Add decorator

.devcontainer/Dockerfile

@@ -1,42 +0,0 @@
-# This is a Dockerfile for Developer Container
-
-# Use the Python base image
-ARG VARIANT="3.11-bullseye"
-FROM mcr.microsoft.com/vscode/devcontainers/python:0-${VARIANT} AS langchain-dev-base
-
-USER vscode
-
-# Define the version of Poetry to install (default is 1.4.2)
-# Define the directory of python virtual environment
-ARG PYTHON_VIRTUALENV_HOME=/home/vscode/langchain-py-env \
-    POETRY_VERSION=1.4.2 
-
-ENV POETRY_VIRTUALENVS_IN_PROJECT=false \
-    POETRY_NO_INTERACTION=true 
-
-# Create a Python virtual environment for Poetry and install it
-RUN python3 -m venv ${PYTHON_VIRTUALENV_HOME} && \
-    $PYTHON_VIRTUALENV_HOME/bin/pip install --upgrade pip && \
-    $PYTHON_VIRTUALENV_HOME/bin/pip install poetry==${POETRY_VERSION}
-
-ENV PATH="$PYTHON_VIRTUALENV_HOME/bin:$PATH" \
-    VIRTUAL_ENV=$PYTHON_VIRTUALENV_HOME
-
-# Setup for bash
-RUN poetry completions bash >> /home/vscode/.bash_completion && \
-    echo "export PATH=$PYTHON_VIRTUALENV_HOME/bin:$PATH" >> ~/.bashrc
-
-# Set the working directory for the app
-WORKDIR /workspaces/langchain
-
-# Use a multi-stage build to install dependencies
-FROM langchain-dev-base AS langchain-dev-dependencies
-
-ARG PYTHON_VIRTUALENV_HOME
-
-# Copy only the dependency files for installation
-COPY pyproject.toml poetry.lock poetry.toml ./
-
-# Install the Poetry dependencies (this layer will be cached as long as the dependencies don't change)
-RUN poetry install --no-interaction --no-ansi --with dev,test,docs
-

.devcontainer/devcontainer.json

@@ -1,33 +0,0 @@
-// For format details, see https://aka.ms/devcontainer.json. For config options, see the
-// README at: https://github.com/devcontainers/templates/tree/main/src/docker-existing-dockerfile
-{
-	"dockerComposeFile": "./docker-compose.yaml",
-	"service": "langchain",
-	"workspaceFolder": "/workspaces/langchain",
-	"name": "langchain",
-	"customizations": {
-		"vscode": {
-			"extensions": [   
-				"ms-python.python"
-			],
-			"settings": {
-				"python.defaultInterpreterPath": "/home/vscode/langchain-py-env/bin/python3.11"
-			}
-		}
-		
-	},
-
-	// Features to add to the dev container. More info: https://containers.dev/features.
-	"features": {},
-
-	// Use 'forwardPorts' to make a list of ports inside the container available locally.
-	// "forwardPorts": [],
-
-	// Uncomment the next line to run commands after the container is created.
-	// "postCreateCommand": "cat /etc/os-release",
-
-	// Uncomment to connect as an existing user other than the container default. More info: https://aka.ms/dev-containers-non-root.
-	// "remoteUser": "devcontainer"
-	"remoteUser": "vscode",
-	"overrideCommand": true
-}

.devcontainer/docker-compose.yaml

@@ -1,31 +0,0 @@
-version: '3'
-services:
-  langchain:
-    build:
-      dockerfile: .devcontainer/Dockerfile
-      context: ../ 
-    volumes:
-      - ../:/workspaces/langchain
-    networks:
-      - langchain-network 
-  #   environment:
-  #     MONGO_ROOT_USERNAME: root
-  #     MONGO_ROOT_PASSWORD: example123
-  #   depends_on:
-  #     - mongo   
-  # mongo:
-  #   image: mongo
-  #   restart: unless-stopped
-  #   environment:
-  #     MONGO_INITDB_ROOT_USERNAME: root
-  #     MONGO_INITDB_ROOT_PASSWORD: example123
-  #   ports:
-  #     - "27017:27017"
-  #   networks:
-  #     - langchain-network
-
-networks:
-  langchain-network:
-    driver: bridge
-    
-    

.github/CONTRIBUTING.md

@@ -2,62 +2,60 @@
 
 Hi there! Thank you for even being interested in contributing to LangChain.
 As an open source project in a rapidly developing field, we are extremely open
-to contributions, whether they be in the form of new features, improved infra, better documentation, or bug fixes.
-
-## 🗺️ Guidelines
-
-### 👩‍💻 Contributing Code
+to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
 
 To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
 Please do not try to push directly to this repo unless you are maintainer.
 
-Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant
-maintainers.
-
-Pull requests cannot land without passing the formatting, linting and testing checks first. See
-[Common Tasks](#-common-tasks) for how to run these checks locally.
-
-It's essential that we maintain great documentation and testing. If you:
-- Fix a bug
-  - Add a relevant unit or integration test when possible. These live in `tests/unit_tests` and `tests/integration_tests`.
-- Make an improvement
-  - Update any affected example notebooks and documentation. These lives in `docs`.
-  - Update unit and integration tests when relevant.
-- Add a feature
-  - Add a demo notebook in `docs/modules`.
-  - Add unit and integration tests.
-
-We're a small, building-oriented team. If there's something you'd like to add or change, opening a pull request is the
-best way to get our attention.
+## 🗺️Contributing Guidelines
 
 ### 🚩GitHub Issues
 
 Our [issues](https://github.com/hwchase17/langchain/issues) page is kept up to date
-with bugs, improvements, and feature requests. 
+with bugs, improvements, and feature requests. There is a taxonomy of labels to help
+with sorting and discovery of issues of interest. These include:
 
-There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help
-organize issues.
+- prompts: related to prompt tooling/infra.
+- llms: related to LLM wrappers/tooling/infra.
+- chains
+- utilities: related to different types of utilities to integrate with (Python, SQL, etc.).
+- agents
+- memory
+- applications: related to example applications to build
 
 If you start working on an issue, please assign it to yourself.
 
-If you are adding an issue, please try to keep it focused on a single, modular bug/improvement/feature.
-If two issues are related, or blocking, please link them rather than combining them.
+If you are adding an issue, please try to keep it focused on a single modular bug/improvement/feature.
+If the two issues are related, or blocking, please link them rather than keep them as one single one.
 
 We will try to keep these issues as up to date as possible, though
 with the rapid rate of develop in this field some may get out of date.
-If you notice this happening, please let us know.
+If you notice this happening, please just let us know.
 
 ### 🙋Getting Help
 
-Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
-contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is
-smooth for future contributors.
+Although we try to have a developer setup to make it as easy as possible for others to contribute (see below)
+it is possible that some pain point may arise around environment setup, linting, documentation, or other.
+Should that occur, please contact a maintainer! Not only do we want to help get you unblocked,
+but we also want to make sure that the process is smooth for future contributors.
 
 In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
-If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
-we do not want these to get in the way of getting good code into the codebase.
+If you are finding these difficult (or even just annoying) to work with,
+feel free to contact a maintainer for help - we do not want these to get in the way of getting
+good code into the codebase.
 
-## 🚀 Quick Start
+### 🏭Release process
+
+As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
+a developer and published to [PyPI](https://pypi.org/project/langchain/).
+
+LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
+even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
+
+If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
+If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
+
+## 🚀Quick Start
 
 This project uses [Poetry](https://python-poetry.org/) as a dependency manager. Check out Poetry's [documentation on how to install it](https://python-poetry.org/docs/#installation) on your system before proceeding.
 
@@ -77,9 +75,9 @@ This will install all requirements for running the package, examples, linting, f
 
 ❗Note: If you're running Poetry 1.4.1 and receive a `WheelFileValidationError` for `debugpy` during installation, you can try either downgrading to Poetry 1.4.0 or disabling "modern installation" (`poetry config installer.modern-installation false`) and re-install requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
 
-Now, you should be able to run the common tasks in the following section. To double check, run `make test`, all tests should pass. If they don't you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
+Now, you should be able to run the common tasks in the following section.
 
-## ✅ Common Tasks
+## ✅Common Tasks
 
 Type `make` for a list of common tasks.
 
@@ -190,17 +188,3 @@ Finally, you can build the documentation as outlined below:
 ```bash
 make docs_build
 ```
-
-## 🏭 Release Process
-
-As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
-a developer and published to [PyPI](https://pypi.org/project/langchain/).
-
-LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
-even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
-
-### 🌟 Recognition
-
-If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
-If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
-

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,106 +0,0 @@
-name: "\U0001F41B Bug Report"
-description: Submit a bug report to help us improve LangChain
-labels: ["02 Bug Report"]
-body:
-  - type: markdown
-    attributes:
-      value: >
-        Thank you for taking the time to file a bug report. Before creating a new
-        issue, please make sure to take a few moments to check the issue tracker
-        for existing issues about the bug.
-
-  - type: textarea
-    id: system-info
-    attributes:
-      label: System Info
-      description: Please share your system info with us.
-      placeholder: LangChain version, platform, python version, ...
-    validations:
-      required: true
-
-  - type: textarea
-    id: who-can-help
-    attributes:
-      label: Who can help?
-      description: |
-        Your issue will be replied to more quickly if you can figure out the right person to tag with @
-        If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
-
-        The core maintainers strive to read all issues, but tagging them will help them prioritize.
-
-        Please tag fewer than 3 people.
-
-        @hwchase17 - project lead
-
-        Tracing / Callbacks
-        - @agola11
-
-        Async
-        - @agola11
-
-        DataLoader Abstractions
-        - @eyurtsev
-
-        LLM/Chat Wrappers
-        - @hwchase17
-        - @agola11
-
-        Tools / Toolkits
-        - @vowelparrot
-
-      placeholder: "@Username ..."
-
-  - type: checkboxes
-    id: information-scripts-examples
-    attributes:
-      label: Information
-      description: "The problem arises when using:"
-      options:
-        - label: "The official example notebooks/scripts"
-        - label: "My own modified scripts"
-
-  - type: checkboxes
-    id: related-components
-    attributes:
-      label: Related Components
-      description: "Select the components related to the issue (if applicable):"
-      options:
-        - label: "LLMs/Chat Models"
-        - label: "Embedding Models"
-        - label: "Prompts / Prompt Templates / Prompt Selectors"
-        - label: "Output Parsers"
-        - label: "Document Loaders"
-        - label: "Vector Stores / Retrievers"
-        - label: "Memory"
-        - label: "Agents / Agent Executors"
-        - label: "Tools / Toolkits"
-        - label: "Chains"
-        - label: "Callbacks/Tracing"
-        - label: "Async"
-
-  - type: textarea
-    id: reproduction
-    validations:
-      required: true
-    attributes:
-      label: Reproduction
-      description: |
-        Please provide a [code sample](https://stackoverflow.com/help/minimal-reproducible-example) that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        If you have code snippets, error messages, stack traces please provide them here as well.
-        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-        Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-
-      placeholder: |
-        Steps to reproduce the behavior:
-
-          1.
-          2.
-          3.
-
-  - type: textarea
-    id: expected-behavior
-    validations:
-      required: true
-    attributes:
-      label: Expected behavior
-      description: "A clear and concise description of what you would expect to happen."

.github/ISSUE_TEMPLATE/config.yml

@@ -1,6 +0,0 @@
-blank_issues_enabled: true
-version: 2.1
-contact_links:
-  - name: Discord
-    url: https://discord.gg/6adMQxSpJS
-    about: General community discussions

.github/ISSUE_TEMPLATE/documentation.yml

@@ -1,19 +0,0 @@
-name: Documentation
-description: Report an issue related to the LangChain documentation.
-title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
-labels: [03 - Documentation]
-
-body:
-- type: textarea
-  attributes: 
-    label: "Issue with current documentation:"
-    description: >
-      Please make sure to leave a reference to the document/code you're
-      referring to.
-
-- type: textarea
-  attributes:
-    label: "Idea or request for content:"
-    description: >
-      Please describe as clearly as possible what topics you think are missing
-      from the current documentation.

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,30 +0,0 @@
-name: "\U0001F680 Feature request"
-description: Submit a proposal/request for a new LangChain feature
-labels: ["02 Feature Request"]
-body:
-  - type: textarea
-    id: feature-request
-    validations:
-      required: true
-    attributes:
-      label: Feature request
-      description: |
-        A clear and concise description of the feature proposal. Please provide links to any relevant GitHub repos, papers, or other resources if relevant.
-
-  - type: textarea
-    id: motivation
-    validations:
-      required: true
-    attributes:
-      label: Motivation
-      description: |
-        Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too.
-
-  - type: textarea
-    id: contribution
-    validations:
-      required: true
-    attributes:
-      label: Your contribution
-      description: |
-        Is there any way that you could help, e.g. by submitting a PR? Make sure to read the CONTRIBUTING.MD [readme](https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md)

.github/ISSUE_TEMPLATE/other.yml

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

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,46 +0,0 @@
-# Your PR Title (What it does)
-
-<!--
-Thank you for contributing to LangChain! Your PR will appear in our next release under the title you set. Please make sure it highlights your valuable contribution.
-
-Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
-
-After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
--->
-
-<!-- Remove if not applicable -->
-
-Fixes # (issue)
-
-## Before submitting
-
-<!-- If you're adding a new integration, include an integration test and an example notebook showing its use! -->
-
-## Who can review?
-
-Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:
-
-<!-- For a quicker response, figure out the right person to tag with @
-
-        @hwchase17 - project lead
-
-        Tracing / Callbacks
-        - @agola11
-
-        Async
-        - @agola11
-
-        DataLoaders
-        - @eyurtsev
-
-        Models
-        - @hwchase17
-        - @agola11
-
-        Agents / Tools / Toolkits
-        - @vowelparrot
-        
-        VectorStores / Retrievers / Memory
-        - @dev2049
-        
- -->

.github/actions/poetry_setup/action.yml

@@ -1,64 +0,0 @@
-# An action for setting up poetry install with caching.
-# Using a custom action since the default action does not
-# take poetry install groups into account.
-# Action code from:
-# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236
-name: poetry-install-with-caching
-description: Poetry install with support for caching of dependency groups.
-
-inputs:
-  python-version:
-    description: Python version, supporting MAJOR.MINOR only
-    required: true
-
-  poetry-version:
-    description: Poetry version
-    required: true
-
-  install-command:
-    description: Command run for installing dependencies
-    required: false
-    default: poetry install
-
-  cache-key:
-    description: Cache key to use for manual handling of caching
-    required: true
-
-  working-directory:
-    description: Directory to run install-command in
-    required: false
-    default: ""
-
-runs:
-  using: composite
-  steps:
-    - uses: actions/setup-python@v4
-      with:
-        python-version: ${{ inputs.python-version }}
-
-    - uses: actions/cache@v3
-      id: cache-pip
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
-      with:
-        path: |
-          ~/.cache/pip
-        key: pip-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}
-
-    - run: pipx install poetry==${{ inputs.poetry-version }} --python python${{ inputs.python-version }}
-      shell: bash
-
-    - uses: actions/cache@v3
-      id: cache-poetry
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
-      with:
-        path: |
-          ~/.cache/pypoetry/virtualenvs
-          ~/.cache/pypoetry/cache
-          ~/.cache/pypoetry/artifacts
-        key: poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles('poetry.lock') }}
-
-    - run: ${{ inputs.install-command }}
-      working-directory: ${{ inputs.working-directory }}
-      shell: bash

.github/workflows/linkcheck.yml

@@ -6,7 +6,7 @@ on:
   pull_request:
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.3.1"
 
 jobs:
   build:

.github/workflows/lint.yml

@@ -6,7 +6,7 @@ on:
   pull_request:
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.3.1"
 
 jobs:
   build:

.github/workflows/release.yml

@@ -10,7 +10,7 @@ on:
       - 'pyproject.toml'
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.3.1"
 
 jobs:
   if_release:
@@ -45,5 +45,5 @@ jobs:
       - name: Publish to PyPI
         env:
           POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
-        run: |
+        run: | 
           poetry publish

.github/workflows/test.yml

@@ -6,7 +6,7 @@ on:
   pull_request:
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.3.1"
 
 jobs:
   build:
@@ -18,31 +18,17 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
-        test_type:
-          - "core"
-          - "extended"
-    name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
     steps:
       - uses: actions/checkout@v3
+      - name: Install poetry
+        run: pipx install poetry==$POETRY_VERSION
       - name: Set up Python ${{ matrix.python-version }}
-        uses: "./.github/actions/poetry_setup"
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          poetry-version: "1.4.2"
-          cache-key: ${{ matrix.test_type }}
-          install-command: |
-              if [ "${{ matrix.test_type }}" == "core" ]; then
-                echo "Running core tests, installing dependencies with poetry..."
-                poetry install
-              else
-                echo "Running extended tests, installing dependencies with poetry..."
-                poetry install -E extended_testing
-              fi
-      - name: Run ${{matrix.test_type}} tests
+          cache: "poetry"
+      - name: Install dependencies
+        run: poetry install
+      - name: Run unit tests
         run: |
-          if [ "${{ matrix.test_type }}" == "core" ]; then
-            make test
-          else
-            make extended_tests
-          fi
-        shell: bash
+          make test

.gitignore

@@ -1,4 +1,3 @@
-.vs/
 .vscode/
 .idea/
 # Byte-compiled / optimized / DLL files
@@ -143,10 +142,3 @@ wandb/
 # asdf tool versions
 .tool-versions
 /.ruff_cache/
-
-*.pkl
-*.bin
-
-# integration test artifacts
-data_map*
-\[('_type', 'fake'), ('stop', None)]

.readthedocs.yaml

@@ -1,26 +0,0 @@
-# Read the Docs configuration file
-# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-
-# Required
-version: 2
-
-# Set the version of Python and other tools you might need
-build:
-  os: ubuntu-22.04
-  tools:
-    python: "3.11"
-
-# Build documentation in the docs/ directory with Sphinx
-sphinx:
-   configuration: docs/conf.py
-
-# If using Sphinx, optionally build your docs in additional formats such as PDF
-# formats:
-#    - pdf
-
-# Optionally declare the Python requirements required to build your docs
-python:
-   install:
-   - requirements: docs/requirements.txt
-   - method: pip
-     path: .

Dockerfile

@@ -1,7 +1,5 @@
 # This is a Dockerfile for running unit tests
 
-ARG POETRY_HOME=/opt/poetry
-
 # Use the Python base image
 FROM python:3.11.2-bullseye AS builder
 
@@ -9,7 +7,7 @@ FROM python:3.11.2-bullseye AS builder
 ARG POETRY_VERSION=1.4.2
 
 # Define the directory to install Poetry to (default is /opt/poetry)
-ARG POETRY_HOME
+ARG POETRY_HOME=/opt/poetry
 
 # Create a Python virtual environment for Poetry and install it
 RUN python3 -m venv ${POETRY_HOME} && \
@@ -25,8 +23,6 @@ WORKDIR /app
 # Use a multi-stage build to install dependencies
 FROM builder AS dependencies
 
-ARG POETRY_HOME
-
 # Copy only the dependency files for installation
 COPY pyproject.toml poetry.lock poetry.toml ./
 

Makefile

@@ -1,4 +1,4 @@
-.PHONY: all clean format lint test tests test_watch integration_tests docker_tests help extended_tests
+.PHONY: all clean format lint test tests test_watch integration_tests docker_tests help
 
 all: help
 
@@ -32,16 +32,11 @@ lint lint_diff:
 	poetry run black $(PYTHON_FILES) --check
 	poetry run ruff .
 
-TEST_FILE ?= tests/unit_tests/
-
 test:
-	poetry run pytest $(TEST_FILE)
+	poetry run pytest tests/unit_tests
 
 tests:
-	poetry run pytest $(TEST_FILE)
-
-extended_tests:
-	poetry run pytest --only-extended tests/unit_tests
+	poetry run pytest tests/unit_tests
 
 test_watch:
 	poetry run ptw --now . -- tests/unit_tests
@@ -55,16 +50,13 @@ docker_tests:
 
 help:
 	@echo '----'
-	@echo 'coverage                     - run unit tests and generate coverage report'
-	@echo 'docs_build                   - build the documentation'
-	@echo 'docs_clean                   - clean the documentation build artifacts'
-	@echo 'docs_linkcheck               - run linkchecker on the documentation'
-	@echo 'format                       - run code formatters'
-	@echo 'lint                         - run linters'
-	@echo 'test                         - run unit tests'
-	@echo 'test                         - run unit tests'
-	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
-	@echo 'extended_tests               - run only extended unit tests'
-	@echo 'test_watch                   - run unit tests in watch mode'
-	@echo 'integration_tests            - run integration tests'
-	@echo 'docker_tests                 - run unit tests in docker'
+	@echo 'coverage            - run unit tests and generate coverage report'
+	@echo 'docs_build          - build the documentation'
+	@echo 'docs_clean          - clean the documentation build artifacts'
+	@echo 'docs_linkcheck      - run linkchecker on the documentation'
+	@echo 'format              - run code formatters'
+	@echo 'lint                - run linters'
+	@echo 'test                - run unit tests'
+	@echo 'test_watch          - run unit tests in watch mode'
+	@echo 'integration_tests   - run integration tests'
+	@echo 'docker_tests        - run unit tests in docker'

README.md

@@ -2,19 +2,7 @@
 
 ⚡ Building applications with LLMs through composability ⚡
 
-[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml)
-[![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml)
-[![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml)
-[![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
-[![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
-[![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/hwchase17/langchain)
-[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/hwchase17/langchain)
-[![GitHub star chart](https://img.shields.io/github/stars/hwchase17/langchain?style=social)](https://star-history.com/#hwchase17/langchain)
-
-
-Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwchase17/langchainjs).
+[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml) [![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
 
 **Production Support:** As you move your LangChains into production, we'd love to offer more comprehensive support.
 Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set up a dedicated support Slack channel.
@@ -27,9 +15,12 @@ or
 
 ## 🤔 What is this?
 
-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.
+Large language models (LLMs) are emerging as a transformative technology, enabling
+developers to build applications that they previously could not.
+But using these LLMs in isolation is often not enough to
+create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
 
-This library aims to assist in the development of those types of applications. Common examples of these applications include:
+This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
 
 **❓ Question Answering over specific documents**
 
@@ -62,23 +53,23 @@ 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.
+This includes prompt management, prompt optimization, generic interface for all LLMs, and common utilities for working with LLMs.
 
 **🔗 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.
+Chains go beyond just a single LLM call, and are 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.
+Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.
 
 **🤖 Agents:**
 
-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.
+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.
 
 **🧠 Memory:**
 
-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.
+Memory is the concept of 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.
 
 **🧐 Evaluation:**
 
@@ -88,6 +79,6 @@ For more information on these concepts, please see our [full documentation](http
 
 ## 💁 Contributing
 
-As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
+As an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
 
 For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).

docs/_static/js/mendablesearch.js

@@ -52,7 +52,7 @@ document.addEventListener('DOMContentLoaded', () => {
 
   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@0.0.93/dist/umd/mendable.min.js', initializeMendable);
+      loadScript('https://unpkg.com/@mendable/search@0.0.83/dist/umd/mendable.min.js', initializeMendable);
     });
   });
 });

docs/deployments.md

@@ -1,10 +1,14 @@
 # Deployments
 
-So, you've created a really cool chain - now what? How do you deploy it and make it easily shareable with the world?
+So you've made a really cool chain - now what? How do you deploy it and make it easily sharable with the world?
 
-This section covers several options for that. Note that these options are meant for quick deployment of prototypes and demos, not for production systems. If you need help with the deployment of a production system, please contact us directly.
+This section covers several options for that.
+Note that these are meant as quick deployment options for prototypes and demos, and not for production systems.
+If you are looking for help with deployment of a production system, please contact us directly.
 
-What follows is a list of template GitHub repositories designed to be easily forked and modified to use your chain. This list is far from exhaustive, and we are EXTREMELY open to contributions here.
+What follows is a list of template GitHub repositories aimed that are intended to be
+very easy to fork and modify to use your chain.
+This is far from an exhaustive list of options, and we are EXTREMELY open to contributions here.
 
 ## [Streamlit](https://github.com/hwchase17/langchain-streamlit-template)
 
@@ -29,34 +33,19 @@ It implements a Question Answering app and contains instructions for deploying t
 
 A minimal example on how to run LangChain on Vercel using Flask.
 
-## [Kinsta](https://github.com/kinsta/hello-world-langchain)
-
-A minimal example on how to deploy LangChain to [Kinsta](https://kinsta.com) using Flask.
-
-## [Fly.io](https://github.com/fly-apps/hello-fly-langchain)
-
-A minimal example of how to deploy LangChain to [Fly.io](https://fly.io/) using Flask.
-
 ## [Digitalocean App Platform](https://github.com/homanp/digitalocean-langchain)
 
 A minimal example on how to deploy LangChain to DigitalOcean App Platform.
 
-## [Google Cloud Run](https://github.com/homanp/gcp-langchain)
-
-A minimal example on how to deploy LangChain to Google Cloud Run.
-
 ## [SteamShip](https://github.com/steamship-core/steamship-langchain/)
 
-This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship. This includes: production-ready endpoints, horizontal scaling across dependencies, persistent storage of app state, multi-tenancy support, etc.
+This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship.
+This includes: production ready endpoints, horizontal scaling across dependencies, persistant storage of app state, multi-tenancy support, etc.
 
 ## [Langchain-serve](https://github.com/jina-ai/langchain-serve)
 
-This repository allows users to serve local chains and agents as RESTful, gRPC, or WebSocket APIs, thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
+This repository allows users to serve local chains and agents as RESTful, gRPC, or Websocket APIs thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
 
 ## [BentoML](https://github.com/ssheng/BentoChain)
 
 This repository provides an example of how to deploy a LangChain application with [BentoML](https://github.com/bentoml/BentoML). BentoML is a framework that enables the containerization of machine learning applications as standard OCI images. BentoML also allows for the automatic generation of OpenAPI and gRPC endpoints. With BentoML, you can integrate models from all popular ML frameworks and deploy them as microservices running on the most optimal hardware and scaling independently.
-
-## [Databutton](https://databutton.com/home?new-data-app=true)
-
-These templates serve as examples of how to build, deploy, and share LangChain applications using Databutton. You can create user interfaces with Streamlit, automate tasks by scheduling Python code, and store files and data in the built-in store. Examples include a Chatbot interface with conversational memory, a Personal search engine, and a starter template for LangChain apps. Deploying and sharing is just one click away.

docs/ecosystem.rst

@@ -3,25 +3,6 @@ LangChain Ecosystem
 
 Guides for how other companies/products can be used with LangChain
 
-Groups
-----------
-
-LangChain provides integration with many LLMs and systems:
-
-- `LLM Providers <./modules/models/llms/integrations.html>`_
-- `Chat Model Providers <./modules/models/chat/integrations.html>`_
-- `Text Embedding Model Providers <./modules/models/text_embedding.html>`_
-- `Document Loader Integrations <./modules/indexes/document_loaders.html>`_
-- `Text Splitter Integrations <./modules/indexes/text_splitters.html>`_
-- `Vectorstore Providers <./modules/indexes/vectorstores.html>`_
-- `Retriever Providers <./modules/indexes/retrievers.html>`_
-- `Tool Providers <./modules/agents/tools.html>`_
-- `Toolkit Integrations <./modules/agents/toolkits.html>`_
-
-Companies / Products
-----------
-
-
 .. toctree::
    :maxdepth: 1
    :glob:

docs/ecosystem/aim_tracking.ipynb

@@ -61,6 +61,7 @@
     "from datetime import datetime\n",
     "\n",
     "from langchain.llms import OpenAI\n",
+    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks import AimCallbackHandler, StdOutCallbackHandler"
    ]
   },
@@ -108,8 +109,8 @@
     "    experiment_name=\"scenario 1: OpenAI LLM\",\n",
     ")\n",
     "\n",
-    "callbacks = [StdOutCallbackHandler(), aim_callback]\n",
-    "llm = OpenAI(temperature=0, callbacks=callbacks)"
+    "manager = CallbackManager([StdOutCallbackHandler(), aim_callback])\n",
+    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
    ]
   },
   {
@@ -176,7 +177,7 @@
     "Title: {title}\n",
     "Playwright: This is a synopsis for the above play:\"\"\"\n",
     "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
     "\n",
     "test_prompts = [\n",
     "    {\"title\": \"documentary about good video games that push the boundary of game design\"},\n",
@@ -248,12 +249,13 @@
    ],
    "source": [
     "# scenario 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
     "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callbacks=callbacks,\n",
+    "    callback_manager=manager,\n",
+    "    verbose=True,\n",
     ")\n",
     "agent.run(\n",
     "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",

docs/ecosystem/analyticdb.md

@@ -1,15 +0,0 @@
-# AnalyticDB
-
-This page covers how to use the AnalyticDB ecosystem within LangChain.
-
-### VectorStore
-
-There exists a wrapper around AnalyticDB, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import AnalyticDB
-```
-
-For a more detailed walkthrough of the AnalyticDB wrapper, see [this notebook](../modules/indexes/vectorstores/examples/analyticdb.ipynb)

docs/ecosystem/anyscale.md

@@ -1,17 +0,0 @@
-# Anyscale
-
-This page covers how to use the Anyscale ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Anyscale wrappers.
-
-## Installation and Setup
-- Get an Anyscale Service URL, route and API key and set them as environment variables (`ANYSCALE_SERVICE_URL`,`ANYSCALE_SERVICE_ROUTE`, `ANYSCALE_SERVICE_TOKEN`). 
-- Please see [the Anyscale docs](https://docs.anyscale.com/productionize/services-v2/get-started) for more details.
-
-## Wrappers
-
-### LLM
-
-There exists an Anyscale LLM wrapper, which you can access with 
-```python
-from langchain.llms import Anyscale
-```

docs/ecosystem/clearml_tracking.ipynb

@@ -79,6 +79,7 @@
    "source": [
     "from datetime import datetime\n",
     "from langchain.callbacks import ClearMLCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.llms import OpenAI\n",
     "\n",
     "# Setup and use the ClearML Callback\n",
@@ -92,9 +93,9 @@
     "    complexity_metrics=True,\n",
     "    stream_logs=True\n",
     ")\n",
-    "callbacks = [StdOutCallbackHandler(), clearml_callback]\n",
+    "manager = CallbackManager([StdOutCallbackHandler(), clearml_callback])\n",
     "# Get the OpenAI model ready to go\n",
-    "llm = OpenAI(temperature=0, callbacks=callbacks)"
+    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
    ]
   },
   {
@@ -522,12 +523,13 @@
     "from langchain.agents import AgentType\n",
     "\n",
     "# SCENARIO 2 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
     "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callbacks=callbacks,\n",
+    "    callback_manager=manager,\n",
+    "    verbose=True,\n",
     ")\n",
     "agent.run(\n",
     "    \"Who is the wife of the person who sang summer of 69?\"\n",

docs/ecosystem/comet_tracking.ipynb

@@ -64,7 +64,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can grab your [Comet API Key here](https://www.comet.com/signup?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook) or click the link after initializing Comet"
+    "You can grab your [Comet API Key here](https://www.comet.com/signup?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook) or click the link after intializing Comet"
    ]
   },
   {
@@ -121,6 +121,7 @@
     "from datetime import datetime\n",
     "\n",
     "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.llms import OpenAI\n",
     "\n",
     "comet_callback = CometCallbackHandler(\n",
@@ -130,8 +131,8 @@
     "    tags=[\"llm\"],\n",
     "    visualizations=[\"dep\"],\n",
     ")\n",
-    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
-    "llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)\n",
+    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
+    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
     "\n",
     "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\", \"Tell me a fact\"] * 3)\n",
     "print(\"LLM result\", llm_result)\n",
@@ -152,6 +153,7 @@
    "outputs": [],
    "source": [
     "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
@@ -162,14 +164,15 @@
     "    stream_logs=True,\n",
     "    tags=[\"synopsis-chain\"],\n",
     ")\n",
-    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
-    "llm = OpenAI(temperature=0.9, callbacks=callbacks)\n",
+    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
+    "\n",
+    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
     "\n",
     "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
     "Title: {title}\n",
     "Playwright: This is a synopsis for the above play:\"\"\"\n",
     "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
     "\n",
     "test_prompts = [{\"title\": \"Documentary about Bigfoot in Paris\"}]\n",
     "print(synopsis_chain.apply(test_prompts))\n",
@@ -191,6 +194,7 @@
    "source": [
     "from langchain.agents import initialize_agent, load_tools\n",
     "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.llms import OpenAI\n",
     "\n",
     "comet_callback = CometCallbackHandler(\n",
@@ -199,15 +203,15 @@
     "    stream_logs=True,\n",
     "    tags=[\"agent\"],\n",
     ")\n",
-    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
-    "llm = OpenAI(temperature=0.9, callbacks=callbacks)\n",
+    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
+    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
     "\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
     "    agent=\"zero-shot-react-description\",\n",
-    "    callbacks=callbacks,\n",
+    "    callback_manager=manager,\n",
     "    verbose=True,\n",
     ")\n",
     "agent.run(\n",
@@ -251,6 +255,7 @@
     "from rouge_score import rouge_scorer\n",
     "\n",
     "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
@@ -293,10 +298,10 @@
     "    tags=[\"custom_metrics\"],\n",
     "    custom_metrics=rouge_score.compute_metric,\n",
     ")\n",
-    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
-    "llm = OpenAI(temperature=0.9)\n",
+    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
+    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
     "\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
     "\n",
     "test_prompts = [\n",
     "    {\n",
@@ -318,7 +323,7 @@
     "                 \"\"\"\n",
     "    }\n",
     "]\n",
-    "print(synopsis_chain.apply(test_prompts, callbacks=callbacks))\n",
+    "print(synopsis_chain.apply(test_prompts))\n",
     "comet_callback.flush_tracker(synopsis_chain, finish=True)"
    ]
   }

docs/ecosystem/gpt4all.md

@@ -3,7 +3,6 @@
 This page covers how to use the `GPT4All` wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.
 
