          | create_self_ask_with_search       | Use LCEL builder over a class                  |
+| StructuredChatAgent             | create_structured_chat_agent      | Use LCEL builder over a class                  |
+| XMLAgent                        | create_xml_agent                  | Use LCEL builder over a class                  |

docs/docs/community.md

@@ -1,53 +0,0 @@
-# Community navigator
-
-Hi! Thanks for being here. We’re lucky to have a community of so many passionate developers building with LangChain–we have so much to teach and learn from each other. Community members contribute code, host meetups, write blog posts, amplify each other’s work, become each other's customers and collaborators, and so much more.
-
-Whether you’re new to LangChain, looking to go deeper, or just want to get more exposure to the world of building with LLMs, this page can point you in the right direction. 
-
-- **🦜 Contribute to LangChain**
-
-- **🌍 Meetups, Events, and Hackathons**
-
-- **📣 Help Us Amplify Your Work**
-
-- **💬 Stay in the loop**
-
-
-# 🦜 Contribute to LangChain
-
-LangChain is the product of over 5,000+ contributions by 1,500+ contributors, and there is ******still****** so much to do together. Here are some ways to get involved:
-
-- **[Open a pull request](https://github.com/langchain-ai/langchain/issues):** We’d appreciate all forms of contributions–new features, infrastructure improvements, better documentation, bug fixes, etc. If you have an improvement or an idea, we’d love to work on it with you.
-- **[Read our contributor guidelines:](./contributing/)** We ask contributors to follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow, run a few local checks for formatting, linting, and testing before submitting, and follow certain documentation and testing conventions.
-    - **First time contributor?** [Try one of these PRs with the “good first issue” tag](https://github.com/langchain-ai/langchain/contribute).
-- **Become an expert:** Our experts help the community by answering product questions in Discord. If that’s a role you’d like to play, we’d be so grateful! (And we have some special experts-only goodies/perks we can tell you more about). Send us an email to introduce yourself at hello@langchain.dev and we’ll take it from there!
-- **Integrate with LangChain:** If your product integrates with LangChain–or aspires to–we want to help make sure the experience is as smooth as possible for you and end users. Send us an email at hello@langchain.dev and tell us what you’re working on.
-    - **Become an Integration Maintainer:** Partner with our team to ensure your integration stays up-to-date and talk directly with users (and answer their inquiries) in our Discord. Introduce yourself at hello@langchain.dev if you’d like to explore this role.
-
-
-# 🌍 Meetups, Events, and Hackathons
-
-One of our favorite things about working in AI is how much enthusiasm there is for building together. We want to help make that as easy and impactful for you as possible! 
-- **Find a meetup, hackathon, or webinar:** You can find the one for you on our [global events calendar](https://mirror-feeling-d80.notion.site/0bc81da76a184297b86ca8fc782ee9a3?v=0d80342540df465396546976a50cfb3f).  
-    - **Submit an event to our calendar:** Email us at events@langchain.dev with a link to your event page! We can also help you spread the word with our local communities.
-- **Host a meetup:** If you want to bring a group of builders together, we want to help! We can publicize your event on our event calendar/Twitter, share it with our local communities in Discord, send swag, or potentially hook you up with a sponsor. Email us at events@langchain.dev to tell us about your event!
-- **Become a meetup sponsor:** We often hear from groups of builders that want to get together, but are blocked or limited on some dimension (space to host, budget for snacks, prizes to distribute, etc.). If you’d like to help, send us an email to events@langchain.dev we can share more about how it works!
-- **Speak at an event:** Meetup hosts are always looking for great speakers, presenters, and panelists. If you’d like to do that at an event, send us an email to hello@langchain.dev with more information about yourself, what you want to talk about, and what city you’re based in and we’ll try to match you with an upcoming event!
-- **Tell us about your LLM community:** If you host or participate in a community that would welcome support from LangChain and/or our team, send us an email at hello@langchain.dev and let us know how we can help.
-
-# 📣 Help Us Amplify Your Work
-
-If you’re working on something you’re proud of, and think the LangChain community would benefit from knowing about it, we want to help you show it off.
-
-- **Post about your work and mention us:** We love hanging out on Twitter to see what people in the space are talking about and working on. If you tag [@langchainai](https://twitter.com/LangChainAI), we’ll almost certainly see it and can show you some love.
-- **Publish something on our blog:** If you’re writing about your experience building with LangChain, we’d love to post (or crosspost) it on our blog! E-mail hello@langchain.dev with a draft of your post! Or even an idea for something you want to write about.
-- **Get your product onto our [integrations hub](https://integrations.langchain.com/):** Many developers take advantage of our seamless integrations with other products, and come to our integrations hub to find out who those are. If you want to get your product up there, tell us about it (and how it works with LangChain) at hello@langchain.dev.
-
-# ☀️ Stay in the loop
-
-Here’s where our team hangs out, talks shop, spotlights cool work, and shares what we’re up to. We’d love to see you there too.
-
-- **[Twitter](https://twitter.com/LangChainAI):** We post about what we’re working on and what cool things we’re seeing in the space. If you tag @langchainai in your post, we’ll almost certainly see it, and can show you some love!
-- **[Discord](https://discord.gg/6adMQxSpJS):** connect with over 30,000 developers who are building with LangChain.
-- **[GitHub](https://github.com/langchain-ai/langchain):** Open pull requests, contribute to a discussion, and/or contribute
-- **[Subscribe to our bi-weekly Release Notes](https://6w1pwbss0py.typeform.com/to/KjZB1auB):** a twice/month email roundup of the coolest things going on in our orbit

