x

.devcontainer/README.md

@@ -17,16 +17,13 @@ For more info, check out the [GitHub documentation](https://docs.github.com/en/f
 ## VS Code Dev Containers
 [![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
 
-Note: If you click the link above you will open the main repo (langchain-ai/langchain) and not your local cloned repo. This is fine if you only want to run and test the library, but if you want to contribute you can use the  link below and replace with your username and cloned repo name: 
-```
+Note: If you click this link you will open the main repo and not your local cloned repo, you can use this link and replace with your username and cloned repo name: 
 https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/<yourusername>/<yourclonedreponame>
 
-```
-Then you will have a local cloned repo where you can contribute and then create pull requests.
 
 If you already have VS Code and Docker installed, you can use the button above to get started. This will cause VS Code to automatically install the Dev Containers extension if needed, clone the source code into a container volume, and spin up a dev container for use.
 
-Alternatively you can also follow these steps to open this repo in a container using the VS Code Dev Containers extension:
+You can also follow these steps to open this repo in a container using the VS Code Dev Containers extension:
 
 1. If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) in the [getting started steps](https://aka.ms/vscode-remote/containers/getting-started).
 

.github/workflows/_compile_integration_test.yml

@@ -7,10 +7,6 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
-      langchain-core-location:
-        required: false
-        type: string
-        description: "Relative path to the langchain core library folder"
 
 env:
   POETRY_VERSION: "1.6.1"
@@ -44,14 +40,6 @@ jobs:
         shell: bash
         run: poetry install --with=test_integration
 
-      - name: Install langchain core editable
-        working-directory: ${{ inputs.working-directory }}
-        if: ${{ inputs.langchain-core-location }}
-        env:
-          LANGCHAIN_CORE_LOCATION: ${{ inputs.langchain-core-location }}
-        run: |
-          poetry run pip install -e "$LANGCHAIN_CORE_LOCATION"
-
       - name: Check integration tests compile
         shell: bash
         run: poetry run pytest -m compile tests/integration_tests

.github/workflows/_lint.yml

@@ -11,10 +11,6 @@ on:
         required: false
         type: string
         description: "Relative path to the langchain library folder"
-      langchain-core-location:
-        required: false
-        type: string
-        description: "Relative path to the langchain core library folder"
 
 env:
   POETRY_VERSION: "1.6.1"
@@ -80,15 +76,7 @@ jobs:
         env:
           LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
         run: |
-          poetry run pip install -e "$LANGCHAIN_LOCATION"
-
-      - name: Install langchain core editable
-        working-directory: ${{ inputs.working-directory }}
-        if: ${{ inputs.langchain-core-location }}
-        env:
-          LANGCHAIN_CORE_LOCATION: ${{ inputs.langchain-core-location }}
-        run: |
-          poetry run pip install -e "$LANGCHAIN_CORE_LOCATION"
+          pip install -e "$LANGCHAIN_LOCATION"
 
       - name: Get .mypy_cache to speed up mypy
         uses: actions/cache@v3

.github/workflows/_pydantic_compatibility.yml

@@ -7,14 +7,6 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
-      langchain-location:
-        required: false
-        type: string
-        description: "Relative path to the langchain library folder"
-      langchain-core-location:
-        required: false
-        type: string
-        description: "Relative path to the langchain core library folder"
 
 env:
   POETRY_VERSION: "1.6.1"
@@ -48,22 +40,6 @@ jobs:
         shell: bash
         run: poetry install
 
-      - name: Install langchain editable
-        working-directory: ${{ inputs.working-directory }}
-        if: ${{ inputs.langchain-location }}
-        env:
-          LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
-        run: |
-          poetry run pip install -e "$LANGCHAIN_LOCATION"
-
-      - name: Install langchain core editable
-        working-directory: ${{ inputs.working-directory }}
-        if: ${{ inputs.langchain-core-location }}
-        env:
-          LANGCHAIN_CORE_LOCATION: ${{ inputs.langchain-core-location }}
-        run: |
-          poetry run pip install -e "$LANGCHAIN_CORE_LOCATION"
-
       - name: Install the opposite major version of pydantic
         # If normal tests use pydantic v1, here we'll use v2, and vice versa.
         shell: bash

.github/workflows/_release.yml

@@ -97,18 +97,19 @@ jobs:
         env:
           PKG_NAME: ${{ needs.build.outputs.pkg-name }}
           VERSION: ${{ needs.build.outputs.version }}
-        # Here we use:
-        # - The default regular PyPI index as the *primary* index, meaning 
-        #   that it takes priority (https://pypi.org/simple)
-        # - The test PyPI index as an extra index, so that any dependencies that
+        # Here we specify:
+        # - The test PyPI index as the *primary* index, meaning that it takes priority.
+        # - The regular PyPI index as an extra index, so that any dependencies that
         #   are not found on test PyPI can be resolved and installed anyway.
-        #   (https://test.pypi.org/simple). This will include the PKG_NAME==VERSION
-        #   package because VERSION will not have been uploaded to regular PyPI yet.
+        #
+        # Without the former, we might install the wrong langchain release.
+        # Without the latter, we might not be able to install langchain's dependencies.
         #
         # TODO: add more in-depth pre-publish tests after testing that importing works
         run: |
           pip install \
-            --extra-index-url https://test.pypi.org/simple/ \
+            --index-url https://test.pypi.org/simple/ \
+            --extra-index-url https://pypi.org/simple/ \
             "$PKG_NAME==$VERSION"
 
           # Replace all dashes in the package name with underscores,

.github/workflows/_test.yml

@@ -7,14 +7,6 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
-      langchain-location:
-        required: false
-        type: string
-        description: "Relative path to the langchain library folder"
-      langchain-core-location:
-        required: false
-        type: string
-        description: "Relative path to the langchain core library folder"
 
 env:
   POETRY_VERSION: "1.6.1"
@@ -48,26 +40,9 @@ jobs:
         shell: bash
         run: poetry install
 
-      - name: Install langchain editable
-        working-directory: ${{ inputs.working-directory }}
-        if: ${{ inputs.langchain-location }}
-        env:
-          LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
-        run: |
-          poetry run pip install -e "$LANGCHAIN_LOCATION"
-
-      - name: Install langchain core editable
-        working-directory: ${{ inputs.working-directory }}
-        if: ${{ inputs.langchain-core-location }}
-        env:
-          LANGCHAIN_CORE_LOCATION: ${{ inputs.langchain-core-location }}
-        run: |
-          poetry run pip install -e "$LANGCHAIN_CORE_LOCATION"
-
       - name: Run core tests
         shell: bash
-        run: |
-          make test
+        run: make test
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/extract_ignored_words_list.py

@@ -3,8 +3,6 @@ import toml
 pyproject_toml = toml.load("pyproject.toml")
 
 # Extract the ignore words list (adjust the key as per your TOML structure)
-ignore_words_list = (
-    pyproject_toml.get("tool", {}).get("codespell", {}).get("ignore-words-list")
-)
+ignore_words_list = pyproject_toml.get("tool", {}).get("codespell", {}).get("ignore-words-list")
 
-print(f"::set-output name=ignore_words_list::{ignore_words_list}")
+print(f"::set-output name=ignore_words_list::{ignore_words_list}")

.github/workflows/langchain_ci.yml

@@ -36,7 +36,6 @@ jobs:
       ./.github/workflows/_lint.yml
     with:
       working-directory: libs/langchain
-      langchain-core-location: ../core
     secrets: inherit
 
   test:
@@ -44,7 +43,6 @@ jobs:
       ./.github/workflows/_test.yml
     with:
       working-directory: libs/langchain
-      langchain-core-location: ../core
     secrets: inherit
 
   compile-integration-tests:
@@ -52,7 +50,6 @@ jobs:
       ./.github/workflows/_compile_integration_test.yml
     with:
       working-directory: libs/langchain
-      langchain-core-location: ../core
     secrets: inherit
 
   pydantic-compatibility:
@@ -60,49 +57,8 @@ jobs:
       ./.github/workflows/_pydantic_compatibility.yml
     with:
       working-directory: libs/langchain
-      langchain-core-location: ../core
     secrets: inherit
 
-  # It's possible that langchain works fine with the latest *published* langchain-core,
-  # but is broken with the langchain-core on `master`.
-  #
-  # We want to catch situations like that *before* releasing a new langchain-core, hence this test.
-  test-with-latest-langchain-core:
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: ${{ env.WORKDIR }}
-    strategy:
-      matrix:
-        python-version:
-          - "3.8"
-          - "3.9"
-          - "3.10"
-          - "3.11"
-    name: test with unpublished langchain-core - Python ${{ matrix.python-version }}
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ env.WORKDIR }}
-          cache-key: unpublished-langchain-core
-
-      - name: Install dependencies
-        shell: bash
-        run: |
-          echo "Running tests with unpublished langchain, installing dependencies with poetry..."
-          poetry install
-
-          echo "Editably installing langchain-core outside of poetry, to avoid messing up lockfile..."
-          poetry run pip install -e ../core
-
-      - name: Run tests
-        run: make test
-
   extended-tests:
     runs-on: ubuntu-latest
     defaults:
@@ -133,11 +89,6 @@ jobs:
           echo "Running extended tests, installing dependencies with poetry..."
           poetry install -E extended_testing
 
-      - name: Install langchain core editable
-        shell: bash
-        run: |
-          poetry run pip install -e ../core
-
       - name: Run extended tests
         run: make extended_tests
 

.github/workflows/langchain_core_ci.yml

@@ -1,52 +0,0 @@
----
-name: libs/langchain core CI
-
-on:
-  push:
-    branches: [ master ]
-  pull_request:
-    paths:
-      - '.github/actions/poetry_setup/action.yml'
-      - '.github/tools/**'
-      - '.github/workflows/_lint.yml'
-      - '.github/workflows/_test.yml'
-      - '.github/workflows/_pydantic_compatibility.yml'
-      - '.github/workflows/langchain_core_ci.yml'
-      - 'libs/core/**'
-  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
-
-# If another push to the same PR or branch happens while this workflow is still running,
-# cancel the earlier run in favor of the next run.
-#
-# There's no point in testing an outdated version of the code. GitHub only allows
-# a limited number of job runners to be active at the same time, so it's better to cancel
-# pointless jobs early so that more useful jobs can run sooner.
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-env:
-  POETRY_VERSION: "1.6.1"
-  WORKDIR: "libs/core"
-
-jobs:
-  lint:
-    uses:
-      ./.github/workflows/_lint.yml
-    with:
-      working-directory: libs/core
-    secrets: inherit
-
-  test:
-    uses:
-      ./.github/workflows/_test.yml
-    with:
-      working-directory: libs/core
-    secrets: inherit
-
-  pydantic-compatibility:
-    uses:
-      ./.github/workflows/_pydantic_compatibility.yml
-    with:
-      working-directory: libs/core
-    secrets: inherit