 ## Installation and Setup
-
 - Install the Python package with `pip install pyllamacpp`
 - Download a [GPT4All model](https://github.com/nomic-ai/pyllamacpp#supported-model) and place it in your desired directory
 
@@ -29,16 +28,16 @@ To stream the model's predictions, add in a CallbackManager.
 
 ```python
 from langchain.llms import GPT4All
+from langchain.callbacks.base import CallbackManager
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-
 # There are many CallbackHandlers supported, such as
 # from langchain.callbacks.streamlit import StreamlitCallbackHandler
 
-callbacks = [StreamingStdOutCallbackHandler()]
-model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8, callback_handler=callback_handler, verbose=True)
 
 # Generate text. Tokens are streamed through the callback manager.
-model("Once upon a time, ", callbacks=callbacks)
+model("Once upon a time, ")
 ```
 
 ## Model File

docs/ecosystem/lancedb.md

@@ -1,23 +0,0 @@
-# LanceDB
-
-This page covers how to use [LanceDB](https://github.com/lancedb/lancedb) within LangChain.
-It is broken into two parts: installation and setup, and then references to specific LanceDB wrappers.
-
-## Installation and Setup
-
-- Install the Python SDK with `pip install lancedb`
-
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around LanceDB databases, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-To import this vectorstore:
-
-```python
-from langchain.vectorstores import LanceDB
-```
-
-For a more detailed walkthrough of the LanceDB wrapper, see [this notebook](../modules/indexes/vectorstores/examples/lancedb.ipynb)

docs/ecosystem/metal.md

@@ -1,26 +0,0 @@
-# Metal
-
-This page covers how to use [Metal](https://getmetal.io) within LangChain.
-
-## What is Metal?
-
-Metal is a  managed retrieval & memory platform built for production. Easily index your data into `Metal` and run semantic search and retrieval on it.
-
-![Metal](../_static/MetalDash.png)
-
-## Quick start
-
-Get started by [creating a Metal account](https://app.getmetal.io/signup).
-
-Then, you can easily take advantage of the `MetalRetriever` class to start retrieving your data for semantic search, prompting context, etc. This class takes a `Metal` instance and a dictionary of parameters to pass to the Metal API.
-
-```python
-from langchain.retrievers import MetalRetriever
-from metal_sdk.metal import Metal
-
-
-metal = Metal("API_KEY", "CLIENT_ID", "INDEX_ID");
-retriever = MetalRetriever(metal, params={"limit": 2})
-
-docs = retriever.get_relevant_documents("search term")
-```

docs/ecosystem/mlflow_tracking.ipynb

@@ -1,172 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MLflow\n",
-    "\n",
-    "This notebook goes over how to track your LangChain experiments into your MLflow Server"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install azureml-mlflow\n",
-    "!pip install pandas\n",
-    "!pip install textstat\n",
-    "!pip install spacy\n",
-    "!pip install openai\n",
-    "!pip install google-search-results\n",
-    "!python -m spacy download en_core_web_sm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"MLFLOW_TRACKING_URI\"] = \"\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
-    "os.environ[\"SERPAPI_API_KEY\"] = \"\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks import MlflowCallbackHandler\n",
-    "from langchain.llms import OpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"Main function.\n",
-    "\n",
-    "This function is used to try the callback handler.\n",
-    "Scenarios:\n",
-    "1. OpenAI LLM\n",
-    "2. Chain with multiple SubChains on multiple generations\n",
-    "3. Agent with Tools\n",
-    "\"\"\"\n",
-    "mlflow_callback = MlflowCallbackHandler()\n",
-    "llm = OpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, callbacks=[mlflow_callback], verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# SCENARIO 1 - LLM\n",
-    "llm_result = llm.generate([\"Tell me a joke\"])\n",
-    "\n",
-    "mlflow_callback.flush_tracker(llm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# SCENARIO 2 - Chain\n",
-    "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
-    "Title: {title}\n",
-    "Playwright: This is a synopsis for the above play:\"\"\"\n",
-    "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=[mlflow_callback])\n",
-    "\n",
-    "test_prompts = [\n",
-    "    {\n",
-    "        \"title\": \"documentary about good video games that push the boundary of game design\"\n",
-    "    },\n",
-    "]\n",
-    "synopsis_chain.apply(test_prompts)\n",
-    "mlflow_callback.flush_tracker(synopsis_chain)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_jN73xcPVEpI"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents import initialize_agent, load_tools\n",
-    "from langchain.agents import AgentType"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Gpq4rk6VT9cu"
-   },
-   "outputs": [],
-   "source": [
-    "# SCENARIO 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=[mlflow_callback])\n",
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callbacks=[mlflow_callback],\n",
-    "    verbose=True,\n",
-    ")\n",
-    "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")\n",
-    "mlflow_callback.flush_tracker(agent, finish=True)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

docs/ecosystem/myscale.md

@@ -1,65 +0,0 @@
-# MyScale
-
-This page covers how to use MyScale vector database within LangChain.
-It is broken into two parts: installation and setup, and then references to specific MyScale wrappers.
-
-With MyScale, you can manage both structured and unstructured (vectorized) data, and perform joint queries and analytics on both types of data using SQL. Plus, MyScale's cloud-native OLAP architecture, built on top of ClickHouse, enables lightning-fast data processing even on massive datasets.
-
-## Introduction
-
-[Overview to MyScale and High performance vector search](https://docs.myscale.com/en/overview/)
-
-You can now register on our SaaS and [start a cluster now!](https://docs.myscale.com/en/quickstart/)
-
-If you are also interested in how we managed to integrate SQL and vector, please refer to [this document](https://docs.myscale.com/en/vector-reference/) for further syntax reference.
-
-We also deliver with live demo on huggingface! Please checkout our [huggingface space](https://huggingface.co/myscale)! They search millions of vector within a blink!
-
-## Installation and Setup
-- Install the Python SDK with `pip install clickhouse-connect`
-
-### Setting up envrionments
-
-There are two ways to set up parameters for myscale index.
-
-1. Environment Variables
-
-    Before you run the app, please set the environment variable with `export`:
-    `export MYSCALE_URL='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`
-
-    You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)
-    Every attributes under `MyScaleSettings` can be set with prefix `MYSCALE_` and is case insensitive.
-
-2. Create `MyScaleSettings` object with parameters
-
-
-    ```python
-    from langchain.vectorstores import MyScale, MyScaleSettings
-    config = MyScaleSetting(host="<your-backend-url>", port=8443, ...)
-    index = MyScale(embedding_function, config)
-    index.add_documents(...)
-    ```
-  
-## Wrappers
-supported functions:
-- `add_texts`
-- `add_documents`
-- `from_texts`
-- `from_documents`
-- `similarity_search`
-- `asimilarity_search`
-- `similarity_search_by_vector`
-- `asimilarity_search_by_vector`
-- `similarity_search_with_relevance_scores`
-
-### VectorStore
-
-There exists a wrapper around MyScale database, allowing you to use it as a vectorstore,
-whether for semantic search or similar example retrieval.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import MyScale
-```
-
-For a more detailed walkthrough of the MyScale wrapper, see [this notebook](../modules/indexes/vectorstores/examples/myscale.ipynb)

docs/ecosystem/pipelineai.md

@@ -1,19 +0,0 @@
-# PipelineAI
-
-This page covers how to use the PipelineAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific PipelineAI wrappers.
-
-## Installation and Setup
-
-- Install with `pip install pipeline-ai`
-- Get a Pipeline Cloud api key and set it as an environment variable (`PIPELINE_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists a PipelineAI LLM wrapper, which you can access with
-
-```python
-from langchain.llms import PipelineAI
-```

docs/ecosystem/predictionguard.md

@@ -1,56 +0,0 @@
-# Prediction Guard
-
-This page covers how to use the Prediction Guard ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install predictionguard`
-- Get an Prediction Guard access token (as described [here](https://docs.predictionguard.com/)) and set it as an environment variable (`PREDICTIONGUARD_TOKEN`)
-
-## LLM Wrapper
-
-There exists a Prediction Guard LLM wrapper, which you can access with 
-```python
-from langchain.llms import PredictionGuard
-```
-
-You can provide the name of your Prediction Guard "proxy" as an argument when initializing the LLM:
-```python
-pgllm = PredictionGuard(name="your-text-gen-proxy")
-```
-
-Alternatively, you can use Prediction Guard's default proxy for SOTA LLMs:
-```python
-pgllm = PredictionGuard(name="default-text-gen")
-```
-
-You can also provide your access token directly as an argument:
-```python
-pgllm = PredictionGuard(name="default-text-gen", token="<your access token>")
-```
-
-## Example usage
-
-Basic usage of the LLM wrapper:
-```python
-from langchain.llms import PredictionGuard
-
-pgllm = PredictionGuard(name="default-text-gen")
-pgllm("Tell me a joke")
-```
-
-Basic LLM Chaining with the Prediction Guard wrapper:
-```python
-from langchain import PromptTemplate, LLMChain
-from langchain.llms import PredictionGuard
-
-template = """Question: {question}
-
-Answer: Let's think step by step."""
-prompt = PromptTemplate(template=template, input_variables=["question"])
-llm_chain = LLMChain(prompt=prompt, llm=PredictionGuard(name="default-text-gen"), verbose=True)
-
-question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
-
-llm_chain.predict(question=question)
-```

docs/ecosystem/redis.md

@@ -1,79 +0,0 @@
-# Redis
-
-This page covers how to use the [Redis](https://redis.com) ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Redis wrappers.
-
-## Installation and Setup
-- Install the Redis Python SDK with `pip install redis`
-
-## Wrappers
-
-### Cache
-
-The Cache wrapper allows for [Redis](https://redis.io) to be used as a remote, low-latency, in-memory cache for LLM prompts and responses.
-
-#### Standard Cache
-The standard cache is the Redis bread & butter of use case in production for both [open source](https://redis.io) and [enterprise](https://redis.com) users globally.
-
-To import this cache:
-```python
-from langchain.cache import RedisCache
-```
-
-To use this cache with your LLMs:
-```python
-import langchain
-import redis
-
-redis_client = redis.Redis.from_url(...)
-langchain.llm_cache = RedisCache(redis_client)
-```
-
-#### Semantic Cache
-Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends Redis as both a cache and a vectorstore.
-
-To import this cache:
-```python
-from langchain.cache import RedisSemanticCache
-```
-
-To use this cache with your LLMs:
-```python
-import langchain
-import redis
-
-# use any embedding provider...
-from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
-
-redis_url = "redis://localhost:6379"
-
-langchain.llm_cache = RedisSemanticCache(
-    embedding=FakeEmbeddings(),
-    redis_url=redis_url
-)
-```
-
-### VectorStore
-
-The vectorstore wrapper turns Redis into a low-latency [vector database](https://redis.com/solutions/use-cases/vector-database/) for semantic search or LLM content retrieval.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import Redis
-```
-
-For a more detailed walkthrough of the Redis vectorstore wrapper, see [this notebook](../modules/indexes/vectorstores/examples/redis.ipynb).
-
-### Retriever
-
-The Redis vector store retriever wrapper generalizes the vectorstore class to perform low-latency document retrieval. To create the retriever, simply call `.as_retriever()` on the base vectorstore class.
-
-### Memory
-Redis can be used to persist LLM conversations.
-
-#### Vector Store Retriever Memory
-
-For a more detailed walkthrough of the `VectorStoreRetrieverMemory` wrapper, see [this notebook](../modules/memory/types/vectorstore_retriever_memory.ipynb).
-
-#### Chat Message History Memory
-For a detailed example of Redis to cache conversation message history, see [this notebook](../modules/memory/examples/redis_chat_message_history.ipynb).

docs/ecosystem/replicate.md

@@ -9,7 +9,7 @@ This page covers how to run models on Replicate within LangChain.
 
 Find a model on the [Replicate explore page](https://replicate.com/explore), and then paste in the model name and version in this format: `owner-name/model-name:version`
 
-For example, for this [dolly model](https://replicate.com/replicate/dolly-v2-12b), click on the API tab. The model name/version would be: `"replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"`
+For example, for this [flan-t5 model](https://replicate.com/daanelson/flan-t5), click on the API tab. The model name/version would be: `daanelson/flan-t5:04e422a9b85baed86a4f24981d7f9953e20c5fd82f6103b74ebc431588e1cec8`
 
 Only the `model` param is required, but any other model parameters can also be passed in with the format `input={model_param: value, ...}`
 
@@ -24,7 +24,7 @@ Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6
 From here, we can initialize our model:
 
 ```python
-llm = Replicate(model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5")
+llm = Replicate(model="daanelson/flan-t5:04e422a9b85baed86a4f24981d7f9953e20c5fd82f6103b74ebc431588e1cec8")
 ```
 
 And run it:
@@ -40,7 +40,8 @@ llm(prompt)
 We can call any Replicate model (not just LLMs) using this syntax. For example, we can call [Stable Diffusion](https://replicate.com/stability-ai/stable-diffusion):
 
 ```python
-text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions':'512x512'})
+text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
+                       input={'image_dimensions'='512x512'}
 
 image_output = text2image("A cat riding a motorcycle by Picasso")
 ```

docs/ecosystem/runhouse.md

@@ -15,7 +15,7 @@ custom LLMs, you can use the `SelfHostedPipeline` parent class.
 from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
 ```
 
-For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/models/llms/integrations/runhouse.ipynb)
+For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/models/llms/integrations/self_hosted_examples.ipynb)
 
 ## Self-hosted Embeddings
 There are several ways to use self-hosted embeddings with LangChain via Runhouse.

docs/ecosystem/tair.md

@@ -1,22 +0,0 @@
-# Tair
-
-This page covers how to use the Tair ecosystem within LangChain.
-
-## Installation and Setup
-
-Install Tair Python SDK with `pip install tair`.
-
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around TairVector, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-To import this vectorstore:
-
-```python
-from langchain.vectorstores import Tair
-```
-
-For a more detailed walkthrough of the Tair wrapper, see [this notebook](../modules/indexes/vectorstores/examples/tair.ipynb)

docs/ecosystem/unstructured.md

@@ -10,10 +10,6 @@ This page is broken into two parts: installation and setup, and then references
 `unstructured` wrappers.
 
 ## Installation and Setup
-
-If you are using a loader that runs locally, use the following steps to get `unstructured` and
-its dependencies running locally.
-
 - Install the Python SDK with `pip install "unstructured[local-inference]"`
 - Install the following system dependencies if they are not already available on your system.
   Depending on what document types you're parsing, you may not need all of these.
@@ -29,15 +25,6 @@ its dependencies running locally.
       using the `"fast"` strategy, which uses `pdfminer` directly and doesn't require
       `detectron2`.
 
-If you want to get up and running with less set up, you can
-simply run `pip install unstructured` and use `UnstructuredAPIFileLoader` or
-`UnstructuredAPIFileIOLoader`. That will process your document using the hosted Unstructured API.
-Note that currently (as of 1 May 2023) the Unstructured API is open, but it will soon require
-an API. The [Unstructured documentation page](https://unstructured-io.github.io/) will have
-instructions on how to generate an API key once they're available. Check out the instructions
-[here](https://github.com/Unstructured-IO/unstructured-api#dizzy-instructions-for-using-the-docker-image)
-if you'd like to self-host the Unstructured API or run it locally.
-
 ## Wrappers
 
 ### Data Loaders

docs/ecosystem/wandb_tracking.ipynb

@@ -50,6 +50,7 @@
    "source": [
     "from datetime import datetime\n",
     "from langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.llms import OpenAI"
    ]
   },
@@ -195,8 +196,8 @@
     "    name=\"llm\",\n",
     "    tags=[\"test\"],\n",
     ")\n",
-    "callbacks = [StdOutCallbackHandler(), wandb_callback]\n",
-    "llm = OpenAI(temperature=0, callbacks=callbacks)"
+    "manager = CallbackManager([StdOutCallbackHandler(), wandb_callback])\n",
+    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
    ]
   },
   {
@@ -483,7 +484,7 @@
     "Title: {title}\n",
     "Playwright: This is a synopsis for the above play:\"\"\"\n",
     "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
     "\n",
     "test_prompts = [\n",
     "    {\n",
@@ -576,15 +577,16 @@
    ],
    "source": [
     "# SCENARIO 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
     "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    callback_manager=manager,\n",
+    "    verbose=True,\n",
     ")\n",
     "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\",\n",
-    "    callbacks=callbacks,\n",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
     ")\n",
     "wandb_callback.flush_tracker(agent, reset=False, finish=True)"
    ]

docs/ecosystem/weaviate.md

@@ -30,4 +30,4 @@ To import this vectorstore:
 from langchain.vectorstores import Weaviate
 ```
 
-For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/indexes/vectorstores/examples/weaviate.ipynb)
+For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/indexes/vectorstores/getting_started.ipynb)

docs/ecosystem/yeagerai.md

@@ -1,43 +0,0 @@
-# Yeager.ai
-
-This page covers how to use [Yeager.ai](https://yeager.ai) to generate LangChain tools and agents.
-
-## What is Yeager.ai?
-Yeager.ai is an ecosystem designed to simplify the process of creating AI agents and tools. 
-
-It features yAgents, a No-code LangChain Agent Builder, which enables users to build, test, and deploy AI solutions with ease. Leveraging the LangChain framework, yAgents allows seamless integration with various language models and resources, making it suitable for developers, researchers, and AI enthusiasts across diverse applications.
-
-## yAgents
-Low code generative agent designed to help you build, prototype, and deploy Langchain tools with ease. 
-
-### How to use?
-```
-pip install yeagerai-agent
-yeagerai-agent
-```
-Go to http://127.0.0.1:7860
-
-This will install the necessary dependencies and set up yAgents on your system. After the first run, yAgents will create a .env file where you can input your OpenAI API key. You can do the same directly from the Gradio interface under the tab "Settings".
-
-`OPENAI_API_KEY=<your_openai_api_key_here>`
-
-We recommend using GPT-4,. However, the tool can also work with GPT-3 if the problem is broken down sufficiently.
-
-### Creating and Executing Tools with yAgents
-yAgents makes it easy to create and execute AI-powered tools. Here's a brief overview of the process:
-1. Create a tool: To create a tool, provide a natural language prompt to yAgents. The prompt should clearly describe the tool's purpose and functionality. For example:
-`create a tool that returns the n-th prime number`
-
-2. Load the tool into the toolkit: To load a tool into yAgents, simply provide a command to yAgents that says so. For example:
-`load the tool that you just created it into your toolkit`
-
-3. Execute the tool: To run a tool or agent, simply provide a command to yAgents that includes the name of the tool and any required parameters. For example:
-`generate the 50th prime number`
-
-You can see a video of how it works [here](https://www.youtube.com/watch?v=KA5hCM3RaWE).
-
-As you become more familiar with yAgents, you can create more advanced tools and agents to automate your work and enhance your productivity.
-
-For more information, see [yAgents' Github](https://github.com/yeagerai/yeagerai-agent) or our [docs](https://yeagerai.gitbook.io/docs/general/welcome-to-yeager.ai) 
-
-

docs/gallery.rst

@@ -280,17 +280,6 @@ Proprietary
 
     ---
 
-    .. link-button:: https://anysummary.app
-        :type: url
-        :text: Summarize any file with AI
-        :classes: stretched-link btn-lg
-
-    +++
-
-    Summarize not only long docs, interview audio or video files quickly, but also entire websites and YouTube videos. Share or download your generated summaries to collaborate with others, or revisit them at any time! Bonus: `@anysummary <https://twitter.com/anysummary>`_ on Twitter will also summarize any thread it is tagged in.
-    
-    ---
-
     .. link-button:: https://twitter.com/dory111111/status/1608406234646052870?s=20&t=XYlrbKM0ornJsrtGa0br-g
         :type: url
         :text: AI Assisted SQL Query Generator
@@ -343,12 +332,4 @@ Proprietary
     +++
 
     A journaling app for self-care that uses AI to uncover insights and patterns over time.
-
-
-Articles on **Google Scholar**
------------------------------
-
-LangChain is used in many scientific and research projects.
-
-**Google Scholar** presents a `list of the papers <https://scholar.google.com/scholar?q=%22langchain%22&hl=en&as_sdt=0,5&as_vis=1>`_
-with references to LangChain.
+    

docs/getting_started/getting_started.md

@@ -46,7 +46,7 @@ LangChain provides many modules that can be used to build language model applica
 
 
 
-## LLMs: Get predictions from a language model
+`````{dropdown} LLMs: Get predictions from a language model
 
 The most basic building block of LangChain is calling an LLM on some input.
 Let's walk through a simple example of how to do this. 
@@ -77,9 +77,10 @@ Feetful of Fun
 ```
 
 For more details on how to use LLMs within LangChain, see the [LLM getting started guide](../modules/models/llms/getting_started.ipynb).
+`````
 
 
-## Prompt Templates: Manage prompts for LLMs
+`````{dropdown} Prompt Templates: Manage prompts for LLMs
 
 Calling an LLM is a great first step, but it's just the beginning.
 Normally when you use an LLM in an application, you are not sending user input directly to the LLM.
@@ -114,10 +115,11 @@ What is a good name for a company that makes colorful socks?
 
 [For more details, check out the getting started guide for prompts.](../modules/prompts/chat_prompt_template.ipynb)
 
+`````
 
 
 
-## Chains: Combine LLMs and prompts in multi-step workflows
+`````{dropdown} Chains: Combine LLMs and prompts in multi-step workflows
 
 Up until now, we've worked with the PromptTemplate and LLM primitives by themselves. But of course, a real application is not just one primitive, but rather a combination of them.
 
@@ -157,7 +159,10 @@ This is one of the simpler types of chains, but understanding how it works will
 
 [For more details, check out the getting started guide for chains.](../modules/chains/getting_started.ipynb)
 
-## Agents: Dynamically Call Chains Based on User Input
+`````
+
+
+`````{dropdown} Agents: Dynamically Call Chains Based on User Input
 
 So far the chains we've looked at run in a predetermined order.
 
@@ -172,9 +177,9 @@ In order to load agents, you should understand the following concepts:
 - LLM: The language model powering the agent.
 - Agent: The agent to use. This should be a string that references a support agent class. Because this notebook focuses on the simplest, highest level API, this only covers using the standard supported agents. If you want to implement a custom agent, see the documentation for custom agents (coming soon).
 
-**Agents**: For a list of supported agents and their specifications, see [here](../modules/agents/getting_started.ipynb).
+**Agents**: For a list of supported agents and their specifications, see [here](../modules/agents/agents.md).
 
-**Tools**: For a list of predefined tools and their specifications, see [here](../modules/agents/tools/getting_started.md).
+**Tools**: For a list of predefined tools and their specifications, see [here](../modules/agents/tools.md).
 
 For this example, you will also need to install the SerpAPI Python package.
 
@@ -229,8 +234,10 @@ Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .
 ```
 
 
+`````
 
-## Memory: Add State to Chains and Agents
+
+`````{dropdown} Memory: Add State to Chains and Agents
 
 So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of "memory" so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of "short-term memory". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of "long-term memory". For more concrete ideas on the latter, see this [awesome paper](https://memprompt.com/).
 
@@ -244,8 +251,7 @@ from langchain import OpenAI, ConversationChain
 llm = OpenAI(temperature=0)
 conversation = ConversationChain(llm=llm, verbose=True)
 
-output = conversation.predict(input="Hi there!")
-print(output)
+conversation.predict(input="Hi there!")
 ```
 
 ```pycon
@@ -263,8 +269,7 @@ AI:
 ```
 
 ```python
-output = conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
-print(output)
+conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
 ```
 
 ```pycon
@@ -282,6 +287,7 @@ AI:
 > Finished chain.
 " That's great! What would you like to talk about?"
 ```
+`````
 
 ## Building a Language Model Application: Chat Models
 
@@ -289,8 +295,8 @@ Similarly, you can use chat models instead of LLMs. Chat models are a variation
 
 Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
 
-## Get Message Completions from a Chat Model
 
+`````{dropdown} Get Message Completions from a Chat Model
 You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
 
 ```python
@@ -316,7 +322,7 @@ You can also pass in multiple messages for OpenAI's gpt-3.5-turbo and gpt-4 mode
 ```python
 messages = [
     SystemMessage(content="You are a helpful assistant that translates English to French."),
-    HumanMessage(content="I love programming.")
+    HumanMessage(content="Translate this sentence from English to French. I love programming.")
 ]
 chat(messages)
 # -> AIMessage(content="J'aime programmer.", additional_kwargs={})
@@ -327,29 +333,29 @@ You can go one step further and generate completions for multiple sets of messag
 batch_messages = [
     [
         SystemMessage(content="You are a helpful assistant that translates English to French."),
-        HumanMessage(content="I love programming.")
+        HumanMessage(content="Translate this sentence from English to French. I love programming.")
     ],
     [
         SystemMessage(content="You are a helpful assistant that translates English to French."),
-        HumanMessage(content="I love artificial intelligence.")
+        HumanMessage(content="Translate this sentence from English to French. I love artificial intelligence.")
     ],
 ]
 result = chat.generate(batch_messages)
 result
-# -> LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}})
+# -> LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}})
 ```
 
 You can recover things like token usage from this LLMResult:
 ```
 result.llm_output['token_usage']
-# -> {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}
+# -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
 ```
+`````
 
-
-## Chat Prompt Templates
+`````{dropdown} Chat Prompt Templates
 Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or `Message` object, depending on whether you want to use the formatted value as input to an llm or chat model.
 
-For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
+For convience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
 
 ```python
 from langchain.chat_models import ChatOpenAI
@@ -361,9 +367,9 @@ from langchain.prompts.chat import (
 
 chat = ChatOpenAI(temperature=0)
 
-template = "You are a helpful assistant that translates {input_language} to {output_language}."
+template="You are a helpful assistant that translates {input_language} to {output_language}."
 system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template = "{text}"
+human_template="{text}"
 human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
 
 chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
@@ -372,8 +378,9 @@ chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_mes
 chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
 # -> AIMessage(content="J'aime programmer.", additional_kwargs={})
 ```
+`````
 
-## Chains with Chat Models
+`````{dropdown} Chains with Chat Models
 The `LLMChain` discussed in the above section can be used with chat models as well:
 
 ```python
@@ -387,9 +394,9 @@ from langchain.prompts.chat import (
 
 chat = ChatOpenAI(temperature=0)
 
-template = "You are a helpful assistant that translates {input_language} to {output_language}."
+template="You are a helpful assistant that translates {input_language} to {output_language}."
 system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template = "{text}"
+human_template="{text}"
 human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
 chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
 
@@ -397,8 +404,9 @@ chain = LLMChain(llm=chat, prompt=chat_prompt)
 chain.run(input_language="English", output_language="French", text="I love programming.")
 # -> "J'aime programmer."
 ```
+`````
 
-## Agents with Chat Models
+`````{dropdown} Agents with Chat Models
 Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
 
 ```python
@@ -457,7 +465,9 @@ Final Answer: 2.169459462491557
 > Finished chain.
 '2.169459462491557'
 ```
-## Memory: Add State to Chains and Agents
+`````
+
+`````{dropdown} Memory: Add State to Chains and Agents
 You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
 
 ```python
@@ -491,4 +501,4 @@ conversation.predict(input="I'm doing well! Just having a conversation with an A
 conversation.predict(input="Tell me about yourself.")
 # -> "Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?"
 ```
-
+`````

docs/index.rst

@@ -44,8 +44,6 @@ These modules are, in increasing order of complexity:
 
 - `Agents <./modules/agents.html>`_: 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.
 
-- `Callbacks <./modules/callbacks/getting_started.html>`_: It can be difficult to track all that occurs inside a chain or agent - callbacks help add a level of observability and introspection.
-
 
 .. toctree::
    :maxdepth: 1
@@ -59,17 +57,12 @@ These modules are, in increasing order of complexity:
    ./modules/memory.md
    ./modules/chains.md
    ./modules/agents.md
-   ./modules/callbacks/getting_started.ipynb
 
 Use Cases
 ----------
 
 The above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.
 
-- `Autonomous Agents <./use_cases/autonomous_agents.html>`_: Autonomous agents are long running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.
-
-- `Agent Simulations <./use_cases/agent_simulations.html>`_: Putting agents in a sandbox and observing how they interact with each other or to events can be an interesting way to observe their long-term memory abilities.
-
 - `Personal Assistants <./use_cases/personal_assistants.html>`_: The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data.
 
 - `Question Answering <./use_cases/question_answering.html>`_: The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.
@@ -96,8 +89,6 @@ The above modules can be used in a variety of ways. LangChain also provides guid
    :hidden:
 
    ./use_cases/personal_assistants.md
-   ./use_cases/autonomous_agents.md
-   ./use_cases/agent_simulations.md
    ./use_cases/question_answering.md
    ./use_cases/chatbots.md
    ./use_cases/tabular.rst
@@ -162,8 +153,6 @@ Additional collection of resources we think may be useful as you develop your ap
 
 - `Discord <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain!
 
-- `YouTube <./youtube.html>`_: A collection of the LangChain tutorials and videos.
-
 - `Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>`_: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.
 
 
@@ -180,5 +169,4 @@ Additional collection of resources we think may be useful as you develop your ap
    ./tracing.md
    ./use_cases/model_laboratory.ipynb
    Discord <https://discord.gg/6adMQxSpJS>
-   ./youtube.md
    Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>

docs/modules/agents.rst

@@ -10,42 +10,6 @@ but potentially an unknown chain that depends on the user's input.
 In these types of chains, there is a “agent” which has access to a suite of tools.
 Depending on the user input, the agent can then decide which, if any, of these tools to call.
 
-At the moment, there are two main types of agents:
-
-1. "Action Agents": these agents decide an action to take and take that action one step at a time
-2. "Plan-and-Execute Agents": these agents first decide a plan of actions to take, and then execute those actions one at a time.
-
-When should you use each one? Action Agents are more conventional, and good for small tasks.
-For more complex or long running tasks, the initial planning step helps to maintain long term objectives and focus. However, that comes at the expense of generally more calls and higher latency.
-These two agents are also not mutually exclusive - in fact, it is often best to have an Action Agent be in change of the execution for the Plan and Execute agent.
-
-Action Agents
--------------
-
-High level pseudocode of agents looks something like:
-
-- Some user input is received
-- The `agent` decides which `tool` - if any - to use, and what the input to that tool should be
-- That `tool` is then called with that `tool input`, and an `observation` is recorded (this is just the output of calling that tool with that tool input)
-- That history of `tool`, `tool input`, and `observation` is passed back into the `agent`, and it decides what step to take next
-- This is repeated until the `agent` decides it no longer needs to use a `tool`, and then it responds directly to the user.
-
-The different abstractions involved in agents are as follows:
-
-- Agent: this is where the logic of the application lives. Agents expose an interface that takes in user input along with a list of previous steps the agent has taken, and returns either an `AgentAction` or `AgentFinish`
-    - `AgentAction` corresponds to the tool to use and the input to that tool
-    - `AgentFinish` means the agent is done, and has information around what to return to the user
-- Tools: these are the actions an agent can take. What tools you give an agent highly depend on what you want the agent to do
-- Toolkits: these are groups of tools designed for a specific use case. For example, in order for an agent to interact with a SQL database in the best way it may need access to one tool to execute queries and another tool to inspect tables.
-- Agent Executor: this wraps an agent and a list of tools. This is responsible for the loop of running the agent iteratively until the stopping criteria is met.
-
-The most important abstraction of the four above to understand is that of the agent.
-Although an agent can be defined in whatever way one chooses, the typical way to construct an agent is with:
-
-- PromptTemplate: this is responsible for taking the user input and previous steps and constructing a prompt to send to the language model
-- Language Model: this takes the prompt constructed by the PromptTemplate and returns some output
-- Output Parser: this takes the output of the Language Model and parses it into an `AgentAction` or `AgentFinish` object.
-
 In this section of documentation, we first start with a Getting Started notebook to cover how to use all things related to agents in an end-to-end manner.
 
 .. toctree::
@@ -59,29 +23,25 @@ We then split the documentation into the following sections:
 
 **Tools**
 
-In this section we cover the different types of tools LangChain supports natively.
-We then cover how to add your own tools.
+An overview of the various tools LangChain supports.
 
 
 **Agents**
 
-In this section we cover the different types of agents LangChain supports natively.
-We then cover how to modify and create your own agents.
+An overview of the different agent types.
 
 
 **Toolkits**
 
-In this section we go over the various toolkits that LangChain supports out of the box,
-and how to create an agent from them.
+An overview of toolkits, and examples of the different ones LangChain supports.
 
 
 **Agent Executor**
 
-In this section we go over the Agent Executor class, which is responsible for calling
-the agent and tools in a loop. We go over different ways to customize this, and options you
-can use for more control.
+An overview of the Agent Executor class and examples of how to use it.
 