docs/docs/contributing/index.mdx

@@ -40,3 +40,8 @@ smooth for future contributors.
 In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
 If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
 we do not want these to get in the way of getting good code into the codebase.
+
+# 🌟 Recognition
+
+If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
+If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.

docs/docs/contributing/integrations.mdx

@@ -53,9 +53,9 @@ And we would write tests in:
 - Integration tests: `libs/community/tests/integration_tests/chat_models/test_parrot_link.py`
 
 And add documentation to:
+
 - `docs/docs/integrations/chat/parrot_link.ipynb`
 
-- `docs/docs/
 ## Partner Packages
 
 Partner packages are in `libs/partners/*` and are installed by users with `pip install langchain-{partner}`, and exported members can be imported with code like 

docs/docs/contributing/packages.mdx

@@ -1,56 +0,0 @@
----
-sidebar_label: Package Versioning
-sidebar_position: 4
----
-
-# 📕 Package Versioning
-
-As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
-a maintainer and published to [PyPI](https://pypi.org/). 
-The different packages are versioned slightly differently.
-
-## `langchain-core`
-
-`langchain-core` is currently on version `0.1.x`. 
-
-As `langchain-core` contains the base abstractions and runtime for the whole LangChain ecosystem, we will communicate any breaking changes with advance notice and version bumps. The exception for this is anything in `langchain_core.beta`. The reason for `langchain_core.beta` is that given the rate of change of the field, being able to move quickly is still a priority, and this module is our attempt to do so.
-
-Minor version increases will occur for:
-
-- Breaking changes for any public interfaces NOT in `langchain_core.beta`
-
-Patch version increases will occur for:
-
-- Bug fixes
-- New features
-- Any changes to private interfaces
-- Any changes to `langchain_core.beta`
-
-## `langchain`
-
-`langchain` is currently on version `0.0.x`
-
-All changes will be accompanied by a patch version increase. Any changes to public interfaces are nearly always done in a backwards compatible way and will be communicated ahead of time when they are not backwards compatible.
-
-We are targeting January 2024 for a release of `langchain` v0.1, at which point `langchain` will adopt the same versioning policy as `langchain-core`.
-
-## `langchain-community`
-
-`langchain-community` is currently on version `0.0.x`
-
-All changes will be accompanied by a patch version increase.
-
-## `langchain-experimental`
-
-`langchain-experimental` is currently on version `0.0.x`
-
-All changes will be accompanied by a patch version increase.
-
-## Partner Packages
-
-Partner packages are versioned independently.
-
-# 🌟 Recognition
-
-If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
-If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.