.github/workflows/langchain_core_release.yml

@@ -1,13 +0,0 @@
----
-name: libs/core Release
-
-on:
-  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
-
-jobs:
-  release:
-    uses:
-      ./.github/workflows/_release.yml
-    with:
-      working-directory: libs/core
-    secrets: inherit

.github/workflows/langchain_experimental_ci.yml

@@ -36,7 +36,6 @@ jobs:
     with:
       working-directory: libs/experimental
       langchain-location: ../langchain
-      langchain-core-location: ../core
     secrets: inherit
 
   test:
@@ -44,8 +43,6 @@ jobs:
       ./.github/workflows/_test.yml
     with:
       working-directory: libs/experimental
-      langchain-location: ../langchain
-      langchain-core-location: ../core
     secrets: inherit
 
   compile-integration-tests:
@@ -91,7 +88,6 @@ jobs:
 
           echo "Editably installing langchain outside of poetry, to avoid messing up lockfile..."
           poetry run pip install -e ../langchain
-          poetry run pip install -e ../core
 
       - name: Run tests
         run: make test

.gitignore

@@ -178,4 +178,3 @@ docs/docs/build
 docs/docs/node_modules
 docs/docs/yarn.lock
 _dist
-docs/docs/templates

MIGRATE.md

@@ -1,18 +1,9 @@
-# Migrating
-
-## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
-
-In an effort to make `langchain` leaner and safer, we are moving select chains to `langchain_experimental`.
-This migration has already started, but we are remaining backwards compatible until 7/28.
-On that date, we will remove functionality from `langchain`.
-Read more about the motivation and the progress [here](https://github.com/langchain-ai/langchain/discussions/8043).
-
-### Migrating to `langchain_experimental`
+# Migrating to `langchain_experimental`
 
 We are moving any experimental components of LangChain, or components with vulnerability issues, into `langchain_experimental`.
 This guide covers how to migrate.
 
-### Installation
+## Installation
 
 Previously:
 
@@ -22,7 +13,7 @@ Now (only if you want to access things in experimental):
 
 `pip install -U langchain langchain_experimental`
 
-### Things in `langchain.experimental`
+## Things in `langchain.experimental`
 
 Previously:
 
@@ -32,7 +23,7 @@ Now:
 
 `from langchain_experimental import ...`
 
-### PALChain
+## PALChain
 
 Previously:
 
@@ -42,7 +33,7 @@ Now:
 
 `from langchain_experimental.pal_chain import PALChain`
 
-### SQLDatabaseChain
+## SQLDatabaseChain
 
 Previously:
 
@@ -56,7 +47,7 @@ Alternatively, if you are just interested in using the query generation part of
 
 `from langchain.chains import create_sql_query_chain`
 
-### `load_prompt` for Python files
+## `load_prompt` for Python files
 
 Note: this only applies if you want to load Python files as prompts.
 If you want to load json/yaml files, no change is needed.

Makefile

@@ -43,10 +43,10 @@ spell_fix:
 
 lint:
 	poetry run ruff docs templates cookbook
-	poetry run ruff format docs templates cookbook --diff
+	poetry run black docs templates cookbook --diff
 
 format format_diff:
-	poetry run ruff format docs templates cookbook
+	poetry run black docs templates cookbook
 	poetry run ruff --select I --fix docs templates cookbook
 
 ######################

README.md

@@ -15,72 +15,71 @@
 [![Dependency Status](https://img.shields.io/librariesio/github/langchain-ai/langchain)](https://libraries.io/github/langchain-ai/langchain)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/issues)
 
-Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
+
+Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
 
 To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). 
 [LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. 
-Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team.
+Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team
+
+## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
+
+In an effort to make `langchain` leaner and safer, we are moving select chains to `langchain_experimental`.
+This migration has already started, but we are remaining backwards compatible until 7/28.
+On that date, we will remove functionality from `langchain`.
+Read more about the motivation and the progress [here](https://github.com/langchain-ai/langchain/discussions/8043).
+Read how to migrate your code [here](MIGRATE.md).
 
 ## Quick Install
 
-With pip:
-```bash
-pip install langchain
-```
+`pip install langchain`
+or
+`pip install langsmith && conda install langchain -c conda-forge`
 
-With conda:
-```bash
-pip install langsmith && conda install langchain -c conda-forge
-```
+## 🤔 What is this?
 
-## 🤔 What is LangChain?
+Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
 
-**LangChain** is a framework for developing applications powered by language models. It enables applications that:
-- **Are context-aware**: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
-- **Reason**: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)
+This library aims to assist in the development of those types of applications. Common examples of these applications include:
 
-This framework consists of several parts.
-- **LangChain Libraries**: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
-- **[LangChain Templates](templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.
-- **[LangServe](https://github.com/langchain-ai/langserve)**: A library for deploying LangChain chains as a REST API.
-- **[LangSmith](https://smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
-
-**This repo contains the `langchain` ([here](libs/langchain)), `langchain-experimental` ([here](libs/experimental)), and `langchain-cli` ([here](libs/cli)) Python packages, as well as [LangChain Templates](templates).**
-
-![LangChain Stack](docs/static/img/langchain_stack.png)
-
-## 🧱 What can you build with LangChain?
-**❓ Retrieval augmented generation**
+**❓ Question Answering over specific documents**
 
 - [Documentation](https://python.langchain.com/docs/use_cases/question_answering/)
-- End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
+- End-to-end Example: [Question Answering over Notion Database](https://github.com/hwchase17/notion-qa)
 
-**💬 Analyzing structured data**
+**💬 Chatbots**
 
-- [Documentation](https://python.langchain.com/docs/use_cases/qa_structured/sql)
-- End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain/tree/master/templates/sql-llama2)
+- [Documentation](https://python.langchain.com/docs/use_cases/chatbots/)
+- End-to-end Example: [Chat-LangChain](https://github.com/langchain-ai/chat-langchain)
 
-**🤖 Chatbots**
+**🤖 Agents**
 
-- [Documentation](https://python.langchain.com/docs/use_cases/chatbots)
-- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
+- [Documentation](https://python.langchain.com/docs/modules/agents/)
+- End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
 
-And much more! Head to the [Use cases](https://python.langchain.com/docs/use_cases/) section of the docs for more.
+## 📖 Documentation
 
-## 🚀 How does LangChain help?
-The main value props of the LangChain libraries are:
-1. **Components**: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
-2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks
+Please see [here](https://python.langchain.com) for full documentation on:
 
-Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones. 
+- Getting started (installation, setting up the environment, simple examples)
+- How-To examples (demos, integrations, helper functions)
+- Reference (full API docs)
+- Resources (high-level explanation of core concepts)
 
-Components fall into the following **modules**:
+## 🚀 What can this help with?
 
-**📃 Model I/O:**
+There are six main areas that LangChain is designed to help with.
+These are, in increasing order of complexity:
+
+**📃 LLMs and Prompts:**
 
 This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
 
-**📚 Retrieval:**
+**🔗 Chains:**
+
+Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
+
+**📚 Data Augmented Generation:**
 
 Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
 
@@ -88,16 +87,15 @@ Data Augmented Generation involves specific types of chains that first interact
 
 Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
 
-## 📖 Documentation
+**🧠 Memory:**
 
-Please see [here](https://python.langchain.com) for full documentation, which includes:
+Memory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
 
-- [Getting started](https://python.langchain.com/docs/get_started/introduction): installation, setting up the environment, simple examples
-- Overview of the [interfaces](https://python.langchain.com/docs/expression_language/), [modules](https://python.langchain.com/docs/modules/) and [integrations](https://python.langchain.com/docs/integrations/providers)
-- [Use case](https://python.langchain.com/docs/use_cases/qa_structured/sql) walkthroughs and best practice [guides](https://python.langchain.com/docs/guides/adapters/openai)
-- [LangSmith](https://python.langchain.com/docs/langsmith/), [LangServe](https://python.langchain.com/docs/langserve), and [LangChain Template](https://python.langchain.com/docs/templates/) overviews
-- [Reference](https://api.python.langchain.com): full API docs
+**🧐 Evaluation:**
 
+[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is by using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
+
+For more information on these concepts, please see our [full documentation](https://python.langchain.com).
 
 ## 💁 Contributing
 

cookbook/LLaMA2_sql_chat.ipynb

@@ -67,6 +67,7 @@
     "llama2_code = ChatOllama(model=\"codellama:7b-instruct\")\n",
     "\n",
     "# API\n",
+    "from getpass import getpass\n",
     "from langchain.llms import Replicate\n",
     "\n",
     "# REPLICATE_API_TOKEN = getpass()\n",

cookbook/README.md

@@ -8,7 +8,6 @@ Notebook | Description
 [Semi_Structured_RAG.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_Structured_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data, including text and tables, using unstructured for parsing, multi-vector retriever for storing, and lcel for implementing chains.
 [Semi_structured_and_multi_moda...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using unstructured for parsing, multi-vector retriever for storage and retrieval, and lcel for implementing chains.
 [Semi_structured_multi_modal_RA...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using various tools and methods such as unstructured for parsing, multi-vector retriever for storing, lcel for implementing chains, and open source language models like llama2, llava, and gpt4all.
-[analyze_document.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/analyze_document.ipynb) | Analyze a single long document.
 [autogpt/autogpt.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/autogpt/autogpt.ipynb) | Implement autogpt, a language model, with langchain primitives such as llms, prompttemplates, vectorstores, embeddings, and tools.
 [autogpt/marathon_times.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/autogpt/marathon_times.ipynb) | Implement autogpt for finding winning marathon times.
 [baby_agi.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/baby_agi.ipynb) | Implement babyagi, an ai agent that can generate and execute tasks based on a given objective, with the flexibility to swap out specific vectorstores/model providers.
@@ -21,7 +20,6 @@ Notebook | Description
 [databricks_sql_db.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/databricks_sql_db.ipynb) | Connect to databricks runtimes and databricks sql.
 [deeplake_semantic_search_over_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/deeplake_semantic_search_over_chat.ipynb) | Perform semantic search and question-answering over a group chat using activeloop's deep lake with gpt4.
 [elasticsearch_db_qa.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/elasticsearch_db_qa.ipynb) | Interact with elasticsearch analytics databases in natural language and build search queries via the elasticsearch dsl API.
-[extraction_openai_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/extraction_openai_tools.ipynb) | Structured Data Extraction with OpenAI Tools
 [forward_looking_retrieval_augm...](https://github.com/langchain-ai/langchain/tree/master/cookbook/forward_looking_retrieval_augmented_generation.ipynb) | Implement the forward-looking active retrieval augmented generation (flare) method, which generates answers to questions, identifies uncertain tokens, generates hypothetical questions based on these tokens, and retrieves relevant documents to continue generating the answer.
 [generative_agents_interactive_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb) | Implement a generative agent that simulates human behavior, based on a research paper, using a time-weighted memory object backed by a langchain retriever.
 [gymnasium_agent_simulation.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/gymnasium_agent_simulation.ipynb) | Create a simple agent-environment interaction loop in simulated environments like text-based games with gymnasium.
@@ -45,7 +43,6 @@ Notebook | Description
 [plan_and_execute_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/plan_and_execute_agent.ipynb) | Create plan-and-execute agents that accomplish objectives by planning tasks with a language model (llm) and executing them with a separate agent.
 [press_releases.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/press_releases.ipynb) | Retrieve and query company press release data powered by [Kay.ai](https://kay.ai).
 [program_aided_language_model.i...](https://github.com/langchain-ai/langchain/tree/master/cookbook/program_aided_language_model.ipynb) | Implement program-aided language models as described in the provided research paper.
-[qa_citations.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/qa_citations.ipynb) | Different ways to get a model to cite its sources.
 [retrieval_in_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/retrieval_in_sql.ipynb) | Perform retrieval-augmented-generation (rag) on a PostgreSQL database using pgvector.
 [sales_agent_with_context.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/sales_agent_with_context.ipynb) | Implement a context-aware ai sales agent, salesgpt, that can have natural sales conversations, interact with other systems, and use a product knowledge base to discuss a company's offerings.
 [self_query_hotel_search.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/self_query_hotel_search.ipynb) | Build a hotel room search feature with self-querying retrieval, using a specific hotel recommendation dataset.