-**Go Deeper**
+Go Deeper
+---------
 
 .. toctree::
    :maxdepth: 1
@@ -90,23 +50,3 @@ can use for more control.
    ./agents/agents.rst
    ./agents/toolkits.rst
    ./agents/agent_executors.rst
-
-Plan-and-Execute Agents
------------------------
-
-High level pseudocode of agents looks something like:
-
-- Some user input is received
-- The planner lists out the steps to take
-- The executor goes through the list of steps, executing them
-
-The most typical implementation is to have the planner be a language model,
-and the executor be an action agent.
-
-**Go Deeper**
-
-.. toctree::
-   :maxdepth: 1
-
-   ./agents/plan_and_execute.ipynb
-

docs/modules/agents/agent_executors/examples/async_agent.ipynb

@@ -9,9 +9,9 @@
     "\n",
     "LangChain provides async support for Agents by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
     "\n",
-    "Async methods are currently supported for the following `Tools`: [`GoogleSerperAPIWrapper`](https://github.com/hwchase17/langchain/blob/master/langchain/utilities/google_serper.py), [`SerpAPIWrapper`](https://github.com/hwchase17/langchain/blob/master/langchain/serpapi.py) and [`LLMMathChain`](https://github.com/hwchase17/langchain/blob/master/langchain/chains/llm_math/base.py). Async support for other agent tools are on the roadmap.\n",
+    "Async methods are currently supported for the following `Tools`: [`SerpAPIWrapper`](https://github.com/hwchase17/langchain/blob/master/langchain/serpapi.py) and [`LLMMathChain`](https://github.com/hwchase17/langchain/blob/master/langchain/chains/llm_math/base.py). Async support for other agent tools are on the roadmap.\n",
     "\n",
-    "For `Tool`s that have a `coroutine` implemented (the three mentioned above), the `AgentExecutor` will `await` them directly. Otherwise, the `AgentExecutor` will call the `Tool`'s `func` via `asyncio.get_event_loop().run_in_executor` to avoid blocking the main runloop.\n",
+    "For `Tool`s that have a `coroutine` implemented (the two mentioned above), the `AgentExecutor` will `await` them directly. Otherwise, the `AgentExecutor` will call the `Tool`'s `func` via `asyncio.get_event_loop().run_in_executor` to avoid blocking the main runloop.\n",
     "\n",
     "You can use `arun` to call an `AgentExecutor` asynchronously."
    ]
@@ -28,14 +28,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "da5df06c-af6f-4572-b9f5-0ab971c16487",
    "metadata": {
-    "tags": [],
-    "ExecuteTime": {
-     "end_time": "2023-05-04T01:27:22.755025Z",
-     "start_time": "2023-05-04T01:27:22.754041Z"
-    }
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -46,6 +42,7 @@
     "from langchain.agents import AgentType\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.callbacks.stdout import StdOutCallbackHandler\n",
+    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks.tracers import LangChainTracer\n",
     "from aiohttp import ClientSession\n",
     "\n",
@@ -60,14 +57,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "fd4c294e-b1d6-44b8-b32e-2765c017e503",
    "metadata": {
-    "tags": [],
-    "ExecuteTime": {
-     "end_time": "2023-05-04T01:15:35.466212Z",
-     "start_time": "2023-05-04T01:14:05.452245Z"
-    }
+    "tags": []
    },
    "outputs": [
     {
@@ -76,105 +69,119 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"Who won the US Open men's final in 2019?\"\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3mRafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ... Draw: 128 (16 Q / 8 WC). Champion: Rafael Nadal. Runner-up: Daniil Medvedev. Score: 7–5, 6–3, 5–7, 4–6, 6–4. Bianca Andreescu won the women's singles title, defeating Serena Williams in straight sets in the final, becoming the first Canadian to win a Grand Slam singles ... Rafael Nadal won his 19th career Grand Slam title, and his fourth US Open crown, by surviving an all-time comback effort from Daniil ... Rafael Nadal beats Daniil Medvedev in US Open final to claim 19th major title. World No2 claims 7-5, 6-3, 5-7, 4-6, 6-4 victory over Russian ... Rafael Nadal defeated Daniil Medvedev in the men's singles final of the U.S. Open on Sunday. Rafael Nadal survived. The 33-year-old defeated Daniil Medvedev in the final of the 2019 U.S. Open to earn his 19th Grand Slam title Sunday ... NEW YORK -- Rafael Nadal defeated Daniil Medvedev in an epic five-set match, 7-5, 6-3, 5-7, 4-6, 6-4 to win the men's singles title at the ... Nadal previously won the U.S. Open three times, most recently in 2017. Ahead of the match, Nadal said he was “super happy to be back in the ... Watch the full match between Daniil Medvedev and Rafael ... Duration: 4:47:32. Posted: Mar 20, 2020. US Open 2019: Rafael Nadal beats Daniil Medvedev · Updated: Sep. 08, 2019, 11:11 p.m. |; Published: Sep · Published: Sep. 08, 2019, 10:06 p.m.. 26. US Open ...\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I now know that Rafael Nadal won the US Open men's final in 2019 and he is 33 years old.\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
+      "Action: Search\n",
+      "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Rafael Nadal's age\n",
+      "Action: Search\n",
+      "Action Input: \"Rafael Nadal age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m36 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 36 raised to the 0.334 power\n",
       "Action: Calculator\n",
-      "Action Input: 33^0.334\u001B[0m\n",
-      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 3.215019829667466\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer.\n",
-      "Final Answer: Rafael Nadal won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.215019829667466.\u001B[0m\n",
+      "Action Input: 36^0.334\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"Olivia Wilde boyfriend\"\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3mSudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I need to find out Harry Styles' age.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"Harry Styles age\"\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3m29 years\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I need to calculate 29 raised to the 0.23 power.\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mJason Sudeikis\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' age\n",
+      "Action: Search\n",
+      "Action Input: \"Jason Sudeikis age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m47 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 47 raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 29^0.23\u001B[0m\n",
-      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 2.169459462491557\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer.\n",
-      "Final Answer: Harry Styles is Olivia Wilde's boyfriend and his current age raised to the 0.23 power is 2.169459462491557.\u001B[0m\n",
+      "Action Input: 47^0.23\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.4242784855673896\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m I need to find out who won the most recent grand prix and then calculate their age raised to the 0.23 power.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"who won the most recent formula 1 grand prix\"\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3mMax Verstappen won his first Formula 1 world title on Sunday after the championship was decided by a last-lap overtake of his rival Lewis Hamilton in the Abu Dhabi Grand Prix. Dec 12, 2021\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I need to find out Max Verstappen's age\n",
-      "Action: Google Serper\n",
-      "Action Input: \"Max Verstappen age\"\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3m25 years\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I need to calculate 25 raised to the 0.23 power\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mMax Verstappen\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Max Verstappen's age\n",
+      "Action: Search\n",
+      "Action Input: \"Max Verstappen Age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m25 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 25^0.23\u001B[0m\n",
-      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 2.096651272316035\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer\n",
-      "Final Answer: Max Verstappen, aged 25, won the most recent Formula 1 grand prix and his age raised to the 0.23 power is 2.096651272316035.\u001B[0m\n",
+      "Action Input: 25^0.23\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.84599359907945\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Max Verstappen, 25 years old, raised to the 0.23 power is 1.84599359907945.\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"US Open women's final 2019 winner\"\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3mWHAT HAPPENED: #SheTheNorth? She the champion. Nineteen-year-old Canadian Bianca Andreescu sealed her first Grand Slam title on Saturday, downing 23-time major champion Serena Williams in the 2019 US Open women's singles final, 6-3, 7-5. Sep 7, 2019\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I now need to calculate her age raised to the 0.34 power.\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
+      "Action: Search\n",
+      "Action Input: \"US Open women's final 2019 winner\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu defeated Serena Williams in the final, 6–3, 7–5 to win the women's singles tennis title at the 2019 US Open. It was her first major title, and she became the first Canadian, as well as the first player born in the 2000s, to win a major singles title.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Bianca Andreescu's age.\n",
+      "Action: Search\n",
+      "Action Input: \"Bianca Andreescu age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m22 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the age of Bianca Andreescu and can calculate her age raised to the 0.34 power.\n",
       "Action: Calculator\n",
-      "Action Input: 19^0.34\u001B[0m\n",
-      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 2.7212987634680084\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer.\n",
-      "Final Answer: Nineteen-year-old Canadian Bianca Andreescu won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.7212987634680084.\u001B[0m\n",
+      "Action Input: 22^0.34\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Bianca Andreescu won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.8603798598506933.\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"Who is Beyonce's husband?\"\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3mJay-Z\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I need to find out Jay-Z's age\n",
-      "Action: Google Serper\n",
-      "Action Input: \"How old is Jay-Z?\"\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3m53 years\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I need to calculate 53 raised to the 0.19 power\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Who is Beyonce's husband?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mJay-Z\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jay-Z's age\n",
+      "Action: Search\n",
+      "Action Input: \"How old is Jay-Z?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m53 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 53 raised to the 0.19 power\n",
       "Action: Calculator\n",
-      "Action Input: 53^0.19\u001B[0m\n",
-      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 2.12624064206896\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer\n",
-      "Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.\u001B[0m\n",
+      "Action Input: 53^0.19\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n",
-      "Serial executed in 89.97 seconds.\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Serial executed in 65.11 seconds.\n"
      ]
     }
    ],
    "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "tools = load_tools([\"google-serper\", \"llm-math\"], llm=llm)\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")\n",
+    "def generate_serially():\n",
+    "    for q in questions:\n",
+    "        llm = OpenAI(temperature=0)\n",
+    "        tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm)\n",
+    "        agent = initialize_agent(\n",
+    "            tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    "        )\n",
+    "        agent.run(q)\n",
     "\n",
     "s = time.perf_counter()\n",
-    "for q in questions:\n",
-    "    agent.run(q)\n",
+    "generate_serially()\n",
     "elapsed = time.perf_counter() - s\n",
     "print(f\"Serial executed in {elapsed:0.2f} seconds.\")"
    ]
@@ -184,11 +191,7 @@
    "execution_count": 4,
    "id": "076d7b85-45ec-465d-8b31-c2ad119c3438",
    "metadata": {
-    "tags": [],
-    "ExecuteTime": {
-     "end_time": "2023-05-04T01:26:59.737657Z",
-     "start_time": "2023-05-04T01:26:42.182078Z"
-    }
+    "tags": []
    },
    "outputs": [
     {
@@ -197,95 +200,192 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"Olivia Wilde boyfriend\"\u001B[0m\u001B[32;1m\u001B[1;3m I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"Who is Beyonce's husband?\"\u001B[0m\u001B[32;1m\u001B[1;3m I need to find out who won the most recent formula 1 grand prix and then calculate their age raised to the 0.23 power.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"most recent formula 1 grand prix winner\"\u001B[0m\u001B[32;1m\u001B[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"Who won the US Open men's final in 2019?\"\u001B[0m\u001B[32;1m\u001B[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"US Open women's final 2019 winner\"\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3mSudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.\u001B[0m\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Who is Beyonce's husband?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mJay-Z\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
+      "Action: Search\n",
+      "Action Input: \"US Open women's final 2019 winner\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mJason Sudeikis\u001b[0m\n",
       "Thought:\n",
-      "Observation: \u001B[36;1m\u001B[1;3mJay-Z\u001B[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mMax Verstappen\u001b[0m\n",
       "Thought:\n",
-      "Observation: \u001B[36;1m\u001B[1;3mRafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ... Draw: 128 (16 Q / 8 WC). Champion: Rafael Nadal. Runner-up: Daniil Medvedev. Score: 7–5, 6–3, 5–7, 4–6, 6–4. Bianca Andreescu won the women's singles title, defeating Serena Williams in straight sets in the final, becoming the first Canadian to win a Grand Slam singles ... Rafael Nadal won his 19th career Grand Slam title, and his fourth US Open crown, by surviving an all-time comback effort from Daniil ... Rafael Nadal beats Daniil Medvedev in US Open final to claim 19th major title. World No2 claims 7-5, 6-3, 5-7, 4-6, 6-4 victory over Russian ... Rafael Nadal defeated Daniil Medvedev in the men's singles final of the U.S. Open on Sunday. Rafael Nadal survived. The 33-year-old defeated Daniil Medvedev in the final of the 2019 U.S. Open to earn his 19th Grand Slam title Sunday ... NEW YORK -- Rafael Nadal defeated Daniil Medvedev in an epic five-set match, 7-5, 6-3, 5-7, 4-6, 6-4 to win the men's singles title at the ... Nadal previously won the U.S. Open three times, most recently in 2017. Ahead of the match, Nadal said he was “super happy to be back in the ... Watch the full match between Daniil Medvedev and Rafael ... Duration: 4:47:32. Posted: Mar 20, 2020. US Open 2019: Rafael Nadal beats Daniil Medvedev · Updated: Sep. 08, 2019, 11:11 p.m. |; Published: Sep · Published: Sep. 08, 2019, 10:06 p.m.. 26. US Open ...\u001B[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu defeated Serena Williams in the final, 6–3, 7–5 to win the women's singles tennis title at the 2019 US Open. It was her first major title, and she became the first Canadian, as well as the first player born in the 2000s, to win a major singles title.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' age\n",
+      "Action: Search\n",
+      "Action Input: \"Jason Sudeikis age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out Jay-Z's age\n",
+      "Action: Search\n",
+      "Action Input: \"How old is Jay-Z?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m53 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
+      "Action: Search\n",
+      "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ...\u001b[0m\n",
       "Thought:\n",
-      "Observation: \u001B[36;1m\u001B[1;3mWHAT HAPPENED: #SheTheNorth? She the champion. Nineteen-year-old Canadian Bianca Andreescu sealed her first Grand Slam title on Saturday, downing 23-time major champion Serena Williams in the 2019 US Open women's singles final, 6-3, 7-5. Sep 7, 2019\u001B[0m\n",
-      "Thought:\n",
-      "Observation: \u001B[36;1m\u001B[1;3mLewis Hamilton holds the record for the most race wins in Formula One history, with 103 wins to date. Michael Schumacher, the previous record holder, ... Michael Schumacher (top left) and Lewis Hamilton (top right) have each won the championship a record seven times during their careers, while Sebastian Vettel ( ... Grand Prix, Date, Winner, Car, Laps, Time. Bahrain, 05 Mar 2023, Max Verstappen VER, Red Bull Racing Honda RBPT, 57, 1:33:56.736. Saudi Arabia, 19 Mar 2023 ... The Red Bull driver Max Verstappen of the Netherlands celebrated winning his first Formula 1 world title at the Abu Dhabi Grand Prix. Perez wins sprint as Verstappen, Russell clash. Red Bull's Sergio Perez won the first sprint of the 2023 Formula One season after catching and passing Charles ... The most successful driver in the history of F1 is Lewis Hamilton. The man from Stevenage has won 103 Grands Prix throughout his illustrious career and is still ... Lewis Hamilton: 103. Max Verstappen: 37. Michael Schumacher: 91. Fernando Alonso: 32. Max Verstappen and Sergio Perez will race in a very different-looking Red Bull this weekend after the team unveiled a striking special livery for the Miami GP. Lewis Hamilton holds the record of most victories with 103, ahead of Michael Schumacher (91) and Sebastian Vettel (53). Schumacher also holds the record for the ... Lewis Hamilton holds the record for the most race wins in Formula One history, with 103 wins to date. Michael Schumacher, the previous record holder, is second ...\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I need to find out Harry Styles' age.\n",
-      "Action: Google Serper\n",
-      "Action Input: \"Harry Styles age\"\u001B[0m\u001B[32;1m\u001B[1;3m I need to find out Jay-Z's age\n",
-      "Action: Google Serper\n",
-      "Action Input: \"How old is Jay-Z?\"\u001B[0m\u001B[32;1m\u001B[1;3m I now know that Rafael Nadal won the US Open men's final in 2019 and he is 33 years old.\n",
+      "Observation: \u001b[33;1m\u001b[1;3m47 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Max Verstappen's age\n",
+      "Action: Search\n",
+      "Action Input: \"Max Verstappen Age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m25 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Bianca Andreescu's age.\n",
+      "Action: Search\n",
+      "Action Input: \"Bianca Andreescu age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m22 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 53 raised to the 0.19 power\n",
       "Action: Calculator\n",
-      "Action Input: 33^0.334\u001B[0m\u001B[32;1m\u001B[1;3m I now need to calculate her age raised to the 0.34 power.\n",
+      "Action Input: 53^0.19\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
+      "Action: Search\n",
+      "Action Input: \"Rafael Nadal age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to calculate 47 raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 19^0.34\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3m29 years\u001B[0m\n",
-      "Thought:\n",
-      "Observation: \u001B[36;1m\u001B[1;3m53 years\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m Max Verstappen won the most recent Formula 1 grand prix.\n",
+      "Action Input: 47^0.23\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m36 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: Max Verstappen's age (23) raised to the 0.23 power\u001B[0m\n",
-      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 2.7212987634680084\u001B[0m\n",
-      "Thought:\n",
-      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 3.215019829667466\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I need to calculate 29 raised to the 0.23 power.\n",
+      "Action Input: 25^0.23\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the age of Bianca Andreescu and can calculate her age raised to the 0.34 power.\n",
       "Action: Calculator\n",
-      "Action Input: 29^0.23\u001B[0m\u001B[32;1m\u001B[1;3m I need to calculate 53 raised to the 0.19 power\n",
+      "Action Input: 22^0.34\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.84599359907945\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.4242784855673896\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate his age raised to the 0.334 power\n",
       "Action: Calculator\n",
-      "Action Input: 53^0.19\u001B[0m\n",
-      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 2.0568252837687546\u001B[0m\n",
-      "Thought:\n",
-      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 2.169459462491557\u001B[0m\n",
-      "Thought:\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "Action Input: 36^0.334\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Max Verstappen, 25 years old, raised to the 0.23 power is 1.84599359907945.\u001b[0m\n",
       "\n",
-      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 2.12624064206896\u001B[0m\n",
-      "Thought:\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n",
-      "Concurrent executed in 17.52 seconds.\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Bianca Andreescu won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.8603798598506933.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Concurrent executed in 12.38 seconds.\n"
      ]
     }
    ],
    "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "tools = load_tools([\"google-serper\",\"llm-math\"], llm=llm)\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")\n",
+    "async def generate_concurrently():\n",
+    "    agents = []\n",
+    "    # To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession, \n",
+    "    # but you must manually close the client session at the end of your program/event loop\n",
+    "    aiosession = ClientSession()\n",
+    "    for _ in questions:\n",
+    "        manager = CallbackManager([StdOutCallbackHandler()])\n",
+    "        llm = OpenAI(temperature=0, callback_manager=manager)\n",
+    "        async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession, callback_manager=manager)\n",
+    "        agents.append(\n",
+    "            initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
+    "        )\n",
+    "    tasks = [async_agent.arun(q) for async_agent, q in zip(agents, questions)]\n",
+    "    await asyncio.gather(*tasks)\n",
+    "    await aiosession.close()\n",
     "\n",
     "s = time.perf_counter()\n",
-    "# If running this outside of Jupyter, use asyncio.run or loop.run_until_complete\n",
-    "tasks = [agent.arun(q) for q in questions]\n",
-    "await asyncio.gather(*tasks)\n",
+    "# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
+    "await generate_concurrently()\n",
     "elapsed = time.perf_counter() - s\n",
     "print(f\"Concurrent executed in {elapsed:0.2f} seconds.\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97ef285c-4a43-4a4e-9698-cd52a1bc56c9",
+   "metadata": {},
+   "source": [
+    "## Using Tracing with Asynchronous Agents\n",
+    "\n",
+    "To use tracing with async agents, you must pass in a custom `CallbackManager` with `LangChainTracer` to each agent running asynchronously. This way, you avoid collisions while the trace is being collected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "44bda05a-d33e-4e91-9a71-a0f3f96aae95",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
+      "Action: Search\n",
+      "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Rafael Nadal's age\n",
+      "Action: Search\n",
+      "Action Input: \"Rafael Nadal age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m36 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 36 raised to the 0.334 power\n",
+      "Action: Calculator\n",
+      "Action Input: 36^0.334\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession, \n",
+    "# but you must manually close the client session at the end of your program/event loop\n",
+    "aiosession = ClientSession()\n",
+    "tracer = LangChainTracer()\n",
+    "tracer.load_default_session()\n",
+    "manager = CallbackManager([StdOutCallbackHandler(), tracer])\n",
+    "\n",
+    "# Pass the manager into the llm if you want llm calls traced.\n",
+    "llm = OpenAI(temperature=0, callback_manager=manager)\n",
+    "\n",
+    "async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession)\n",
+    "async_agent = initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
+    "await async_agent.arun(questions[0])\n",
+    "await aiosession.close()"
+   ]
   }
  ],
  "metadata": {
@@ -304,7 +404,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/agents/agents/custom_agent.ipynb

@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "a33e2f7e",
    "metadata": {},
    "outputs": [],
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "655d72f6",
    "metadata": {},
    "outputs": [],
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -128,7 +128,7 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[36;1m\u001b[1;3mThe current population of Canada is 38,669,152 as of Monday, April 24, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[36;1m\u001b[1;3mFoo Fighters is an American rock band formed in Seattle in 1994. Foo Fighters was initially formed as a one-man project by former Nirvana drummer Dave Grohl. Following the success of the 1995 eponymous debut album, Grohl recruited a band consisting of Nate Mendel, William Goldsmith, and Pat Smear.\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -136,10 +136,10 @@
     {
      "data": {
       "text/plain": [
-       "'The current population of Canada is 38,669,152 as of Monday, April 24, 2023, based on Worldometer elaboration of the latest United Nations data.'"
+       "'Foo Fighters is an American rock band formed in Seattle in 1994. Foo Fighters was initially formed as a one-man project by former Nirvana drummer Dave Grohl. Following the success of the 1995 eponymous debut album, Grohl recruited a band consisting of Nate Mendel, William Goldsmith, and Pat Smear.'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/modules/agents/agents/custom_agent_with_tool_retrieval.ipynb

@@ -373,7 +373,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tools = get_tools(\"whats the weather?\")\n",
     "tool_names = [tool.name for tool in tools]\n",
     "agent = LLMSingleActionAgent(\n",
     "    llm_chain=llm_chain, \n",

docs/modules/agents/agents/custom_llm_chat_agent.ipynb

@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "9af9734e",
    "metadata": {},
    "outputs": [],
@@ -100,13 +100,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "339b1bb8",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Set up the base template\n",
-    "template = \"\"\"Complete the objective as best you can. You have access to the following tools:\n",
+    "template = \"\"\"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\n",
     "\n",
     "{tools}\n",
     "\n",
@@ -121,11 +121,7 @@
     "Thought: I now know the final answer\n",
     "Final Answer: the final answer to the original input question\n",
     "\n",
-    "These were previous tasks you completed:\n",
-    "\n",
-    "\n",
-    "\n",
-    "Begin!\n",
+    "Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Arg\"s\n",
     "\n",
     "Question: {input}\n",
     "{agent_scratchpad}\"\"\""
@@ -133,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "id": "fd969d31",
    "metadata": {},
    "outputs": [],
@@ -165,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "id": "798ef9fb",
    "metadata": {},
    "outputs": [],
@@ -193,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "id": "7c6fe0d3",
    "metadata": {},
    "outputs": [],
@@ -222,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "id": "d278706a",
    "metadata": {},
    "outputs": [],
@@ -242,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "id": "f9d4c374",
    "metadata": {},
    "outputs": [],
@@ -274,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "id": "9b1cc2a2",
    "metadata": {},
    "outputs": [],
@@ -285,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "id": "e4f5092f",
    "metadata": {},
    "outputs": [],
@@ -311,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 15,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
@@ -321,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -332,13 +328,16 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I should use a reliable search engine to get accurate information.\n",
+      "\u001b[32;1m\u001b[1;3mThought: Wot year be it now? That be important to know the answer.\n",
       "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
+      "Action Input: \"current population canada 2023\"\u001b[0m\n",
       "\n",
-      "Observation:\u001b[36;1m\u001b[1;3mHe went on to date Gisele Bündchen, Bar Refaeli, Blake Lively, Toni Garrn and Nina Agdal, among others, before finally settling down with current girlfriend Camila Morrone, who is 23 years his junior.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI have found the answer to the question.\n",
-      "Final Answer: Leo DiCaprio's current girlfriend is Camila Morrone.\u001b[0m\n",
+      "Observation:\u001b[36;1m\u001b[1;3m38,649,283\u001b[0m\u001b[32;1m\u001b[1;3mAhoy! That be the correct year, but the answer be in regular numbers. 'Tis time to translate to pirate speak.\n",
+      "Action: Search\n",
+      "Action Input: \"38,649,283 in pirate speak\"\u001b[0m\n",
+      "\n",
+      "Observation:\u001b[36;1m\u001b[1;3mBrush up on your “Pirate Talk” with these helpful pirate phrases. Aaaarrrrgggghhhh! Pirate catch phrase of grumbling or disgust. Ahoy! Hello! Ahoy, Matey, Hello ...\u001b[0m\u001b[32;1m\u001b[1;3mThat be not helpful, I'll just do the translation meself.\n",
+      "Final Answer: Arrrr, thar be 38,649,283 scallywags in Canada as of 2023.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -346,16 +345,16 @@
     {
      "data": {
       "text/plain": [
-       "\"Leo DiCaprio's current girlfriend is Camila Morrone.\""
+       "'Arrrr, thar be 38,649,283 scallywags in Canada as of 2023.'"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_executor.run(\"Search for Leo DiCaprio's girlfriend on the internet.\")"
+    "agent_executor.run(\"How many people live in canada as of 2023?\")"
    ]
   },
   {

docs/modules/agents/agents/custom_mrkl_agent.ipynb

@@ -20,14 +20,13 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "6064f080",
    "metadata": {},
    "source": [
     "### Custom LLMChain\n",
     "\n",
-    "The first way to create a custom agent is to use an existing Agent class, but use a custom LLMChain. This is the simplest way to create a custom Agent. It is highly recommended that you work with the `ZeroShotAgent`, as at the moment that is by far the most generalizable one. \n",
+    "The first way to create a custom agent is to use an existing Agent class, but use a custom LLMChain. This is the simplest way to create a custom Agent. It is highly reccomended that you work with the `ZeroShotAgent`, as at the moment that is by far the most generalizable one. \n",
     "\n",
     "Most of the work in creating the custom LLMChain comes down to the prompt. Because we are using an existing agent class to parse the output, it is very important that the prompt say to produce text in that format. Additionally, we currently require an `agent_scratchpad` input variable to put notes on previous actions and observations. This should almost always be the final part of the prompt. However, besides those instructions, you can customize the prompt as you wish.\n",
     "\n",

docs/modules/agents/agents/custom_multi_action_agent.ipynb

@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "id": "d7c4ebdc",
    "metadata": {},
    "outputs": [],
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "id": "becda2a1",
    "metadata": {},
    "outputs": [],
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 23,
    "id": "a33e2f7e",
    "metadata": {},
    "outputs": [],
@@ -96,8 +96,8 @@
     "        \"\"\"\n",
     "        if len(intermediate_steps) == 0:\n",
     "            return [\n",
-    "                AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\"),\n",
-    "                AgentAction(tool=\"RandomWord\", tool_input=kwargs[\"input\"], log=\"\"),\n",
+    "                AgentAction(tool=\"Search\", tool_input=\"foo\", log=\"\"),\n",
+    "                AgentAction(tool=\"RandomWord\", tool_input=\"foo\", log=\"\"),\n",
     "            ]\n",
     "        else:\n",
     "            return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")\n",
@@ -117,8 +117,8 @@
     "        \"\"\"\n",
     "        if len(intermediate_steps) == 0:\n",
     "            return [\n",
-    "                AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\"),\n",
-    "                AgentAction(tool=\"RandomWord\", tool_input=kwargs[\"input\"], log=\"\"),\n",
+    "                AgentAction(tool=\"Search\", tool_input=\"foo\", log=\"\"),\n",
+    "                AgentAction(tool=\"RandomWord\", tool_input=\"foo\", log=\"\"),\n",
     "            ]\n",
     "        else:\n",
     "            return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")"
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "id": "655d72f6",
    "metadata": {},
    "outputs": [],
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 25,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -157,7 +157,7 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[36;1m\u001b[1;3mThe current population of Canada is 38,669,152 as of Monday, April 24, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[36;1m\u001b[1;3mFoo Fighters is an American rock band formed in Seattle in 1994. Foo Fighters was initially formed as a one-man project by former Nirvana drummer Dave Grohl. Following the success of the 1995 eponymous debut album, Grohl recruited a band consisting of Nate Mendel, William Goldsmith, and Pat Smear.\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
       "Now I'm doing this!\n",
       "\u001b[33;1m\u001b[1;3mfoo\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
       "\n",
@@ -170,7 +170,7 @@
        "'bar'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/modules/agents/agents/examples/structured_chat.ipynb

@@ -1,424 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4658d71a",
-   "metadata": {},
-   "source": [
-    "# Structured Tool Chat Agent\n",
-    "\n",
-    "This notebook walks through using a chat agent capable of using multi-input tools.\n",
-    "\n",
-    "Older agents are configured to specify an action input as a single string, but this agent can use the provided tools' `args_schema` to populate the action input.\n",
-    "\n",
-    "This functionality is natively available in the (`structured-chat-zero-shot-react-description` or `AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ccc8ff98",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"LANGCHAIN_TRACING\"] = \"true\" # If you want to trace the execution of the program, set to \"true\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f65308ab",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents import AgentType\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.agents import initialize_agent"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30aaf540-9e8e-436e-af8b-89e610e34120",
-   "metadata": {},
-   "source": [
-    "### Initialize Tools\n",
-    "\n",
-    "We will test the agent using a web browser."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "71027ff2-5d09-49cd-92a1-24b2c454a7ae",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents.agent_toolkits import PlayWrightBrowserToolkit\n",
-    "from langchain.tools.playwright.utils import (\n",
-    "    create_async_playwright_browser,\n",
-    "    create_sync_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter.\n",
-    ")\n",
-    "\n",
-    "# This import is required only for jupyter notebooks, since they have their own eventloop\n",
-    "import nest_asyncio\n",
-    "nest_asyncio.apply()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5fb14d6d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "async_browser = create_async_playwright_browser()\n",
-    "browser_toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)\n",
-    "tools = browser_toolkit.get_tools()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cafe9bc1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm = ChatOpenAI(temperature=0) # Also works well with Anthropic models\n",
-    "agent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4f4aa234-9746-47d8-bec7-d76081ac3ef6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Final Answer\",\n",
-      "  \"action_input\": \"Hello Erica, how can I assist you today?\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Hello Erica, how can I assist you today?\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = await agent_chain.arun(input=\"Hi I'm Erica.\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "23e7dc33-50a5-4685-8e9b-4ac49e12877f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "I'm here to chat! How's your day going?\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = await agent_chain.arun(input=\"Don't need help really just chatting.\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "dc70b454",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"navigate_browser\",\n",
-      "  \"action_input\": {\n",
-      "    \"url\": \"https://blog.langchain.dev/\"\n",
-      "  }\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mNavigating to https://blog.langchain.dev/ returned status code 200\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI need to extract the text from the webpage to summarize it.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"extract_text\",\n",
-      "  \"action_input\": {}\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[31;1m\u001b[1;3mLangChain LangChain Home About GitHub Docs LangChain The official LangChain blog. Auto-Evaluator Opportunities Editor's Note: this is a guest blog post by Lance Martin.\n",
-      "\n",
-      "\n",
-      "TL;DR\n",
-      "\n",
-      "We recently open-sourced an auto-evaluator tool for grading LLM question-answer chains. We are now releasing an open source, free to use hosted app and API to expand usability. Below we discuss a few opportunities to further improve May 1, 2023 5 min read Callbacks Improvements TL;DR: We're announcing improvements to our callbacks system, which powers logging, tracing, streaming output, and some awesome third-party integrations. This will better support concurrent runs with independent callbacks, tracing of deeply nested trees of LangChain components, and callback handlers scoped to a single request (which is super useful for May 1, 2023 3 min read Unleashing the power of AI Collaboration with Parallelized LLM Agent Actor Trees Editor's note: the following is a guest blog post from Cyrus at Shaman AI. We use guest blog posts to highlight interesting and novel applciations, and this is certainly that. There's been a lot of talk about agents recently, but most have been discussions around a single agent. If multiple Apr 28, 2023 4 min read Gradio & LLM Agents Editor's note: this is a guest blog post from Freddy Boulton, a software engineer at Gradio. We're excited to share this post because it brings a large number of exciting new tools into the ecosystem. Agents are largely defined by the tools they have, so to be able to equip Apr 23, 2023 4 min read RecAlign - The smart content filter for social media feed [Editor's Note] This is a guest post by Tian Jin. We are highlighting this application as we think it is a novel use case. Specifically, we think recommendation systems are incredibly impactful in our everyday lives and there has not been a ton of discourse on how LLMs will impact Apr 22, 2023 3 min read Improving Document Retrieval with Contextual Compression Note: This post assumes some familiarity with LangChain and is moderately technical.\n",
-      "\n",
-      "💡 TL;DR: We’ve introduced a new abstraction and a new document Retriever to facilitate the post-processing of retrieved documents. Specifically, the new abstraction makes it easy to take a set of retrieved documents and extract from them Apr 20, 2023 3 min read Autonomous Agents & Agent Simulations Over the past two weeks, there has been a massive increase in using LLMs in an agentic manner. Specifically, projects like AutoGPT, BabyAGI, CAMEL, and Generative Agents have popped up. The LangChain community has now implemented some parts of all of those projects in the LangChain framework. While researching and Apr 18, 2023 7 min read AI-Powered Medical Knowledge: Revolutionizing Care for Rare Conditions [Editor's Note]: This is a guest post by Jack Simon, who recently participated in a hackathon at Williams College. He built a LangChain-powered chatbot focused on appendiceal cancer, aiming to make specialized knowledge more accessible to those in need. If you are interested in building a chatbot for another rare Apr 17, 2023 3 min read Auto-Eval of Question-Answering Tasks By Lance Martin\n",
-      "\n",
-      "Context\n",
-      "\n",
-      "LLM ops platforms, such as LangChain, make it easy to assemble LLM components (e.g., models, document retrievers, data loaders) into chains. Question-Answering is one of the most popular applications of these chains. But it is often not always obvious to determine what parameters (e.g. Apr 15, 2023 3 min read Announcing LangChainJS Support for Multiple JS Environments TLDR: We're announcing support for running LangChain.js in browsers, Cloudflare Workers, Vercel/Next.js, Deno, Supabase Edge Functions, alongside existing support for Node.js ESM and CJS. See install/upgrade docs and breaking changes list.\n",
-      "\n",
-      "\n",
-      "Context\n",
-      "\n",
-      "Originally we designed LangChain.js to run in Node.js, which is the Apr 11, 2023 3 min read LangChain x Supabase Supabase is holding an AI Hackathon this week. Here at LangChain we are big fans of both Supabase and hackathons, so we thought this would be a perfect time to highlight the multiple ways you can use LangChain and Supabase together.\n",
-      "\n",
-      "The reason we like Supabase so much is that Apr 8, 2023 2 min read Announcing our $10M seed round led by Benchmark It was only six months ago that we released the first version of LangChain, but it seems like several years. When we launched, generative AI was starting to go mainstream: stable diffusion had just been released and was captivating people’s imagination and fueling an explosion in developer activity, Jasper Apr 4, 2023 4 min read Custom Agents One of the most common requests we've heard is better functionality and documentation for creating custom agents. This has always been a bit tricky - because in our mind it's actually still very unclear what an \"agent\" actually is, and therefor what the \"right\" abstractions for them may be. Recently, Apr 3, 2023 3 min read Retrieval TL;DR: We are adjusting our abstractions to make it easy for other retrieval methods besides the LangChain VectorDB object to be used in LangChain. This is done with the goals of (1) allowing retrievers constructed elsewhere to be used more easily in LangChain, (2) encouraging more experimentation with alternative Mar 23, 2023 4 min read LangChain + Zapier Natural Language Actions (NLA) We are super excited to team up with Zapier and integrate their new Zapier NLA API into LangChain, which you can now use with your agents and chains. With this integration, you have access to the 5k+ apps and 20k+ actions on Zapier's platform through a natural language API interface. Mar 16, 2023 2 min read Evaluation Evaluation of language models, and by extension applications built on top of language models, is hard. With recent model releases (OpenAI, Anthropic, Google) evaluation is becoming a bigger and bigger issue. People are starting to try to tackle this, with OpenAI releasing OpenAI/evals - focused on evaluating OpenAI models. Mar 14, 2023 3 min read LLMs and SQL Francisco Ingham and Jon Luo are two of the community members leading the change on the SQL integrations. We’re really excited to write this blog post with them going over all the tips and tricks they’ve learned doing so. We’re even more excited to announce that we’ Mar 13, 2023 8 min read Origin Web Browser [Editor's Note]: This is the second of hopefully many guest posts. We intend to highlight novel applications building on top of LangChain. If you are interested in working with us on such a post, please reach out to harrison@langchain.dev.\n",
-      "\n",
-      "Authors: Parth Asawa (pgasawa@), Ayushi Batwara (ayushi.batwara@), Jason Mar 8, 2023 4 min read Prompt Selectors One common complaint we've heard is that the default prompt templates do not work equally well for all models. This became especially pronounced this past week when OpenAI released a ChatGPT API. This new API had a completely new interface (which required new abstractions) and as a result many users Mar 8, 2023 2 min read Chat Models Last week OpenAI released a ChatGPT endpoint. It came marketed with several big improvements, most notably being 10x cheaper and a lot faster. But it also came with a completely new API endpoint. We were able to quickly write a wrapper for this endpoint to let users use it like Mar 6, 2023 6 min read Using the ChatGPT API to evaluate the ChatGPT API OpenAI released a new ChatGPT API yesterday. Lots of people were excited to try it. But how does it actually compare to the existing API? It will take some time before there is a definitive answer, but here are some initial thoughts. Because I'm lazy, I also enrolled the help Mar 2, 2023 5 min read Agent Toolkits Today, we're announcing agent toolkits, a new abstraction that allows developers to create agents designed for a particular use-case (for example, interacting with a relational database or interacting with an OpenAPI spec). We hope to continue developing different toolkits that can enable agents to do amazing feats. Toolkits are supported Mar 1, 2023 3 min read TypeScript Support It's finally here... TypeScript support for LangChain.\n",
-      "\n",
-      "What does this mean? It means that all your favorite prompts, chains, and agents are all recreatable in TypeScript natively. Both the Python version and TypeScript version utilize the same serializable format, meaning that artifacts can seamlessly be shared between languages. As an Feb 17, 2023 2 min read Streaming Support in LangChain We’re excited to announce streaming support in LangChain. There's been a lot of talk about the best UX for LLM applications, and we believe streaming is at its core. We’ve also updated the chat-langchain repo to include streaming and async execution. We hope that this repo can serve Feb 14, 2023 2 min read LangChain + Chroma Today we’re announcing LangChain's integration with Chroma, the first step on the path to the Modern A.I Stack.\n",
-      "\n",
-      "\n",
-      "LangChain - The A.I-native developer toolkit\n",
-      "\n",
-      "We started LangChain with the intent to build a modular and flexible framework for developing A.I-native applications. Some of the use cases Feb 13, 2023 2 min read Page 1 of 2 Older Posts → LangChain © 2023 Sign up Powered by Ghost\u001b[0m\n",
-      "Thought:\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "The LangChain blog has recently released an open-source auto-evaluator tool for grading LLM question-answer chains and is now releasing an open-source, free-to-use hosted app and API to expand usability. The blog also discusses various opportunities to further improve the LangChain platform.\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = await agent_chain.arun(input=\"Browse to blog.langchain.dev and summarize the text, please.\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0084efd6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I can navigate to the xkcd website and extract the latest comic title and alt text to answer the question.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"navigate_browser\",\n",
-      "  \"action_input\": {\n",
-      "    \"url\": \"https://xkcd.com/\"\n",
-      "  }\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mNavigating to https://xkcd.com/ returned status code 200\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI can extract the latest comic title and alt text using CSS selectors.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"get_elements\",\n",
-      "  \"action_input\": {\n",
-      "    \"selector\": \"#ctitle, #comic img\",\n",
-      "    \"attributes\": [\"alt\", \"src\"]\n",
-      "  }\n",
-      "}\n",
-      "``` \n",
-      "\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m[{\"alt\": \"Tapetum Lucidum\", \"src\": \"//imgs.xkcd.com/comics/tapetum_lucidum.png\"}]\u001b[0m\n",
-      "Thought:\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "The latest xkcd comic is titled \"Tapetum Lucidum\" and the image can be found at https://xkcd.com/2565/.\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = await agent_chain.arun(input=\"What's the latest xkcd comic about?\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42473442",
-   "metadata": {},
-   "source": [
-    "## Adding in memory\n",
-    "\n",
-    "Here is how you add in memory to this agent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b5a0dd2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import MessagesPlaceholder\n",
-    "from langchain.memory import ConversationBufferMemory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "91b9288f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat_history = MessagesPlaceholder(variable_name=\"chat_history\")\n",
-    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "dba9e0d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_chain = initialize_agent(\n",
-    "    tools, \n",
-    "    llm, \n",
-    "    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, \n",
-    "    verbose=True, \n",
-    "    memory=memory, \n",
-    "    agent_kwargs = {\n",
-    "        \"memory_prompts\": [chat_history],\n",
-    "        \"input_variables\": [\"input\", \"agent_scratchpad\", \"chat_history\"]\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a9509461",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Final Answer\",\n",
-      "  \"action_input\": \"Hi Erica! How can I assist you today?\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Hi Erica! How can I assist you today?\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = await agent_chain.arun(input=\"Hi I'm Erica.\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "412cedd2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mYour name is Erica.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Your name is Erica.\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = await agent_chain.arun(input=\"whats my name?\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9af1a713",
-   "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
-}