docs/docs/expression_language/cookbook/code_writing.ipynb

@@ -10,6 +10,16 @@
     "Example of how to use LCEL to write Python code."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0653c7c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain-core langchain-experimental langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -17,12 +27,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import (\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import (\n",
     "    ChatPromptTemplate,\n",
     ")\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_experimental.utilities import PythonREPL"
+    "from langchain_experimental.utilities import PythonREPL\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

docs/docs/expression_language/cookbook/embedding_router.ipynb

@@ -12,6 +12,16 @@
     "One especially useful technique is to use embeddings to route a query to the most relevant prompt. Here's a very simple example."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b793a0aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain-core langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -19,12 +29,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.utils.math import cosine_similarity\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "\n",
     "physics_template = \"\"\"You are a very smart physics professor. \\\n",
     "You are great at answering questions about physics in a concise and easy to understand manner. \\\n",

docs/docs/expression_language/cookbook/memory.ipynb

@@ -10,6 +10,16 @@
     "This shows how to add memory to an arbitrary chain. Right now, you can use the memory classes but need to hook it up manually"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18753dee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -20,9 +30,9 @@
     "from operator import itemgetter\n",
     "\n",
     "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI()\n",
     "prompt = ChatPromptTemplate.from_messages(\n",

docs/docs/expression_language/cookbook/moderation.ipynb

@@ -10,6 +10,16 @@
     "This shows how to add in moderation (or other safeguards) around your LLM application."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6acf3505",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -18,8 +28,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import OpenAIModerationChain\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.llms import OpenAI"
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {

docs/docs/expression_language/cookbook/multiple_chains.ipynb

@@ -19,6 +19,14 @@
     "Runnables can easily be used to string together multiple Chains"
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "0f316b5c",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -39,9 +47,9 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema import StrOutputParser\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "prompt1 = ChatPromptTemplate.from_template(\"what is the city {person} is from?\")\n",
     "prompt2 = ChatPromptTemplate.from_template(\n",

docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb

@@ -35,6 +35,14 @@
     "Note, you can mix and match PromptTemplate/ChatPromptTemplates and LLMs/ChatModels as you like here."
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "ef79a54b",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -42,8 +50,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "prompt = ChatPromptTemplate.from_template(\"tell me a joke about {foo}\")\n",
     "model = ChatOpenAI()\n",

docs/docs/expression_language/cookbook/prompt_size.ipynb

@@ -12,6 +12,16 @@
     "With LCEL, it's easy to add custom functionality for managing the size of prompts within your chain or agent. Let's look at simple agent example that can search Wikipedia for information."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1846587d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai wikipedia"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -19,19 +29,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !pip install langchain wikipedia\n",
-    "\n",
     "from operator import itemgetter\n",
     "\n",
     "from langchain.agents import AgentExecutor, load_tools\n",
     "from langchain.agents.format_scratchpad import format_to_openai_function_messages\n",
     "from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser\n",
-    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "from langchain.prompts.chat import ChatPromptValue\n",
     "from langchain.tools import WikipediaQueryRun\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_community.tools.convert_to_openai import format_tool_to_openai_function\n",
-    "from langchain_community.utilities import WikipediaAPIWrapper"
+    "from langchain_community.utilities import WikipediaAPIWrapper\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