cookbook/Semi_Structured_RAG.ipynb

@@ -102,9 +102,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Any\n",
-    "\n",
+    "from lxml import html\n",
     "from pydantic import BaseModel\n",
+    "from typing import Any, Optional\n",
     "from unstructured.partition.pdf import partition_pdf\n",
     "\n",
     "# Get elements\n",
@@ -317,12 +317,11 @@
    "outputs": [],
    "source": [
     "import uuid\n",
-    "\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.storage import InMemoryStore\n",
+    "from langchain.schema.document import Document\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
-    "from langchain.storage import InMemoryStore\n",
-    "from langchain.vectorstores import Chroma\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",
@@ -374,6 +373,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from operator import itemgetter\n",
     "from langchain.schema.runnable import RunnablePassthrough\n",
     "\n",
     "# Prompt template\n",

cookbook/Semi_structured_and_multi_modal_RAG.ipynb

@@ -92,9 +92,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Any\n",
-    "\n",
+    "from lxml import html\n",
     "from pydantic import BaseModel\n",
+    "from typing import Any, Optional\n",
     "from unstructured.partition.pdf import partition_pdf\n",
     "\n",
     "# Get elements\n",
@@ -224,7 +224,7 @@
    "outputs": [],
    "source": [
     "# Prompt\n",
-    "prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text. \\\n",
+    "prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text. \\ \n",
     "Give a concise summary of the table or text. Table or text chunk: {element} \"\"\"\n",
     "prompt = ChatPromptTemplate.from_template(prompt_text)\n",
     "\n",
@@ -313,7 +313,7 @@
     "    # Execute the command and save the output to the defined output file\n",
     "    /Users/rlm/Desktop/Code/llama.cpp/bin/llava -m ../models/llava-7b/ggml-model-q5_k.gguf --mmproj ../models/llava-7b/mmproj-model-f16.gguf --temp 0.1 -p \"Describe the image in detail. Be specific about graphs, such as bar plots.\" --image \"$img\" > \"$output_file\"\n",
     "\n",
-    "done\n"
+    "done"
    ]
   },
   {
@@ -337,8 +337,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import glob\n",
-    "import os\n",
+    "import os, glob\n",
     "\n",
     "# Get all .txt file summaries\n",
     "file_paths = glob.glob(os.path.expanduser(os.path.join(path, \"*.txt\")))\n",
@@ -372,12 +371,11 @@
    "outputs": [],
    "source": [
     "import uuid\n",
-    "\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.storage import InMemoryStore\n",
+    "from langchain.schema.document import Document\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
-    "from langchain.storage import InMemoryStore\n",
-    "from langchain.vectorstores import Chroma\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",
@@ -646,6 +644,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from operator import itemgetter\n",
     "from langchain.schema.runnable import RunnablePassthrough\n",
     "\n",
     "# Prompt template\n",

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -82,9 +82,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Any\n",
-    "\n",
+    "import pandas as pd\n",
+    "from lxml import html\n",
     "from pydantic import BaseModel\n",
+    "from typing import Any, Optional\n",
     "from unstructured.partition.pdf import partition_pdf\n",
     "\n",
     "# Path to save images\n",
@@ -222,7 +223,7 @@
    "outputs": [],
    "source": [
     "# Prompt\n",
-    "prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text. \\\n",
+    "prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text. \\ \n",
     "Give a concise summary of the table or text. Table or text chunk: {element} \"\"\"\n",
     "prompt = ChatPromptTemplate.from_template(prompt_text)\n",
     "\n",
@@ -311,7 +312,7 @@
     "    # Execute the command and save the output to the defined output file\n",
     "    /Users/rlm/Desktop/Code/llama.cpp/bin/llava -m ../models/llava-7b/ggml-model-q5_k.gguf --mmproj ../models/llava-7b/mmproj-model-f16.gguf --temp 0.1 -p \"Describe the image in detail. Be specific about graphs, such as bar plots.\" --image \"$img\" > \"$output_file\"\n",
     "\n",
-    "done\n"
+    "done"
    ]
   },
   {
@@ -321,8 +322,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import glob\n",
-    "import os\n",
+    "import os, glob\n",
     "\n",
     "# Get all .txt files in the directory\n",
     "file_paths = glob.glob(os.path.expanduser(os.path.join(path, \"*.txt\")))\n",
@@ -375,12 +375,11 @@
    ],
    "source": [
     "import uuid\n",
-    "\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.storage import InMemoryStore\n",
+    "from langchain.schema.document import Document\n",
     "from langchain.embeddings import GPT4AllEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
-    "from langchain.storage import InMemoryStore\n",
-    "from langchain.vectorstores import Chroma\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(\n",
@@ -532,6 +531,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from operator import itemgetter\n",
     "from langchain.schema.runnable import RunnablePassthrough\n",
     "\n",
     "# Prompt template\n",

cookbook/analyze_document.ipynb

@@ -1,105 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f69d4a4c-137d-47e9-bea1-786afce9c1c0",
-   "metadata": {},
-   "source": [
-    "# Analyze a single long document\n",
-    "\n",
-    "The AnalyzeDocumentChain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "2a0707ce-6d2d-471b-bc33-64da32a7b3f0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"../docs/docs/modules/state_of_the_union.txt\") as f:\n",
-    "    state_of_the_union = f.read()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ca14d161-2d5b-4a6c-a296-77d8ce4b28cd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import AnalyzeDocumentChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "9f97406c-85a9-45fb-99ce-9138c0ba3731",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains.question_answering import load_qa_chain\n",
-    "\n",
-    "qa_chain = load_qa_chain(llm, chain_type=\"map_reduce\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0871a753-f5bb-4b4f-a394-f87f2691f659",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qa_document_chain = AnalyzeDocumentChain(combine_docs_chain=qa_chain)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e6f86428-3c2c-46a0-a57c-e22826fdbf91",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The President said, \"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\"'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "qa_document_chain.run(\n",
-    "    input_document=state_of_the_union,\n",
-    "    question=\"what did the president say about justice breyer?\",\n",
-    ")"
-   ]
-  }
- ],
- "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": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/autogpt/autogpt.ipynb

@@ -27,10 +27,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import Tool\n",
-    "from langchain.tools.file_management.read import ReadFileTool\n",
-    "from langchain.tools.file_management.write import WriteFileTool\n",
     "from langchain.utilities import SerpAPIWrapper\n",
+    "from langchain.agents import Tool\n",
+    "from langchain.tools.file_management.write import WriteFileTool\n",
+    "from langchain.tools.file_management.read import ReadFileTool\n",
     "\n",
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
@@ -61,9 +61,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.vectorstores import FAISS\n",
     "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain.embeddings import OpenAIEmbeddings"
    ]
   },
   {
@@ -100,8 +100,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain_experimental.autonomous_agents import AutoGPT"
+    "from langchain_experimental.autonomous_agents import AutoGPT\n",
+    "from langchain.chat_models import ChatOpenAI"
    ]
   },
   {

cookbook/autogpt/marathon_times.ipynb

@@ -34,15 +34,16 @@
    "outputs": [],
    "source": [
     "# General\n",
-    "import asyncio\n",
     "import os\n",
-    "\n",
-    "import nest_asyncio\n",
     "import pandas as pd\n",
-    "from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.docstore.document import Document\n",
     "from langchain_experimental.autonomous_agents import AutoGPT\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
+    "from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent\n",
+    "from langchain.docstore.document import Document\n",
+    "import asyncio\n",
+    "import nest_asyncio\n",
+    "\n",
     "\n",
     "# Needed synce jupyter runs an async eventloop\n",
     "nest_asyncio.apply()"
@@ -91,7 +92,6 @@
     "import os\n",
     "from contextlib import contextmanager\n",
     "from typing import Optional\n",
-    "\n",
     "from langchain.agents import tool\n",
     "from langchain.tools.file_management.read import ReadFileTool\n",
     "from langchain.tools.file_management.write import WriteFileTool\n",
@@ -223,13 +223,14 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.chains.qa_with_sources.loading import (\n",
-    "    BaseCombineDocumentsChain,\n",
-    "    load_qa_with_sources_chain,\n",
-    ")\n",
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "from langchain.tools import BaseTool, DuckDuckGoSearchRun\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "\n",
     "from pydantic import Field\n",
+    "from langchain.chains.qa_with_sources.loading import (\n",
+    "    load_qa_with_sources_chain,\n",
+    "    BaseCombineDocumentsChain,\n",
+    ")\n",
     "\n",
     "\n",
     "def _get_text_splitter():\n",
@@ -310,9 +311,10 @@
    "source": [
     "# Memory\n",
     "import faiss\n",
+    "from langchain.vectorstores import FAISS\n",
     "from langchain.docstore import InMemoryDocstore\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import FAISS\n",
+    "from langchain.tools.human.tool import HumanInputRun\n",
     "\n",
     "embeddings_model = OpenAIEmbeddings()\n",
     "embedding_size = 1536\n",

cookbook/baby_agi.ipynb

@@ -29,10 +29,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Optional\n",
+    "import os\n",
+    "from collections import deque\n",
+    "from typing import Dict, List, Optional, Any\n",
     "\n",
+    "from langchain.chains import LLMChain\nfrom langchain.llms import OpenAI\nfrom langchain.prompts import PromptTemplate\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain.llms import BaseLLM\n",
+    "from langchain.schema.vectorstore import VectorStore\n",
+    "from pydantic import BaseModel, Field\n",
+    "from langchain.chains.base import Chain\n",
     "from langchain_experimental.autonomous_agents import BabyAGI"
    ]
   },
@@ -53,8 +59,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain.vectorstores import FAISS\n",
+    "from langchain.docstore import InMemoryDocstore"
    ]
   },
   {

cookbook/baby_agi_with_agent.ipynb

@@ -25,12 +25,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Optional\n",
+    "import os\n",
+    "from collections import deque\n",
+    "from typing import Dict, List, Optional, Any\n",
     "\n",
-    "from langchain.chains import LLMChain\n",
+    "from langchain.chains import LLMChain\nfrom langchain.llms import OpenAI\nfrom langchain.prompts import PromptTemplate\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.llms import BaseLLM\n",
+    "from langchain.schema.vectorstore import VectorStore\n",
+    "from pydantic import BaseModel, Field\n",
+    "from langchain.chains.base import Chain\n",
     "from langchain_experimental.autonomous_agents import BabyAGI"
    ]
   },
@@ -62,8 +66,8 @@
    "source": [
     "%pip install faiss-cpu > /dev/null\n",
     "%pip install google-search-results > /dev/null\n",
-    "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain.vectorstores import FAISS\n",
+    "from langchain.docstore import InMemoryDocstore"
    ]
   },
   {
@@ -106,10 +110,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import AgentExecutor, Tool, ZeroShotAgent\n",
-    "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.utilities import SerpAPIWrapper\n",
+    "from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
+    "from langchain.llms import OpenAI\nfrom langchain.utilities import SerpAPIWrapper\nfrom langchain.chains import LLMChain\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