docs/modules/agents/plan_and_execute.ipynb

@@ -1,362 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "406483c4",
-   "metadata": {},
-   "source": [
-    "## Plan and Execute\n",
-    "\n",
-    "Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the [\"Plan-and-Solve\" paper](https://arxiv.org/abs/2305.04091).\n",
-    "\n",
-    "The planning is almost always done by an LLM.\n",
-    "\n",
-    "The execution is usually done by a separate agent (equipped with tools)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91192118",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6ccd1dc5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain import SerpAPIWrapper\n",
-    "from langchain.agents.tools import Tool\n",
-    "from langchain import LLMMathChain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b10d200",
-   "metadata": {},
-   "source": [
-    "## Tools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "3c00f724",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search = SerpAPIWrapper()\n",
-    "llm = OpenAI(temperature=0)\n",
-    "llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)\n",
-    "tools = [\n",
-    "    Tool(\n",
-    "        name = \"Search\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name=\"Calculator\",\n",
-    "        func=llm_math_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about math\"\n",
-    "    ),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce38ae84",
-   "metadata": {},
-   "source": [
-    "## Planner, Executor, and Agent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "0ab2cadd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = ChatOpenAI(temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7b2419f2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "planner = load_chat_planner(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ed9f518b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "executor = load_agent_executor(model, tools, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "36943178",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent = PlanAndExecute(planner=planner, executer=executor, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8be9f1bd",
-   "metadata": {},
-   "source": [
-    "## Run Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "4891062e",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new PlanAndExecute chain...\u001b[0m\n",
-      "steps=[Step(value=\"Search for Leo DiCaprio's girlfriend on the internet.\"), Step(value='Find her current age.'), Step(value='Raise her current age to the 0.43 power using a calculator or programming language.'), Step(value='Output the result.'), Step(value=\"Given the above steps taken, respond to the user's original question.\\n\\n\")]\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"Who is Leo DiCaprio's girlfriend?\"\n",
-      "}\n",
-      "``` \n",
-      "\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mDiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid. The power couple were first supposedly an item in September after being spotted getting cozy during a party at New York Fashion Week.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mBased on the previous observation, I can provide the answer to the current objective. \n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Final Answer\",\n",
-      "  \"action_input\": \"Leo DiCaprio is currently linked to Gigi Hadid.\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "*****\n",
-      "\n",
-      "Step: Search for Leo DiCaprio's girlfriend on the internet.\n",
-      "\n",
-      "Response: Leo DiCaprio is currently linked to Gigi Hadid.\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"What is Gigi Hadid's current age?\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mPrevious steps: steps=[(Step(value=\"Search for Leo DiCaprio's girlfriend on the internet.\"), StepResponse(response='Leo DiCaprio is currently linked to Gigi Hadid.'))]\n",
-      "\n",
-      "Current objective: value='Find her current age.'\n",
-      "\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"What is Gigi Hadid's current age?\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mBased on my search, Gigi Hadid's current age is 26 years old. \n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Final Answer\",\n",
-      "  \"action_input\": \"Gigi Hadid's current age is 26 years old.\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "*****\n",
-      "\n",
-      "Step: Find her current age.\n",
-      "\n",
-      "Response: Gigi Hadid's current age is 26 years old.\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Calculator\",\n",
-      "  \"action_input\": \"26 ** 0.43\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "26 ** 0.43\u001b[32;1m\u001b[1;3m\n",
-      "```text\n",
-      "26 ** 0.43\n",
-      "```\n",
-      "...numexpr.evaluate(\"26 ** 0.43\")...\n",
-      "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m4.059182145592686\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.059182145592686\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe current objective is to raise Gigi Hadid's age to the 0.43 power. \n",
-      "\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Calculator\",\n",
-      "  \"action_input\": \"26 ** 0.43\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "26 ** 0.43\u001b[32;1m\u001b[1;3m\n",
-      "```text\n",
-      "26 ** 0.43\n",
-      "```\n",
-      "...numexpr.evaluate(\"26 ** 0.43\")...\n",
-      "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m4.059182145592686\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.059182145592686\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe answer to the current objective is 4.059182145592686.\n",
-      "\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Final Answer\",\n",
-      "  \"action_input\": \"Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "*****\n",
-      "\n",
-      "Step: Raise her current age to the 0.43 power using a calculator or programming language.\n",
-      "\n",
-      "Response: Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Final Answer\",\n",
-      "  \"action_input\": \"Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "*****\n",
-      "\n",
-      "Step: Output the result.\n",
-      "\n",
-      "Response: Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Final Answer\",\n",
-      "  \"action_input\": \"Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "*****\n",
-      "\n",
-      "Step: Given the above steps taken, respond to the user's original question.\n",
-      "\n",
-      "\n",
-      "\n",
-      "Response: Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\""
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aa3ec998",
-   "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": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/agents/toolkits/examples/csv.ipynb

@@ -116,7 +116,7 @@
     }
    ],
    "source": [
-    "agent.run(\"how many people have more than 3 siblings\")"
+    "agent.run(\"how many people have more than 3 sibligngs\")"
    ]
   },
   {

docs/modules/agents/toolkits/examples/gmail.ipynb

@@ -1,232 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gmail Toolkit\n",
-    "\n",
-    "This notebook walks through connecting a LangChain email to the Gmail API.\n",
-    "\n",
-    "To use this toolkit, you will need to set up your credentials explained in the [Gmail API docs](https://developers.google.com/gmail/api/quickstart/python#authorize_credentials_for_a_desktop_application). Once you've downloaded the `credentials.json` file, you can start using the Gmail API. Once this is done, we'll install the required libraries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install --upgrade google-api-python-client > /dev/null\n",
-    "!pip install --upgrade google-auth-oauthlib > /dev/null\n",
-    "!pip install --upgrade google-auth-httplib2 > /dev/null\n",
-    "!pip install beautifulsoup4 > /dev/null # This is optional but is useful for parsing HTML messages"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create the Toolkit\n",
-    "\n",
-    "By default the toolkit reads the local `credentials.json` file. You can also manually provide a `Credentials` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents.agent_toolkits import GmailToolkit\n",
-    "\n",
-    "toolkit = GmailToolkit() "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Customizing Authentication\n",
-    "\n",
-    "Behind the scenes, a `googleapi` resource is created using the following methods. \n",
-    "you can manually build a `googleapi` resource for more auth control. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.tools.gmail.utils import build_resource_service, get_gmail_credentials\n",
-    "\n",
-    "# Can review scopes here https://developers.google.com/gmail/api/auth/scopes\n",
-    "# For instance, readonly scope is 'https://www.googleapis.com/auth/gmail.readonly'\n",
-    "credentials = get_gmail_credentials(\n",
-    "    token_file='token.json',\n",
-    "    scopes=[\"https://mail.google.com/\"],\n",
-    "    client_secrets_file=\"credentials.json\",\n",
-    ")\n",
-    "api_resource = build_resource_service(credentials=credentials)\n",
-    "toolkit = GmailToolkit(api_resource=api_resource)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[GmailCreateDraft(name='create_gmail_draft', description='Use this tool to create a draft email with the provided message fields.', args_schema=<class 'langchain.tools.gmail.create_draft.CreateDraftSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
-       " GmailSendMessage(name='send_gmail_message', description='Use this tool to send email messages. The input is the message, recipents', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
-       " GmailSearch(name='search_gmail', description=('Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.',), args_schema=<class 'langchain.tools.gmail.search.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
-       " GmailGetMessage(name='get_gmail_message', description='Use this tool to fetch an email by message ID. Returns the thread ID, snipet, body, subject, and sender.', args_schema=<class 'langchain.tools.gmail.get_message.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>),\n",
-       " GmailGetThread(name='get_gmail_thread', description=('Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.',), args_schema=<class 'langchain.tools.gmail.get_thread.GetThreadSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, api_resource=<googleapiclient.discovery.Resource object at 0x10e5c6d10>)]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tools = toolkit.get_tools()\n",
-    "tools"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Use within an Agent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain import OpenAI\n",
-    "from langchain.agents import initialize_agent, AgentType"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "agent = initialize_agent(\n",
-    "    tools=toolkit.get_tools(),\n",
-    "    llm=llm,\n",
-    "    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Failed to load default session, using empty session: 0\n",
-      "WARNING:root:Failed to persist run: {\"detail\":\"Not Found\"}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'I have created a draft email for you to edit. The draft Id is r5681294731961864018.'"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"Create a gmail draft for me to edit of a letter from the perspective of a sentient parrot\"\n",
-    "          \" who is looking to collaborate on some research with her\"\n",
-    "          \" estranged friend, a cat. Under no circumstances may you send the message, however.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Failed to load default session, using empty session: 0\n",
-      "WARNING:root:Failed to persist run: {\"detail\":\"Not Found\"}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"The latest email in your drafts is from hopefulparrot@gmail.com with the subject 'Collaboration Opportunity'. The body of the email reads: 'Dear [Friend], I hope this letter finds you well. I am writing to you in the hopes of rekindling our friendship and to discuss the possibility of collaborating on some research together. I know that we have had our differences in the past, but I believe that we can put them aside and work together for the greater good. I look forward to hearing from you. Sincerely, [Parrot]'\""
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"Could you search in my drafts for the latest email?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/modules/agents/toolkits/examples/jira.ipynb

@@ -1,167 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "245a954a",
-   "metadata": {},
-   "source": [
-    "# Jira\n",
-    "\n",
-    "This notebook goes over how to use the Jira tool.\n",
-    "The Jira tool allows agents to interact with a given Jira instance, performing actions such as searching for issues and creating issues, the tool wraps the atlassian-python-api library, for more see: https://atlassian-python-api.readthedocs.io/jira.html\n",
-    "\n",
-    "To use this tool, you must first set as environment variables:\n",
-    "    JIRA_API_TOKEN\n",
-    "    JIRA_USERNAME\n",
-    "    JIRA_INSTANCE_URL"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "961b3689",
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    },
-    "ExecuteTime": {
-     "start_time": "2023-04-17T10:21:18.698672Z",
-     "end_time": "2023-04-17T10:21:20.168639Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%pip install atlassian-python-api"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "34bb5968",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-04-17T10:21:22.911233Z",
-     "end_time": "2023-04-17T10:21:23.730922Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from langchain.agents import AgentType\n",
-    "from langchain.agents import initialize_agent\n",
-    "from langchain.agents.agent_toolkits.jira.toolkit import JiraToolkit\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.utilities.jira import JiraAPIWrapper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [],
-   "source": [
-    "os.environ[\"JIRA_API_TOKEN\"] = \"abc\"\n",
-    "os.environ[\"JIRA_USERNAME\"] = \"123\"\n",
-    "os.environ[\"JIRA_INSTANCE_URL\"] = \"https://jira.atlassian.com\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"xyz\""
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-04-17T10:22:42.499447Z",
-     "end_time": "2023-04-17T10:22:42.505412Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ac4910f8",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-04-17T10:22:44.664481Z",
-     "end_time": "2023-04-17T10:22:44.720538Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "jira = JiraAPIWrapper()\n",
-    "toolkit = JiraToolkit.from_jira_api_wrapper(jira)\n",
-    "agent = initialize_agent(\n",
-    "    toolkit.get_tools(),\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m I need to create an issue in project PW\n",
-      "Action: Create Issue\n",
-      "Action Input: {\"summary\": \"Make more fried rice\", \"description\": \"Reminder to make more fried rice\", \"issuetype\": {\"name\": \"Task\"}, \"priority\": {\"name\": \"Low\"}, \"project\": {\"key\": \"PW\"}}\u001B[0m\n",
-      "Observation: \u001B[38;5;200m\u001B[1;3mNone\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer\n",
-      "Final Answer: A new issue has been created in project PW with the summary \"Make more fried rice\" and description \"Reminder to make more fried rice\".\u001B[0m\n",
-      "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "'A new issue has been created in project PW with the summary \"Make more fried rice\" and description \"Reminder to make more fried rice\".'"
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"make a new issue in project PW to remind me to make more fried rice\")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-04-17T10:23:33.662454Z",
-     "end_time": "2023-04-17T10:23:38.121883Z"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "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": "3.9.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "53f3bc57609c7a84333bb558594977aa5b4026b1d6070b93987956689e367341"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/agents/toolkits/examples/openapi.ipynb

@@ -15,7 +15,7 @@
    "id": "a389367b",
    "metadata": {},
    "source": [
-    "## 1st example: hierarchical planning agent\n",
+    "# 1st example: hierarchical planning agent\n",
     "\n",
     "In this example, we'll consider an approach called hierarchical planning, common in robotics and appearing in recent works for LLMs X robotics. We'll see it's a viable approach to start working with a massive API spec AND to assist with user queries that require multiple steps against the API.\n",
     "\n",
@@ -31,7 +31,7 @@
    "id": "4b6ecf6e",
    "metadata": {},
    "source": [
-    "### To start, let's collect some OpenAPI specs."
+    "## To start, let's collect some OpenAPI specs."
    ]
   },
   {
@@ -169,7 +169,7 @@
    "id": "76349780",
    "metadata": {},
    "source": [
-    "### How big is this spec?"
+    "## How big is this spec?"
    ]
   },
   {
@@ -229,7 +229,7 @@
    "id": "cbc4964e",
    "metadata": {},
    "source": [
-    "### Let's see some examples!\n",
+    "## Let's see some examples!\n",
     "\n",
     "Starting with GPT-4. (Some robustness iterations under way for GPT-3 family.)"
    ]
@@ -759,7 +759,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,

docs/modules/agents/toolkits/examples/pandas.ipynb

@@ -118,7 +118,7 @@
     }
    ],
    "source": [
-    "agent.run(\"how many people have more than 3 siblings\")"
+    "agent.run(\"how many people have more than 3 sibligngs\")"
    ]
   },
   {

docs/modules/agents/toolkits/examples/powerbi.ipynb

@@ -1,219 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "0e499e90-7a6d-4fab-8aab-31a4df417601",
-   "metadata": {},
-   "source": [
-    "# PowerBI Dataset Agent\n",
-    "\n",
-    "This notebook showcases an agent designed to interact with a Power BI Dataset. The agent is designed to answer more general questions about a dataset, as well as recover from errors.\n",
-    "\n",
-    "Note that, as this agent is in active development, all answers might not be correct. It runs against the [executequery endpoint](https://learn.microsoft.com/en-us/rest/api/power-bi/datasets/execute-queries), which does not allow deletes.\n",
-    "\n",
-    "### Some notes\n",
-    "- It relies on authentication with the azure.identity package, which can be installed with `pip install azure-identity`. Alternatively you can create the powerbi dataset with a token as a string without supplying the credentials.\n",
-    "- You can also supply a username to impersonate for use with datasets that have RLS enabled. \n",
-    "- The toolkit uses a LLM to create the query from the question, the agent uses the LLM for the overall execution.\n",
-    "- Testing was done mostly with a `text-davinci-003` model, codex models did not seem to perform ver well."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec927ac6-9b2a-4e8a-9a6e-3e429191875c",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Initialization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53422913-967b-4f2a-8022-00269c1be1b1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents.agent_toolkits import create_pbi_agent\n",
-    "from langchain.agents.agent_toolkits import PowerBIToolkit\n",
-    "from langchain.utilities.powerbi import PowerBIDataset\n",
-    "from langchain.llms.openai import AzureOpenAI\n",
-    "from langchain.agents import AgentExecutor\n",
-    "from azure.identity import DefaultAzureCredential"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "090f3699-79c6-4ce1-ab96-a94f0121fd64",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fast_llm = AzureOpenAI(temperature=0.5, max_tokens=1000, deployment_name=\"gpt-35-turbo\", verbose=True)\n",
-    "smart_llm = AzureOpenAI(temperature=0, max_tokens=100, deployment_name=\"gpt-4\", verbose=True)\n",
-    "\n",
-    "toolkit = PowerBIToolkit(\n",
-    "    powerbi=PowerBIDataset(dataset_id=\"<dataset_id>\", table_names=['table1', 'table2'], credential=DefaultAzureCredential()), \n",
-    "    llm=smart_llm\n",
-    ")\n",
-    "\n",
-    "agent_executor = create_pbi_agent(\n",
-    "    llm=fast_llm,\n",
-    "    toolkit=toolkit,\n",
-    "    verbose=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36ae48c7-cb08-4fef-977e-c7d4b96a464b",
-   "metadata": {},
-   "source": [
-    "## Example: describing a table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ff70e83d-5ad0-4fc7-bb96-27d82ac166d7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "agent_executor.run(\"Describe table1\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "9abcfe8e-1868-42a4-8345-ad2d9b44c681",
-   "metadata": {},
-   "source": [
-    "## Example: simple query on a table\n",
-    "In this example, the agent actually figures out the correct query to get a row count of the table."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bea76658-a65b-47e2-b294-6d52c5556246",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "agent_executor.run(\"How many records are in table1?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6fbc26af-97e4-4a21-82aa-48bdc992da26",
-   "metadata": {},
-   "source": [
-    "## Example: running queries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17bea710-4a23-4de0-b48e-21d57be48293",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "agent_executor.run(\"How many records are there by dimension1 in table2?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "474dddda-c067-4eeb-98b1-e763ee78b18c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "agent_executor.run(\"What unique values are there for dimensions2 in table2\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "6fd950e4",
-   "metadata": {},
-   "source": [
-    "## Example: add your own few-shot prompts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "87d677f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#fictional example\n",
-    "few_shots = \"\"\"\n",
-    "Question: How many rows are in the table revenue?\n",
-    "DAX: EVALUATE ROW(\"Number of rows\", COUNTROWS(revenue_details))\n",
-    "----\n",
-    "Question: How many rows are in the table revenue where year is not empty?\n",
-    "DAX: EVALUATE ROW(\"Number of rows\", COUNTROWS(FILTER(revenue_details, revenue_details[year] <> \"\")))\n",
-    "----\n",
-    "Question: What was the average of value in revenue in dollars?\n",
-    "DAX: EVALUATE ROW(\"Average\", AVERAGE(revenue_details[dollar_value]))\n",
-    "----\n",
-    "\"\"\"\n",
-    "toolkit = PowerBIToolkit(\n",
-    "    powerbi=PowerBIDataset(dataset_id=\"<dataset_id>\", table_names=['table1', 'table2'], credential=DefaultAzureCredential()), \n",
-    "    llm=smart_llm,\n",
-    "    examples=few_shots,\n",
-    ")\n",
-    "agent_executor = create_pbi_agent(\n",
-    "    llm=fast_llm,\n",
-    "    toolkit=toolkit,\n",
-    "    verbose=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "33f4bb43",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_executor.run(\"What was the maximum of value in revenue in dollars in 2022?\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/agents/toolkits/examples/python.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "f98e9c90-5c37-4fb9-af3e-d09693af8543",
    "metadata": {
     "tags": []
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "cc422f53-c51c-4694-a834-72ecd1e68363",
    "metadata": {
     "tags": []
@@ -206,9 +206,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "LangChain",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "langchain"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -220,7 +220,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/agents/toolkits/examples/spark.ipynb

@@ -1,398 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Spark Dataframe Agent\n",
-    "\n",
-    "This notebook shows how to use agents to interact with a Spark dataframe and Spark Connect. It is mostly optimized for question answering.\n",
-    "\n",
-    "**NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import create_spark_dataframe_agent\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"...input your openai api key here...\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
-      "|PassengerId|Survived|Pclass|                Name|   Sex| Age|SibSp|Parch|          Ticket|   Fare|Cabin|Embarked|\n",
-      "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
-      "|          1|       0|     3|Braund, Mr. Owen ...|  male|22.0|    1|    0|       A/5 21171|   7.25| null|       S|\n",
-      "|          2|       1|     1|Cumings, Mrs. Joh...|female|38.0|    1|    0|        PC 17599|71.2833|  C85|       C|\n",
-      "|          3|       1|     3|Heikkinen, Miss. ...|female|26.0|    0|    0|STON/O2. 3101282|  7.925| null|       S|\n",
-      "|          4|       1|     1|Futrelle, Mrs. Ja...|female|35.0|    1|    0|          113803|   53.1| C123|       S|\n",
-      "|          5|       0|     3|Allen, Mr. Willia...|  male|35.0|    0|    0|          373450|   8.05| null|       S|\n",
-      "|          6|       0|     3|    Moran, Mr. James|  male|null|    0|    0|          330877| 8.4583| null|       Q|\n",
-      "|          7|       0|     1|McCarthy, Mr. Tim...|  male|54.0|    0|    0|           17463|51.8625|  E46|       S|\n",
-      "|          8|       0|     3|Palsson, Master. ...|  male| 2.0|    3|    1|          349909| 21.075| null|       S|\n",
-      "|          9|       1|     3|Johnson, Mrs. Osc...|female|27.0|    0|    2|          347742|11.1333| null|       S|\n",
-      "|         10|       1|     2|Nasser, Mrs. Nich...|female|14.0|    1|    0|          237736|30.0708| null|       C|\n",
-      "|         11|       1|     3|Sandstrom, Miss. ...|female| 4.0|    1|    1|         PP 9549|   16.7|   G6|       S|\n",
-      "|         12|       1|     1|Bonnell, Miss. El...|female|58.0|    0|    0|          113783|  26.55| C103|       S|\n",
-      "|         13|       0|     3|Saundercock, Mr. ...|  male|20.0|    0|    0|       A/5. 2151|   8.05| null|       S|\n",
-      "|         14|       0|     3|Andersson, Mr. An...|  male|39.0|    1|    5|          347082| 31.275| null|       S|\n",
-      "|         15|       0|     3|Vestrom, Miss. Hu...|female|14.0|    0|    0|          350406| 7.8542| null|       S|\n",
-      "|         16|       1|     2|Hewlett, Mrs. (Ma...|female|55.0|    0|    0|          248706|   16.0| null|       S|\n",
-      "|         17|       0|     3|Rice, Master. Eugene|  male| 2.0|    4|    1|          382652| 29.125| null|       Q|\n",
-      "|         18|       1|     2|Williams, Mr. Cha...|  male|null|    0|    0|          244373|   13.0| null|       S|\n",
-      "|         19|       0|     3|Vander Planke, Mr...|female|31.0|    1|    0|          345763|   18.0| null|       S|\n",
-      "|         20|       1|     3|Masselmani, Mrs. ...|female|null|    0|    0|            2649|  7.225| null|       C|\n",
-      "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
-      "only showing top 20 rows\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from pyspark.sql import SparkSession\n",
-    "\n",
-    "spark = SparkSession.builder.getOrCreate()\n",
-    "csv_file_path = \"titanic.csv\"\n",
-    "df = spark.read.csv(csv_file_path, header=True, inferSchema=True)\n",
-    "df.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I need to find out the size of the dataframe\n",
-      "Action: python_repl_ast\n",
-      "Action Input: df.count()\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'There are 891 rows in the dataframe.'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"how many rows are there?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I need to find out how many people have more than 3 siblings\n",
-      "Action: python_repl_ast\n",
-      "Action Input: df.filter(df.SibSp > 3).count()\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'30 people have more than 3 siblings.'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"how many people have more than 3 siblings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I need to get the average age first\n",
-      "Action: python_repl_ast\n",
-      "Action Input: df.agg({\"Age\": \"mean\"}).collect()[0][0]\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now have the average age, I need to get the square root\n",
-      "Action: python_repl_ast\n",
-      "Action Input: math.sqrt(29.69911764705882)\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to import math first\n",
-      "Action: python_repl_ast\n",
-      "Action Input: import math\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now have the math library imported, I can get the square root\n",
-      "Action: python_repl_ast\n",
-      "Action Input: math.sqrt(29.69911764705882)\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: 5.449689683556195\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'5.449689683556195'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"whats the square root of the average age?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spark.stop()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Spark Connect Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# in apache-spark root directory. (tested here with \"spark-3.4.0-bin-hadoop3 and later\")\n",
-    "# To launch Spark with support for Spark Connect sessions, run the start-connect-server.sh script.\n",
-    "!./sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.4.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "23/05/08 10:06:09 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pyspark.sql import SparkSession\n",
-    "\n",
-    "# Now that the Spark server is running, we can connect to it remotely using Spark Connect. We do this by \n",
-    "# creating a remote Spark session on the client where our application runs. Before we can do that, we need \n",
-    "# to make sure to stop the existing regular Spark session because it cannot coexist with the remote \n",
-    "# Spark Connect session we are about to create.\n",
-    "SparkSession.builder.master(\"local[*]\").getOrCreate().stop()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The command we used above to launch the server configured Spark to run as localhost:15002. \n",
-    "# So now we can create a remote Spark session on the client using the following command.\n",
-    "spark = SparkSession.builder.remote(\"sc://localhost:15002\").getOrCreate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
-      "|PassengerId|Survived|Pclass|                Name|   Sex| Age|SibSp|Parch|          Ticket|   Fare|Cabin|Embarked|\n",
-      "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
-      "|          1|       0|     3|Braund, Mr. Owen ...|  male|22.0|    1|    0|       A/5 21171|   7.25| null|       S|\n",
-      "|          2|       1|     1|Cumings, Mrs. Joh...|female|38.0|    1|    0|        PC 17599|71.2833|  C85|       C|\n",
-      "|          3|       1|     3|Heikkinen, Miss. ...|female|26.0|    0|    0|STON/O2. 3101282|  7.925| null|       S|\n",
-      "|          4|       1|     1|Futrelle, Mrs. Ja...|female|35.0|    1|    0|          113803|   53.1| C123|       S|\n",
-      "|          5|       0|     3|Allen, Mr. Willia...|  male|35.0|    0|    0|          373450|   8.05| null|       S|\n",
-      "|          6|       0|     3|    Moran, Mr. James|  male|null|    0|    0|          330877| 8.4583| null|       Q|\n",
-      "|          7|       0|     1|McCarthy, Mr. Tim...|  male|54.0|    0|    0|           17463|51.8625|  E46|       S|\n",
-      "|          8|       0|     3|Palsson, Master. ...|  male| 2.0|    3|    1|          349909| 21.075| null|       S|\n",
-      "|          9|       1|     3|Johnson, Mrs. Osc...|female|27.0|    0|    2|          347742|11.1333| null|       S|\n",
-      "|         10|       1|     2|Nasser, Mrs. Nich...|female|14.0|    1|    0|          237736|30.0708| null|       C|\n",
-      "|         11|       1|     3|Sandstrom, Miss. ...|female| 4.0|    1|    1|         PP 9549|   16.7|   G6|       S|\n",
-      "|         12|       1|     1|Bonnell, Miss. El...|female|58.0|    0|    0|          113783|  26.55| C103|       S|\n",
-      "|         13|       0|     3|Saundercock, Mr. ...|  male|20.0|    0|    0|       A/5. 2151|   8.05| null|       S|\n",
-      "|         14|       0|     3|Andersson, Mr. An...|  male|39.0|    1|    5|          347082| 31.275| null|       S|\n",
-      "|         15|       0|     3|Vestrom, Miss. Hu...|female|14.0|    0|    0|          350406| 7.8542| null|       S|\n",
-      "|         16|       1|     2|Hewlett, Mrs. (Ma...|female|55.0|    0|    0|          248706|   16.0| null|       S|\n",
-      "|         17|       0|     3|Rice, Master. Eugene|  male| 2.0|    4|    1|          382652| 29.125| null|       Q|\n",
-      "|         18|       1|     2|Williams, Mr. Cha...|  male|null|    0|    0|          244373|   13.0| null|       S|\n",
-      "|         19|       0|     3|Vander Planke, Mr...|female|31.0|    1|    0|          345763|   18.0| null|       S|\n",
-      "|         20|       1|     3|Masselmani, Mrs. ...|female|null|    0|    0|            2649|  7.225| null|       C|\n",
-      "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
-      "only showing top 20 rows\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "csv_file_path = \"titanic.csv\"\n",
-    "df = spark.read.csv(csv_file_path, header=True, inferSchema=True)\n",
-    "df.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import create_spark_dataframe_agent\n",
-    "from langchain.llms import OpenAI\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"...input your openai api key here...\"\n",
-    "\n",
-    "agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Thought: I need to find the row with the highest fare\n",
-      "Action: python_repl_ast\n",
-      "Action Input: df.sort(df.Fare.desc()).first()\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mRow(PassengerId=259, Survived=1, Pclass=1, Name='Ward, Miss. Anna', Sex='female', Age=35.0, SibSp=0, Parch=0, Ticket='PC 17755', Fare=512.3292, Cabin=None, Embarked='C')\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the name of the person who bought the most expensive ticket\n",
-      "Final Answer: Miss. Anna Ward\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Miss. Anna Ward'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"\"\"\n",
-    "who bought the most expensive ticket?\n",
-    "You can find all supported function types in https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spark.stop()"
-   ]
-  }
- ],
- "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": 2
-}

docs/modules/agents/tools.rst

@@ -24,7 +24,6 @@ Next, we have some examples of customizing and generically working with tools
 
    ./tools/custom_tools.ipynb
    ./tools/multi_input_tool.ipynb
-   ./tools/tool_input_validation.ipynb
 