docs/docs/expression_language/cookbook/retrieval.ipynb

@@ -26,7 +26,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install langchain openai faiss-cpu tiktoken"
+    "%pip install --upgrade --quiet  langchain langchain-openai faiss-cpu tiktoken"
    ]
   },
   {
@@ -38,12 +38,11 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import FAISS\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough"
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings"
    ]
   },
   {

docs/docs/expression_language/cookbook/sql_db.ipynb

@@ -19,6 +19,14 @@
     "We can replicate our SQLDatabaseChain with Runnables."
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "b3121aa8",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -26,7 +34,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
     "template = \"\"\"Based on the table schema below, write a SQL query that would answer the user's question:\n",
     "{schema}\n",
@@ -93,9 +101,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI()\n",
     "\n",

docs/docs/expression_language/cookbook/tools.ipynb

@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install duckduckgo-search"
+    "%pip install --upgrade --quiet  langchain langchain-openai duckduckgo-search"
    ]
   },
   {
@@ -27,10 +27,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.tools import DuckDuckGoSearchRun\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_core.output_parsers import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

docs/docs/expression_language/get_started.ipynb

@@ -30,30 +30,38 @@
     "The most basic and common use case is chaining a prompt template and a model together. To see how this works, let's create a chain that takes a topic and generates a joke:"
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "278b0027",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain-core langchain-community langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "id": "466b65b3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\"Why did the ice cream go to therapy?\\n\\nBecause it had too many toppings and couldn't find its cone-fidence!\""
+       "\"Why don't ice creams ever get invited to parties?\\n\\nBecause they always drip when things heat up!\""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "prompt = ChatPromptTemplate.from_template(\"tell me a short joke about {topic}\")\n",
-    "model = ChatOpenAI()\n",
+    "model = ChatOpenAI(model=\"gpt-4\")\n",
     "output_parser = StrOutputParser()\n",
     "\n",
     "chain = prompt | model | output_parser\n",
@@ -89,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "b8656990",
    "metadata": {},
    "outputs": [
@@ -99,7 +107,7 @@
        "ChatPromptValue(messages=[HumanMessage(content='tell me a short joke about ice cream')])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -111,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "e6034488",
    "metadata": {},
    "outputs": [
@@ -121,7 +129,7 @@
        "[HumanMessage(content='tell me a short joke about ice cream')]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -132,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "60565463",
    "metadata": {},
    "outputs": [
@@ -142,7 +150,7 @@
        "'Human: tell me a short joke about ice cream'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,17 +171,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "id": "33cf5f72",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"Why did the ice cream go to therapy? \\n\\nBecause it had too many toppings and couldn't find its cone-fidence!\")"
+       "AIMessage(content=\"Why don't ice creams ever get invited to parties?\\n\\nBecause they always bring a melt down!\")"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -193,23 +201,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "8feb05da",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'\\n\\nRobot: Why did the ice cream go to therapy? Because it had a rocky road.'"
+       "'\\n\\nRobot: Why did the ice cream truck break down? Because it had a meltdown!'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_community.llms import OpenAI\n",
+    "from langchain_openai.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(model=\"gpt-3.5-turbo-instruct\")\n",
     "llm.invoke(prompt_value)"
@@ -324,12 +332,12 @@
     "# Requires:\n",
     "# pip install langchain docarray tiktoken\n",
     "\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import DocArrayInMemorySearch\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
+    "from langchain_openai.chat_models import ChatOpenAI\n",
+    "from langchain_openai.embeddings import OpenAIEmbeddings\n",
     "\n",
     "vectorstore = DocArrayInMemorySearch.from_texts(\n",
     "    [\"harrison worked at kensho\", \"bears like to eat honey\"],\n",
@@ -486,7 +494,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/binding.ipynb