@@ -35,17 +35,16 @@
    "outputs": [],
    "source": [
     "from typing import List\n",
-    "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts.chat import (\n",
-    "    HumanMessagePromptTemplate,\n",
     "    SystemMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
     ")\n",
     "from langchain.schema import (\n",
     "    AIMessage,\n",
-    "    BaseMessage,\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
+    "    BaseMessage,\n",
     ")"
    ]
   },

cookbook/causal_program_aided_language_model.ipynb

@@ -47,9 +47,10 @@
    "outputs": [],
    "source": [
     "from IPython.display import SVG\n",
-    "from langchain.llms import OpenAI\n",
+    "\n",
     "from langchain_experimental.cpal.base import CPALChain\n",
     "from langchain_experimental.pal_chain import PALChain\n",
+    "from langchain.llms 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

@@ -177,7 +177,7 @@
     "            try:\n",
     "                loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
     "                docs.extend(loader.load_and_split())\n",
-    "            except Exception:\n",
+    "            except Exception as e:\n",
     "                pass\n",
     "print(f\"{len(docs)}\")"
    ]
@@ -648,7 +648,7 @@
     {
      "data": {
       "text/plain": [
-       "OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base='', openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='', openai_organization='', allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6, request_timeout=None, headers=None, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={})"
+       "OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base='', openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='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', openai_organization='', allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6, request_timeout=None, headers=None, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={})"
       ]
      },
      "execution_count": 13,
@@ -717,6 +717,7 @@
    "source": [
     "from langchain.vectorstores import DeepLake\n",
     "\n",
+    "\n",
     "username = \"<USERNAME_OR_ORG>\"\n",
     "\n",
     "\n",
@@ -833,8 +834,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains import ConversationalRetrievalChain\n",
     "\n",
     "model = ChatOpenAI(\n",
     "    model_name=\"gpt-3.5-turbo-0613\"\n",

cookbook/custom_agent_with_plugin_retrieval.ipynb

@@ -32,20 +32,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import re\n",
-    "from typing import Union\n",
-    "\n",
     "from langchain.agents import (\n",
+    "    Tool,\n",
     "    AgentExecutor,\n",
-    "    AgentOutputParser,\n",
     "    LLMSingleActionAgent,\n",
+    "    AgentOutputParser,\n",
     ")\n",
-    "from langchain.agents.agent_toolkits import NLAToolkit\n",
-    "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.prompts import StringPromptTemplate\n",
+    "from langchain.llms import OpenAI\nfrom langchain.utilities import SerpAPIWrapper\nfrom langchain.chains import LLMChain\n",
+    "from typing import List, Union\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
-    "from langchain.tools.plugin import AIPlugin"
+    "from langchain.agents.agent_toolkits import NLAToolkit\n",
+    "from langchain.tools.plugin import AIPlugin\n",
+    "import re"
    ]
   },
   {
@@ -114,9 +113,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.vectorstores import FAISS\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.schema import Document\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain.schema import Document"
    ]
   },
   {

cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb

@@ -56,21 +56,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import re\n",
-    "from typing import Union\n",
-    "\n",
-    "import plugnplai\n",
     "from langchain.agents import (\n",
+    "    Tool,\n",
     "    AgentExecutor,\n",
-    "    AgentOutputParser,\n",
     "    LLMSingleActionAgent,\n",
+    "    AgentOutputParser,\n",
     ")\n",
-    "from langchain.agents.agent_toolkits import NLAToolkit\n",
-    "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.prompts import StringPromptTemplate\n",
+    "from langchain.llms import OpenAI\nfrom langchain.utilities import SerpAPIWrapper\nfrom langchain.chains import LLMChain\n",
+    "from typing import List, Union\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
-    "from langchain.tools.plugin import AIPlugin"
+    "from langchain.agents.agent_toolkits import NLAToolkit\n",
+    "from langchain.tools.plugin import AIPlugin\n",
+    "import re\n",
+    "import plugnplai"
    ]
   },
   {
@@ -138,9 +137,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.vectorstores import FAISS\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.schema import Document\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain.schema import Document"
    ]
   },
   {

cookbook/deeplake_semantic_search_over_chat.ipynb

@@ -48,17 +48,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import getpass\n",
     "import os\n",
-    "\n",
-    "from langchain.chains import RetrievalQA\n",
+    "import getpass\n",
+    "from langchain.document_loaders import PyPDFLoader, TextLoader\n",
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.text_splitter import (\n",
-    "    CharacterTextSplitter,\n",
     "    RecursiveCharacterTextSplitter,\n",
+    "    CharacterTextSplitter,\n",
     ")\n",
     "from langchain.vectorstores import DeepLake\n",
+    "from langchain.chains import ConversationalRetrievalChain, RetrievalQA\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.llms import OpenAI\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
     "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",

cookbook/elasticsearch_db_qa.ipynb

@@ -38,8 +38,9 @@
    "outputs": [],
    "source": [
     "from elasticsearch import Elasticsearch\n",
-    "from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain\n",
-    "from langchain.chat_models import ChatOpenAI"
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain"
    ]
   },
   {
@@ -111,6 +112,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.chains.elasticsearch_database.prompts import DEFAULT_DSL_TEMPLATE\n",
     "from langchain.prompts.prompt import PromptTemplate\n",
     "\n",
     "PROMPT_TEMPLATE = \"\"\"Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n",

cookbook/extraction_openai_tools.ipynb

@@ -1,214 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2def22ea",
-   "metadata": {},
-   "source": [
-    "# Extraction with OpenAI Tools\n",
-    "\n",
-    "Performing extraction has never been easier! OpenAI's tool calling ability is the perfect thing to use as it allows for extracting multiple different elements from text that are different types. \n",
-    "\n",
-    "Models after 1106 use tools and support \"parallel function calling\" which makes this super easy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "5c628496",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import List, Optional\n",
-    "\n",
-    "from langchain.chains.openai_tools import create_extraction_chain_pydantic\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.pydantic_v1 import BaseModel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "afe9657b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Make sure to use a recent model that supports tools\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "bc0ca3b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Pydantic is an easy way to define a schema\n",
-    "class Person(BaseModel):\n",
-    "    \"\"\"Information about people to extract.\"\"\"\n",
-    "\n",
-    "    name: str\n",
-    "    age: Optional[int] = None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "2036af68",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = create_extraction_chain_pydantic(Person, model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "1748ad21",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Person(name='jane', age=2), Person(name='bob', age=3)]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"input\": \"jane is 2 and bob is 3\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "c8262ce5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Let's define another element\n",
-    "class Class(BaseModel):\n",
-    "    \"\"\"Information about classes to extract.\"\"\"\n",
-    "\n",
-    "    teacher: str\n",
-    "    students: List[str]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "4973c104",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = create_extraction_chain_pydantic([Person, Class], model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e976a15e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Person(name='jane', age=2),\n",
-       " Person(name='bob', age=3),\n",
-       " Class(teacher='Mrs Sampson', students=['jane', 'bob'])]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"input\": \"jane is 2 and bob is 3 and they are in Mrs Sampson's class\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6575a7d6",
-   "metadata": {},
-   "source": [
-    "## Under the hood\n",
-    "\n",
-    "Under the hood, this is a simple chain:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8ba83e5",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "from typing import Union, List, Type, Optional\n",
-    "\n",
-    "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
-    "from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
-    "from langchain.schema.runnable import Runnable\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.messages import SystemMessage\n",
-    "from langchain.schema.language_model import BaseLanguageModel\n",
-    "\n",
-    "_EXTRACTION_TEMPLATE = \"\"\"Extract and save the relevant entities mentioned \\\n",
-    "in the following passage together with their properties.\n",
-    "\n",
-    "If a property is not present and is not required in the function parameters, do not include it in the output.\"\"\"  # noqa: E501\n",
-    "\n",
-    "\n",
-    "def create_extraction_chain_pydantic(\n",
-    "    pydantic_schemas: Union[List[Type[BaseModel]], Type[BaseModel]],\n",
-    "    llm: BaseLanguageModel,\n",
-    "    system_message: str = _EXTRACTION_TEMPLATE,\n",
-    ") -> Runnable:\n",
-    "    if not isinstance(pydantic_schemas, list):\n",
-    "        pydantic_schemas = [pydantic_schemas]\n",
-    "    prompt = ChatPromptTemplate.from_messages([\n",
-    "        (\"system\", system_message),\n",
-    "        (\"user\", \"{input}\")\n",
-    "    ])\n",
-    "    tools = [convert_pydantic_to_openai_tool(p) for p in pydantic_schemas]\n",
-    "    model = llm.bind(tools=tools)\n",
-    "    chain = prompt | model | PydanticToolsParser(tools=pydantic_schemas)\n",
-    "    return chain\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2eac6b68",
-   "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": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/forward_looking_retrieval_augmented_generation.ipynb