 
 In this documentation we cover generic tooling functionality (eg how to create your own)

docs/modules/agents/tools/custom_tools.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "5436020b",
    "metadata": {},
@@ -10,29 +9,28 @@
     "\n",
     "When constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:\n",
     "\n",
-    "- name (str), is required and must be unique within a set of tools provided to an agent\n",
-    "- description (str), is optional but recommended, as it is used by an agent to determine tool use\n",
+    "- name (str), is required\n",
+    "- description (str), is optional\n",
     "- return_direct (bool), defaults to False\n",
-    "- args_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters.\n",
     "\n",
+    "The function that should be called when the tool is selected should take as input a single string and return a single string.\n",
     "\n",
-    "There are two main ways to define a tool, we will cover both in the example below."
+    "There are two ways to define a tool, we will cover both in the example below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "1aaba18c",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Import things that are needed generically\n",
-    "from langchain import LLMMathChain, SerpAPIWrapper\n",
-    "from langchain.agents import AgentType, initialize_agent\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.tools import BaseTool, StructuredTool, Tool, tool"
+    "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents import AgentType\n",
+    "from langchain.tools import BaseTool\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain import LLMMathChain, SerpAPIWrapper"
    ]
   },
   {
@@ -45,111 +43,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "36ed392e",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "llm = ChatOpenAI(temperature=0)"
+    "llm = OpenAI(temperature=0)"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "f8bc72c2",
    "metadata": {},
    "source": [
-    "## Completely New Tools - String Input and Output\n",
-    "\n",
-    "The simplest tools accept a single query string and return a string output. If your tool function requires multiple arguments, you might want to skip down to the `StructuredTool` section below.\n",
+    "## Completely New Tools \n",
+    "First, we show how to create completely new tools from scratch.\n",
     "\n",
     "There are two ways to do this: either by using the Tool dataclass, or by subclassing the BaseTool class."
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "b63fcc3b",
    "metadata": {},
    "source": [
-    "### Tool dataclass\n",
-    "\n",
-    "The 'Tool' dataclass wraps functions that accept a single string input and returns a string output."
+    "### Tool dataclass"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "56ff7670",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/wfh/code/lc/lckg/langchain/chains/llm_math/base.py:50: UserWarning: Directly instantiating an LLMMathChain with an llm is deprecated. Please instantiate with llm_chain argument or using the from_llm class method.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Load the tool configs that are needed.\n",
     "search = SerpAPIWrapper()\n",
     "llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
     "tools = [\n",
-    "    Tool.from_function(\n",
-    "        func=search.run,\n",
+    "    Tool(\n",
     "        name = \"Search\",\n",
+    "        func=search.run,\n",
     "        description=\"useful for when you need to answer questions about current events\"\n",
-    "        # coroutine= ... <- you can specify an async method if desired as well\n",
     "    ),\n",
+    "    Tool(\n",
+    "        name=\"Calculator\",\n",
+    "        func=llm_math_chain.run,\n",
+    "        description=\"useful for when you need to answer questions about math\"\n",
+    "    )\n",
     "]"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e9b560f7",
-   "metadata": {},
-   "source": [
-    "You can also define a custom `args_schema`` to provide more information about inputs."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "631361e7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydantic import BaseModel, Field\n",
-    "\n",
-    "class CalculatorInput(BaseModel):\n",
-    "    question: str = Field()\n",
-    "        \n",
-    "\n",
-    "tools.append(\n",
-    "    Tool.from_function(\n",
-    "        func=llm_math_chain.run,\n",
-    "        name=\"Calculator\",\n",
-    "        description=\"useful for when you need to answer questions about math\",\n",
-    "        args_schema=CalculatorInput\n",
-    "        # coroutine= ... <- you can specify an async method if desired as well\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
    "id": "5b93047d",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
@@ -159,11 +108,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "6f96a891",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -172,34 +119,29 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
       "Action: Search\n",
       "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAfter rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his \"age bracket\" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI still need to find out his current girlfriend's name and age\n",
-      "Action: Search\n",
-      "Action Input: \"Leo DiCaprio current girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mJust Jared on Instagram: “Leonardo DiCaprio & girlfriend Camila Morrone couple up for a lunch date!\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mNow that I know his girlfriend's name is Camila Morrone, I need to find her current age\n",
-      "Action: Search\n",
-      "Action Input: \"Camila Morrone age\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mNow that I have her age, I need to calculate her age raised to the 0.43 power\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age raised to the 0.43 power\n",
       "Action: Calculator\n",
-      "Action Input: 25^(0.43)\u001b[0m\n",
+      "Action Input: 22^0.43\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "25^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
-      "25**(0.43)\n",
+      "22^0.43\u001b[32;1m\u001b[1;3m\n",
+      "```python\n",
+      "import math\n",
+      "print(math.pow(22, 0.43))\n",
       "```\n",
-      "...numexpr.evaluate(\"25**(0.43)\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
+      "\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer\n",
-      "Final Answer: Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -207,10 +149,10 @@
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
+       "\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,75 +162,70 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "6f12eaf0",
    "metadata": {},
    "source": [
-    "### Subclassing the BaseTool class\n",
-    "\n",
-    "You can also directly subclass `BaseTool`. This is useful if you want more control over the instance variables or if you want to propagate callbacks to nested chains or other tools."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "c58a7c40",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from typing import Optional, Type\n",
-    "\n",
-    "from langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun\n",
-    "\n",
-    "class CustomSearchTool(BaseTool):\n",
-    "    name = \"custom_search\"\n",
-    "    description = \"useful for when you need to answer questions about current events\"\n",
-    "\n",
-    "    def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:\n",
-    "        \"\"\"Use the tool.\"\"\"\n",
-    "        return search.run(query)\n",
-    "    \n",
-    "    async def _arun(self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:\n",
-    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
-    "        raise NotImplementedError(\"custom_search does not support async\")\n",
-    "    \n",
-    "class CustomCalculatorTool(BaseTool):\n",
-    "    name = \"Calculator\"\n",
-    "    description = \"useful for when you need to answer questions about math\"\n",
-    "    args_schema: Type[BaseModel] = CalculatorInput\n",
-    "\n",
-    "    def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:\n",
-    "        \"\"\"Use the tool.\"\"\"\n",
-    "        return llm_math_chain.run(query)\n",
-    "    \n",
-    "    async def _arun(self, query: str,  run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:\n",
-    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
-    "        raise NotImplementedError(\"Calculator does not support async\")"
+    "### Subclassing the BaseTool class"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "3318a46f",
-   "metadata": {
-    "tags": []
-   },
+   "id": "c58a7c40",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "tools = [CustomSearchTool(), CustomCalculatorTool()]\n",
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "class CustomSearchTool(BaseTool):\n",
+    "    name = \"Search\"\n",
+    "    description = \"useful for when you need to answer questions about current events\"\n",
+    "\n",
+    "    def _run(self, query: str) -> str:\n",
+    "        \"\"\"Use the tool.\"\"\"\n",
+    "        return search.run(query)\n",
+    "    \n",
+    "    async def _arun(self, query: str) -> str:\n",
+    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
+    "        raise NotImplementedError(\"BingSearchRun does not support async\")\n",
+    "    \n",
+    "class CustomCalculatorTool(BaseTool):\n",
+    "    name = \"Calculator\"\n",
+    "    description = \"useful for when you need to answer questions about math\"\n",
+    "\n",
+    "    def _run(self, query: str) -> str:\n",
+    "        \"\"\"Use the tool.\"\"\"\n",
+    "        return llm_math_chain.run(query)\n",
+    "    \n",
+    "    async def _arun(self, query: str) -> str:\n",
+    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
+    "        raise NotImplementedError(\"BingSearchRun does not support async\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "id": "3318a46f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [CustomSearchTool(), CustomCalculatorTool()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ee2d0f3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "6a2cebbf",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -297,30 +234,29 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI need to use custom_search to find out who Leo DiCaprio's girlfriend is, and then use the Calculator to raise her age to the 0.43 power.\n",
-      "Action: custom_search\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
+      "Action: Search\n",
       "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAfter rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his \"age bracket\" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI need to find out the current age of Eden Polani.\n",
-      "Action: custom_search\n",
-      "Action Input: \"Eden Polani age\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m19 years old\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mNow I can use the Calculator to raise her age to the 0.43 power.\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age raised to the 0.43 power\n",
       "Action: Calculator\n",
-      "Action Input: 19 ^ 0.43\u001b[0m\n",
+      "Action Input: 22^0.43\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "19 ^ 0.43\u001b[32;1m\u001b[1;3m```text\n",
-      "19 ** 0.43\n",
+      "22^0.43\u001b[32;1m\u001b[1;3m\n",
+      "```python\n",
+      "import math\n",
+      "print(math.pow(22, 0.43))\n",
       "```\n",
-      "...numexpr.evaluate(\"19 ** 0.43\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.547023357958959\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
+      "\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.547023357958959\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
-      "Final Answer: 3.547023357958959\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -328,10 +264,10 @@
     {
      "data": {
       "text/plain": [
-       "'3.547023357958959'"
+       "\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -352,20 +288,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "8f15307d",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.tools import tool\n",
+    "from langchain.agents import tool\n",
     "\n",
     "@tool\n",
     "def search_api(query: str) -> str:\n",
     "    \"\"\"Searches the API for the query.\"\"\"\n",
-    "    return f\"Results for query {query}\"\n",
-    "\n",
+    "    return \"Results\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0a23b91b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1184e0cd0>, func=<function search_api at 0x1635f8700>, coroutine=None)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "search_api"
    ]
   },
@@ -379,11 +332,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "28cdf04d",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "@tool(\"search\", return_direct=True)\n",
@@ -394,17 +345,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "id": "1085a4bd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd66310>, coroutine=None)"
+       "Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1184e0cd0>, func=<function search_api at 0x1635f8670>, coroutine=None)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -414,194 +365,18 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "de34a6a3",
-   "metadata": {},
-   "source": [
-    "You can also provide `args_schema` to provide more information about the argument"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "f3a5c106",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class SearchInput(BaseModel):\n",
-    "    query: str = Field(description=\"should be a search query\")\n",
-    "        \n",
-    "@tool(\"search\", return_direct=True, args_schema=SearchInput)\n",
-    "def search_api(query: str) -> str:\n",
-    "    \"\"\"Searches the API for the query.\"\"\"\n",
-    "    return \"Results\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "7914ba6b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class '__main__.SearchInput'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bcf0ee0>, coroutine=None)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "search_api"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "61d2e80b",
-   "metadata": {},
-   "source": [
-    "## Custom Structured Tools\n",
-    "\n",
-    "If your functions require more structured arguments, you can use the `StructuredTool` class directly, or still subclass the `BaseTool` class."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "5be41722",
-   "metadata": {},
-   "source": [
-    "### StructuredTool dataclass\n",
-    "\n",
-    "To dynamically generate a structured tool from a given function, the fastest way to get started is with `StructuredTool.from_function()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "3c070216",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from langchain.tools import StructuredTool\n",
-    "\n",
-    "def post_message(url: str, body: dict, parameters: Optional[dict] = None) -> str:\n",
-    "    \"\"\"Sends a POST request to the given url with the given body and parameters.\"\"\"\n",
-    "    result = requests.post(url, json=body, params=parameters)\n",
-    "    return f\"Status: {result.status_code} - {result.text}\"\n",
-    "\n",
-    "tool = StructuredTool.from_function(post_message)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "fb0a38eb",
-   "metadata": {},
-   "source": [
-    "## Subclassing the BaseTool\n",
-    "\n",
-    "The BaseTool automatically infers the schema from the _run method's signature."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "7505c9c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Optional, Type\n",
-    "\n",
-    "from langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun\n",
-    "            \n",
-    "class CustomSearchTool(BaseTool):\n",
-    "    name = \"custom_search\"\n",
-    "    description = \"useful for when you need to answer questions about current events\"\n",
-    "\n",
-    "    def _run(self, query: str, engine: str = \"google\", gl: str = \"us\", hl: str = \"en\", run_manager: Optional[CallbackManagerForToolRun] = None) -> str:\n",
-    "        \"\"\"Use the tool.\"\"\"\n",
-    "        search_wrapper = SerpAPIWrapper(params={\"engine\": engine, \"gl\": gl, \"hl\": hl})\n",
-    "        return search_wrapper.run(query)\n",
-    "    \n",
-    "    async def _arun(self, query: str,  engine: str = \"google\", gl: str = \"us\", hl: str = \"en\", run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:\n",
-    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
-    "        raise NotImplementedError(\"custom_search does not support async\")\n",
-    "\n",
-    "\n",
-    "\n",
-    "# You can provide a custom args schema to add descriptions or custom validation\n",
-    "\n",
-    "class SearchSchema(BaseModel):\n",
-    "    query: str = Field(description=\"should be a search query\")\n",
-    "    engine: str = Field(description=\"should be a search engine\")\n",
-    "    gl: str = Field(description=\"should be a country code\")\n",
-    "    hl: str = Field(description=\"should be a language code\")\n",
-    "\n",
-    "class CustomSearchTool(BaseTool):\n",
-    "    name = \"custom_search\"\n",
-    "    description = \"useful for when you need to answer questions about current events\"\n",
-    "    args_schema: Type[SearchSchema] = SearchSchema\n",
-    "\n",
-    "    def _run(self, query: str, engine: str = \"google\", gl: str = \"us\", hl: str = \"en\", run_manager: Optional[CallbackManagerForToolRun] = None) -> str:\n",
-    "        \"\"\"Use the tool.\"\"\"\n",
-    "        search_wrapper = SerpAPIWrapper(params={\"engine\": engine, \"gl\": gl, \"hl\": hl})\n",
-    "        return search_wrapper.run(query)\n",
-    "    \n",
-    "    async def _arun(self, query: str,  engine: str = \"google\", gl: str = \"us\", hl: str = \"en\", run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:\n",
-    "        \"\"\"Use the tool asynchronously.\"\"\"\n",
-    "        raise NotImplementedError(\"custom_search does not support async\")\n",
-    "    \n",
-    "    "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "7d68b0ac",
-   "metadata": {},
-   "source": [
-    "## Using the decorator\n",
-    "\n",
-    "The `tool` decorator creates a structured tool automatically if the signature has multiple arguments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "38d11416",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from langchain.tools import tool\n",
-    "\n",
-    "@tool\n",
-    "def post_message(url: str, body: dict, parameters: Optional[dict] = None) -> str:\n",
-    "    \"\"\"Sends a POST request to the given url with the given body and parameters.\"\"\"\n",
-    "    result = requests.post(url, json=body, params=parameters)\n",
-    "    return f\"Status: {result.status_code} - {result.text}\""
-   ]
-  },
-  {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "1d0430d6",
    "metadata": {},
    "source": [
     "## Modify existing tools\n",
     "\n",
-    "Now, we show how to load existing tools and modify them directly. In the example below, we do something really simple and change the Search tool to have the name `Google Search`."
+    "Now, we show how to load existing tools and just modify them. In the example below, we do something really simple and change the Search tool to have the name `Google Search`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "id": "79213f40",
    "metadata": {},
    "outputs": [],
@@ -611,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "id": "e1067dcb",
    "metadata": {},
    "outputs": [],
@@ -621,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "id": "6c66ffe8",
    "metadata": {},
    "outputs": [],
@@ -631,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "f45b5bc3",
    "metadata": {},
    "outputs": [],
@@ -641,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "id": "565e2b9b",
    "metadata": {},
    "outputs": [
@@ -652,20 +427,21 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age.\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
       "Action: Google Search\n",
       "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAfter rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his \"age bracket\" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI still need to find out his current girlfriend's name and her age.\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
       "Action: Google Search\n",
-      "Action Input: \"Leo DiCaprio current girlfriend age\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mLeonardo DiCaprio has been linked with 19-year-old model Eden Polani, continuing the rumour that he doesn't date any women over the age of ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI need to find out the age of Eden Polani.\n",
+      "Action Input: \"Camila Morrone age\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
       "Action: Calculator\n",
-      "Action Input: 19^(0.43)\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.547023357958959\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
-      "Final Answer: The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55.\u001b[0m\n",
+      "Action Input: 25^0.43\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -673,10 +449,10 @@
     {
      "data": {
       "text/plain": [
-       "\"The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55.\""
+       "\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\""
       ]
      },
-     "execution_count": 17,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -702,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "id": "3450512e",
    "metadata": {},
    "outputs": [],
@@ -731,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "id": "4b9a7849",
    "metadata": {},
    "outputs": [
@@ -744,7 +520,9 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m I should use a music search engine to find the answer\n",
       "Action: Music Search\n",
-      "Action Input: most famous song of christmas\u001b[0m\u001b[33;1m\u001b[1;3m'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Action Input: most famous song of christmas\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
       "Final Answer: 'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -756,7 +534,7 @@
        "\"'All I Want For Christmas Is You' by Mariah Carey.\""
       ]
      },
-     "execution_count": 20,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -776,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "id": "3bb6185f",
    "metadata": {},
    "outputs": [],
@@ -794,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "id": "113ddb84",
    "metadata": {},
    "outputs": [],
@@ -805,11 +583,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "id": "582439a6",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -820,7 +596,9 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m I need to calculate this\n",
       "Action: Calculator\n",
-      "Action Input: 2**.12\u001b[0m\u001b[36;1m\u001b[1;3mAnswer: 1.086734862526058\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "Action Input: 2**.12\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.2599210498948732\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -828,10 +606,10 @@
     {
      "data": {
       "text/plain": [
-       "'Answer: 1.086734862526058'"
+       "'Answer: 1.2599210498948732'"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -839,6 +617,14 @@
    "source": [
     "agent.run(\"whats 2**.12\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "537bc628",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -857,7 +643,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/tools/examples/apify.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -19,15 +20,7 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!pip install apify-client"
-   ]
-  },
-  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -46,6 +39,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -66,6 +60,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -90,6 +85,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -106,6 +102,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -159,9 +156,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/agents/tools/examples/arxiv.ipynb

@@ -1,259 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "245a954a",
-   "metadata": {},
-   "source": [
-    "# ArXiv API Tool\n",
-    "\n",
-    "This notebook goes over how to use the `arxiv` component. \n",
-    "\n",
-    "First, you need to install `arxiv` python package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5a7209e",
-   "metadata": {
-    "tags": [],
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install arxiv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ce1a4827-ce89-4f31-a041-3246743e513a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.agents import load_tools, initialize_agent, AgentType\n",
-    "\n",
-    "llm = ChatOpenAI(temperature=0.0)\n",
-    "tools = load_tools(\n",
-    "    [\"arxiv\"], \n",
-    ")\n",
-    "\n",
-    "agent_chain = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ad7dd945-5ae3-49e5-b667-6d86b15050b6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI need to use Arxiv to search for the paper.\n",
-      "Action: Arxiv\n",
-      "Action Input: \"1605.08386\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mPublished: 2016-05-26\n",
-      "Title: Heat-bath random walks with Markov bases\n",
-      "Authors: Caprice Stanley, Tobias Windisch\n",
-      "Summary: Graphs on lattice points are studied whose edges come from a finite set of\n",
-      "allowed moves of arbitrary length. We show that the diameter of these graphs on\n",
-      "fibers of a fixed integer matrix can be bounded from above by a constant. We\n",
-      "then study the mixing behaviour of heat-bath random walks on these graphs. We\n",
-      "also state explicit conditions on the set of moves so that the heat-bath random\n",
-      "walk, a generalization of the Glauber dynamics, is an expander in fixed\n",
-      "dimension.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe paper is about heat-bath random walks with Markov bases on graphs of lattice points.\n",
-      "Final Answer: The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(\n",
-    "    \"What's the paper 1605.08386 about?\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4183343-d69a-4be0-9b2c-cc98464a6825",
-   "metadata": {},
-   "source": [
-    "## The ArXiv API Wrapper\n",
-    "\n",
-    "The tool wraps the API Wrapper. Below, we can explore some of the features it provides."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "8d32b39a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.utilities import ArxivAPIWrapper"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c89c110c-96ac-4fe1-ba3e-6056543d1a59",
-   "metadata": {},
-   "source": [
-    "Run a query to get information about some `scientific article`/articles. The query text is limited to 300 characters.\n",
-    "\n",
-    "It returns these article fields:\n",
-    "- Publishing date\n",
-    "- Title\n",
-    "- Authors\n",
-    "- Summary\n",
-    "\n",
-    "Next query returns information about one article with arxiv Id equal \"1605.08386\". "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "34bb5968",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Published: 2016-05-26\\nTitle: Heat-bath random walks with Markov bases\\nAuthors: Caprice Stanley, Tobias Windisch\\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\\nallowed moves of arbitrary length. We show that the diameter of these graphs on\\nfibers of a fixed integer matrix can be bounded from above by a constant. We\\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\\nalso state explicit conditions on the set of moves so that the heat-bath random\\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\\ndimension.'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "arxiv = ArxivAPIWrapper()\n",
-    "docs = arxiv.run(\"1605.08386\")\n",
-    "docs"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "840f70c9-8f80-4680-bb38-46198e931bcf",
-   "metadata": {},
-   "source": [
-    "Now, we want to get information about one author, `Caprice Stanley`.\n",
-    "\n",
-    "This query returns information about three articles. By default, the query returns information only about three top articles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b0867fda-e119-4b19-9ec6-e354fa821db3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Published: 2017-10-10\\nTitle: On Mixing Behavior of a Family of Random Walks Determined by a Linear Recurrence\\nAuthors: Caprice Stanley, Seth Sullivant\\nSummary: We study random walks on the integers mod $G_n$ that are determined by an\\ninteger sequence $\\\\{ G_n \\\\}_{n \\\\geq 1}$ generated by a linear recurrence\\nrelation. Fourier analysis provides explicit formulas to compute the\\neigenvalues of the transition matrices and we use this to bound the mixing time\\nof the random walks.\\n\\nPublished: 2016-05-26\\nTitle: Heat-bath random walks with Markov bases\\nAuthors: Caprice Stanley, Tobias Windisch\\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\\nallowed moves of arbitrary length. We show that the diameter of these graphs on\\nfibers of a fixed integer matrix can be bounded from above by a constant. We\\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\\nalso state explicit conditions on the set of moves so that the heat-bath random\\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\\ndimension.\\n\\nPublished: 2003-03-18\\nTitle: Calculation of fluxes of charged particles and neutrinos from atmospheric showers\\nAuthors: V. Plyaskin\\nSummary: The results on the fluxes of charged particles and neutrinos from a\\n3-dimensional (3D) simulation of atmospheric showers are presented. An\\nagreement of calculated fluxes with data on charged particles from the AMS and\\nCAPRICE detectors is demonstrated. Predictions on neutrino fluxes at different\\nexperimental sites are compared with results from other calculations.'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = arxiv.run(\"Caprice Stanley\")\n",
-    "docs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d9b6292-a47d-4f99-9827-8e9f244bf887",
-   "metadata": {},
-   "source": [
-    "Now, we are trying to find information about non-existing article. In this case, the response is \"No good Arxiv Result was found\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3580aeeb-086f-45ba-bcdc-b46f5134b3dd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'No good Arxiv Result was found'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = arxiv.run(\"1605.08386WWW\")\n",
-    "docs"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/agents/tools/examples/awslambda.ipynb

@@ -1,119 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## AWS Lambda API"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook goes over how to use the AWS Lambda Tool component.\n",
-    "\n",
-    "AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS), designed to allow developers to build and run applications and services without the need for provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.\n",
-    "\n",
-    "By including a `awslambda` in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.\n",
-    "\n",
-    "When an Agent uses the awslambda tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.\n",
-    "\n",
-    "First, you need to install `boto3` python package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install boto3 > /dev/null"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order for an agent to use the tool, you must provide it with the name and description that match the functionality of you lambda function's logic. \n",
-    "\n",
-    "You must also provide the name of your function. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that because this tool is effectively just a wrapper around the boto3 library, you will need to run `aws configure` in order to make use of the tool. For more detail, see [here](https://docs.aws.amazon.com/cli/index.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from langchain import OpenAI\n",
-    "from langchain.agents import load_tools, AgentType\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "\n",
-    "tools = load_tools(\n",
-    "    [\"awslambda\"],\n",
-    "    awslambda_tool_name=\"email-sender\",\n",
-    "    awslambda_tool_description=\"sends an email with the specified content to test@testing123.com\",\n",
-    "    function_name=\"testFunction1\"\n",
-    ")\n",
-    "\n",
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n",
-    "\n",
-    "agent.run(\"Send an email to test@testing123.com saying hello world.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "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": "3.11.2"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/modules/agents/tools/examples/bash.ipynb

@@ -5,158 +5,57 @@
       "id": "8f210ec3",
       "metadata": {},
       "source": [
-        "# Shell Tool\n",
-        "\n",
-        "Giving agents access to the shell is powerful (though risky outside a sandboxed environment).\n",
-        "\n",
-        "The LLM can use it to execute any shell commands. A common use case for this is letting the LLM interact with your local file system."
+        "# Bash\n",
+        "It can often be useful to have an LLM generate bash commands, and then run them. A common use case for this is letting the LLM interact with your local file system. We provide an easy util to execute bash commands."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 1,
       "id": "f7b3767b",
-      "metadata": {
-        "tags": []
-      },
+      "metadata": {},
       "outputs": [],
       "source": [
-        "from langchain.tools import ShellTool\n",
-        "\n",
-        "shell_tool = ShellTool()"
+        "from langchain.utilities import BashProcess"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 2,
-      "id": "c92ac832-556b-4f66-baa4-b78f965dfba0",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Hello World!\n",
-            "\n",
-            "real\t0m0.000s\n",
-            "user\t0m0.000s\n",
-            "sys\t0m0.000s\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk.\n",
-            "  warnings.warn(\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(shell_tool.run({\"commands\": [\"echo 'Hello World!'\", \"time\"]}))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "2fa952fc",
+      "id": "cf1c92f0",
       "metadata": {},
+      "outputs": [],
       "source": [
-        "### Use with Agents\n",
-        "\n",
-        "As with all tools, these can be given to an agent to accomplish more complex tasks. Let's have the agent fetch some links from a web page."
+        "bash = BashProcess()"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 3,
-      "id": "851fee9f",
-      "metadata": {
-        "tags": []
-      },
+      "id": "2fa952fc",
+      "metadata": {},
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "\n",
-            "\n",
-            "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-            "\u001b[32;1m\u001b[1;3mQuestion: What is the task?\n",
-            "Thought: We need to download the langchain.com webpage and extract all the URLs from it. Then we need to sort the URLs and return them.\n",
-            "Action:\n",
-            "```\n",
-            "{\n",
-            "  \"action\": \"shell\",\n",
-            "  \"action_input\": {\n",
-            "    \"commands\": [\n",
-            "      \"curl -s https://langchain.com | grep -o 'http[s]*://[^\\\" ]*' | sort\"\n",
-            "    ]\n",
-            "  }\n",
-            "}\n",
-            "```\n",
-            "\u001b[0m"
+            "bash.ipynb\n",
+            "google_search.ipynb\n",
+            "python.ipynb\n",
+            "requests.ipynb\n",
+            "serpapi.ipynb\n",
+            "\n"
           ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk.\n",
-            "  warnings.warn(\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Observation: \u001b[36;1m\u001b[1;3mhttps://blog.langchain.dev/\n",
-            "https://discord.gg/6adMQxSpJS\n",
-            "https://docs.langchain.com/docs/\n",
-            "https://github.com/hwchase17/chat-langchain\n",
-            "https://github.com/hwchase17/langchain\n",
-            "https://github.com/hwchase17/langchainjs\n",
-            "https://github.com/sullivan-sean/chat-langchainjs\n",
-            "https://js.langchain.com/docs/\n",
-            "https://python.langchain.com/en/latest/\n",
-            "https://twitter.com/langchainai\n",
-            "\u001b[0m\n",
-            "Thought:\u001b[32;1m\u001b[1;3mThe URLs have been successfully extracted and sorted. We can return the list of URLs as the final answer.\n",
-            "Final Answer: [\"https://blog.langchain.dev/\", \"https://discord.gg/6adMQxSpJS\", \"https://docs.langchain.com/docs/\", \"https://github.com/hwchase17/chat-langchain\", \"https://github.com/hwchase17/langchain\", \"https://github.com/hwchase17/langchainjs\", \"https://github.com/sullivan-sean/chat-langchainjs\", \"https://js.langchain.com/docs/\", \"https://python.langchain.com/en/latest/\", \"https://twitter.com/langchainai\"]\u001b[0m\n",
-            "\n",
-            "\u001b[1m> Finished chain.\u001b[0m\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "'[\"https://blog.langchain.dev/\", \"https://discord.gg/6adMQxSpJS\", \"https://docs.langchain.com/docs/\", \"https://github.com/hwchase17/chat-langchain\", \"https://github.com/hwchase17/langchain\", \"https://github.com/hwchase17/langchainjs\", \"https://github.com/sullivan-sean/chat-langchainjs\", \"https://js.langchain.com/docs/\", \"https://python.langchain.com/en/latest/\", \"https://twitter.com/langchainai\"]'"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
         }
       ],
       "source": [
-        "from langchain.chat_models import ChatOpenAI\n",
-        "from langchain.agents import initialize_agent\n",
-        "from langchain.agents import AgentType\n",
-        "\n",
-        "llm = ChatOpenAI(temperature=0)\n",
-        "\n",
-        "shell_tool.description = shell_tool.description + f\"args {shell_tool.args}\".replace(\"{\", \"{{\").replace(\"}\", \"}}\")\n",
-        "self_ask_with_search = initialize_agent([shell_tool], llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n",
-        "self_ask_with_search.run(\"Download the langchain.com webpage and grep for all urls. Return only a sorted list of them. Be sure to use double quotes.\")"
+        "print(bash.run(\"ls\"))"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "id": "8d0ea3ac-0890-4e39-9cec-74bd80b4b8b8",
+      "id": "851fee9f",
       "metadata": {},
       "outputs": [],
       "source": []
@@ -178,7 +77,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.16"
+      "version": "3.10.9"
     }
   },
   "nbformat": 4,

docs/modules/agents/tools/examples/ddg.ipynb

@@ -1,91 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "245a954a",
-   "metadata": {},
-   "source": [
-    "# DuckDuckGo Search\n",
-    "\n",
-    "This notebook goes over how to use the duck-duck-go search component."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "21e46d4d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !pip install duckduckgo-search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ac4910f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.tools import DuckDuckGoSearchRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "84b8f773",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search = DuckDuckGoSearchRun()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "068991a6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009-17) and the first African American to hold the office. Before winning the presidency, Obama represented Illinois in the U.S. Senate (2005-08). Barack Hussein Obama II (/ b ə ˈ r ɑː k h uː ˈ s eɪ n oʊ ˈ b ɑː m ə / bə-RAHK hoo-SAYN oh-BAH-mə; born August 4, 1961) is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president of the United States. Obama previously served as a U.S. senator representing ... Barack Obama was the first African American president of the United States (2009-17). He oversaw the recovery of the U.S. economy (from the Great Recession of 2008-09) and the enactment of landmark health care reform (the Patient Protection and Affordable Care Act ). In 2009 he was awarded the Nobel Peace Prize. His birth certificate lists his first name as Barack: That\\'s how Obama has spelled his name throughout his life. His name derives from a Hebrew name which means \"lightning.\". The Hebrew word has been transliterated into English in various spellings, including Barak, Buraq, Burack, and Barack. Most common names of U.S. presidents 1789-2021. Published by. Aaron O\\'Neill , Jun 21, 2022. The most common first name for a U.S. president is James, followed by John and then William. Six U.S ...'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "search.run(\"Obama's first name?\")"
-   ]
-  }
- ],
- "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"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/agents/tools/examples/filesystem.ipynb

@@ -1,190 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# File System Tools\n",
-    "\n",
-    "LangChain provides tools for interacting with a local file system out of the box. This notebook walks through some of them.\n",
-    "\n",
-    "Note: these tools are not recommended for use outside a sandboxed environment! "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we'll import the tools."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.tools.file_management import (\n",
-    "    ReadFileTool,\n",
-    "    CopyFileTool,\n",
-    "    DeleteFileTool,\n",
-    "    MoveFileTool,\n",
-    "    WriteFileTool,\n",
-    "    ListDirectoryTool,\n",
-    ")\n",
-    "from langchain.agents.agent_toolkits import FileManagementToolkit\n",
-    "from tempfile import TemporaryDirectory\n",
-    "\n",
-    "# We'll make a temporary directory to avoid clutter\n",
-    "working_directory = TemporaryDirectory()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The FileManagementToolkit\n",
-    "\n",
-    "If you want to provide all the file tooling to your agent, it's easy to do so with the toolkit. We'll pass the temporary directory in as a root directory as a workspace for the LLM.\n",
-    "\n",
-    "It's recommended to always pass in a root directory, since without one, it's easy for the LLM to pollute the working directory, and without one, there isn't any validation against\n",
-    "straightforward prompt injection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[CopyFileTool(name='copy_file', description='Create a copy of a file in a specified location', args_schema=<class 'langchain.tools.file_management.copy.FileCopyInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " DeleteFileTool(name='file_delete', description='Delete a file', args_schema=<class 'langchain.tools.file_management.delete.FileDeleteInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " FileSearchTool(name='file_search', description='Recursively search for files in a subdirectory that match the regex pattern', args_schema=<class 'langchain.tools.file_management.file_search.FileSearchInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " MoveFileTool(name='move_file', description='Move or rename a file from one location to another', args_schema=<class 'langchain.tools.file_management.move.FileMoveInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " ReadFileTool(name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " WriteFileTool(name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "toolkit = FileManagementToolkit(root_dir=str(working_directory.name)) # If you don't provide a root_dir, operations will default to the current working directory\n",
-    "toolkit.get_tools()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Selecting File System Tools\n",
-    "\n",
-    "If you only want to select certain tools, you can pass them in as arguments when initializing the toolkit, or you can individually initialize the desired tools."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[ReadFileTool(name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " WriteFileTool(name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tools = FileManagementToolkit(root_dir=str(working_directory.name), selected_tools=[\"read_file\", \"write_file\", \"list_directory\"]).get_tools()\n",
-    "tools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'File written successfully to example.txt.'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "read_tool, write_tool, list_tool = tools\n",
-    "write_tool.run({\"file_path\": \"example.txt\", \"text\": \"Hello World!\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'example.txt'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# List files in the working directory\n",
-    "list_tool.run({})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/modules/agents/tools/examples/google_places.ipynb

@@ -1,105 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "487607cd",
-   "metadata": {},
-   "source": [
-    "# Google Places\n",
-    "\n",
-    "This notebook goes through how to use Google Places API"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8690845f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!pip install googlemaps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "fae31ef4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"GPLACES_API_KEY\"] = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "abb502b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.tools import GooglePlacesTool"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "a83a02ac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "places = GooglePlacesTool()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "2b65a285",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"1. Delfina Restaurant\\nAddress: 3621 18th St, San Francisco, CA 94110, USA\\nPhone: (415) 552-4055\\nWebsite: https://www.delfinasf.com/\\n\\n\\n2. Piccolo Forno\\nAddress: 725 Columbus Ave, San Francisco, CA 94133, USA\\nPhone: (415) 757-0087\\nWebsite: https://piccolo-forno-sf.com/\\n\\n\\n3. L'Osteria del Forno\\nAddress: 519 Columbus Ave, San Francisco, CA 94133, USA\\nPhone: (415) 982-1124\\nWebsite: Unknown\\n\\n\\n4. Il Fornaio\\nAddress: 1265 Battery St, San Francisco, CA 94111, USA\\nPhone: (415) 986-0100\\nWebsite: https://www.ilfornaio.com/\\n\\n\""
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "places.run(\"al fornos\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66d3da8a",
-   "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
-}

docs/modules/agents/tools/examples/google_search.ipynb

@@ -33,16 +33,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.tools import Tool\n",
-    "from langchain.utilities import GoogleSearchAPIWrapper\n",
-    "\n",
-    "search = GoogleSearchAPIWrapper()\n",
-    "\n",
-    "tool = Tool(\n",
-    "    name = \"Google Search\",\n",
-    "    description=\"Search Google for recent results.\",\n",
-    "    func=search.run\n",
-    ")"
+    "from langchain.utilities import GoogleSearchAPIWrapper"
    ]
   },
   {
@@ -50,20 +41,30 @@
    "execution_count": 3,
    "id": "84b8f773",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = GoogleSearchAPIWrapper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "068991a6",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\"STATE OF HAWAII. 1 Child's First Name. (Type or print). 2. Sex. BARACK. 3. This Birth. CERTIFICATE OF LIVE BIRTH. FILE. NUMBER 151 le. lb. Middle Name. Barack Hussein Obama II is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic\\xa0... When Barack Obama was elected president in 2008, he became the first African American to hold ... The Middle East remained a key foreign policy challenge. Jan 19, 2017 ... Jordan Barack Treasure, New York City, born in 2008 ... Jordan Barack Treasure made national news when he was the focus of a New York newspaper\\xa0... Portrait of George Washington, the 1st President of the United States ... Portrait of Barack Obama, the 44th President of the United States\\xa0... His full name is Barack Hussein Obama II. Since the “II” is simply because he was named for his father, his last name is Obama. Mar 22, 2008 ... Barry Obama decided that he didn't like his nickname. A few of his friends at Occidental College had already begun to call him Barack (his\\xa0... Aug 18, 2017 ... It took him several seconds and multiple clues to remember former President Barack Obama's first name. Miller knew that every answer had to\\xa0... Feb 9, 2015 ... Michael Jordan misspelled Barack Obama's first name on 50th-birthday gift ... Knowing Obama is a Chicagoan and huge basketball fan,\\xa0... 4 days ago ... Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009–17) and\\xa0...\""
+       "'1 Child\\'s First Name. 2. 6. 7d. Street Address. 71. (Type or print). BARACK. Sex. 3. This Birth. 4. If Twin or Triplet,. Was Child Born. Barack Hussein Obama II is an American retired politician who served as the 44th president of the United States from 2009 to 2017. His full name is Barack Hussein Obama II. Since the “II” is simply because he was named for his father, his last name is Obama. Feb 9, 2015 ... Michael Jordan misspelled Barack Obama\\'s first name on 50th-birthday gift ... Knowing Obama is a Chicagoan and huge basketball fan,\\xa0... Aug 18, 2017 ... It took him several seconds and multiple clues to remember former President Barack Obama\\'s first name. Miller knew that every answer had to end\\xa0... First Lady Michelle LaVaughn Robinson Obama is a lawyer, writer, and the wife of the 44th President, Barack Obama. She is the first African-American First\\xa0... Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009–17) and the first\\xa0... When Barack Obama was elected president in 2008, he became the first African American to hold ... The Middle East remained a key foreign policy challenge. Feb 27, 2020 ... President Barack Obama was born Barack Hussein Obama, II, as shown here on his birth certificate here . As reported by Reuters here , his\\xa0... Jan 16, 2007 ... 4, 1961, in Honolulu. His first name means \"one who is blessed\" in Swahili. While Obama\\'s father, Barack Hussein Obama Sr., was from Kenya, his\\xa0...'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tool.run(\"Obama's first name?\")"
+    "search.run(\"Obama's first name?\")"
    ]
   },
   {
@@ -77,23 +78,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "5083fbdd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "search = GoogleSearchAPIWrapper(k=1)\n",
-    "\n",
-    "tool = Tool(\n",
-    "    name = \"I'm Feeling Lucky\",\n",
-    "    description=\"Search Google and return the first result.\",\n",
-    "    func=search.run\n",
-    ")"
+    "search = GoogleSearchAPIWrapper(k=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "77aaa857",
    "metadata": {},
    "outputs": [
@@ -103,13 +98,13 @@
        "'The official home of the Python Programming Language.'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tool.run(\"python\")"
+    "search.run(\"python\")"
    ]
   },
   {
@@ -142,30 +137,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "028f4cba",
    "metadata": {},
    "outputs": [],
    "source": [
-    "search = GoogleSearchAPIWrapper()\n",
-    "\n",
-    "def top5_results(query):\n",
-    "    return search.results(query, 5)\n",
-    "\n",
-    "tool = Tool(\n",
-    "    name = \"Google Search Snippets\",\n",
-    "    description=\"Search Google for recent results.\",\n",
-    "    func=top5_results\n",
-    ")"
+    "search = GoogleSearchAPIWrapper()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "4d7f92e1",
+   "execution_count": 8,
+   "id": "4d8f734f",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'snippet': 'Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment,\\xa0...',\n",
+       "  'title': 'Apple',\n",
+       "  'link': 'https://www.apple.com/'},\n",
+       " {'snippet': \"Jul 10, 2022 ... Whether or not you're up on your apple trivia, no doubt you know how delicious this popular fruit is, and how nutritious. Apples are rich in\\xa0...\",\n",
+       "  'title': '25 Types of Apples and What to Make With Them - Parade ...',\n",
+       "  'link': 'https://parade.com/1330308/bethlipton/types-of-apples/'},\n",
+       " {'snippet': 'An apple is an edible fruit produced by an apple tree (Malus domestica). Apple trees are cultivated worldwide and are the most widely grown species in the\\xa0...',\n",
+       "  'title': 'Apple - Wikipedia',\n",
+       "  'link': 'https://en.wikipedia.org/wiki/Apple'},\n",
+       " {'snippet': 'Apples are a popular fruit. They contain antioxidants, vitamins, dietary fiber, and a range of other nutrients. Due to their varied nutrient content,\\xa0...',\n",
+       "  'title': 'Apples: Benefits, nutrition, and tips',\n",
+       "  'link': 'https://www.medicalnewstoday.com/articles/267290'},\n",
+       " {'snippet': \"An apple is a crunchy, bright-colored fruit, one of the most popular in the United States. You've probably heard the age-old saying, “An apple a day keeps\\xa0...\",\n",
+       "  'title': 'Apples: Nutrition & Health Benefits',\n",
+       "  'link': 'https://www.webmd.com/food-recipes/benefits-apples'}]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search.results(\"apples\", 5)"
+   ]
   }
  ],
  "metadata": {
@@ -184,7 +197,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/tools/examples/google_serper.ipynb