@@ -12,6 +12,16 @@
     "Suppose we have a simple prompt + model sequence:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5dad8b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -19,10 +29,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema import StrOutputParser\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_core.runnables import RunnablePassthrough"
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

docs/docs/expression_language/how_to/configure.ipynb

@@ -34,6 +34,16 @@
     "With LLMs we can configure things like temperature"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40ed76a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 35,
@@ -42,8 +52,8 @@
    "outputs": [],
    "source": [
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.runnables import ConfigurableField\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(temperature=0).configurable_fields(\n",
     "    temperature=ConfigurableField(\n",
@@ -264,8 +274,9 @@
    "outputs": [],
    "source": [
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.chat_models import ChatAnthropic, ChatOpenAI\n",
-    "from langchain_core.runnables import ConfigurableField"
+    "from langchain_community.chat_models import ChatAnthropic\n",
+    "from langchain_core.runnables import ConfigurableField\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

docs/docs/expression_language/how_to/decorator.ipynb

@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b45110ef",
+   "metadata": {},
+   "source": [
+    "# Create a runnable with the `@chain` decorator\n",
+    "\n",
+    "You can also turn an arbitrary function into a chain by adding a `@chain` decorator. This is functionaly equivalent to wrapping in a [`RunnableLambda`](./functions).\n",
+    "\n",
+    "This will have the benefit of improved observability by tracing your chain correctly. Any calls to runnables inside this function will be traced as nested childen.\n",
+    "\n",
+    "It will also allow you to use this as any other runnable, compose it in chain, etc.\n",
+    "\n",
+    "Let's take a look at this in action!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23b2b564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d9370420",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import chain\n",
+    "from langchain_openai import ChatOpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b7f74f7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt1 = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
+    "prompt2 = ChatPromptTemplate.from_template(\"What is the subject of this joke: {joke}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2b0365c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@chain\n",
+    "def custom_chain(text):\n",
+    "    prompt_val1 = prompt1.invoke({\"topic\": text})\n",
+    "    output1 = ChatOpenAI().invoke(prompt_val1)\n",
+    "    parsed_output1 = StrOutputParser().invoke(output1)\n",
+    "    chain2 = prompt2 | ChatOpenAI() | StrOutputParser()\n",
+    "    return chain2.invoke({\"joke\": parsed_output1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "904d6872",
+   "metadata": {},
+   "source": [
+    "`custom_chain` is now a runnable, meaning you will need to use `invoke`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6448bdd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The subject of this joke is bears.'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "custom_chain.invoke(\"bears\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa767ea9",
+   "metadata": {},
+   "source": [
+    "If you check out your LangSmith traces, you should see a `custom_chain` trace in there, with the calls to OpenAI nested underneath"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1245bdc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/expression_language/how_to/fallbacks.ipynb

@@ -24,6 +24,16 @@
     "IMPORTANT: By default, a lot of the LLM wrappers catch errors and retry. You will most likely want to turn those off when working with fallbacks. Otherwise the first wrapper will keep on retrying and not failing."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebb61b1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -31,7 +41,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatAnthropic, ChatOpenAI"
+    "from langchain_community.chat_models import ChatAnthropic\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -141,7 +152,7 @@
     }
    ],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
@@ -241,7 +252,7 @@
    "source": [
     "# Now lets create a chain with the normal OpenAI model\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_community.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "prompt_template = \"\"\"Instructions: You should always include a compliment in your response.\n",
     "\n",
@@ -291,7 +302,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/functions.ipynb

@@ -24,6 +24,14 @@
     "Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single input and unpacks it into multiple argument."
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "9a5fe916",
+   "metadata": {},
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -33,9 +41,9 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnableLambda\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "\n",
     "def length_function(text):\n",
@@ -190,7 +198,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/generators.ipynb