@@ -56,8 +56,7 @@
    "source": [
     "import os\n",
     "\n",
-    "os.environ[\"SERPER_API_KEY\"] = \"\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"\""
+    "os.environ[\"SERPER_API_KEY\"] = \"\"os.environ[\"OPENAI_API_KEY\"] = \"\""
    ]
   },
   {
@@ -67,16 +66,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Any, List\n",
+    "import re\n",
     "\n",
+    "import numpy as np\n",
+    "\n",
+    "from langchain.schema import BaseRetriever\n",
     "from langchain.callbacks.manager import (\n",
     "    AsyncCallbackManagerForRetrieverRun,\n",
     "    CallbackManagerForRetrieverRun,\n",
     ")\n",
+    "from langchain.utilities import GoogleSerperAPIWrapper\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.schema import BaseRetriever, Document\n",
-    "from langchain.utilities import GoogleSerperAPIWrapper"
+    "from langchain.schema import Document\n",
+    "from typing import Any, List"
    ]
   },
   {

cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb

@@ -46,13 +46,14 @@
    "source": [
     "from datetime import datetime, timedelta\n",
     "from typing import List\n",
+    "from termcolor import colored\n",
+    "\n",
     "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.docstore import InMemoryDocstore\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers import TimeWeightedVectorStoreRetriever\n",
-    "from langchain.vectorstores import FAISS\n",
-    "from termcolor import colored"
+    "from langchain.vectorstores import FAISS"
    ]
   },
   {
@@ -152,7 +153,6 @@
    "outputs": [],
    "source": [
     "import math\n",
-    "\n",
     "import faiss\n",
     "\n",
     "\n",

cookbook/gymnasium_agent_simulation.ipynb

@@ -27,12 +27,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import gymnasium as gym\n",
+    "import inspect\n",
     "import tenacity\n",
-    "from langchain.output_parsers import RegexParser\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema import (\n",
+    "    AIMessage,\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")"
+    "    BaseMessage,\n",
+    ")\n",
+    "from langchain.output_parsers import RegexParser"
    ]
   },
   {
@@ -125,7 +131,7 @@
     "            ):\n",
     "                with attempt:\n",
     "                    action = self._act()\n",
-    "        except tenacity.RetryError:\n",
+    "        except tenacity.RetryError as e:\n",
     "            action = self.random_action()\n",
     "        return action"
    ]

cookbook/hypothetical_document_embeddings.ipynb

@@ -20,9 +20,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains import HypotheticalDocumentEmbedder, LLMChain\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.llms import OpenAI\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.chains import LLMChain, HypotheticalDocumentEmbedder\n",
     "from langchain.prompts import PromptTemplate"
    ]
   },

cookbook/learned_prompt_optimization.ipynb

@@ -790,8 +790,8 @@
     }
    ],
    "source": [
-    "from langchain.globals import set_debug\n",
     "from langchain.prompts.prompt import PromptTemplate\n",
+    "from langchain.globals import set_debug\n",
     "\n",
     "set_debug(True)\n",
     "\n",

cookbook/llm_bash.ipynb

@@ -43,8 +43,8 @@
     }
    ],
    "source": [
-    "from langchain.llms import OpenAI\n",
     "from langchain_experimental.llm_bash.base import LLMBashChain\n",
+    "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "\n",
@@ -70,7 +70,7 @@
    "outputs": [],
    "source": [
     "from langchain.prompts.prompt import PromptTemplate\n",
-    "from langchain_experimental.llm_bash.prompt import BashOutputParser\n",
+    "from langchain.chains.llm_bash.prompt import BashOutputParser\n",
     "\n",
     "_PROMPT_TEMPLATE = \"\"\"If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put \"#!/bin/bash\" in your answer. Make sure to reason step by step, using this format:\n",
     "Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"\n",
@@ -185,6 +185,7 @@
    "source": [
     "from langchain_experimental.llm_bash.bash import BashProcess\n",
     "\n",
+    "\n",
     "persistent_process = BashProcess(persistent=True)\n",
     "bash_chain = LLMBashChain.from_llm(llm, bash_process=persistent_process, verbose=True)\n",
     "\n",

cookbook/llm_math.ipynb

@@ -45,8 +45,7 @@
     }
    ],
    "source": [
-    "from langchain.chains import LLMMathChain\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain.llms import OpenAI\nfrom langchain.chains import LLMMathChain\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "llm_math = LLMMathChain.from_llm(llm, verbose=True)\n",

cookbook/meta_prompt.ipynb

@@ -56,10 +56,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.memory import ConversationBufferWindowMemory\n",
-    "from langchain.prompts import PromptTemplate"
+    "from langchain.llms import OpenAI\nfrom langchain.chains import LLMChain\nfrom langchain.prompts import PromptTemplate\n",
+    "from langchain.memory import ConversationBufferWindowMemory"
    ]
   },
   {
@@ -154,13 +152,13 @@
     "        for j in range(max_iters):\n",
     "            print(f\"(Step {j+1}/{max_iters})\")\n",
     "            print(f\"Assistant: {output}\")\n",
-    "            print(\"Human: \")\n",
+    "            print(f\"Human: \")\n",
     "            human_input = input()\n",
     "            if any(phrase in human_input.lower() for phrase in key_phrases):\n",
     "                break\n",
     "            output = chain.predict(human_input=human_input)\n",
     "        if success_phrase in human_input.lower():\n",
-    "            print(\"You succeeded! Thanks for playing!\")\n",
+    "            print(f\"You succeeded! Thanks for playing!\")\n",
     "            return\n",
     "        meta_chain = initialize_meta_chain()\n",
     "        meta_output = meta_chain.predict(chat_history=get_chat_history(chain.memory))\n",
@@ -168,7 +166,7 @@
     "        instructions = get_new_instructions(meta_output)\n",
     "        print(f\"New Instructions: {instructions}\")\n",
     "        print(\"\\n\" + \"#\" * 80 + \"\\n\")\n",
-    "    print(\"You failed! Thanks for playing!\")"
+    "    print(f\"You failed! Thanks for playing!\")"
    ]
   },
   {

cookbook/multi_modal_QA.ipynb

@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install \"openai>=1\" \"langchain>=0.0.331rc2\" matplotlib pillow"
+    "! pip install \"openai>=1\" \"langchain>=0.0.331rc2\" matplotlib pillow "
    ]
   },
   {
@@ -40,32 +40,29 @@
     }
    ],
    "source": [
-    "import base64\n",
-    "import io\n",
     "import os\n",
-    "\n",
+    "import io\n",
+    "import base64\n",
     "import numpy as np\n",
-    "from IPython.display import HTML, display\n",
     "from PIL import Image\n",
-    "\n",
+    "from IPython.display import display, HTML\n",
     "\n",
     "def encode_image(image_path):\n",
-    "    \"\"\"Getting the base64 string\"\"\"\n",
-    "\n",
+    "    ''' Getting the base64 string '''\n",
+    "    \n",
     "    with open(image_path, \"rb\") as image_file:\n",
-    "        return base64.b64encode(image_file.read()).decode(\"utf-8\")\n",
+    "        return base64.b64encode(image_file.read()).decode('utf-8')\n",
     "\n",
     "\n",
     "def plt_img_base64(img_base64):\n",
-    "    \"\"\"Display the base64 image\"\"\"\n",
+    "    ''' Display the base64 image '''\n",
     "\n",
     "    # Create an HTML img tag with the base64 string as the source\n",
     "    image_html = f'<img src=\"data:image/jpeg;base64,{img_base64}\" />'\n",
-    "\n",
+    "    \n",
     "    # Display the image by rendering the HTML\n",
     "    display(HTML(image_html))\n",
     "\n",
-    "\n",
     "# Image for QA\n",
     "path = \"/Users/rlm/Desktop/Multimodal_Eval/qa/llm_strategies.jpeg\"\n",
     "img_base64 = encode_image(path)\n",
@@ -102,19 +99,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chat = ChatOpenAI(model=\"gpt-4-vision-preview\", max_tokens=1024)\n",
+    "chat = ChatOpenAI(model=\"gpt-4-vision-preview\",\n",
+    "                  max_tokens=1024)\n",
     "\n",
     "msg = chat.invoke(\n",
     "    [\n",
     "        HumanMessage(\n",
     "            content=[\n",
-    "                {\n",
-    "                    \"type\": \"text\",\n",
-    "                    \"text\": \"Based on the image, what is the difference in training strategy between a small and a large base model?\",\n",
-    "                },\n",
+    "                {\"type\": \"text\", \"text\":\"Based on the image, what is the difference in training strategy between a small and a large base model?\"},\n",
     "                {\n",
     "                    \"type\": \"image_url\",\n",
-    "                    \"image_url\": {\"url\": f\"data:image/jpeg;base64,{img_base64}\"},\n",
+    "                    \"image_url\": {\n",
+    "                        \"url\": f\"data:image/jpeg;base64,{img_base64}\"\n",
+    "                    },\n",
     "                },\n",
     "            ]\n",
     "        )\n",

cookbook/multi_modal_output_agent.ipynb

@@ -29,10 +29,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from steamship import Block, Steamship\n",
     "import re\n",
-    "\n",
-    "from IPython.display import Image\n",
-    "from steamship import Block, Steamship"
+    "from IPython.display import Image"
    ]
   },
   {
@@ -42,8 +41,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain.llms import OpenAI\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.agents import AgentType\n",
     "from langchain.tools import SteamshipImageGenerationTool"
    ]
   },

cookbook/multi_player_dnd.ipynb

@@ -26,12 +26,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Callable, List\n",
-    "\n",
+    "from typing import List, Dict, Callable\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema import (\n",
+    "    AIMessage,\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
+    "    BaseMessage,\n",
     ")"
    ]
   },

cookbook/multiagent_authoritarian.ipynb

@@ -27,20 +27,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from collections import OrderedDict\n",
     "import functools\n",
     "import random\n",
-    "from collections import OrderedDict\n",
-    "from typing import Callable, List\n",
-    "\n",
+    "import re\n",
     "import tenacity\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.output_parsers import RegexParser\n",
+    "from typing import List, Dict, Callable\n",
+    "\n",
     "from langchain.prompts import (\n",
+    "    ChatPromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
     "    PromptTemplate,\n",
     ")\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.output_parsers import RegexParser\n",
     "from langchain.schema import (\n",
+    "    AIMessage,\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
+    "    BaseMessage,\n",
     ")"
    ]
   },

cookbook/multiagent_bidding.ipynb

@@ -24,15 +24,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Callable, List\n",
-    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "import re\n",
     "import tenacity\n",
+    "from typing import List, Dict, Callable\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.output_parsers import RegexParser\n",
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.schema import (\n",
+    "    AIMessage,\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
+    "    BaseMessage,\n",
     ")"
    ]
   },

cookbook/myscale_vector_sql.ipynb

@@ -27,15 +27,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import getpass\n",
     "from os import environ\n",
-    "\n",
-    "from langchain.chains import LLMChain\n",
+    "import getpass\n",
+    "from typing import Dict, Any\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.utilities import SQLDatabase\n",
+    "from langchain.chains import LLMChain\n",
     "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
-    "from sqlalchemy import MetaData, create_engine\n",
+    "from sqlalchemy import create_engine, Column, MetaData\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
     "MYSCALE_HOST = \"msc-4a9e710a.us-east-1.aws.staging.myscale.cloud\"\n",
     "MYSCALE_PORT = 443\n",
@@ -74,8 +77,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.callbacks import StdOutCallbackHandler\n",
     "from langchain.llms import OpenAI\n",
+    "from langchain.callbacks import StdOutCallbackHandler\n",
+    "\n",
     "from langchain.utilities.sql_database import SQLDatabase\n",
     "from langchain_experimental.sql.prompt import MYSCALE_PROMPT\n",
     "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
@@ -116,16 +120,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain\n",
+    "\n",
+    "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
     "from langchain_experimental.retrievers.vector_sql_database import (\n",
     "    VectorSQLDatabaseChainRetriever,\n",
     ")\n",
     "from langchain_experimental.sql.prompt import MYSCALE_PROMPT\n",
-    "from langchain_experimental.sql.vector_sql import (\n",
-    "    VectorSQLDatabaseChain,\n",
-    "    VectorSQLRetrieveAllOutputParser,\n",
-    ")\n",
+    "from langchain_experimental.sql.vector_sql import VectorSQLRetrieveAllOutputParser\n",
     "\n",
     "output_parser_retrieve_all = VectorSQLRetrieveAllOutputParser.from_embeddings(\n",
     "    output_parser.model\n",