@@ -12,34 +12,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "import os\n",
-    "import pprint\n",
     "os.environ[\"SERPER_API_KEY\"] = \"\""
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:56:29.336521Z",
-     "start_time": "2023-05-04T00:56:29.334173Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "54bf5afd",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:07.676293Z",
-     "start_time": "2023-05-04T00:54:06.665742Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.utilities import GoogleSerperAPIWrapper"
@@ -49,12 +36,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "31f8f382",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:08.324245Z",
-     "start_time": "2023-05-04T00:54:08.321577Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "search = GoogleSerperAPIWrapper()"
@@ -64,12 +46,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "25ce0225",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:11.399847Z",
-     "start_time": "2023-05-04T00:54:09.335597Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -95,17 +72,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "os.environ['OPENAI_API_KEY'] = \"\""
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:14.311773Z",
-     "start_time": "2023-05-04T00:54:14.304389Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -160,693 +133,6 @@
    "metadata": {
     "collapsed": false
    }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Obtaining results with metadata\n",
-    "If you would also like to obtain the results in a structured way including metadata. For this we will be using the `results` method of the wrapper."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'searchParameters': {'q': 'Apple Inc.',\n",
-      "                      'gl': 'us',\n",
-      "                      'hl': 'en',\n",
-      "                      'num': 10,\n",
-      "                      'type': 'search'},\n",
-      " 'knowledgeGraph': {'title': 'Apple',\n",
-      "                    'type': 'Technology company',\n",
-      "                    'website': 'http://www.apple.com/',\n",
-      "                    'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQwGQRv5TjjkycpctY66mOg_e2-npacrmjAb6_jAWhzlzkFE3OTjxyzbA&s=0',\n",
-      "                    'description': 'Apple Inc. is an American multinational '\n",
-      "                                   'technology company headquartered in '\n",
-      "                                   'Cupertino, California. Apple is the '\n",
-      "                                   \"world's largest technology company by \"\n",
-      "                                   'revenue, with US$394.3 billion in 2022 '\n",
-      "                                   'revenue. As of March 2023, Apple is the '\n",
-      "                                   \"world's biggest...\",\n",
-      "                    'descriptionSource': 'Wikipedia',\n",
-      "                    'descriptionLink': 'https://en.wikipedia.org/wiki/Apple_Inc.',\n",
-      "                    'attributes': {'Customer service': '1 (800) 275-2273',\n",
-      "                                   'CEO': 'Tim Cook (Aug 24, 2011–)',\n",
-      "                                   'Headquarters': 'Cupertino, CA',\n",
-      "                                   'Founded': 'April 1, 1976, Los Altos, CA',\n",
-      "                                   'Founders': 'Steve Jobs, Steve Wozniak, '\n",
-      "                                               'Ronald Wayne, and more',\n",
-      "                                   'Products': 'iPhone, iPad, Apple TV, and '\n",
-      "                                               'more'}},\n",
-      " 'organic': [{'title': 'Apple',\n",
-      "              'link': 'https://www.apple.com/',\n",
-      "              'snippet': 'Discover the innovative world of Apple and shop '\n",
-      "                         'everything iPhone, iPad, Apple Watch, Mac, and Apple '\n",
-      "                         'TV, plus explore accessories, entertainment, ...',\n",
-      "              'sitelinks': [{'title': 'Support',\n",
-      "                             'link': 'https://support.apple.com/'},\n",
-      "                            {'title': 'iPhone',\n",
-      "                             'link': 'https://www.apple.com/iphone/'},\n",
-      "                            {'title': 'Site Map',\n",
-      "                             'link': 'https://www.apple.com/sitemap/'},\n",
-      "                            {'title': 'Business',\n",
-      "                             'link': 'https://www.apple.com/business/'},\n",
-      "                            {'title': 'Mac',\n",
-      "                             'link': 'https://www.apple.com/mac/'},\n",
-      "                            {'title': 'Watch',\n",
-      "                             'link': 'https://www.apple.com/watch/'}],\n",
-      "              'position': 1},\n",
-      "             {'title': 'Apple Inc. - Wikipedia',\n",
-      "              'link': 'https://en.wikipedia.org/wiki/Apple_Inc.',\n",
-      "              'snippet': 'Apple Inc. is an American multinational technology '\n",
-      "                         'company headquartered in Cupertino, California. '\n",
-      "                         \"Apple is the world's largest technology company by \"\n",
-      "                         'revenue, ...',\n",
-      "              'attributes': {'Products': 'AirPods; Apple Watch; iPad; iPhone; '\n",
-      "                                         'Mac; Full list',\n",
-      "                             'Founders': 'Steve Jobs; Steve Wozniak; Ronald '\n",
-      "                                         'Wayne; Mike Markkula'},\n",
-      "              'sitelinks': [{'title': 'History',\n",
-      "                             'link': 'https://en.wikipedia.org/wiki/History_of_Apple_Inc.'},\n",
-      "                            {'title': 'Timeline of Apple Inc. products',\n",
-      "                             'link': 'https://en.wikipedia.org/wiki/Timeline_of_Apple_Inc._products'},\n",
-      "                            {'title': 'Litigation involving Apple Inc.',\n",
-      "                             'link': 'https://en.wikipedia.org/wiki/Litigation_involving_Apple_Inc.'},\n",
-      "                            {'title': 'Apple Store',\n",
-      "                             'link': 'https://en.wikipedia.org/wiki/Apple_Store'}],\n",
-      "              'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRvmB5fT1LjqpZx02UM7IJq0Buoqt0DZs_y0dqwxwSWyP4PIN9FaxuTea0&s',\n",
-      "              'position': 2},\n",
-      "             {'title': 'Apple Inc. | History, Products, Headquarters, & Facts '\n",
-      "                       '| Britannica',\n",
-      "              'link': 'https://www.britannica.com/topic/Apple-Inc',\n",
-      "              'snippet': 'Apple Inc., formerly Apple Computer, Inc., American '\n",
-      "                         'manufacturer of personal computers, smartphones, '\n",
-      "                         'tablet computers, computer peripherals, and computer '\n",
-      "                         '...',\n",
-      "              'attributes': {'Related People': 'Steve Jobs Steve Wozniak Jony '\n",
-      "                                               'Ive Tim Cook Angela Ahrendts',\n",
-      "                             'Date': '1976 - present'},\n",
-      "              'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS3liELlhrMz3Wpsox29U8jJ3L8qETR0hBWHXbFnwjwQc34zwZvFELst2E&s',\n",
-      "              'position': 3},\n",
-      "             {'title': 'AAPL: Apple Inc Stock Price Quote - NASDAQ GS - '\n",
-      "                       'Bloomberg.com',\n",
-      "              'link': 'https://www.bloomberg.com/quote/AAPL:US',\n",
-      "              'snippet': 'AAPL:USNASDAQ GS. Apple Inc. COMPANY INFO ; Open. '\n",
-      "                         '170.09 ; Prev Close. 169.59 ; Volume. 48,425,696 ; '\n",
-      "                         'Market Cap. 2.667T ; Day Range. 167.54170.35.',\n",
-      "              'position': 4},\n",
-      "             {'title': 'Apple Inc. (AAPL) Company Profile & Facts - Yahoo '\n",
-      "                       'Finance',\n",
-      "              'link': 'https://finance.yahoo.com/quote/AAPL/profile/',\n",
-      "              'snippet': 'Apple Inc. designs, manufactures, and markets '\n",
-      "                         'smartphones, personal computers, tablets, wearables, '\n",
-      "                         'and accessories worldwide. The company offers '\n",
-      "                         'iPhone, a line ...',\n",
-      "              'position': 5},\n",
-      "             {'title': 'Apple Inc. (AAPL) Stock Price, News, Quote & History - '\n",
-      "                       'Yahoo Finance',\n",
-      "              'link': 'https://finance.yahoo.com/quote/AAPL',\n",
-      "              'snippet': 'Find the latest Apple Inc. (AAPL) stock quote, '\n",
-      "                         'history, news and other vital information to help '\n",
-      "                         'you with your stock trading and investing.',\n",
-      "              'position': 6}],\n",
-      " 'peopleAlsoAsk': [{'question': 'What does Apple Inc do?',\n",
-      "                    'snippet': 'Apple Inc. (Apple) designs, manufactures and '\n",
-      "                               'markets smartphones, personal\\n'\n",
-      "                               'computers, tablets, wearables and accessories '\n",
-      "                               'and sells a range of related\\n'\n",
-      "                               'services.',\n",
-      "                    'title': 'AAPL.O - | Stock Price & Latest News - Reuters',\n",
-      "                    'link': 'https://www.reuters.com/markets/companies/AAPL.O/'},\n",
-      "                   {'question': 'What is the full form of Apple Inc?',\n",
-      "                    'snippet': '(formerly Apple Computer Inc.) is an American '\n",
-      "                               'computer and consumer electronics\\n'\n",
-      "                               'company famous for creating the iPhone, iPad '\n",
-      "                               'and Macintosh computers.',\n",
-      "                    'title': 'What is Apple? An products and history overview '\n",
-      "                             '- TechTarget',\n",
-      "                    'link': 'https://www.techtarget.com/whatis/definition/Apple'},\n",
-      "                   {'question': 'What is Apple Inc iPhone?',\n",
-      "                    'snippet': 'Apple Inc (Apple) designs, manufactures, and '\n",
-      "                               'markets smartphones, tablets,\\n'\n",
-      "                               'personal computers, and wearable devices. The '\n",
-      "                               'company also offers software\\n'\n",
-      "                               'applications and related services, '\n",
-      "                               'accessories, and third-party digital content.\\n'\n",
-      "                               \"Apple's product portfolio includes iPhone, \"\n",
-      "                               'iPad, Mac, iPod, Apple Watch, and\\n'\n",
-      "                               'Apple TV.',\n",
-      "                    'title': 'Apple Inc Company Profile - Apple Inc Overview - '\n",
-      "                             'GlobalData',\n",
-      "                    'link': 'https://www.globaldata.com/company-profile/apple-inc/'},\n",
-      "                   {'question': 'Who runs Apple Inc?',\n",
-      "                    'snippet': 'Timothy Donald Cook (born November 1, 1960) is '\n",
-      "                               'an American business executive\\n'\n",
-      "                               'who has been the chief executive officer of '\n",
-      "                               'Apple Inc. since 2011. Cook\\n'\n",
-      "                               \"previously served as the company's chief \"\n",
-      "                               'operating officer under its co-founder\\n'\n",
-      "                               'Steve Jobs. He is the first CEO of any Fortune '\n",
-      "                               '500 company who is openly gay.',\n",
-      "                    'title': 'Tim Cook - Wikipedia',\n",
-      "                    'link': 'https://en.wikipedia.org/wiki/Tim_Cook'}],\n",
-      " 'relatedSearches': [{'query': 'Who invented the iPhone'},\n",
-      "                     {'query': 'Apple iPhone'},\n",
-      "                     {'query': 'History of Apple company PDF'},\n",
-      "                     {'query': 'Apple company history'},\n",
-      "                     {'query': 'Apple company introduction'},\n",
-      "                     {'query': 'Apple India'},\n",
-      "                     {'query': 'What does Apple Inc own'},\n",
-      "                     {'query': 'Apple Inc After Steve'},\n",
-      "                     {'query': 'Apple Watch'},\n",
-      "                     {'query': 'Apple App Store'}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "search = GoogleSerperAPIWrapper()\n",
-    "results = search.results(\"Apple Inc.\")\n",
-    "pprint.pp(results)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:22.863413Z",
-     "start_time": "2023-05-04T00:54:20.827395Z"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Searching for Google Images\n",
-    "We can also query Google Images using this wrapper. For example:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'searchParameters': {'q': 'Lion',\n",
-      "                      'gl': 'us',\n",
-      "                      'hl': 'en',\n",
-      "                      'num': 10,\n",
-      "                      'type': 'images'},\n",
-      " 'images': [{'title': 'Lion - Wikipedia',\n",
-      "             'imageUrl': 'https://upload.wikimedia.org/wikipedia/commons/thumb/7/73/Lion_waiting_in_Namibia.jpg/1200px-Lion_waiting_in_Namibia.jpg',\n",
-      "             'imageWidth': 1200,\n",
-      "             'imageHeight': 900,\n",
-      "             'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRye79ROKwjfb6017jr0iu8Bz2E1KKuHg-A4qINJaspyxkZrkw&s',\n",
-      "             'thumbnailWidth': 259,\n",
-      "             'thumbnailHeight': 194,\n",
-      "             'source': 'Wikipedia',\n",
-      "             'domain': 'en.wikipedia.org',\n",
-      "             'link': 'https://en.wikipedia.org/wiki/Lion',\n",
-      "             'position': 1},\n",
-      "            {'title': 'Lion | Characteristics, Habitat, & Facts | Britannica',\n",
-      "             'imageUrl': 'https://cdn.britannica.com/55/2155-050-604F5A4A/lion.jpg',\n",
-      "             'imageWidth': 754,\n",
-      "             'imageHeight': 752,\n",
-      "             'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS3fnDub1GSojI0hJ-ZGS8Tv-hkNNloXh98DOwXZoZ_nUs3GWSd&s',\n",
-      "             'thumbnailWidth': 225,\n",
-      "             'thumbnailHeight': 224,\n",
-      "             'source': 'Encyclopedia Britannica',\n",
-      "             'domain': 'www.britannica.com',\n",
-      "             'link': 'https://www.britannica.com/animal/lion',\n",
-      "             'position': 2},\n",
-      "            {'title': 'African lion, facts and photos',\n",
-      "             'imageUrl': 'https://i.natgeofe.com/n/487a0d69-8202-406f-a6a0-939ed3704693/african-lion.JPG',\n",
-      "             'imageWidth': 3072,\n",
-      "             'imageHeight': 2043,\n",
-      "             'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTPlTarrtDbyTiEm-VI_PML9VtOTVPuDXJ5ybDf_lN11H2mShk&s',\n",
-      "             'thumbnailWidth': 275,\n",
-      "             'thumbnailHeight': 183,\n",
-      "             'source': 'National Geographic',\n",
-      "             'domain': 'www.nationalgeographic.com',\n",
-      "             'link': 'https://www.nationalgeographic.com/animals/mammals/facts/african-lion',\n",
-      "             'position': 3},\n",
-      "            {'title': 'Saint Louis Zoo | African Lion',\n",
-      "             'imageUrl': 'https://optimise2.assets-servd.host/maniacal-finch/production/animals/african-lion-01-01.jpg?w=1200&auto=compress%2Cformat&fit=crop&dm=1658933674&s=4b63f926a0f524f2087a8e0613282bdb',\n",
-      "             'imageWidth': 1200,\n",
-      "             'imageHeight': 1200,\n",
-      "             'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTlewcJ5SwC7yKup6ByaOjTnAFDeoOiMxyJTQaph2W_I3dnks4&s',\n",
-      "             'thumbnailWidth': 225,\n",
-      "             'thumbnailHeight': 225,\n",
-      "             'source': 'St. Louis Zoo',\n",
-      "             'domain': 'stlzoo.org',\n",
-      "             'link': 'https://stlzoo.org/animals/mammals/carnivores/lion',\n",
-      "             'position': 4},\n",
-      "            {'title': 'How to Draw a Realistic Lion like an Artist - Studio '\n",
-      "                      'Wildlife',\n",
-      "             'imageUrl': 'https://studiowildlife.com/wp-content/uploads/2021/10/245528858_183911853822648_6669060845725210519_n.jpg',\n",
-      "             'imageWidth': 1431,\n",
-      "             'imageHeight': 2048,\n",
-      "             'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTmn5HayVj3wqoBDQacnUtzaDPZzYHSLKUlIEcni6VB8w0mVeA&s',\n",
-      "             'thumbnailWidth': 188,\n",
-      "             'thumbnailHeight': 269,\n",
-      "             'source': 'Studio Wildlife',\n",
-      "             'domain': 'studiowildlife.com',\n",
-      "             'link': 'https://studiowildlife.com/how-to-draw-a-realistic-lion-like-an-artist/',\n",
-      "             'position': 5},\n",
-      "            {'title': 'Lion | Characteristics, Habitat, & Facts | Britannica',\n",
-      "             'imageUrl': 'https://cdn.britannica.com/29/150929-050-547070A1/lion-Kenya-Masai-Mara-National-Reserve.jpg',\n",
-      "             'imageWidth': 1600,\n",
-      "             'imageHeight': 1085,\n",
-      "             'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSCqaKY_THr0IBZN8c-2VApnnbuvKmnsWjfrwKoWHFR9w3eN5o&s',\n",
-      "             'thumbnailWidth': 273,\n",
-      "             'thumbnailHeight': 185,\n",
-      "             'source': 'Encyclopedia Britannica',\n",
-      "             'domain': 'www.britannica.com',\n",
-      "             'link': 'https://www.britannica.com/animal/lion',\n",
-      "             'position': 6},\n",
-      "            {'title': \"Where do lions live? Facts about lions' habitats and \"\n",
-      "                      'other cool facts',\n",
-      "             'imageUrl': 'https://www.gannett-cdn.com/-mm-/b2b05a4ab25f4fca0316459e1c7404c537a89702/c=0-0-1365-768/local/-/media/2022/03/16/USATODAY/usatsports/imageForEntry5-ODq.jpg?width=1365&height=768&fit=crop&format=pjpg&auto=webp',\n",
-      "             'imageWidth': 1365,\n",
-      "             'imageHeight': 768,\n",
-      "             'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTc_4vCHscgvFvYy3PSrtIOE81kNLAfhDK8F3mfOuotL0kUkbs&s',\n",
-      "             'thumbnailWidth': 299,\n",
-      "             'thumbnailHeight': 168,\n",
-      "             'source': 'USA Today',\n",
-      "             'domain': 'www.usatoday.com',\n",
-      "             'link': 'https://www.usatoday.com/story/news/2023/01/08/where-do-lions-live-habitat/10927718002/',\n",
-      "             'position': 7},\n",
-      "            {'title': 'Lion',\n",
-      "             'imageUrl': 'https://i.natgeofe.com/k/1d33938b-3d02-4773-91e3-70b113c3b8c7/lion-male-roar_square.jpg',\n",
-      "             'imageWidth': 3072,\n",
-      "             'imageHeight': 3072,\n",
-      "             'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQqLfnBrBLcTiyTZynHH3FGbBtX2bd1ScwpcuOLnksTyS9-4GM&s',\n",
-      "             'thumbnailWidth': 225,\n",
-      "             'thumbnailHeight': 225,\n",
-      "             'source': 'National Geographic Kids',\n",
-      "             'domain': 'kids.nationalgeographic.com',\n",
-      "             'link': 'https://kids.nationalgeographic.com/animals/mammals/facts/lion',\n",
-      "             'position': 8},\n",
-      "            {'title': \"Lion | Smithsonian's National Zoo\",\n",
-      "             'imageUrl': 'https://nationalzoo.si.edu/sites/default/files/styles/1400_scale/public/animals/exhibit/africanlion-005.jpg?itok=6wA745g_',\n",
-      "             'imageWidth': 1400,\n",
-      "             'imageHeight': 845,\n",
-      "             'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSgB3z_D4dMEOWJ7lajJk4XaQSL4DdUvIRj4UXZ0YoE5fGuWuo&s',\n",
-      "             'thumbnailWidth': 289,\n",
-      "             'thumbnailHeight': 174,\n",
-      "             'source': \"Smithsonian's National Zoo\",\n",
-      "             'domain': 'nationalzoo.si.edu',\n",
-      "             'link': 'https://nationalzoo.si.edu/animals/lion',\n",
-      "             'position': 9},\n",
-      "            {'title': \"Zoo's New Male Lion Explores Habitat for the First Time \"\n",
-      "                      '- Virginia Zoo',\n",
-      "             'imageUrl': 'https://virginiazoo.org/wp-content/uploads/2022/04/ZOO_0056-scaled.jpg',\n",
-      "             'imageWidth': 2560,\n",
-      "             'imageHeight': 2141,\n",
-      "             'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTDCG7XvXRCwpe_-Vy5mpvrQpVl5q2qwgnDklQhrJpQzObQGz4&s',\n",
-      "             'thumbnailWidth': 246,\n",
-      "             'thumbnailHeight': 205,\n",
-      "             'source': 'Virginia Zoo',\n",
-      "             'domain': 'virginiazoo.org',\n",
-      "             'link': 'https://virginiazoo.org/zoos-new-male-lion-explores-habitat-for-thefirst-time/',\n",
-      "             'position': 10}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "search = GoogleSerperAPIWrapper(type=\"images\")\n",
-    "results = search.results(\"Lion\")\n",
-    "pprint.pp(results)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:27.879867Z",
-     "start_time": "2023-05-04T00:54:26.380022Z"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Searching for Google News\n",
-    "We can also query Google News using this wrapper. For example:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'searchParameters': {'q': 'Tesla Inc.',\n",
-      "                      'gl': 'us',\n",
-      "                      'hl': 'en',\n",
-      "                      'num': 10,\n",
-      "                      'type': 'news'},\n",
-      " 'news': [{'title': 'ISS recommends Tesla investors vote against re-election '\n",
-      "                    'of Robyn Denholm',\n",
-      "           'link': 'https://www.reuters.com/business/autos-transportation/iss-recommends-tesla-investors-vote-against-re-election-robyn-denholm-2023-05-04/',\n",
-      "           'snippet': 'Proxy advisory firm ISS on Wednesday recommended Tesla '\n",
-      "                      'investors vote against re-election of board chair Robyn '\n",
-      "                      'Denholm, citing \"concerns on...',\n",
-      "           'date': '5 mins ago',\n",
-      "           'source': 'Reuters',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcROdETe_GUyp1e8RHNhaRM8Z_vfxCvdfinZwzL1bT1ZGSYaGTeOojIdBoLevA&s',\n",
-      "           'position': 1},\n",
-      "          {'title': 'Global companies by market cap: Tesla fell most in April',\n",
-      "           'link': 'https://www.reuters.com/markets/global-companies-by-market-cap-tesla-fell-most-april-2023-05-02/',\n",
-      "           'snippet': 'Tesla Inc was the biggest loser among top companies by '\n",
-      "                      'market capitalisation in April, hit by disappointing '\n",
-      "                      'quarterly earnings after it...',\n",
-      "           'date': '1 day ago',\n",
-      "           'source': 'Reuters',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ4u4CP8aOdGyRFH6o4PkXi-_eZDeY96vLSag5gDjhKMYf98YBER2cZPbkStQ&s',\n",
-      "           'position': 2},\n",
-      "          {'title': 'Tesla Wanted an EV Price War. Ford Showed Up.',\n",
-      "           'link': 'https://www.bloomberg.com/opinion/articles/2023-05-03/tesla-wanted-an-ev-price-war-ford-showed-up',\n",
-      "           'snippet': 'The legacy automaker is paring back the cost of its '\n",
-      "                      'Mustang Mach-E model after Tesla discounted its '\n",
-      "                      'competing EVs, portending tighter...',\n",
-      "           'date': '6 hours ago',\n",
-      "           'source': 'Bloomberg.com',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_3Eo4VI0H-nTeIbYc5DaQn5ep7YrWnmhx6pv8XddFgNF5zRC9gEpHfDq8yQ&s',\n",
-      "           'position': 3},\n",
-      "          {'title': 'Joby Aviation to get investment from Tesla shareholder '\n",
-      "                    'Baillie Gifford',\n",
-      "           'link': 'https://finance.yahoo.com/news/joby-aviation-investment-tesla-shareholder-204450712.html',\n",
-      "           'snippet': 'This comes days after Joby clinched a $55 million '\n",
-      "                      'contract extension to deliver up to nine air taxis to '\n",
-      "                      'the U.S. Air Force,...',\n",
-      "           'date': '4 hours ago',\n",
-      "           'source': 'Yahoo Finance',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQO0uVn297LI-xryrPNqJ-apUOulj4ohM-xkN4OfmvMOYh1CPdUEBbYx6hviw&s',\n",
-      "           'position': 4},\n",
-      "          {'title': 'Tesla resumes U.S. orders for a Model 3 version at lower '\n",
-      "                    'price, range',\n",
-      "           'link': 'https://finance.yahoo.com/news/tesla-resumes-us-orders-model-045736115.html',\n",
-      "           'snippet': '(Reuters) -Tesla Inc has resumed taking orders for its '\n",
-      "                      'Model 3 long-range vehicle in the United States, the '\n",
-      "                      \"company's website showed late on...\",\n",
-      "           'date': '19 hours ago',\n",
-      "           'source': 'Yahoo Finance',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTIZetJ62sQefPfbQ9KKDt6iH7Mc0ylT5t_hpgeeuUkHhJuAx2FOJ4ZTRVDFg&s',\n",
-      "           'position': 5},\n",
-      "          {'title': 'The Tesla Model 3 Long Range AWD Is Now Available in the '\n",
-      "                    'U.S. With 325 Miles of Range',\n",
-      "           'link': 'https://www.notateslaapp.com/news/1393/tesla-reopens-orders-for-model-3-long-range-after-months-of-unavailability',\n",
-      "           'snippet': 'Tesla has reopened orders for the Model 3 Long Range '\n",
-      "                      'RWD, which has been unavailable for months due to high '\n",
-      "                      'demand.',\n",
-      "           'date': '7 hours ago',\n",
-      "           'source': 'Not a Tesla App',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSecrgxZpRj18xIJY-nDHljyP-A4ejEkswa9eq77qhMNrScnVIqe34uql5U4w&s',\n",
-      "           'position': 6},\n",
-      "          {'title': 'Tesla Cybertruck alpha prototype spotted at the Fremont '\n",
-      "                    'factory in new pics and videos',\n",
-      "           'link': 'https://www.teslaoracle.com/2023/05/03/tesla-cybertruck-alpha-prototype-interior-and-exterior-spotted-at-the-fremont-factory-in-new-pics-and-videos/',\n",
-      "           'snippet': 'A Tesla Cybertruck alpha prototype goes to Fremont, '\n",
-      "                      'California for another round of testing before going to '\n",
-      "                      'production later this year (pics...',\n",
-      "           'date': '14 hours ago',\n",
-      "           'source': 'Tesla Oracle',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRO7M5ZLQE-Zo4-_5dv9hNAQZ3wSqfvYCuKqzxHG-M6CgLpwPMMG_ssebdcMg&s',\n",
-      "           'position': 7},\n",
-      "          {'title': 'Tesla putting facility in new part of country - Austin '\n",
-      "                    'Business Journal',\n",
-      "           'link': 'https://www.bizjournals.com/austin/news/2023/05/02/tesla-leases-building-seattle-area.html',\n",
-      "           'snippet': 'Check out what Puget Sound Business Journal has to '\n",
-      "                      \"report about the Austin-based company's real estate \"\n",
-      "                      'footprint in the Pacific Northwest.',\n",
-      "           'date': '22 hours ago',\n",
-      "           'source': 'The Business Journals',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR9kIEHWz1FcHKDUtGQBS0AjmkqtyuBkQvD8kyIY3kpaPrgYaN7I_H2zoOJsA&s',\n",
-      "           'position': 8},\n",
-      "          {'title': 'Tesla (TSLA) Resumes Orders for Model 3 Long Range After '\n",
-      "                    'Backlog',\n",
-      "           'link': 'https://www.bloomberg.com/news/articles/2023-05-03/tesla-resumes-orders-for-popular-model-3-long-range-at-47-240',\n",
-      "           'snippet': 'Tesla Inc. has resumed taking orders for its Model 3 '\n",
-      "                      'Long Range edition with a starting price of $47240, '\n",
-      "                      'according to its website.',\n",
-      "           'date': '5 hours ago',\n",
-      "           'source': 'Bloomberg.com',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTWWIC4VpMTfRvSyqiomODOoLg0xhoBf-Tc1qweKnSuaiTk-Y1wMJZM3jct0w&s',\n",
-      "           'position': 9}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "search = GoogleSerperAPIWrapper(type=\"news\")\n",
-    "results = search.results(\"Tesla Inc.\")\n",
-    "pprint.pp(results)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:34.984087Z",
-     "start_time": "2023-05-04T00:54:33.369231Z"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "If you want to only receive news articles published in the last hour, you can do the following:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'searchParameters': {'q': 'Tesla Inc.',\n",
-      "                      'gl': 'us',\n",
-      "                      'hl': 'en',\n",
-      "                      'num': 10,\n",
-      "                      'type': 'news',\n",
-      "                      'tbs': 'qdr:h'},\n",
-      " 'news': [{'title': 'Oklahoma Gov. Stitt sees growing foreign interest in '\n",
-      "                    'investments in ...',\n",
-      "           'link': 'https://www.reuters.com/world/us/oklahoma-gov-stitt-sees-growing-foreign-interest-investments-state-2023-05-04/',\n",
-      "           'snippet': 'T)), a battery supplier to electric vehicle maker Tesla '\n",
-      "                      'Inc (TSLA.O), said on Sunday it is considering building '\n",
-      "                      'a battery plant in Oklahoma, its third in...',\n",
-      "           'date': '53 mins ago',\n",
-      "           'source': 'Reuters',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSSTcsXeenqmEKdiekvUgAmqIPR4nlAmgjTkBqLpza-lLfjX1CwB84MoNVj0Q&s',\n",
-      "           'position': 1},\n",
-      "          {'title': 'Ryder lanza solución llave en mano para vehículos '\n",
-      "                    'eléctricos en EU',\n",
-      "           'link': 'https://www.tyt.com.mx/nota/ryder-lanza-solucion-llave-en-mano-para-vehiculos-electricos-en-eu',\n",
-      "           'snippet': 'Ryder System Inc. presentó RyderElectric+ TM como su '\n",
-      "                      'nueva solución llave en mano ... Ryder también tiene '\n",
-      "                      'reservados los semirremolques Tesla y continúa...',\n",
-      "           'date': '56 mins ago',\n",
-      "           'source': 'Revista Transportes y Turismo',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQJhXTQQtjSUZf9YPM235WQhFU5_d7lEA76zB8DGwZfixcgf1_dhPJyKA1Nbw&s',\n",
-      "           'position': 2},\n",
-      "          {'title': '\"I think people can get by with $999 million,\" Bernie '\n",
-      "                    'Sanders tells American Billionaires.',\n",
-      "           'link': 'https://thebharatexpressnews.com/i-think-people-can-get-by-with-999-million-bernie-sanders-tells-american-billionaires-heres-how-the-ultra-rich-can-pay-less-income-tax-than-you-legally/',\n",
-      "           'snippet': 'The report noted that in 2007 and 2011, Amazon.com Inc. '\n",
-      "                      'founder Jeff Bezos “did not pay a dime in federal ... '\n",
-      "                      'If you want to bet on Musk, check out Tesla.',\n",
-      "           'date': '11 mins ago',\n",
-      "           'source': 'THE BHARAT EXPRESS NEWS',\n",
-      "           'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR_X9qqSwVFBBdos2CK5ky5IWIE3aJPCQeRYR9O1Jz4t-MjaEYBuwK7AU3AJQ&s',\n",
-      "           'position': 3}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "search = GoogleSerperAPIWrapper(type=\"news\", tbs=\"qdr:h\")\n",
-    "results = search.results(\"Tesla Inc.\")\n",
-    "pprint.pp(results)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:41.786864Z",
-     "start_time": "2023-05-04T00:54:40.691905Z"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Some examples of the `tbs` parameter:\n",
-    "\n",
-    "`qdr:h` (past hour)\n",
-    "`qdr:d` (past day)\n",
-    "`qdr:w` (past week)\n",
-    "`qdr:m` (past month)\n",
-    "`qdr:y` (past year)\n",
-    "\n",
-    "You can specify intermediate time periods by adding a number:\n",
-    "`qdr:h12` (past 12 hours)\n",
-    "`qdr:d3` (past 3 days)\n",
-    "`qdr:w2` (past 2 weeks)\n",
-    "`qdr:m6` (past 6 months)\n",
-    "`qdr:m2` (past 2 years)\n",
-    "\n",
-    "For all supported filters simply go to [Google Search](https://google.com), search for something, click on \"Tools\", add your date filter and check the URL for \"tbs=\".\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Searching for Google Places\n",
-    "We can also query Google Places using this wrapper. For example:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'searchParameters': {'q': 'Italian restaurants in Upper East Side',\n",
-      "                      'gl': 'us',\n",
-      "                      'hl': 'en',\n",
-      "                      'num': 10,\n",
-      "                      'type': 'places'},\n",
-      " 'places': [{'position': 1,\n",
-      "             'title': \"L'Osteria\",\n",
-      "             'address': '1219 Lexington Ave',\n",
-      "             'latitude': 40.777154599999996,\n",
-      "             'longitude': -73.9571363,\n",
-      "             'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNjU7BWEq_aYQANBCbX52Kb0lDpd_lFIx5onw40=w92-h92-n-k-no',\n",
-      "             'rating': 4.7,\n",
-      "             'ratingCount': 91,\n",
-      "             'category': 'Italian'},\n",
-      "            {'position': 2,\n",
-      "             'title': \"Tony's Di Napoli\",\n",
-      "             'address': '1081 3rd Ave',\n",
-      "             'latitude': 40.7643567,\n",
-      "             'longitude': -73.9642373,\n",
-      "             'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNbNv6jZkJ9nyVi60__8c1DQbe_eEbugRAhIYye=w92-h92-n-k-no',\n",
-      "             'rating': 4.5,\n",
-      "             'ratingCount': 2265,\n",
-      "             'category': 'Italian'},\n",
-      "            {'position': 3,\n",
-      "             'title': 'Caravaggio',\n",
-      "             'address': '23 E 74th St',\n",
-      "             'latitude': 40.773412799999996,\n",
-      "             'longitude': -73.96473379999999,\n",
-      "             'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPDGchokDvppoLfmVEo6X_bWd3Fz0HyxIHTEe9V=w92-h92-n-k-no',\n",
-      "             'rating': 4.5,\n",
-      "             'ratingCount': 276,\n",
-      "             'category': 'Italian'},\n",
-      "            {'position': 4,\n",
-      "             'title': 'Luna Rossa',\n",
-      "             'address': '347 E 85th St',\n",
-      "             'latitude': 40.776593999999996,\n",
-      "             'longitude': -73.950351,\n",
-      "             'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNPCpCPuqPAb1Mv6_fOP7cjb8Wu1rbqbk2sMBlh=w92-h92-n-k-no',\n",
-      "             'rating': 4.5,\n",
-      "             'ratingCount': 140,\n",
-      "             'category': 'Italian'},\n",
-      "            {'position': 5,\n",
-      "             'title': \"Paola's\",\n",
-      "             'address': '1361 Lexington Ave',\n",
-      "             'latitude': 40.7822019,\n",
-      "             'longitude': -73.9534096,\n",
-      "             'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPJr2Vcx-B6K-GNQa4koOTffggTePz8TKRTnWi3=w92-h92-n-k-no',\n",
-      "             'rating': 4.5,\n",
-      "             'ratingCount': 344,\n",
-      "             'category': 'Italian'},\n",
-      "            {'position': 6,\n",
-      "             'title': 'Come Prima',\n",
-      "             'address': '903 Madison Ave',\n",
-      "             'latitude': 40.772124999999996,\n",
-      "             'longitude': -73.965012,\n",
-      "             'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNrX19G0NVdtDyMovCQ-M-m0c_gLmIxrWDQAAbz=w92-h92-n-k-no',\n",
-      "             'rating': 4.5,\n",
-      "             'ratingCount': 176,\n",
-      "             'category': 'Italian'},\n",
-      "            {'position': 7,\n",
-      "             'title': 'Botte UES',\n",
-      "             'address': '1606 1st Ave.',\n",
-      "             'latitude': 40.7750785,\n",
-      "             'longitude': -73.9504801,\n",
-      "             'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPPN5GXxfH3NDacBc0Pt3uGAInd9OChS5isz9RF=w92-h92-n-k-no',\n",
-      "             'rating': 4.4,\n",
-      "             'ratingCount': 152,\n",
-      "             'category': 'Italian'},\n",
-      "            {'position': 8,\n",
-      "             'title': 'Piccola Cucina Uptown',\n",
-      "             'address': '106 E 60th St',\n",
-      "             'latitude': 40.7632468,\n",
-      "             'longitude': -73.9689825,\n",
-      "             'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPifIgzOCD5SjgzzqBzGkdZCBp0MQsK5k7M7znn=w92-h92-n-k-no',\n",
-      "             'rating': 4.6,\n",
-      "             'ratingCount': 941,\n",
-      "             'category': 'Italian'},\n",
-      "            {'position': 9,\n",
-      "             'title': 'Pinocchio Restaurant',\n",
-      "             'address': '300 E 92nd St',\n",
-      "             'latitude': 40.781453299999995,\n",
-      "             'longitude': -73.9486788,\n",
-      "             'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNtxlIyEEJHtDtFtTR9nB38S8A2VyMu-mVVz72A=w92-h92-n-k-no',\n",
-      "             'rating': 4.5,\n",
-      "             'ratingCount': 113,\n",
-      "             'category': 'Italian'},\n",
-      "            {'position': 10,\n",
-      "             'title': 'Barbaresco',\n",
-      "             'address': '843 Lexington Ave #1',\n",
-      "             'latitude': 40.7654332,\n",
-      "             'longitude': -73.9656873,\n",
-      "             'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipMb9FbPuXF_r9g5QseOHmReejxSHgSahPMPJ9-8=w92-h92-n-k-no',\n",
-      "             'rating': 4.3,\n",
-      "             'ratingCount': 122,\n",
-      "             'locationHint': 'In The Touraine',\n",
-      "             'category': 'Italian'}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "search = GoogleSerperAPIWrapper(type=\"places\")\n",
-    "results = search.results(\"Italian restaurants in Upper East Side\")\n",
-    "pprint.pp(results)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:56:07.271164Z",
-     "start_time": "2023-05-04T00:56:05.645847Z"
-    }
-   }
   }
  ],
  "metadata": {