@@ -24,6 +24,15 @@
     "## Sync version"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -33,8 +42,8 @@
     "from typing import Iterator, List\n",
     "\n",
     "from langchain.prompts.chat import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "prompt = ChatPromptTemplate.from_template(\n",
     "    \"Write a comma-separated list of 5 animals similar to: {animal}\"\n",

docs/docs/expression_language/how_to/inspect.ipynb

@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8c5eb99a",
+   "metadata": {},
+   "source": [
+    "# Inspect your runnables\n",
+    "\n",
+    "Once you create a runnable with LCEL, you may often want to inspect it to get a better sense for what is going on. This notebook covers some methods for doing so.\n",
+    "\n",
+    "First, let's create an example LCEL. We will create one that does retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d816e954",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai faiss-cpu tiktoken"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a88f4b24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.vectorstores import FAISS\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "139228c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorstore = FAISS.from_texts(\n",
+    "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "template = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    "\n",
+    "Question: {question}\n",
+    "\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "\n",
+    "model = ChatOpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "70e3fe93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = (\n",
+    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
+    "    | prompt\n",
+    "    | model\n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "849e3c42",
+   "metadata": {},
+   "source": [
+    "## Get a graph\n",
+    "\n",
+    "You can get a graph of the runnable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2448b6c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Graph(nodes={'7308e6063c6d40818c5a0cc1cc7444f2': Node(id='7308e6063c6d40818c5a0cc1cc7444f2', data=<class 'pydantic.main.RunnableParallel<context,question>Input'>), '292bbd8021d44ec3a31fbe724d9002c1': Node(id='292bbd8021d44ec3a31fbe724d9002c1', data=<class 'pydantic.main.RunnableParallel<context,question>Output'>), '9212f219cf05488f95229c56ea02b192': Node(id='9212f219cf05488f95229c56ea02b192', data=VectorStoreRetriever(tags=['FAISS', 'OpenAIEmbeddings'], vectorstore=<langchain_community.vectorstores.faiss.FAISS object at 0x117334f70>)), 'c7a8e65fa5cf44b99dbe7d1d6e36886f': Node(id='c7a8e65fa5cf44b99dbe7d1d6e36886f', data=RunnablePassthrough()), '818b9bfd40a341008373d5b9f9d0784b': Node(id='818b9bfd40a341008373d5b9f9d0784b', data=ChatPromptTemplate(input_variables=['context', 'question'], messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template='Answer the question based only on the following context:\\n{context}\\n\\nQuestion: {question}\\n'))])), 'b9f1d3ddfa6b4334a16ea439df22b11e': Node(id='b9f1d3ddfa6b4334a16ea439df22b11e', data=ChatOpenAI(client=<class 'openai.api_resources.chat_completion.ChatCompletion'>, openai_api_key='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', openai_proxy='')), '2bf84f6355c44731848345ca7d0f8ab9': Node(id='2bf84f6355c44731848345ca7d0f8ab9', data=StrOutputParser()), '1aeb2da5da5a43bb8771d3f338a473a2': Node(id='1aeb2da5da5a43bb8771d3f338a473a2', data=<class 'pydantic.main.StrOutputParserOutput'>)}, edges=[Edge(source='7308e6063c6d40818c5a0cc1cc7444f2', target='9212f219cf05488f95229c56ea02b192'), Edge(source='9212f219cf05488f95229c56ea02b192', target='292bbd8021d44ec3a31fbe724d9002c1'), Edge(source='7308e6063c6d40818c5a0cc1cc7444f2', target='c7a8e65fa5cf44b99dbe7d1d6e36886f'), Edge(source='c7a8e65fa5cf44b99dbe7d1d6e36886f', target='292bbd8021d44ec3a31fbe724d9002c1'), Edge(source='292bbd8021d44ec3a31fbe724d9002c1', target='818b9bfd40a341008373d5b9f9d0784b'), Edge(source='818b9bfd40a341008373d5b9f9d0784b', target='b9f1d3ddfa6b4334a16ea439df22b11e'), Edge(source='2bf84f6355c44731848345ca7d0f8ab9', target='1aeb2da5da5a43bb8771d3f338a473a2'), Edge(source='b9f1d3ddfa6b4334a16ea439df22b11e', target='2bf84f6355c44731848345ca7d0f8ab9')])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.get_graph()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "065b02fb",
+   "metadata": {},
+   "source": [
+    "## Print a graph\n",
+    "\n",
+    "While that is not super legible, you can print it to get a display that's easier to understand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d5ab1515",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           +---------------------------------+         \n",
+      "           | Parallel<context,question>Input |         \n",
+      "           +---------------------------------+         \n",
+      "                    **               **                \n",
+      "                 ***                   ***             \n",
+      "               **                         **           \n",
+      "+----------------------+              +-------------+  \n",
+      "| VectorStoreRetriever |              | Passthrough |  \n",
+      "+----------------------+              +-------------+  \n",