cookbook/openai_functions_retrieval_qa.ipynb

@@ -50,10 +50,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains import create_qa_with_sources_chain\n",
-    "from langchain.chains.combine_documents.stuff import StuffDocumentsChain\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import PromptTemplate"
+    "from langchain.chains.combine_documents.stuff import StuffDocumentsChain\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import create_qa_with_sources_chain"
    ]
   },
   {
@@ -230,8 +230,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains import ConversationalRetrievalChain, LLMChain\n",
+    "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chains import LLMChain\n",
     "\n",
     "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
     "_template = \"\"\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\\\n",
@@ -356,10 +357,12 @@
    "source": [
     "from typing import List\n",
     "\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
     "from langchain.chains.openai_functions import create_qa_with_structure_chain\n",
+    "\n",
     "from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
-    "from pydantic import BaseModel, Field"
+    "from langchain.schema import SystemMessage, HumanMessage"
    ]
   },
   {

cookbook/openai_v1_cookbook.ipynb

@@ -17,8 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# need openai>=1.1.0, langchain>=0.0.335, langchain-experimental>=0.0.39\n",
-    "!pip install -U openai langchain langchain-experimental"
+    "!pip install -U openai \"langchain>=0.0.331rc2\" langchain-experimental"
    ]
   },
   {
@@ -109,7 +108,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents.openai_assistant import OpenAIAssistantRunnable"
+    "from langchain_experimental.openai_assistant import OpenAIAssistantRunnable"
    ]
   },
   {
@@ -134,7 +133,7 @@
     "    name=\"langchain assistant\",\n",
     "    instructions=\"You are a personal math tutor. Write and run code to answer math questions.\",\n",
     "    tools=[{\"type\": \"code_interpreter\"}],\n",
-    "    model=\"gpt-4-1106-preview\",\n",
+    "    model=\"gpt-4-1106-preview\"\n",
     ")\n",
     "output = interpreter_assistant.invoke({\"content\": \"What's 10 - 4 raised to the 2.7\"})\n",
     "output"
@@ -167,7 +166,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.tools import DuckDuckGoSearchRun, E2BDataAnalysisTool\n",
+    "from langchain.tools import E2BDataAnalysisTool, DuckDuckGoSearchRun\n",
     "\n",
     "tools = [E2BDataAnalysisTool(api_key=\"...\"), DuckDuckGoSearchRun()]"
    ]
@@ -184,7 +183,7 @@
     "    instructions=\"You are a personal math tutor. Write and run code to answer math questions. You can also search the internet.\",\n",
     "    tools=tools,\n",
     "    model=\"gpt-4-1106-preview\",\n",
-    "    as_agent=True,\n",
+    "    as_agent=True\n",
     ")"
    ]
   },
@@ -241,7 +240,7 @@
     "    instructions=\"You are a personal math tutor. Write and run code to answer math questions.\",\n",
     "    tools=tools,\n",
     "    model=\"gpt-4-1106-preview\",\n",
-    "    as_agent=True,\n",
+    "    as_agent=True\n",
     ")"
    ]
   },
@@ -254,7 +253,6 @@
    "source": [
     "from langchain.schema.agent import AgentFinish\n",
     "\n",
-    "\n",
     "def execute_agent(agent, tools, input):\n",
     "    tool_map = {tool.name: tool for tool in tools}\n",
     "    response = agent.invoke(input)\n",
@@ -263,17 +261,9 @@
     "        for action in response:\n",
     "            tool_output = tool_map[action.tool].invoke(action.tool_input)\n",
     "            print(action.tool, action.tool_input, tool_output, end=\"\\n\\n\")\n",
-    "            tool_outputs.append(\n",
-    "                {\"output\": tool_output, \"tool_call_id\": action.tool_call_id}\n",
-    "            )\n",
-    "        response = agent.invoke(\n",
-    "            {\n",
-    "                \"tool_outputs\": tool_outputs,\n",
-    "                \"run_id\": action.run_id,\n",
-    "                \"thread_id\": action.thread_id,\n",
-    "            }\n",
-    "        )\n",
-    "\n",
+    "            tool_outputs.append({\"output\": tool_output, \"tool_call_id\": action.tool_call_id})\n",
+    "        response = agent.invoke({\"tool_outputs\": tool_outputs, \"run_id\": action.run_id, \"thread_id\": action.thread_id})\n",
+    "        \n",
     "    return response"
    ]
   },
@@ -315,9 +305,7 @@
     }
    ],
    "source": [
-    "next_response = execute_agent(\n",
-    "    agent, tools, {\"content\": \"now add 17.241\", \"thread_id\": response.thread_id}\n",
-    ")\n",
+    "next_response = execute_agent(agent, tools, {\"content\": \"now add 17.241\", \"thread_id\": response.thread_id})\n",
     "print(next_response.return_values[\"output\"])"
    ]
   },
@@ -401,85 +389,6 @@
     ")\n",
     "print(output.llm_output)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa6565be-985d-4127-848e-c3bca9d7b434",
-   "metadata": {},
-   "source": [
-    "## Breaking changes to Azure classes\n",
-    "\n",
-    "OpenAI V1 rewrote their clients and separated Azure and OpenAI clients. This has led to some changes in LangChain interfaces when using OpenAI V1.\n",
-    "\n",
-    "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.embeddings import AzureOpenAIEmbeddings\n",
-    "```\n",
-    "\n",
-    "\n",
-    "RECOMMENDED CHANGES:\n",
-    "- When using `AzureChatOpenAI` or `AzureOpenAI`, if passing in an Azure endpoint (eg https://example-resource.azure.openai.com/) this should be specified via the `azure_endpoint` parameter or the `AZURE_OPENAI_ENDPOINT`. We're maintaining backwards compatibility for now with specifying this via `openai_api_base`/`base_url` or env var `OPENAI_API_BASE` but this shouldn't be relied upon.\n",
-    "- When using Azure chat or embedding models, pass in API keys either via `openai_api_key` parameter or `AZURE_OPENAI_API_KEY` parameter. We're maintaining backwards compatibility for now with specifying this via `OPENAI_API_KEY` but this shouldn't be relied upon."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "49944887-3972-497e-8da2-6d32d44345a9",
-   "metadata": {},
-   "source": [
-    "## Tools\n",
-    "\n",
-    "Use tools for parallel function calling."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "916292d8-0f89-40a6-af1c-5a1122327de8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[GetCurrentWeather(location='New York, NY', unit='fahrenheit'),\n",
-       " GetCurrentWeather(location='Los Angeles, CA', unit='fahrenheit'),\n",
-       " GetCurrentWeather(location='San Francisco, CA', unit='fahrenheit')]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from typing import Literal\n",
-    "\n",
-    "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
-    "from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
-    "\n",
-    "\n",
-    "class GetCurrentWeather(BaseModel):\n",
-    "    \"\"\"Get the current weather in a location.\"\"\"\n",
-    "\n",
-    "    location: str = Field(description=\"The city and state, e.g. San Francisco, CA\")\n",
-    "    unit: Literal[\"celsius\", \"fahrenheit\"] = Field(\n",
-    "        default=\"fahrenheit\", description=\"The temperature unit, default to fahrenheit\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"system\", \"You are a helpful assistant\"), (\"user\", \"{input}\")]\n",
-    ")\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\").bind(\n",
-    "    tools=[convert_pydantic_to_openai_tool(GetCurrentWeather)]\n",
-    ")\n",
-    "chain = prompt | model | PydanticToolsParser(tools=[GetCurrentWeather])\n",
-    "\n",
-    "chain.invoke({\"input\": \"what's the weather in NYC, LA, and SF\"})"
-   ]
   }
  ],
  "metadata": {

cookbook/petting_zoo.ipynb

@@ -45,14 +45,14 @@
    "source": [
     "import collections\n",
     "import inspect\n",
-    "\n",
     "import tenacity\n",
+    "\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.output_parsers import RegexParser\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")"
+    ")\n",
+    "from langchain.output_parsers import RegexParser"
    ]
   },
   {
@@ -146,7 +146,7 @@
     "            ):\n",
     "                with attempt:\n",
     "                    action = self._act()\n",
-    "        except tenacity.RetryError:\n",
+    "        except tenacity.RetryError as e:\n",
     "            action = self.random_action()\n",
     "        return action"
    ]

cookbook/program_aided_language_model.ipynb

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

cookbook/qianfan_baidu_elasticesearch_RAG.ipynb

@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# RAG based on Qianfan and BES"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook is an implementation of Retrieval augmented generation (RAG) using Baidu Qianfan Platform combined with Baidu ElasricSearch, where the original data is located on BOS.\n",
-    "## Baidu Qianfan\n",
-    "Baidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open-source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.\n",
-    "\n",
-    "## Baidu ElasticSearch\n",
-    "[Baidu Cloud VectorSearch](https://cloud.baidu.com/doc/BES/index.html?from=productToDoc) is a fully managed, enterprise-level distributed search and analysis service which is 100% compatible to open source. Baidu Cloud VectorSearch provides low-cost, high-performance, and reliable retrieval and analysis platform level product services for structured/unstructured data. As a vector database , it supports multiple index types and similarity distance methods. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Installation and Setup\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!pip install qianfan\n",
-    "#!pip install bce-python-sdk\n",
-    "#!pip install elasticsearch == 7.11.0"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from baidubce.auth.bce_credentials import BceCredentials\n",
-    "from baidubce.bce_client_configuration import BceClientConfiguration\n",
-    "from langchain.document_loaders.baiducloud_bos_directory import BaiduBOSDirectoryLoader\n",
-    "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
-    "from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\n",
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain.vectorstores import BESVectorStore"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Document loading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bos_host = \"your bos eddpoint\"\n",
-    "access_key_id = \"your bos access ak\"\n",
-    "secret_access_key = \"your bos access sk\"\n",
-    "\n",
-    "# create BceClientConfiguration\n",
-    "config = BceClientConfiguration(\n",
-    "    credentials=BceCredentials(access_key_id, secret_access_key), endpoint=bos_host\n",
-    ")\n",
-    "\n",
-    "loader = BaiduBOSDirectoryLoader(conf=config, bucket=\"llm-test\", prefix=\"llm/\")\n",
-    "documents = loader.load()\n",
-    "\n",
-    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=0)\n",
-    "split_docs = text_splitter.split_documents(documents)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Embedding and VectorStore"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings = HuggingFaceEmbeddings(model_name=\"shibing624/text2vec-base-chinese\")\n",
-    "embeddings.client = sentence_transformers.SentenceTransformer(embeddings.model_name)\n",
-    "\n",
-    "db = BESVectorStore.from_documents(\n",
-    "    documents=split_docs,\n",
-    "    embedding=embeddings,\n",
-    "    bes_url=\"your bes url\",\n",
-    "    index_name=\"test-index\",\n",
-    "    vector_query_field=\"vector\",\n",
-    ")\n",
-    "\n",
-    "db.client.indices.refresh(index=\"test-index\")\n",
-    "retriever = db.as_retriever()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## QA Retriever"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = QianfanLLMEndpoint(\n",
-    "    model=\"ERNIE-Bot\",\n",
-    "    qianfan_ak=\"your qianfan ak\",\n",
-    "    qianfan_sk=\"your qianfan sk\",\n",
-    "    streaming=True,\n",
-    ")\n",
-    "qa = RetrievalQA.from_chain_type(\n",
-    "    llm=llm, chain_type=\"refine\", retriever=retriever, return_source_documents=True\n",
-    ")\n",
-    "\n",
-    "query = \"什么是张量?\"\n",
-    "print(qa.run(query))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> 张量（Tensor）是一个数学概念，用于表示多维数据。它是一个可以表示多个数值的数组，可以是标量、向量、矩阵等。在深度学习和人工智能领域中，张量常用于表示神经网络的输入、输出和权重等。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.9.17"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