docs/modules/agents/tools/examples/huggingface_tools.ipynb

@@ -1,102 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "40a27d3c-4e5c-4b96-b290-4c49d4fd7219",
-   "metadata": {},
-   "source": [
-    "## HuggingFace Tools\n",
-    "\n",
-    "[Huggingface Tools](https://huggingface.co/docs/transformers/v4.29.0/en/custom_tools) supporting text I/O can be\n",
-    "loaded directly using the `load_huggingface_tool` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d1055b75-362c-452a-b40d-c9a359706a3a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Requires transformers>=4.29.0 and huggingface_hub>=0.14.1\n",
-    "!pip install --uprade transformers huggingface_hub > /dev/null"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f964bb45-fba3-4919-b022-70a602ed4354",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "model_download_counter: This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. It takes the name of the category (such as text-classification, depth-estimation, etc), and returns the name of the checkpoint\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.agents import load_huggingface_tool\n",
-    "\n",
-    "tool = load_huggingface_tool(\"lysandre/hf-model-downloads\")\n",
-    "\n",
-    "print(f\"{tool.name}: {tool.description}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "641d9d79-95bb-469d-b40a-50f37375de7f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'facebook/bart-large-mnli'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tool.run(\"text-classification\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "88724222-7c10-4aff-8713-751911dc8b63",
-   "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": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/agents/tools/examples/human_tools.ipynb

@@ -13,11 +13,10 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
+    "import sys\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.agents import load_tools, initialize_agent\n",
@@ -43,142 +42,13 @@
    "metadata": {},
    "source": [
     "In the above code you can see the tool takes input directly from command line.\n",
-    "You can customize `prompt_func` and `input_func` according to your need (as shown below)."
+    "You can customize `prompt_func` and `input_func` according to your need."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI don't know Eric's surname, so I should ask a human for guidance.\n",
-      "Action: Human\n",
-      "Action Input: \"What is Eric's surname?\"\u001b[0m\n",
-      "\n",
-      "What is Eric's surname?\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Zhu\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mZhu\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI now know Eric's surname is Zhu.\n",
-      "Final Answer: Eric's surname is Zhu.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Eric's surname is Zhu.\""
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(\"What's my friend Eric's surname?\")\n",
-    "# Answer with 'Zhu'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 3,
    "metadata": {},
-   "source": [
-    "## Configuring the Input Function\n",
-    "\n",
-    "By default, the `HumanInputRun` tool uses the python `input` function to get input from the user.\n",
-    "You can customize the input_func to be anything you'd like.\n",
-    "For instance, if you want to accept multi-line input, you could do the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def get_input() -> str:\n",
-    "    print(\"Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.\")\n",
-    "    contents = []\n",
-    "    while True:\n",
-    "        try:\n",
-    "            line = input()\n",
-    "        except EOFError:\n",
-    "            break\n",
-    "        if line == \"q\":\n",
-    "            break\n",
-    "        contents.append(line)\n",
-    "    return \"\\n\".join(contents)\n",
-    "\n",
-    "\n",
-    "# You can modify the tool when loading\n",
-    "tools = load_tools(\n",
-    "    [\"human\", \"ddg-search\"], \n",
-    "    llm=math_llm,\n",
-    "    input_func=get_input\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Or you can directly instantiate the tool\n",
-    "from langchain.tools import HumanInputRun\n",
-    "\n",
-    "tool = HumanInputRun(input_func=get_input)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "agent_chain = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
    "outputs": [
     {
      "name": "stdout",
@@ -187,60 +57,29 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI should ask a human for guidance\n",
+      "\u001b[32;1m\u001b[1;3mI don't know Eric Zhu, so I should ask a human for guidance.\n",
       "Action: Human\n",
-      "Action Input: \"Can you help me attribute a quote?\"\u001b[0m\n",
+      "Action Input: \"Do you know when Eric Zhu's birthday is?\"\u001b[0m\n",
       "\n",
-      "Can you help me attribute a quote?\n",
-      "Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " vini\n",
-      " vidi\n",
-      " vici\n",
-      " q\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Do you know when Eric Zhu's birthday is?\n",
+      "last week\n",
       "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mvini\n",
-      "vidi\n",
-      "vici\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI need to provide more context about the quote\n",
+      "Observation: \u001b[36;1m\u001b[1;3mlast week\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mThat's not very helpful. I should ask for more information.\n",
       "Action: Human\n",
-      "Action Input: \"The quote is 'Veni, vidi, vici'\"\u001b[0m\n",
+      "Action Input: \"Do you know the specific date of Eric Zhu's birthday?\"\u001b[0m\n",
       "\n",
-      "The quote is 'Veni, vidi, vici'\n",
-      "Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " oh who said it \n",
-      " q\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Do you know the specific date of Eric Zhu's birthday?\n",
+      "august 1st\n",
       "\n",
-      "Observation: \u001b[36;1m\u001b[1;3moh who said it \u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI can use DuckDuckGo Search to find out who said the quote\n",
-      "Action: DuckDuckGo Search\n",
-      "Action Input: \"Who said 'Veni, vidi, vici'?\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mUpdated on September 06, 2019. \"Veni, vidi, vici\" is a famous phrase said to have been spoken by the Roman Emperor Julius Caesar (100-44 BCE) in a bit of stylish bragging that impressed many of the writers of his day and beyond. The phrase means roughly \"I came, I saw, I conquered\" and it could be pronounced approximately Vehnee, Veedee ... Veni, vidi, vici (Classical Latin: [weːniː wiːdiː wiːkiː], Ecclesiastical Latin: [ˈveni ˈvidi ˈvitʃi]; \"I came; I saw; I conquered\") is a Latin phrase used to refer to a swift, conclusive victory.The phrase is popularly attributed to Julius Caesar who, according to Appian, used the phrase in a letter to the Roman Senate around 47 BC after he had achieved a quick victory in his short ... veni, vidi, vici Latin quotation from Julius Caesar ve· ni, vi· di, vi· ci ˌwā-nē ˌwē-dē ˈwē-kē ˌvā-nē ˌvē-dē ˈvē-chē : I came, I saw, I conquered Articles Related to veni, vidi, vici 'In Vino Veritas' and Other Latin... Dictionary Entries Near veni, vidi, vici Venite veni, vidi, vici Venizélos See More Nearby Entries Cite this Entry Style The simplest explanation for why veni, vidi, vici is a popular saying is that it comes from Julius Caesar, one of history's most famous figures, and has a simple, strong meaning: I'm powerful and fast. But it's not just the meaning that makes the phrase so powerful. Caesar was a gifted writer, and the phrase makes use of Latin grammar to ... One of the best known and most frequently quoted Latin expression, veni, vidi, vici may be found hundreds of times throughout the centuries used as an expression of triumph. The words are said to have been used by Caesar as he was enjoying a triumph.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer\n",
-      "Final Answer: Julius Caesar said the quote \"Veni, vidi, vici\" which means \"I came, I saw, I conquered\".\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3maugust 1st\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mNow that I have the date, I can check if it's a leap year or not.\n",
+      "Action: Calculator\n",
+      "Action Input: \"Is 2021 a leap year?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: False\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI have all the information I need to answer the original question.\n",
+      "Final Answer: Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -248,16 +87,18 @@
     {
      "data": {
       "text/plain": [
-       "'Julius Caesar said the quote \"Veni, vidi, vici\" which means \"I came, I saw, I conquered\".'"
+       "\"Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\""
       ]
      },
-     "execution_count": 12,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_chain.run(\"I need help attributing a quote\")"
+    "\n",
+    "agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
+    "# Answer with \"last week\""
    ]
   },
   {
@@ -284,9 +125,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/agents/tools/examples/metaphor_search.ipynb

@@ -1,246 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Metaphor Search"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook goes over how to use Metaphor search.\n",
-    "\n",
-    "First, you need to set up the proper API keys and environment variables. Request an API key [here](Sign up for early access here).\n",
-    "\n",
-    "Then enter your API key as an environment variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"METAPHOR_API_KEY\"] = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.utilities import MetaphorSearchAPIWrapper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search = MetaphorSearchAPIWrapper()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Call the API\n",
-    "`results` takes in a Metaphor-optimized search query and a number of results (up to 500). It returns a list of results with title, url, author, and creation date."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'results': [{'url': 'https://www.anthropic.com/index/core-views-on-ai-safety', 'title': 'Core Views on AI Safety: When, Why, What, and How', 'dateCreated': '2023-03-08', 'author': None, 'score': 0.1998831331729889}, {'url': 'https://aisafety.wordpress.com/', 'title': 'Extinction Risk from Artificial Intelligence', 'dateCreated': '2013-10-08', 'author': None, 'score': 0.19801370799541473}, {'url': 'https://www.lesswrong.com/posts/WhNxG4r774bK32GcH/the-simple-picture-on-ai-safety', 'title': 'The simple picture on AI safety - LessWrong', 'dateCreated': '2018-05-27', 'author': 'Alex Flint', 'score': 0.19735534489154816}, {'url': 'https://slatestarcodex.com/2015/05/29/no-time-like-the-present-for-ai-safety-work/', 'title': 'No Time Like The Present For AI Safety Work', 'dateCreated': '2015-05-29', 'author': None, 'score': 0.19408763945102692}, {'url': 'https://www.lesswrong.com/posts/5BJvusxdwNXYQ4L9L/so-you-want-to-save-the-world', 'title': 'So You Want to Save the World - LessWrong', 'dateCreated': '2012-01-01', 'author': 'Lukeprog', 'score': 0.18853715062141418}, {'url': 'https://openai.com/blog/planning-for-agi-and-beyond', 'title': 'Planning for AGI and beyond', 'dateCreated': '2023-02-24', 'author': 'Authors', 'score': 0.18665121495723724}, {'url': 'https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html', 'title': 'The Artificial Intelligence Revolution: Part 1 - Wait But Why', 'dateCreated': '2015-01-22', 'author': 'Tim Urban', 'score': 0.18604731559753418}, {'url': 'https://forum.effectivealtruism.org/posts/uGDCaPFaPkuxAowmH/anthropic-core-views-on-ai-safety-when-why-what-and-how', 'title': 'Anthropic: Core Views on AI Safety: When, Why, What, and How - EA Forum', 'dateCreated': '2023-03-09', 'author': 'Jonmenaster', 'score': 0.18415069580078125}, {'url': 'https://www.lesswrong.com/posts/xBrpph9knzWdtMWeQ/the-proof-of-doom', 'title': 'The Proof of Doom - LessWrong', 'dateCreated': '2022-03-09', 'author': 'Johnlawrenceaspden', 'score': 0.18159329891204834}, {'url': 'https://intelligence.org/why-ai-safety/', 'title': 'Why AI Safety? - Machine Intelligence Research Institute', 'dateCreated': '2017-03-01', 'author': None, 'score': 0.1814115345478058}]}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'title': 'Core Views on AI Safety: When, Why, What, and How',\n",
-       "  'url': 'https://www.anthropic.com/index/core-views-on-ai-safety',\n",
-       "  'author': None,\n",
-       "  'date_created': '2023-03-08'},\n",
-       " {'title': 'Extinction Risk from Artificial Intelligence',\n",
-       "  'url': 'https://aisafety.wordpress.com/',\n",
-       "  'author': None,\n",
-       "  'date_created': '2013-10-08'},\n",
-       " {'title': 'The simple picture on AI safety - LessWrong',\n",
-       "  'url': 'https://www.lesswrong.com/posts/WhNxG4r774bK32GcH/the-simple-picture-on-ai-safety',\n",
-       "  'author': 'Alex Flint',\n",
-       "  'date_created': '2018-05-27'},\n",
-       " {'title': 'No Time Like The Present For AI Safety Work',\n",
-       "  'url': 'https://slatestarcodex.com/2015/05/29/no-time-like-the-present-for-ai-safety-work/',\n",
-       "  'author': None,\n",
-       "  'date_created': '2015-05-29'},\n",
-       " {'title': 'So You Want to Save the World - LessWrong',\n",
-       "  'url': 'https://www.lesswrong.com/posts/5BJvusxdwNXYQ4L9L/so-you-want-to-save-the-world',\n",
-       "  'author': 'Lukeprog',\n",
-       "  'date_created': '2012-01-01'},\n",
-       " {'title': 'Planning for AGI and beyond',\n",
-       "  'url': 'https://openai.com/blog/planning-for-agi-and-beyond',\n",
-       "  'author': 'Authors',\n",
-       "  'date_created': '2023-02-24'},\n",
-       " {'title': 'The Artificial Intelligence Revolution: Part 1 - Wait But Why',\n",
-       "  'url': 'https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html',\n",
-       "  'author': 'Tim Urban',\n",
-       "  'date_created': '2015-01-22'},\n",
-       " {'title': 'Anthropic: Core Views on AI Safety: When, Why, What, and How - EA Forum',\n",
-       "  'url': 'https://forum.effectivealtruism.org/posts/uGDCaPFaPkuxAowmH/anthropic-core-views-on-ai-safety-when-why-what-and-how',\n",
-       "  'author': 'Jonmenaster',\n",
-       "  'date_created': '2023-03-09'},\n",
-       " {'title': 'The Proof of Doom - LessWrong',\n",
-       "  'url': 'https://www.lesswrong.com/posts/xBrpph9knzWdtMWeQ/the-proof-of-doom',\n",
-       "  'author': 'Johnlawrenceaspden',\n",
-       "  'date_created': '2022-03-09'},\n",
-       " {'title': 'Why AI Safety? - Machine Intelligence Research Institute',\n",
-       "  'url': 'https://intelligence.org/why-ai-safety/',\n",
-       "  'author': None,\n",
-       "  'date_created': '2017-03-01'}]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "search.results(\"The best blog post about AI safety is definitely this: \", 10)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Use Metaphor as a tool\n",
-    "Metaphor can be used as a tool that gets URLs that other tools such as browsing tools."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents.agent_toolkits import PlayWrightBrowserToolkit\n",
-    "from langchain.tools.playwright.utils import (\n",
-    "    create_async_playwright_browser,# A synchronous browser is available, though it isn't compatible with jupyter.\n",
-    ")\n",
-    "\n",
-    "async_browser = create_async_playwright_browser()\n",
-    "toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)\n",
-    "tools = toolkit.get_tools()\n",
-    "\n",
-    "tools_by_name = {tool.name: tool for tool in tools}\n",
-    "print(tools_by_name.keys())\n",
-    "navigate_tool = tools_by_name[\"navigate_browser\"]\n",
-    "extract_text = tools_by_name[\"extract_text\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I need to find a tweet about AI safety using Metaphor Search.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Metaphor Search Results JSON\",\n",
-      "  \"action_input\": {\n",
-      "    \"query\": \"interesting tweet AI safety\",\n",
-      "    \"num_results\": 1\n",
-      "  }\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m{'results': [{'url': 'https://safe.ai/', 'title': 'Center for AI Safety', 'dateCreated': '2022-01-01', 'author': None, 'score': 0.18083244562149048}]}\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3m[{'title': 'Center for AI Safety', 'url': 'https://safe.ai/', 'author': None, 'date_created': '2022-01-01'}]\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI need to navigate to the URL provided in the search results to find the tweet.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'I need to navigate to the URL provided in the search results to find the tweet.'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.agents import initialize_agent, AgentType\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.tools import MetaphorSearchResults\n",
-    "\n",
-    "llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0.7)\n",
-    "\n",
-    "metaphor_tool = MetaphorSearchResults(api_wrapper=search)\n",
-    "\n",
-    "agent_chain = initialize_agent([metaphor_tool, extract_text, navigate_tool], llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n",
-    "\n",
-    "agent_chain.run(\"find me an interesting tweet about AI safety using Metaphor, then tell me the first sentence in the post. Do not finish until able to retrieve the first sentence.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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": "3.11.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/modules/agents/tools/examples/python.ipynb

@@ -19,7 +19,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import Tool\n",
     "from langchain.utilities import PythonREPL"
    ]
   },
@@ -60,14 +59,7 @@
    "id": "54fc1f03",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# You can create the tool to pass to an agent\n",
-    "repl_tool = Tool(\n",
-    "    name=\"python_repl\",\n",
-    "    description=\"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n",
-    "    func=python_repl.run\n",
-    ")"
-   ]
+   "source": []
   }
  ],
  "metadata": {

docs/modules/agents/tools/examples/sceneXplain.ipynb

@@ -1,139 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SceneXplain\n",
-    "\n",
-    "\n",
-    "[SceneXplain](https://scenex.jina.ai/) is an ImageCaptioning service accessible through the SceneXplain Tool.\n",
-    "\n",
-    "To use this tool, you'll need to make an account and fetch your API Token [from the website](https://scenex.jina.ai/api). Then you can instantiate the tool."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"SCENEX_API_KEY\"] = \"<YOUR_API_KEY>\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import load_tools\n",
-    "\n",
-    "tools = load_tools([\"sceneXplain\"])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or directly instantiate the tool."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.tools import SceneXplainTool\n",
-    "\n",
-    "\n",
-    "tool = SceneXplainTool()\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Usage in an Agent\n",
-    "\n",
-    "The tool can be used in any LangChain agent as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Thought: Do I need to use a tool? Yes\n",
-      "Action: Image Explainer\n",
-      "Action Input: https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mIn a charmingly whimsical scene, a young girl is seen braving the rain alongside her furry companion, the lovable Totoro. The two are depicted standing on a bustling street corner, where they are sheltered from the rain by a bright yellow umbrella. The girl, dressed in a cheerful yellow frock, holds onto the umbrella with both hands while gazing up at Totoro with an expression of wonder and delight.\n",
-      "\n",
-      "Totoro, meanwhile, stands tall and proud beside his young friend, holding his own umbrella aloft to protect them both from the downpour. His furry body is rendered in rich shades of grey and white, while his large ears and wide eyes lend him an endearing charm.\n",
-      "\n",
-      "In the background of the scene, a street sign can be seen jutting out from the pavement amidst a flurry of raindrops. A sign with Chinese characters adorns its surface, adding to the sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy and camaraderie in this heartwarming image.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
-      "AI: This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.agents import initialize_agent\n",
-    "from langchain.memory import ConversationBufferMemory\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, memory=memory, agent=\"conversational-react-description\", verbose=True\n",
-    ")\n",
-    "output = agent.run(\n",
-    "    input=(\n",
-    "        \"What is in this image https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png. \"\n",
-    "        \"Is it movie or a game? If it is a movie, what is the name of the movie?\"\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "print(output)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "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": "3.11.2"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/modules/agents/tools/examples/serpapi.ipynb

@@ -102,15 +102,7 @@
    "id": "e0a1dc1c",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from langchain.agents import Tool\n",
-    "# You can create the tool to pass to an agent\n",
-    "repl_tool = Tool(\n",
-    "    name=\"python_repl\",\n",
-    "    description=\"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n",
-    "    func=search.run,\n",
-    ")"
-   ]
+   "source": []
   }
  ],
  "metadata": {

docs/modules/agents/tools/examples/youtube.ipynb

@@ -1,125 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "acb64858",
-   "metadata": {},
-   "source": [
-    "# YouTubeSearchTool\n",
-    "\n",
-    "This notebook shows how to use a tool to search YouTube\n",
-    "\n",
-    "Adapted from [https://github.com/venuv/langchain_yt_tools](https://github.com/venuv/langchain_yt_tools)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9bb15d4a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#! pip install youtube_search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cc1c83e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.tools import YouTubeSearchTool"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "becb262b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tool = YouTubeSearchTool()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6bbc4211",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu']\""
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tool.run(\"lex friedman\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f772147",
-   "metadata": {},
-   "source": [
-    "You can also specify the number of results that are returned"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "682fdb33",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=YVJ8gTnDC4Y&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=Udh22kuLebg&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=L_Guz73e6fw&pp=ygUMbGV4IGZyaWVkbWFu']\""
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tool.run(\"lex friedman,5\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bb5e1659",
-   "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
-}

docs/modules/agents/tools/multi_input_tool.ipynb

@@ -1,144 +1,23 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "87455ddb",
    "metadata": {},
    "source": [
     "# Multi-Input Tools\n",
     "\n",
-    "This notebook shows how to use a tool that requires multiple inputs with an agent. The recommended way to do so is with the `StructuredTool` class.\n",
-    "\n"
+    "This notebook shows how to use a tool that requires multiple inputs with an agent.\n",
+    "\n",
+    "The difficulty in doing so comes from the fact that an agent decides its next step from a language model, which outputs a string. So if that step requires multiple inputs, they need to be parsed from that. Therefore, the currently supported way to do this is to write a smaller wrapper function that parses a string into multiple inputs.\n",
+    "\n",
+    "For a concrete example, let's work on giving an agent access to a multiplication function, which takes as input two integers. In order to use this, we will tell the agent to generate the \"Action Input\" as a comma-separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "113c8805",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"LANGCHAIN_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9c257017",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain import OpenAI\n",
-    "from langchain.agents import initialize_agent, AgentType\n",
-    "\n",
-    "llm = OpenAI(temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "21623e8f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.tools import StructuredTool\n",
-    "\n",
-    "def multiplier(a: float, b: float) -> float:\n",
-    "    \"\"\"Multiply the provided floats.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "tool = StructuredTool.from_function(multiplier)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ae7e8e07",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Structured tools are compatible with the STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent type. \n",
-    "agent_executor = initialize_agent([tool], llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "6cfa22d7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Thought: I need to multiply 3 and 4\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"multiplier\",\n",
-      "  \"action_input\": {\"a\": 3, \"b\": 4}\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m12\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I know what to respond\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Final Answer\",\n",
-      "  \"action_input\": \"3 times 4 is 12\"\n",
-      "}\n",
-      "```\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'3 times 4 is 12'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_executor.run(\"What is 3 times 4\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e643b307",
-   "metadata": {},
-   "source": [
-    "## Multi-Input Tools with a string format\n",
-    "\n",
-    "An alternative to the structured tool would be to use the regular `Tool` class and accept a single string. The tool would then have to handle the parsing logic to extract the relavent values from the text, which tightly couples the tool representation to the agent prompt. This is still useful if the underlying language model can't reliabl generate structured schema. \n",
-    "\n",
-    "Let's take the multiplication function as an example. In order to use this, we will tell the agent to generate the \"Action Input\" as a comma-separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
    "id": "291149b6",
    "metadata": {},
    "outputs": [],
@@ -158,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "f0b82020",
    "metadata": {},
    "outputs": [],
@@ -173,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "6db1d43f",
    "metadata": {},
    "outputs": [],
@@ -191,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "id": "aa25d0ca",
    "metadata": {},
    "outputs": [
@@ -218,7 +97,7 @@
        "'3 times 4 is 12'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -252,7 +131,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/tools/tool_input_validation.ipynb

@@ -1,184 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Tool Input Schema\n",
-    "\n",
-    "By default, tools infer the argument schema by inspecting the function signature. For more strict requirements, custom input schema can be specified, along with custom validation logic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from typing import Any, Dict\n",
-    "\n",
-    "from langchain.agents import AgentType, initialize_agent\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper\n",
-    "from pydantic import BaseModel, Field, root_validator\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm = OpenAI(temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install tldextract > /dev/null"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import tldextract\n",
-    "\n",
-    "_APPROVED_DOMAINS = {\n",
-    "    \"langchain\",\n",
-    "    \"wikipedia\",\n",
-    "}\n",
-    "\n",
-    "class ToolInputSchema(BaseModel):\n",
-    "\n",
-    "    url: str = Field(...)\n",
-    "    \n",
-    "    @root_validator\n",
-    "    def validate_query(cls, values: Dict[str, Any]) -> Dict:\n",
-    "        url = values[\"url\"]\n",
-    "        domain = tldextract.extract(url).domain\n",
-    "        if domain not in _APPROVED_DOMAINS:\n",
-    "            raise ValueError(f\"Domain {domain} is not on the approved list:\"\n",
-    "                             f\" {sorted(_APPROVED_DOMAINS)}\")\n",
-    "        return values\n",
-    "    \n",
-    "tool = RequestsGetTool(args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The main title of langchain.com is \"LANG CHAIN 🦜️🔗 Official Home Page\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "# This will succeed, since there aren't any arguments that will be triggered during validation\n",
-    "answer = agent.run(\"What's the main title on langchain.com?\")\n",
-    "print(answer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "ename": "ValidationError",
-     "evalue": "1 validation error for ToolInputSchema\n__root__\n  Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m agent\u001b[39m.\u001b[39;49mrun(\u001b[39m\"\u001b[39;49m\u001b[39mWhat\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39ms the main title on google.com?\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:213\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m    212\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m`run` supports only one positional argument.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 213\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m(args[\u001b[39m0\u001b[39;49m])[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n\u001b[1;32m    215\u001b[0m \u001b[39mif\u001b[39;00m kwargs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m args:\n\u001b[1;32m    216\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m(kwargs)[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:116\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    115\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_error(e, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n\u001b[0;32m--> 116\u001b[0m     \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m    117\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_end(outputs, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n\u001b[1;32m    118\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprep_outputs(inputs, outputs, return_only_outputs)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:113\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_start(\n\u001b[1;32m    108\u001b[0m     {\u001b[39m\"\u001b[39m\u001b[39mname\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m},\n\u001b[1;32m    109\u001b[0m     inputs,\n\u001b[1;32m    110\u001b[0m     verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose,\n\u001b[1;32m    111\u001b[0m )\n\u001b[1;32m    112\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m     outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(inputs)\n\u001b[1;32m    114\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    115\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_error(e, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:792\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[39m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m    791\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m--> 792\u001b[0m     next_step_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_take_next_step(\n\u001b[1;32m    793\u001b[0m         name_to_tool_map, color_mapping, inputs, intermediate_steps\n\u001b[1;32m    794\u001b[0m     )\n\u001b[1;32m    795\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m    796\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_return(next_step_output, intermediate_steps)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:695\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)\u001b[0m\n\u001b[1;32m    693\u001b[0m         tool_run_kwargs[\u001b[39m\"\u001b[39m\u001b[39mllm_prefix\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    694\u001b[0m     \u001b[39m# We then call the tool on the tool input to get an observation\u001b[39;00m\n\u001b[0;32m--> 695\u001b[0m     observation \u001b[39m=\u001b[39m tool\u001b[39m.\u001b[39;49mrun(\n\u001b[1;32m    696\u001b[0m         agent_action\u001b[39m.\u001b[39;49mtool_input,\n\u001b[1;32m    697\u001b[0m         verbose\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[1;32m    698\u001b[0m         color\u001b[39m=\u001b[39;49mcolor,\n\u001b[1;32m    699\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mtool_run_kwargs,\n\u001b[1;32m    700\u001b[0m     )\n\u001b[1;32m    701\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    702\u001b[0m     tool_run_kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39magent\u001b[39m.\u001b[39mtool_run_logging_kwargs()\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:110\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, **kwargs)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun\u001b[39m(\n\u001b[1;32m    102\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    103\u001b[0m     tool_input: Union[\u001b[39mstr\u001b[39m, Dict],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any,\n\u001b[1;32m    108\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mstr\u001b[39m:\n\u001b[1;32m    109\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Run the tool.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 110\u001b[0m     run_input \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_input(tool_input)\n\u001b[1;32m    111\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39mand\u001b[39;00m verbose \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    112\u001b[0m         verbose_ \u001b[39m=\u001b[39m verbose\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:71\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(input_args, BaseModel):\n\u001b[1;32m     70\u001b[0m     key_ \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39m(\u001b[39miter\u001b[39m(input_args\u001b[39m.\u001b[39m__fields__\u001b[39m.\u001b[39mkeys()))\n\u001b[0;32m---> 71\u001b[0m     input_args\u001b[39m.\u001b[39;49mparse_obj({key_: tool_input})\n\u001b[1;32m     72\u001b[0m \u001b[39m# Passing as a positional argument is more straightforward for\u001b[39;00m\n\u001b[1;32m     73\u001b[0m \u001b[39m# backwards compatability\u001b[39;00m\n\u001b[1;32m     74\u001b[0m \u001b[39mreturn\u001b[39;00m tool_input\n",
-      "File \u001b[0;32m~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for ToolInputSchema\n__root__\n  Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)"
-     ]
-    }
-   ],
-   "source": [
-    "agent.run(\"What's the main title on google.com?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/modules/chains/examples/constitutional_chain.ipynb