+      "                    **               **                \n",
+      "                      ***         ***                  \n",
+      "                         **     **                     \n",
+      "           +----------------------------------+        \n",
+      "           | Parallel<context,question>Output |        \n",
+      "           +----------------------------------+        \n",
+      "                             *                         \n",
+      "                             *                         \n",
+      "                             *                         \n",
+      "                  +--------------------+               \n",
+      "                  | ChatPromptTemplate |               \n",
+      "                  +--------------------+               \n",
+      "                             *                         \n",
+      "                             *                         \n",
+      "                             *                         \n",
+      "                      +------------+                   \n",
+      "                      | ChatOpenAI |                   \n",
+      "                      +------------+                   \n",
+      "                             *                         \n",
+      "                             *                         \n",
+      "                             *                         \n",
+      "                   +-----------------+                 \n",
+      "                   | StrOutputParser |                 \n",
+      "                   +-----------------+                 \n",
+      "                             *                         \n",
+      "                             *                         \n",
+      "                             *                         \n",
+      "                +-----------------------+              \n",
+      "                | StrOutputParserOutput |              \n",
+      "                +-----------------------+              \n"
+     ]
+    }
+   ],
+   "source": [
+    "chain.get_graph().print_ascii()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2babf851",
+   "metadata": {},
+   "source": [
+    "## Get the prompts\n",
+    "\n",
+    "An important part of every chain is the prompts that are used. You can get the prompts present in the chain:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "34b2118d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[ChatPromptTemplate(input_variables=['context', 'question'], messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template='Answer the question based only on the following context:\\n{context}\\n\\nQuestion: {question}\\n'))])]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.get_prompts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed965769",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/expression_language/how_to/map.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "id": "e2596041-9b76-4e74-836f-e6235086bbf0",
    "metadata": {},
    "source": [
@@ -26,6 +26,16 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2627ffd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -44,12 +54,11 @@
     }
    ],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import FAISS\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "\n",
     "vectorstore = FAISS.from_texts(\n",
     "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
@@ -128,12 +137,11 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import FAISS\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "\n",
     "vectorstore = FAISS.from_texts(\n",
     "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
@@ -192,9 +200,9 @@
     }
    ],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnableParallel\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI()\n",
     "joke_chain = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",

docs/docs/expression_language/how_to/message_history.ipynb

@@ -41,7 +41,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -U langchain redis anthropic"
+    "%pip install --upgrade --quiet  langchain redis anthropic"
    ]
   },
   {
@@ -131,10 +131,10 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "from langchain_community.chat_message_histories import RedisChatMessageHistory\n",
     "from langchain_community.chat_models import ChatAnthropic\n",
     "from langchain_core.chat_history import BaseChatMessageHistory\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "from langchain_core.runnables.history import RunnableWithMessageHistory"
    ]
   },

docs/docs/expression_language/how_to/passthrough.ipynb

@@ -28,6 +28,16 @@
     "See the example below:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e169b952",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain langchain-openai"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -97,12 +107,11 @@
     }
    ],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain_community.chat_models import ChatOpenAI\n",
-    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import FAISS\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "\n",
     "vectorstore = FAISS.from_texts(\n",
     "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",