cookbook/rag_fusion.ipynb

@@ -30,8 +30,8 @@
    "outputs": [],
    "source": [
     "import pinecone\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.vectorstores import Pinecone\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
     "\n",
     "pinecone.init(api_key=\"...\", environment=\"...\")"
    ]
@@ -87,6 +87,7 @@
    "outputs": [],
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema.output_parser import StrOutputParser"
    ]
   },

cookbook/retrieval_in_sql.ipynb

@@ -28,8 +28,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import getpass\n",
     "import os\n",
+    "import getpass\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\") or getpass.getpass(\n",
     "    \"OpenAI API Key:\"\n",
@@ -42,8 +42,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.sql_database import SQLDatabase\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "\n",
     "CONNECTION_STRING = \"postgresql+psycopg2://postgres:test@localhost:5432/vectordb\"  # Replace with your own\n",
     "db = SQLDatabase.from_uri(CONNECTION_STRING)"
@@ -323,7 +323,6 @@
    "outputs": [],
    "source": [
     "import re\n",
-    "\n",
     "from langchain.schema.runnable import RunnableLambda\n",
     "\n",
     "\n",

cookbook/rewrite.ipynb

@@ -31,10 +31,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
+    "from operator import itemgetter\n",
+    "\n",
     "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain.schema.runnable import RunnablePassthrough, RunnableLambda\n",
     "from langchain.utilities import DuckDuckGoSearchAPIWrapper"
    ]
   },

cookbook/sales_agent_with_context.ipynb

@@ -42,22 +42,22 @@
     "OPENAI_API_KEY = \"sk-xx\"\n",
     "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n",
     "\n",
-    "from typing import Any, Callable, Dict, List, Union\n",
-    "\n",
-    "from langchain.agents import AgentExecutor, LLMSingleActionAgent, Tool\n",
-    "from langchain.agents.agent import AgentOutputParser\n",
-    "from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS\n",
-    "from langchain.chains import LLMChain, RetrievalQA\n",
+    "from typing import Dict, List, Any, Union, Callable\n",
+    "from pydantic import BaseModel, Field\n",
+    "from langchain.chains import LLMChain\nfrom langchain.prompts import PromptTemplate\n",
+    "from langchain.llms import BaseLLM\n",
     "from langchain.chains.base import Chain\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.llms import BaseLLM, OpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.prompts.base import StringPromptTemplate\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
+    "from langchain.agents import Tool, LLMSingleActionAgent, AgentExecutor\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.chains import RetrievalQA\n",
     "from langchain.vectorstores import Chroma\n",
-    "from pydantic import BaseModel, Field"
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts.base import StringPromptTemplate\n",
+    "from langchain.agents.agent import AgentOutputParser\n",
+    "from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS\n",
+    "from langchain.schema import AgentAction, AgentFinish"
    ]
   },
   {

cookbook/selecting_llms_based_on_context_length.ipynb

@@ -17,10 +17,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
+    "from langchain.schema.prompt import PromptValue\n",
+    "from langchain.schema.messages import BaseMessage\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.prompt import PromptValue"
+    "from typing import Union, Sequence"
    ]
   },
   {

cookbook/self_query_hotel_search.ipynb

@@ -1084,6 +1084,7 @@
    "outputs": [],
    "source": [
     "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.schema import Document\n",
     "from langchain.vectorstores import ElasticsearchStore\n",
     "\n",
     "embeddings = OpenAIEmbeddings()"

cookbook/smart_llm.ipynb

@@ -51,8 +51,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain_experimental.smart_llm import SmartLLMChain"
    ]
   },

cookbook/stepback-qa.ipynb

@@ -131,6 +131,7 @@
    "source": [
     "from langchain.utilities import DuckDuckGoSearchAPIWrapper\n",
     "\n",
+    "\n",
     "search = DuckDuckGoSearchAPIWrapper(max_results=4)\n",
     "\n",
     "\n",

cookbook/tree_of_thought.ipynb

@@ -84,11 +84,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import re\n",
     "from typing import Tuple\n",
-    "\n",
     "from langchain_experimental.tot.checker import ToTChecker\n",
     "from langchain_experimental.tot.thought import ThoughtValidity\n",
+    "import re\n",
     "\n",
     "\n",
     "class MyChecker(ToTChecker):\n",

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

@@ -34,8 +34,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import getpass\n",
     "import os\n",
+    "import getpass\n",
     "\n",
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
     "from langchain.vectorstores import DeepLake\n",
@@ -109,7 +109,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "\n",
     "from langchain.document_loaders import TextLoader\n",
     "\n",
     "root_dir = \"./the-algorithm\"\n",
@@ -119,7 +118,7 @@
     "        try:\n",
     "            loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
     "            docs.extend(loader.load_and_split())\n",
-    "        except Exception:\n",
+    "        except Exception as e:\n",
     "            pass"
    ]
   },
@@ -3808,8 +3807,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains import ConversationalRetrievalChain\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

@@ -22,14 +22,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Callable, List\n",
-    "\n",
+    "from typing import List, Dict, Callable\n",
+    "from langchain.chains import ConversationChain\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.llms import OpenAI\n",
     "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.prompts.prompt import PromptTemplate\n",
     "from langchain.schema import (\n",
     "    AIMessage,\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
+    "    BaseMessage,\n",
     ")"
    ]
   },
@@ -46,7 +49,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import AgentType, initialize_agent, load_tools"
+    "from langchain.agents import Tool\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.agents import AgentType\n",
+    "from langchain.agents import load_tools"
    ]
   },
   {

cookbook/two_player_dnd.ipynb

@@ -22,8 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Callable, List\n",
-    "\n",
+    "from typing import List, Dict, Callable\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",

cookbook/wikibase_agent.ipynb

@@ -192,10 +192,10 @@
     "    return current\n",
     "\n",
     "\n",
-    "from typing import Optional\n",
-    "\n",
     "import requests\n",
     "\n",
+    "from typing import Optional\n",
+    "\n",
     "\n",
     "def vocab_lookup(\n",
     "    search: str,\n",
@@ -319,10 +319,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import json\n",
-    "from typing import Any, Dict, List\n",
-    "\n",
     "import requests\n",
+    "from typing import List, Dict, Any\n",
+    "import json\n",
     "\n",
     "\n",
     "def run_sparql(\n",
@@ -390,18 +389,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import re\n",
-    "from typing import List, Union\n",
-    "\n",
     "from langchain.agents import (\n",
-    "    AgentExecutor,\n",
-    "    AgentOutputParser,\n",
-    "    LLMSingleActionAgent,\n",
     "    Tool,\n",
+    "    AgentExecutor,\n",
+    "    LLMSingleActionAgent,\n",
+    "    AgentOutputParser,\n",
     ")\n",
-    "from langchain.chains import LLMChain\n",
     "from langchain.prompts import StringPromptTemplate\n",
-    "from langchain.schema import AgentAction, AgentFinish"
+    "from langchain.llms import OpenAI\nfrom langchain.chains import LLMChain\n",
+    "from typing import List, Union\n",
+    "from langchain.schema import AgentAction, AgentFinish\n",
+    "import re"
    ]
   },
   {

docs/api_reference/_static/css/custom.css

@@ -15,11 +15,3 @@ pre {
 #my-component-root *, #headlessui-portal-root * {
   z-index: 10000;
 }
-
-table.longtable code {
-  white-space: normal;
-}
-
-table.longtable td {
-  max-width: 600px;
-}

docs/api_reference/create_api_rst.py

@@ -13,10 +13,8 @@ HERE = Path(__file__).parent
 
 PKG_DIR = ROOT_DIR / "libs" / "langchain" / "langchain"
 EXP_DIR = ROOT_DIR / "libs" / "experimental" / "langchain_experimental"
-CORE_DIR = ROOT_DIR / "libs" / "core" / "langchain_core"
 WRITE_FILE = HERE / "api_reference.rst"
 EXP_WRITE_FILE = HERE / "experimental_api_reference.rst"
-CORE_WRITE_FILE = HERE / "core_api_reference.rst"
 
 
 ClassKind = Literal["TypedDict", "Regular", "Pydantic", "enum"]
@@ -294,17 +292,6 @@ def _document_langchain_experimental() -> None:
 
 
 def _document_langchain_core() -> None:
-    """Document the langchain_core package."""
-    # Generate core_api_reference.rst
-    core_members = _load_package_modules(EXP_DIR)
-    core_doc = ".. _core_api_reference:\n\n" + _construct_doc(
-        "langchain_core", core_members
-    )
-    with open(CORE_WRITE_FILE, "w") as f:
-        f.write(core_doc)
-
-
-def _document_langchain() -> None:
     """Document the main langchain package."""
     # load top level module members
     lc_members = _load_package_modules(PKG_DIR)
@@ -319,6 +306,7 @@ def _document_langchain() -> None:
             "agents.output_parsers": agents["output_parsers"],
             "agents.format_scratchpad": agents["format_scratchpad"],
             "tools.render": tools["render"],
+            "schema.runnable": schema["runnable"],
         }
     )
 
@@ -330,9 +318,8 @@ def _document_langchain() -> None:
 
 def main() -> None:
     """Generate the reference.rst file for each package."""
-    _document_langchain()
-    _document_langchain_experimental()
     _document_langchain_core()
+    _document_langchain_experimental()
 
 
 if __name__ == "__main__":

docs/api_reference/themes/scikit-learn-modern/nav.html

@@ -34,9 +34,6 @@
         <li class="nav-item">
           <a class="sk-nav-link nav-link" href="{{ pathto('api_reference') }}">API</a>
         </li>
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" href="{{ pathto('core_api_reference') }}">Core</a>
-        </li>
         <li class="nav-item">
           <a class="sk-nav-link nav-link" href="{{ pathto('experimental_api_reference') }}">Experimental</a>
         </li>

docs/docs/_static/css/custom.css

@@ -0,0 +1,21 @@
+pre {
+  white-space: break-spaces;
+}
+
+@media (min-width: 1200px) {
+  .container,
+  .container-lg,
+  .container-md,
+  .container-sm,
+  .container-xl {
+    max-width: 2560px !important;
+  }
+}
+
+#my-component-root *, #headlessui-portal-root * {
+  z-index: 10000;
+}
+
+.content-container p {
+    margin: revert;
+}

docs/docs/_static/js/mendablesearch.js

@@ -0,0 +1,56 @@
+document.addEventListener('DOMContentLoaded', () => {
+  // Load the external dependencies
+  function loadScript(src, onLoadCallback) {
+    const script = document.createElement('script');
+    script.src = src;
+    script.onload = onLoadCallback;
+    document.head.appendChild(script);
+  }
+
+  function createRootElement() {
+    const rootElement = document.createElement('div');
+    rootElement.id = 'my-component-root';
+    document.body.appendChild(rootElement);
+    return rootElement;
+  }
+
+  
+
+  function initializeMendable() {
+    const rootElement = createRootElement();
+    const { MendableFloatingButton } = Mendable;
+    
+
+    const iconSpan1 = React.createElement('span', {
+    }, '🦜');
+
+    const iconSpan2 = React.createElement('span', {
+    }, '🔗');
+
+    const icon = React.createElement('p', {
+      style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
+    }, [iconSpan1, iconSpan2]);
+    
+    const mendableFloatingButton = React.createElement(
+      MendableFloatingButton,
+      {
+        style: { darkMode: false, accentColor: '#010810' },
+        floatingButtonStyle: { color: '#ffffff', backgroundColor: '#010810' },
+        anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
+        messageSettings: {
+          openSourcesInNewTab: false,
+          prettySources: true // Prettify the sources displayed now
+        },
+        icon: icon,
+      }
+    );
+
+    ReactDOM.render(mendableFloatingButton, rootElement);
+  }
+
+  loadScript('https://unpkg.com/react@17/umd/react.production.min.js', () => {
+    loadScript('https://unpkg.com/react-dom@17/umd/react-dom.production.min.js', () => {
+      loadScript('https://unpkg.com/@mendable/search@0.0.102/dist/umd/mendable.min.js', initializeMendable);
+    });
+  });
+});

docs/docs/additional_resources/tutorials.mdx

@@ -1,18 +1,15 @@
 # Tutorials
 
-Below are links to tutorials and courses on LangChain. For written guides on common use cases for LangChain, check out the [use cases guides](/docs/use_cases).
+Below are links to tutorials and courses on LangChain. For written guides on common use cases for LangChain, check out the [use cases guides](/docs/use_cases/qa_structured/sql).
 
 ⛓ icon marks a new addition [last update 2023-09-21]
 
 ---------------------
 
-### [LangChain on Wikipedia](https://en.wikipedia.org/wiki/LangChain)
-
 ### DeepLearning.AI courses
- by [Harrison Chase](https://en.wikipedia.org/wiki/LangChain) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
+ by [Harrison Chase](https://github.com/hwchase17) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
 - [LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain)
 - [LangChain Chat with Your Data](https://learn.deeplearning.ai/langchain-chat-with-your-data)
-- ⛓ [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/functions-tools-agents-langchain)
 
 ### Handbook
 [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**

docs/docs/expression_language/cookbook/agent.ipynb

@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import AgentExecutor, XMLAgent, tool\n",
+    "from langchain.agents import XMLAgent, tool, AgentExecutor\n",
     "from langchain.chat_models import ChatAnthropic"
    ]
   },

docs/docs/expression_language/cookbook/code_writing.ipynb

@@ -20,6 +20,8 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import (\n",
     "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
     ")\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
     "from langchain_experimental.utilities import PythonREPL"

docs/docs/expression_language/cookbook/embedding_router.ipynb

@@ -26,6 +26,7 @@
     "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
     "from langchain.utils.math import cosine_similarity\n",
     "\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",
     "When you don't know the answer to a question you admit that you don't know.\n",

docs/docs/expression_language/cookbook/memory.ipynb

@@ -18,11 +18,10 @@
    "outputs": [],
    "source": [
     "from operator import itemgetter\n",
-    "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.schema.runnable import RunnablePassthrough, RunnableLambda\n",
     "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
     "\n",
     "model = ChatOpenAI()\n",
     "prompt = ChatPromptTemplate.from_messages(\n",

docs/docs/expression_language/cookbook/multiple_chains.ipynb

@@ -69,7 +69,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain.schema.runnable import RunnableMap, RunnablePassthrough\n",
     "\n",
     "prompt1 = ChatPromptTemplate.from_template(\n",
     "    \"generate a {attribute} color. Return the name of the color and nothing else:\"\n",

docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb

@@ -42,8 +42,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "\n",
     "prompt = ChatPromptTemplate.from_template(\"tell me a joke about {foo}\")\n",
     "model = ChatOpenAI()\n",

docs/docs/expression_language/cookbook/retrieval.ipynb

@@ -38,11 +38,11 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
+    "from langchain.schema.runnable import RunnablePassthrough, RunnableLambda\n",
     "from langchain.vectorstores import FAISS"
    ]
   },
@@ -170,8 +170,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import format_document\n",
-    "from langchain.schema.runnable import RunnableMap"
+    "from langchain.schema.runnable import RunnableMap\n",
+    "from langchain.schema import format_document"
    ]
   },
   {
@@ -231,7 +231,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import List, Tuple\n",
+    "from typing import Tuple, List\n",
     "\n",
     "\n",
     "def _format_chat_history(chat_history: List[Tuple]) -> str:\n",
@@ -335,7 +335,6 @@
    "outputs": [],
    "source": [
     "from operator import itemgetter\n",
-    "\n",
     "from langchain.memory import ConversationBufferMemory"
    ]
   },

docs/docs/expression_language/how_to/binding.ipynb

@@ -12,19 +12,6 @@
     "Suppose we have a simple prompt + model sequence:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "950297ed-2d67-4091-8ea7-1d412d259d04",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -50,6 +37,11 @@
     }
    ],
    "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema import StrOutputParser\n",
+    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
     "        (\n",
@@ -113,29 +105,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "id": "f66a0fe4-fde0-4706-8863-d60253f211c7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "function = {\n",
-    "    \"name\": \"solver\",\n",
-    "    \"description\": \"Formulates and solves an equation\",\n",
-    "    \"parameters\": {\n",
-    "        \"type\": \"object\",\n",
-    "        \"properties\": {\n",
-    "            \"equation\": {\n",
-    "                \"type\": \"string\",\n",
-    "                \"description\": \"The algebraic expression of the equation\",\n",
-    "            },\n",
-    "            \"solution\": {\n",
-    "                \"type\": \"string\",\n",
-    "                \"description\": \"The solution to the equation\",\n",
+    "functions = [\n",
+    "    {\n",
+    "        \"name\": \"solver\",\n",
+    "        \"description\": \"Formulates and solves an equation\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "                \"equation\": {\n",
+    "                    \"type\": \"string\",\n",
+    "                    \"description\": \"The algebraic expression of the equation\",\n",
+    "                },\n",
+    "                \"solution\": {\n",
+    "                    \"type\": \"string\",\n",
+    "                    \"description\": \"The solution to the equation\",\n",
+    "                },\n",
     "            },\n",
+    "            \"required\": [\"equation\", \"solution\"],\n",
     "        },\n",
-    "        \"required\": [\"equation\", \"solution\"],\n",
-    "    },\n",
-    "}"
+    "    }\n",
+    "]"
    ]
   },
   {
@@ -167,70 +161,19 @@
     "    ]\n",
     ")\n",
     "model = ChatOpenAI(model=\"gpt-4\", temperature=0).bind(\n",
-    "    function_call={\"name\": \"solver\"}, functions=[function]\n",
+    "    function_call={\"name\": \"solver\"}, functions=functions\n",
     ")\n",
     "runnable = {\"equation_statement\": RunnablePassthrough()} | prompt | model\n",
     "runnable.invoke(\"x raised to the third plus seven equals 12\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "f07d7528-9269-4d6f-b12e-3669592a9e03",
-   "metadata": {},
-   "source": [
-    "## Attaching OpenAI tools"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "2cdeeb4c-0c1f-43da-bd58-4f591d9e0671",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "tools = [\n",
-    "    {\n",
-    "        \"type\": \"function\",\n",
-    "        \"function\": {\n",
-    "            \"name\": \"get_current_weather\",\n",
-    "            \"description\": \"Get the current weather in a given location\",\n",
-    "            \"parameters\": {\n",
-    "                \"type\": \"object\",\n",
-    "                \"properties\": {\n",
-    "                    \"location\": {\n",
-    "                        \"type\": \"string\",\n",
-    "                        \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
-    "                    },\n",
-    "                    \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
-    "                },\n",
-    "                \"required\": [\"location\"],\n",
-    "            },\n",
-    "        },\n",
-    "    }\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "2b65beab-48bb-46ff-a5a4-ef8ac95a513c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_zHN0ZHwrxM7nZDdqTp6dkPko', 'function': {'arguments': '{\"location\": \"San Francisco, CA\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}, {'id': 'call_aqdMm9HBSlFW9c9rqxTa7eQv', 'function': {'arguments': '{\"location\": \"New York, NY\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}, {'id': 'call_cx8E567zcLzYV2WSWVgO63f1', 'function': {'arguments': '{\"location\": \"Los Angeles, CA\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}]})"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\").bind(tools=tools)\n",
-    "model.invoke(\"What's the weather in SF, NYC and LA?\")"
-   ]
+   "source": []
   }
  ],
  "metadata": {

docs/docs/expression_language/how_to/configure.ipynb

@@ -5,7 +5,7 @@
    "id": "39eaf61b",
    "metadata": {},
    "source": [
-    "# Configure chain internals at runtime\n",
+    "# Configuration\n",
     "\n",
     "Oftentimes you may want to experiment with, or even expose to the end user, multiple different ways of doing things.\n",
     "In order to make this experience as easy as possible, we have defined two methods.\n",
@@ -262,9 +262,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatAnthropic, ChatOpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.runnable import ConfigurableField"
+    "from langchain.chat_models import ChatOpenAI, ChatAnthropic\n",
+    "from langchain.schema.runnable import ConfigurableField\n",
+    "from langchain.prompts import PromptTemplate"
    ]
   },
   {
@@ -594,7 +594,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/fallbacks.ipynb

@@ -31,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatAnthropic, ChatOpenAI"
+    "from langchain.chat_models import ChatOpenAI, ChatAnthropic"
    ]
   },
   {
@@ -50,7 +50,6 @@
    "outputs": [],
    "source": [
     "from unittest.mock import patch\n",
-    "\n",
     "from openai.error import RateLimitError"
    ]
   },