@@ -9,6 +9,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -17,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -26,7 +27,7 @@
        "' Break into a pet store at night and take as many kittens as you can carry.'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -54,66 +55,16 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new ConstitutionalChain chain...\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mInitial response:  Break into a pet store at night and take as many kittens as you can carry.\n",
-      "\n",
-      "\u001b[0m\u001b[32;1m\u001b[1;3mApplying illegal...\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response encourages illegal and dangerous activity, as it suggests breaking into a pet store to steal kittens. This is not acceptable and should be avoided.\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3mUpdated response: Stealing kittens is illegal and unethical. I would advise against it. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.\n",
-      "\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Stealing kittens is illegal and unethical. I would advise against it. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains.constitutional_ai.base import ConstitutionalChain\n",
-    "\n",
-    "principles = ConstitutionalChain.get_principles([\"illegal\"])\n",
-    "constitutional_chain = ConstitutionalChain.from_llm(\n",
-    "    chain=evil_qa_chain,\n",
-    "    constitutional_principles=principles,\n",
-    "    llm=llm,\n",
-    "    verbose=True,\n",
-    ")\n",
-    "\n",
-    "constitutional_chain.run(question=\"How can I steal kittens?\")"
-   ]
-  },
-  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Custom Principles\n",
-    "\n",
-    "We can easily add in custom principles."
+    "Let's try adding a constitutional principle against outputs that are illegal or unethical."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +78,7 @@
       "\n",
       "\u001b[0m\u001b[32;1m\u001b[1;3mApplying Ethical Principle...\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, and should not be condoned.\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, as it encourages stealing kittens.\n",
       "\n",
       "\u001b[0m\u001b[33;1m\u001b[1;3mUpdated response: It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.\n",
       "\n",
@@ -141,12 +92,13 @@
        "'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "from langchain.chains.constitutional_ai.base import ConstitutionalChain\n",
     "from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple\n",
     "\n",
     "ethical_principle = ConstitutionalPrinciple(\n",
@@ -166,6 +118,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -214,7 +167,7 @@
     }
    ],
    "source": [
-    "master_yoda_principle = ConstitutionalPrinciple(\n",
+    "master_yoda_principal = ConstitutionalPrinciple(\n",
     "    name='Master Yoda Principle',\n",
     "    critique_request='Identify specific ways in which the model\\'s response is not in the style of Master Yoda.',\n",
     "    revision_request='Please rewrite the model response to be in the style of Master Yoda using his teachings and wisdom.',\n",
@@ -222,224 +175,18 @@
     "\n",
     "constitutional_chain = ConstitutionalChain.from_llm(\n",
     "    chain=evil_qa_chain,\n",
-    "    constitutional_principles=[ethical_principle, master_yoda_principle],\n",
+    "    constitutional_principles=[ethical_principle, master_yoda_principal],\n",
     "    llm=llm,\n",
     "    verbose=True,\n",
     ")\n",
     "\n",
     "constitutional_chain.run(question=\"How can I steal kittens?\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Intermediate Steps\n",
-    "\n",
-    "You can also get the constitutional chain to return it's intermediate steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new ConstitutionalChain chain...\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mInitial response:  Break into a pet store at night and take as many kittens as you can carry.\n",
-      "\n",
-      "\u001b[0m\u001b[32;1m\u001b[1;3mApplying Ethical Principle...\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, and should not be condoned.\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3mUpdated response: It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.\n",
-      "\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'question': 'How can I steal kittens?',\n",
-       " 'output': 'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.',\n",
-       " 'initial_output': ' Break into a pet store at night and take as many kittens as you can carry.',\n",
-       " 'critiques_and_revisions': [(\"The model's response is unethical and illegal, and should not be condoned.\",\n",
-       "   'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.')]}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "constitutional_chain = ConstitutionalChain.from_llm(\n",
-    "    chain=evil_qa_chain,\n",
-    "    constitutional_principles=[ethical_principle],\n",
-    "    llm=llm,\n",
-    "    verbose=True,\n",
-    "    return_intermediate_steps=True\n",
-    ")\n",
-    "\n",
-    "constitutional_chain({\"question\":\"How can I steal kittens?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## No revision necessary\n",
-    "\n",
-    "We can also see that the chain recognizes when no revision is necessary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "good_qa_prompt = PromptTemplate(\n",
-    "    template=\"\"\"You are good and must only give good and ethical answers. If someone asks you how to do something illegal, make sure you respond in an ethical and legal way.\n",
-    "\n",
-    "Question: {question}\n",
-    "\n",
-    "Ethical answer:\"\"\",\n",
-    "    input_variables=[\"question\"],\n",
-    ")\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "\n",
-    "good_qa_chain = LLMChain(llm=llm, prompt=good_qa_prompt)\n",
-    "\n",
-    "good_qa_chain.run(question=\"How can I steal kittens?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new ConstitutionalChain chain...\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mInitial response:  Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.\n",
-      "\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'question': 'How can I steal kittens?',\n",
-       " 'output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',\n",
-       " 'initial_output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',\n",
-       " 'critiques_and_revisions': [('No critique needed.', '')]}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "constitutional_chain = ConstitutionalChain.from_llm(\n",
-    "    chain=good_qa_chain,\n",
-    "    constitutional_principles=[ethical_principle],\n",
-    "    llm=llm,\n",
-    "    verbose=True,\n",
-    "    return_intermediate_steps=True\n",
-    ")\n",
-    "\n",
-    "constitutional_chain({\"question\":\"How can I steal kittens?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## All Principles\n",
-    "\n",
-    "For a list of all principles, see:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains.constitutional_ai.principles import PRINCIPLES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'harmful1': ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant’s last response is harmful, unethical, racist, sexist, toxic, dangerous, or illegal.', revision_request='Please rewrite the assistant response to remove any and all harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.', name='harmful1'),\n",
-       " 'harmful2': ConstitutionalPrinciple(critique_request='Explain ways in which the assistant’s preceding response may be harmful to either the human or others. For example, if the assistant says anything that encourages dangerous or illegal activity, make sure to point it out. Other examples of harm include racism, sexism, and other social biases.', revision_request='Please rewrite the assistant response to remove any and all harmful content, including anything dangerous, illegal, racist, sexist or socially biased.', name='harmful2'),\n",
-       " 'harmful3': ConstitutionalPrinciple(critique_request='Identify all ways in which the assistant’s last response is harmful, unethical, or socially biased. Furthermore, provide specific details on how the assistant can improve its response.', revision_request='Please rewrite the assistant response to remove all harmful, unethical, or socially biased content, and move the conversation in a positive direction.', name='harmful3'),\n",
-       " 'harmful4': ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', revision_request='Rewrite the assistant response to remove all harmful content, and to politely point out any harmful assumptions from the human, such as racism, sexism, toxicity.', name='harmful4'),\n",
-       " 'insensitive': ConstitutionalPrinciple(critique_request='Discuss the assistant’s last response to the human and whether it is in any way insensitive, sexist, racist, or socially inappropriate.', revision_request='Revise the assistant’s last response to remove content that is in any way insensitive, sexist, racist, or socially inappropriate.', name='insensitive'),\n",
-       " 'offensive': ConstitutionalPrinciple(critique_request='Discuss whether the assistant said anything that may be interpreted as offensive or harmful. If possible, also point out any harmful assumptions in the human’s dialogue.', revision_request='Please write a new version of the assistant’s response that removes offensive and harmful content, and politely addresses any problematic assumptions from the human.', name='offensive'),\n",
-       " 'harmful5': ConstitutionalPrinciple(critique_request='What’s the strongest argument you could give that the assistant’s last response was in some way harmful, unethical, racist, sexist, toxic, dangerous, or illegal.', revision_request='Amend the assistant’s last response to remove any and all harmful, unethical, racist, toxic, dangerous, and illegal content.', name='harmful5'),\n",
-       " 'age-innappropriate': ConstitutionalPrinciple(critique_request='Did the assistant’s preceding response include anything that may be inappropriate for young children? If so, discuss how it can be revised to be more appropriate.', revision_request='Please rewrite the assistant response to make it more age appropriate for children.', name='age-innappropriate'),\n",
-       " 'derogatory': ConstitutionalPrinciple(critique_request='Think carefully about whether the human’s request succeeded in eliciting responses from the assistant that are derogatory, toxic, racist, sexist or socially harmful in any way, and discuss how the assistant should’ve responded instead.', revision_request='Please rewrite the assistant’s response so as to remove all harmful content, especially anything derogatory, toxic, racist, sexist or socially harmful.', name='derogatory'),\n",
-       " 'illegal': ConstitutionalPrinciple(critique_request='Discuss whether the assistant provided any advice that may encourage illegal or dangerous activity from either the human or others.', revision_request='Revise the assistant’s response so as to refrain from giving legally questionable or dangerous advice.', name='illegal'),\n",
-       " 'controversial': ConstitutionalPrinciple(critique_request='Discuss ways in which the assistant’s response may be considered controversal or objectionable based on commonsense ethical and moral standards.', revision_request='Revise the assistant’s response if possible so that it’s less controversial and objectionable based on commonsense ethical and moral standards.', name='controversial'),\n",
-       " 'harmful6': ConstitutionalPrinciple(critique_request='Discuss whether the human is deliberately trying to bait the assistant into generating harmful content, and whether they succeeded in doing so.', revision_request='Write a revision of the assistant’s response that refrains from saying anything harmful.', name='harmful6'),\n",
-       " 'thoughtful': ConstitutionalPrinciple(critique_request='Please comment on whether the assistant’s last response is thoughtful and empathetic. Does it seem like the kind of thing a sensitive friend or therapist might say? Give the strongest argument you can for how it could improve.', revision_request='Please write a new assistant response so that it is more like what a thoughtful, empathetic, caring, sensitive friend or therapist would say.', name='thoughtful'),\n",
-       " 'misogynistic': ConstitutionalPrinciple(critique_request='Take a look at the assistant’s last response and note if it was in any way misogynistic or otherwise gender biased. Give the strongest argument you can that something the assistant said could be interpreted as misogynistic.', revision_request='Please write a new response that does not have any trace of misogyny or gender bias.', name='misogynistic'),\n",
-       " 'criminal': ConstitutionalPrinciple(critique_request='Read the assistant’s last response carefully and identify anything it said that provides advice or assistance in potentially criminal activities, such as violence, theft, hacking, robbery, or any other illegal activity. Consider the strongest argument you could give that the assistant response might encourage crime.', revision_request='Rewrite the assistant response to avoid assisting the human with criminal plans or activities, such as violence, theft, hacking, robbery, or anything illegal.', name='criminal'),\n",
-       " 'harmful7': ConstitutionalPrinciple(critique_request='Critique the assistant’s last response and identify anything it said that was harmful, unethical, dangerous, or illegal.', revision_request='Please rewrite the assistant response to remove any and all harmful, unethical, dangerous, or illegal content.', name='harmful7')}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "PRINCIPLES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langchain",
    "language": "python",
    "name": "python3"
   },
@@ -453,8 +200,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.16"
   },
+  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "06ba49dd587e86cdcfee66b9ffe769e1e94f0e368e54c2d6c866e38e33c0d9b1"

docs/modules/chains/examples/flare.ipynb

@@ -1,483 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0f0b9afa",
-   "metadata": {},
-   "source": [
-    "# FLARE\n",
-    "\n",
-    "This notebook is an implementation of Forward-Looking Active REtrieval augmented generation (FLARE).\n",
-    "\n",
-    "Please see the original repo [here](https://github.com/jzbjyb/FLARE/tree/main).\n",
-    "\n",
-    "The basic idea is:\n",
-    "\n",
-    "- Start answering a question\n",
-    "- If you start generating tokens the model is uncertain about, look up relevant documents\n",
-    "- Use those documents to continue generating\n",
-    "- Repeat until finished\n",
-    "\n",
-    "There is a lot of cool detail in how the lookup of relevant documents is done.\n",
-    "Basically, the tokens that model is uncertain about are highlighted, and then an LLM is called to generate a question that would lead to that answer. For example, if the generated text is `Joe Biden went to Harvard`, and the tokens the model was uncertain about was `Harvard`, then a good generated question would be `where did Joe Biden go to college`. This generated question is then used in a retrieval step to fetch relevant documents.\n",
-    "\n",
-    "In order to set up this chain, we will need three things:\n",
-    "\n",
-    "- An LLM to generate the answer\n",
-    "- An LLM to generate hypothetical questions to use in retrieval\n",
-    "- A retriever to use to look up answers for\n",
-    "\n",
-    "The LLM that we use to generate the answer needs to return logprobs so we can identify uncertain tokens. For that reason, we HIGHLY recommend that you use the OpenAI wrapper (NB: not the ChatOpenAI wrapper, as that does not return logprobs).\n",
-    "\n",
-    "The LLM we use to generate hypothetical questions to use in retrieval can be anything. In this notebook we will use ChatOpenAI because it is fast and cheap.\n",
-    "\n",
-    "The retriever can be anything. In this notebook we will use [SERPER](https://serper.dev/) search engine, because it is cheap.\n",
-    "\n",
-    "Other important parameters to understand:\n",
-    "\n",
-    "- `max_generation_len`: The maximum number of tokens to generate before stopping to check if any are uncertain\n",
-    "- `min_prob`: Any tokens generated with probability below this will be considered uncertain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7e4b63d",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "042bb161",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"SERPER_API_KEY\"] = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a7888f4a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "from langchain.schema import BaseRetriever\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 Document"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f552dce",
-   "metadata": {},
-   "source": [
-    "## Retriever"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "59c7d875",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class SerperSearchRetriever(BaseRetriever):\n",
-    "    def __init__(self, search):\n",
-    "        self.search = search\n",
-    "    \n",
-    "    def get_relevant_documents(self, query: str):\n",
-    "        return [Document(page_content=self.search.run(query))]\n",
-    "    \n",
-    "    async def aget_relevant_documents(self, query: str):\n",
-    "        raise NotImplemented\n",
-    "        \n",
-    "        \n",
-    "retriever = SerperSearchRetriever(GoogleSerperAPIWrapper())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "92478194",
-   "metadata": {},
-   "source": [
-    "## FLARE Chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "577e7c2c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We set this so we can see what exactly is going on\n",
-    "import langchain\n",
-    "langchain.verbose = True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "300d783e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import FlareChain\n",
-    "\n",
-    "flare = FlareChain.from_llm(\n",
-    "    ChatOpenAI(temperature=0), \n",
-    "    retriever=retriever,\n",
-    "    max_generation_len=164,\n",
-    "    min_prob=.3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1f3d5e90",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"explain in great detail the difference between the langchain framework and baby agi\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4b1bfa8c",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new FlareChain chain...\u001b[0m\n",
-      "\u001b[36;1m\u001b[1;3mCurrent Response: \u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
-      "\n",
-      ">>> CONTEXT: \n",
-      ">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
-      ">>> RESPONSE: \u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new QuestionGeneratorChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
-      "\n",
-      "Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
-      "\n",
-      "In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" decentralized platform for natural language processing\" is:\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
-      "\n",
-      "Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
-      "\n",
-      "In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" uses a blockchain\" is:\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
-      "\n",
-      "Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
-      "\n",
-      "In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" distributed ledger to\" is:\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
-      "\n",
-      "Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
-      "\n",
-      "In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" process data, allowing for secure and transparent data sharing.\" is:\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
-      "\n",
-      "Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
-      "\n",
-      "In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" set of tools\" is:\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
-      "\n",
-      "Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
-      "\n",
-      "In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" help developers create\" is:\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
-      "\n",
-      "Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
-      "\n",
-      "In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" create an AI system\" is:\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
-      "\n",
-      "Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
-      "\n",
-      "In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" NLP applications\" is:\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mGenerated Questions: ['What is the Langchain Framework?', 'What technology does the Langchain Framework use to store and process data for secure and transparent data sharing?', 'What technology does the Langchain Framework use to store and process data?', 'What does the Langchain Framework use a blockchain-based distributed ledger for?', 'What does the Langchain Framework provide in addition to a decentralized platform for natural language processing applications?', 'What set of tools and services does the Langchain Framework provide?', 'What is the purpose of Baby AGI?', 'What type of applications is the Langchain Framework designed for?']\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new _OpenAIResponseChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
-      "\n",
-      ">>> CONTEXT: LangChain: Software. LangChain is a software development framework designed to simplify the creation of applications using large language models. LangChain Initial release date: October 2022. LangChain Programming languages: Python and JavaScript. LangChain Developer(s): Harrison Chase. LangChain License: MIT License. LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only ... Type: Software framework. At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. LangChain is a powerful tool that can be used to work with Large Language Models (LLMs). LLMs are very general in nature, which means that while they can ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. LangChain is a software development framework designed to simplify the creation of applications using large language models (LLMs). Written in: Python and JavaScript. Initial release: October 2022. LangChain - The A.I-native developer toolkit We started LangChain with the intent to build a modular and flexible framework for developing A.I- ... LangChain explained in 3 minutes - LangChain is a ... Duration: 3:03. Posted: Apr 13, 2023. LangChain is a framework built to help you build LLM-powered applications more easily by providing you with the following:. LangChain is a framework that enables quick and easy development of applications that make use of Large Language Models, for example, GPT-3. LangChain is a powerful open-source framework for developing applications powered by language models. It connects to the AI models you want to ...\n",
-      "\n",
-      "LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... Missing: secure | Must include:secure. Blockchain is the best way to secure the data of the shared community. Utilizing the capabilities of the blockchain nobody can read or interfere ... This modern technology consists of a chain of blocks that allows to securely store all committed transactions using shared and distributed ... A Blockchain network is used in the healthcare system to preserve and exchange patient data through hospitals, diagnostic laboratories, pharmacy firms, and ... In this article, I will walk you through the process of using the LangChain.js library with Google Cloud Functions, helping you leverage the ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. Missing: transparent | Must include:transparent. This technology keeps a distributed ledger on each blockchain node, making it more secure and transparent. The blockchain network can operate smart ... blockchain technology can offer a highly secured health data ledger to ... framework can be employed to store encrypted healthcare data in a ... In a simplified way, Blockchain is a data structure that stores transactions in an ordered way and linked to the previous block, serving as a ... Blockchain technology is a decentralized, distributed ledger that stores the record of ownership of digital assets. Missing: Langchain | Must include:Langchain.\n",
-      "\n",
-      "LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. This documentation covers the steps to integrate Pinecone, a high-performance vector database, with LangChain, a framework for building applications powered ... The ability to connect to any model, ingest any custom database, and build upon a framework that can take action provides numerous use cases for ... With LangChain, developers can use a framework that abstracts the core building blocks of LLM applications. LangChain empowers developers to ... Build a question-answering tool based on financial data with LangChain & Deep Lake's unified & streamable data store. Browse applications built on LangChain technology. Explore PoC and MVP applications created by our community and discover innovative use cases for LangChain ... LangChain is a great framework that can be used for developing applications powered by LLMs. When you intend to enhance your application ... In this blog, we'll introduce you to LangChain and Ray Serve and how to use them to build a search engine using LLM embeddings and a vector ... The LinkChain Framework simplifies embedding creation and storage using Pinecone and Chroma, with code that loads files, splits documents, and creates embedding ... Missing: technology | Must include:technology.\n",
-      "\n",
-      "Blockchain is one type of a distributed ledger. Distributed ledgers use independent computers (referred to as nodes) to record, share and ... Missing: Langchain | Must include:Langchain. Blockchain is used in distributed storage software where huge data is broken down into chunks. This is available in encrypted data across a ... People sometimes use the terms 'Blockchain' and 'Distributed Ledger' interchangeably. This post aims to analyze the features of each. A distributed ledger ... Missing: Framework | Must include:Framework. Think of a “distributed ledger” that uses cryptography to allow each participant in the transaction to add to the ledger in a secure way without ... In this paper, we provide an overview of the history of trade settlement and discuss this nascent technology that may now transform traditional ... Missing: Langchain | Must include:Langchain. LangChain is a blockchain-based language education platform that aims to revolutionize the way people learn languages. Missing: Framework | Must include:Framework. It uses the distributed ledger technology framework and Smart contract engine for building scalable Business Blockchain applications. The fabric ... It looks at the assets the use case is handling, the different parties conducting transactions, and the smart contract, distributed ... Are you curious to know how Blockchain and Distributed ... Duration: 44:31. Posted: May 4, 2021. A blockchain is a distributed and immutable ledger to transfer ownership, record transactions, track assets, and ensure transparency, security, trust and value ... Missing: Langchain | Must include:Langchain.\n",
-      "\n",
-      "LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. Missing: decentralized | Must include:decentralized. LangChain, created by Harrison Chase, is a Python library that provides out-of-the-box support to build NLP applications using LLMs. Missing: decentralized | Must include:decentralized. LangChain provides a standard interface for chains, enabling developers to create sequences of calls that go beyond a single LLM call. Chains ... Missing: decentralized platform natural. LangChain is a powerful framework that simplifies the process of building advanced language model applications. Missing: platform | Must include:platform. Are your language models ignoring previous instructions ... Duration: 32:23. Posted: Feb 21, 2023. LangChain is a framework that enables quick and easy development of applications ... Prompting is the new way of programming NLP models. Missing: decentralized platform. It then uses natural language processing and machine learning algorithms to search ... Summarization is handled via cohere, QnA is handled via langchain, ... LangChain is a framework for developing applications powered by language models. ... There are several main modules that LangChain provides support for. Missing: decentralized platform. In the healthcare-chain system, blockchain provides an appreciated secure ... The entire process of adding new and previous block data is performed based on ... ChatGPT is a large language model developed by OpenAI, ... tool for a wide range of applications, including natural language processing, ...\n",
-      "\n",
-      "LangChain is a powerful tool that can be used to work with Large Language ... If an API key has been provided, create an OpenAI language model instance At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. A tutorial of the six core modules of the LangChain Python package covering models, prompts, chains, agents, indexes, and memory with OpenAI ... LangChain's collection of tools refers to a set of tools provided by the LangChain framework for developing applications powered by language models. LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only ... LangChain is an open-source library that provides developers with the tools to build applications powered by large language models (LLMs). LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... Plan-and-Execute Agents · Feature Stores and LLMs · Structured Tools · Auto-Evaluator Opportunities · Callbacks Improvements · Unleashing the power ... Tool: A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. · LLM: The language model ... LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\n",
-      "\n",
-      "Baby AGI has the ability to complete tasks, generate new tasks based on previous results, and prioritize tasks in real-time. This system is exploring and demonstrating to us the potential of large language models, such as GPT and how it can autonomously perform tasks. Apr 17, 2023\n",
-      "\n",
-      "At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.\n",
-      ">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
-      ">>> RESPONSE: \u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "' LangChain is a framework for developing applications powered by language models. It provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications. On the other hand, Baby AGI is an AI system that is exploring and demonstrating the potential of large language models, such as GPT, and how it can autonomously perform tasks. Baby AGI has the ability to complete tasks, generate new tasks based on previous results, and prioritize tasks in real-time. '"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flare.run(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7bed8944",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\n\\nThe Langchain framework and Baby AGI are both artificial intelligence (AI) frameworks that are used to create intelligent agents. The Langchain framework is a supervised learning system that is based on the concept of “language chains”. It uses a set of rules to map natural language inputs to specific outputs. It is a general-purpose AI framework and can be used to build applications such as natural language processing (NLP), chatbots, and more.\\n\\nBaby AGI, on the other hand, is an unsupervised learning system that uses neural networks and reinforcement learning to learn from its environment. It is used to create intelligent agents that can adapt to changing environments. It is a more advanced AI system and can be used to build more complex applications such as game playing, robotic vision, and more.\\n\\nThe main difference between the two is that the Langchain framework uses supervised learning while Baby AGI uses unsupervised learning. The Langchain framework is a general-purpose AI framework that can be used for various applications, while Baby AGI is a more advanced AI system that can be used to create more complex applications.'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm = OpenAI()\n",
-    "llm(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "8fb76286",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new FlareChain chain...\u001b[0m\n",
-      "\u001b[36;1m\u001b[1;3mCurrent Response: \u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
-      "\n",
-      ">>> CONTEXT: \n",
-      ">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?\n",
-      ">>> RESPONSE: \u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new QuestionGeneratorChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "\n",
-      "Langchain and Bitcoin have very different origin stories. Bitcoin was created by the mysterious Satoshi Nakamoto in 2008 as a decentralized digital currency. Langchain, on the other hand, was created in 2020 by a team of developers as a platform for creating and managing decentralized language learning applications. \n",
-      "\n",
-      "FINISHED\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" very different origin\" is:\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "\n",
-      "Langchain and Bitcoin have very different origin stories. Bitcoin was created by the mysterious Satoshi Nakamoto in 2008 as a decentralized digital currency. Langchain, on the other hand, was created in 2020 by a team of developers as a platform for creating and managing decentralized language learning applications. \n",
-      "\n",
-      "FINISHED\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" 2020 by a\" is:\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
-      "\n",
-      ">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?\n",
-      ">>> EXISTING PARTIAL RESPONSE:  \n",
-      "\n",
-      "Langchain and Bitcoin have very different origin stories. Bitcoin was created by the mysterious Satoshi Nakamoto in 2008 as a decentralized digital currency. Langchain, on the other hand, was created in 2020 by a team of developers as a platform for creating and managing decentralized language learning applications. \n",
-      "\n",
-      "FINISHED\n",
-      "\n",
-      "The question to which the answer is the term/entity/phrase \" developers as a platform for creating and managing decentralized language learning applications.\" is:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mGenerated Questions: ['How would you describe the origin stories of Langchain and Bitcoin in terms of their similarities or differences?', 'When was Langchain created and by whom?', 'What was the purpose of creating Langchain?']\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new _OpenAIResponseChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
-      "\n",
-      ">>> CONTEXT: Bitcoin and Ethereum have many similarities but different long-term visions and limitations. Ethereum changed from proof of work to proof of ... Bitcoin will be around for many years and examining its white paper origins is a great exercise in understanding why. Satoshi Nakamoto's blueprint describes ... Bitcoin is a new currency that was created in 2009 by an unknown person using the alias Satoshi Nakamoto. Transactions are made with no middle men – meaning, no ... Missing: Langchain | Must include:Langchain. By comparison, Bitcoin transaction speeds are tremendously lower. ... learn about its history and its role in the emergence of the Bitcoin ... LangChain is a powerful framework that simplifies the process of ... tasks like document retrieval, clustering, and similarity comparisons. Key terms: Bitcoin System, Blockchain Technology, ... Furthermore, the research paper will discuss and compare the five payment. Blockchain first appeared in Nakamoto's Bitcoin white paper that describes a new decentralized cryptocurrency [1]. Bitcoin takes the blockchain technology ... Missing: stories | Must include:stories. A score of 0 means there were not enough data for this term. Google trends was accessed on 5 November 2018 with searches for bitcoin, euro, gold ... Contracts, transactions, and records of them provide critical structure in our economic system, but they haven't kept up with the world's digital ... Missing: Langchain | Must include:Langchain. Of course, traders try to make a profit on their portfolio in this way.The difference between investing and trading is the regularity with which ...\n",
-      "\n",
-      "After all these giant leaps forward in the LLM space, OpenAI released ChatGPT — thrusting LLMs into the spotlight. LangChain appeared around the same time. Its creator, Harrison Chase, made the first commit in late October 2022. Leaving a short couple of months of development before getting caught in the LLM wave.\n",
-      "\n",
-      "At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.\n",
-      ">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?\n",
-      ">>> RESPONSE: \u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "' The origin stories of LangChain and Bitcoin are quite different. Bitcoin was created in 2009 by an unknown person using the alias Satoshi Nakamoto. LangChain was created in late October 2022 by Harrison Chase. Bitcoin is a decentralized cryptocurrency, while LangChain is a framework built around LLMs. '"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flare.run(\"how are the origin stories of langchain and bitcoin similar or different?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fbadd022",
-   "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
-}

docs/modules/chains/examples/llm_bash.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -24,8 +24,8 @@
       "\n",
       "```bash\n",
       "echo \"Hello World\"\n",
-      "```\u001b[0m\n",
-      "Code: \u001b[33;1m\u001b[1;3m['echo \"Hello World\"']\u001b[0m\n",
+      "```\u001b[0m['```bash', 'echo \"Hello World\"', '```']\n",
+      "\n",
       "Answer: \u001b[33;1m\u001b[1;3mHello World\n",
       "\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -37,7 +37,7 @@
        "'Hello World\\n'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
     "\n",
     "text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
     "\n",
-    "bash_chain = LLMBashChain.from_llm(llm, verbose=True)\n",
+    "bash_chain = LLMBashChain(llm=llm, verbose=True)\n",
     "\n",
     "bash_chain.run(text)"
    ]
@@ -65,12 +65,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.prompts.prompt import PromptTemplate\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",
@@ -89,12 +88,12 @@
     "That is the format. Begin!\n",
     "Question: {question}\"\"\"\n",
     "\n",
-    "PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE, output_parser=BashOutputParser())"
+    "PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -108,8 +107,8 @@
       "\n",
       "```bash\n",
       "printf \"Hello World\\n\"\n",
-      "```\u001b[0m\n",
-      "Code: \u001b[33;1m\u001b[1;3m['printf \"Hello World\\\\n\"']\u001b[0m\n",
+      "```\u001b[0m['```bash', 'printf \"Hello World\\\\n\"', '```']\n",
+      "\n",
       "Answer: \u001b[33;1m\u001b[1;3mHello World\n",
       "\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -121,125 +120,18 @@
        "'Hello World\\n'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bash_chain = LLMBashChain.from_llm(llm, prompt=PROMPT, verbose=True)\n",
+    "bash_chain = LLMBashChain(llm=llm, prompt=PROMPT, verbose=True)\n",
     "\n",
     "text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
     "\n",
     "bash_chain.run(text)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Persistent Terminal\n",
-    "\n",
-    "By default, the chain will run in a separate subprocess each time it is called. This behavior can be changed by instantiating with a persistent bash process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
-      "List the current directory then move up a level.\u001b[32;1m\u001b[1;3m\n",
-      "\n",
-      "```bash\n",
-      "ls\n",
-      "cd ..\n",
-      "```\u001b[0m\n",
-      "Code: \u001b[33;1m\u001b[1;3m['ls', 'cd ..']\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3mapi.ipynb\t\t\tllm_summarization_checker.ipynb\n",
-      "constitutional_chain.ipynb\tmoderation.ipynb\n",
-      "llm_bash.ipynb\t\t\topenai_openapi.yaml\n",
-      "llm_checker.ipynb\t\topenapi.ipynb\n",
-      "llm_math.ipynb\t\t\tpal.ipynb\n",
-      "llm_requests.ipynb\t\tsqlite.ipynb\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'api.ipynb\\t\\t\\tllm_summarization_checker.ipynb\\r\\nconstitutional_chain.ipynb\\tmoderation.ipynb\\r\\nllm_bash.ipynb\\t\\t\\topenai_openapi.yaml\\r\\nllm_checker.ipynb\\t\\topenapi.ipynb\\r\\nllm_math.ipynb\\t\\t\\tpal.ipynb\\r\\nllm_requests.ipynb\\t\\tsqlite.ipynb'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.utilities.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",
-    "text = \"List the current directory then move up a level.\"\n",
-    "\n",
-    "bash_chain.run(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
-      "List the current directory then move up a level.\u001b[32;1m\u001b[1;3m\n",
-      "\n",
-      "```bash\n",
-      "ls\n",
-      "cd ..\n",
-      "```\u001b[0m\n",
-      "Code: \u001b[33;1m\u001b[1;3m['ls', 'cd ..']\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3mexamples\t\tgetting_started.ipynb\tindex_examples\n",
-      "generic\t\t\thow_to_guides.rst\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'examples\\t\\tgetting_started.ipynb\\tindex_examples\\r\\ngeneric\\t\\t\\thow_to_guides.rst'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Run the same command again and see that the state is maintained between calls\n",
-    "bash_chain.run(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -258,7 +150,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/chains/examples/llm_checker.ipynb

@@ -23,16 +23,28 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
+      "\u001b[1mChain 0\u001b[0m:\n",
+      "{'statement': '\\nNone. Mammals do not lay eggs.'}\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1mChain 1\u001b[0m:\n",
+      "{'assertions': '\\n• Mammals reproduce using live birth\\n• Mammals do not lay eggs\\n• Animals that lay eggs are not mammals'}\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001b[1mChain 2\u001b[0m:\n",
+      "{'checked_assertions': '\\n1. True\\n\\n2. True\\n\\n3. False - Mammals are a class of animals that includes animals that lay eggs, such as monotremes (platypus and echidna).'}\n",
+      "\n",
+      "\u001b[1mChain 3\u001b[0m:\n",
+      "{'revised_statement': ' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'}\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Finished SequentialChain chain.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished LLMCheckerChain chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "' No mammal lays the biggest eggs. The Elephant Bird, which was a species of giant bird, laid the largest eggs of any bird.'"
+       "' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'"
       ]
      },
      "execution_count": 1,
@@ -48,7 +60,7 @@
     "\n",
     "text = \"What type of mammal lays the biggest eggs?\"\n",
     "\n",
-    "checker_chain = LLMCheckerChain.from_llm(llm, verbose=True)\n",
+    "checker_chain = LLMCheckerChain(llm=llm, verbose=True)\n",
     "\n",
     "checker_chain.run(text)"
    ]
@@ -77,7 +89,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/chains/examples/llm_math.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "44e9ba31",
    "metadata": {},
    "outputs": [
@@ -24,22 +24,23 @@
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
       "What is 13 raised to the .3432 power?\u001b[32;1m\u001b[1;3m\n",
-      "```text\n",
-      "13 ** .3432\n",
+      "```python\n",
+      "import math\n",
+      "print(math.pow(13, .3432))\n",
       "```\n",
-      "...numexpr.evaluate(\"13 ** .3432\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\n",
+      "\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'Answer: 2.4116004626599237'"
+       "'Answer: 2.4116004626599237\\n'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -48,7 +49,102 @@
     "from langchain import OpenAI, LLMMathChain\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
-    "llm_math = LLMMathChain.from_llm(llm, verbose=True)\n",
+    "llm_math = LLMMathChain(llm=llm, verbose=True)\n",
+    "\n",
+    "llm_math.run(\"What is 13 raised to the .3432 power?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bdd5fc6",
+   "metadata": {},
+   "source": [
+    "## Customize Prompt\n",
+    "You can also customize the prompt that is used. Here is an example prompting it to use numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "76be17b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "\n",
+    "_PROMPT_TEMPLATE = \"\"\"You are GPT-3, and you can't do math.\n",
+    "\n",
+    "You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to just make up highly specific, but wrong, answers.\n",
+    "\n",
+    "So we hooked you up to a Python 3 kernel, and now you can execute code. If you execute code, you must print out the final answer using the print function. You MUST use the python package numpy to answer your question. You must import numpy as np.\n",
+    "\n",
+    "\n",
+    "Question: ${{Question with hard calculation.}}\n",
+    "```python\n",
+    "${{Code that prints what you need to know}}\n",
+    "print(${{code}})\n",
+    "```\n",
+    "```output\n",
+    "${{Output of your code}}\n",
+    "```\n",
+    "Answer: ${{Answer}}\n",
+    "\n",
+    "Begin.\n",
+    "\n",
+    "Question: What is 37593 * 67?\n",
+    "\n",
+    "```python\n",
+    "import numpy as np\n",
+    "print(np.multiply(37593, 67))\n",
+    "```\n",
+    "```output\n",
+    "2518731\n",
+    "```\n",
+    "Answer: 2518731\n",
+    "\n",
+    "Question: {question}\"\"\"\n",
+    "\n",
+    "PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "0c42faa0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
+      "What is 13 raised to the .3432 power?\u001b[32;1m\u001b[1;3m\n",
+      "\n",
+      "```python\n",
+      "import numpy as np\n",
+      "print(np.power(13, .3432))\n",
+      "```\n",
+      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\n",
+      "\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Answer: 2.4116004626599237\\n'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_math = LLMMathChain(llm=llm, prompt=PROMPT, verbose=True)\n",
     "\n",
     "llm_math.run(\"What is 13 raised to the .3432 power?\")"
    ]
@@ -56,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e978bb8e",
+   "id": "0c62951b",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -78,7 +174,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/chains/examples/llm_summarization_checker.ipynb

@@ -221,11 +221,11 @@
       "\n",
       "• The light from these galaxies has been traveling for over 13 billion years to reach us. - True \n",
       "\n",
-      "• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 2004. \n",
+      "• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 1995. \n",
       "\n",
       "• Exoplanets were first discovered in 1992. - True \n",
       "\n",
-      "• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. The JWST has not yet been launched, so it is not yet known how much detail it will be able to provide.\n",
+      "• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. It is too early to tell as the JWST has not been launched yet.\n",
       "\"\"\"\n",
       "\n",
       "Original Summary:\n",
@@ -296,11 +296,11 @@
       "\n",
       "• The light from these galaxies has been traveling for over 13 billion years to reach us. - True \n",
       "\n",
-      "• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 2004. \n",
+      "• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 1995. \n",
       "\n",
       "• Exoplanets were first discovered in 1992. - True \n",
       "\n",
-      "• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. The JWST has not yet been launched, so it is not yet known how much detail it will be able to provide.\n",
+      "• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. It is too early to tell as the JWST has not been launched yet.\n",
       "\"\"\"\n",
       "Result:\u001b[0m\n",
       "\n",
@@ -312,7 +312,7 @@
       "Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\n",
       "• In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\n",
       "• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\n",
-      "• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail when it is launched in 2023.\n",
+      "• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail than ever before.\n",
       "These discoveries can spark a child's imagination about the infinite wonders of the universe.\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -321,7 +321,7 @@
     {
      "data": {
       "text/plain": [
-       "'Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\\n• In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\\n• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\\n• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail when it is launched in 2023.\\nThese discoveries can spark a child\\'s imagination about the infinite wonders of the universe.'"
+       "'Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\\n• In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\\n• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\\n• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail than ever before.\\nThese discoveries can spark a child\\'s imagination about the infinite wonders of the universe.'"
       ]
      },
      "execution_count": 1,
@@ -334,7 +334,7 @@
     "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
-    "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=2)\n",
+    "checker_chain = LLMSummarizationCheckerChain(llm=llm, verbose=True, max_checks=2)\n",
     "text = \"\"\"\n",
     "Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\n",
     "• In 2023, The JWST spotted a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\n",
@@ -407,8 +407,7 @@
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.\n",
       "\n",
-      "Checked Assertions:\n",
-      "\"\"\"\n",
+      "Checked Assertions:\"\"\"\n",
       "\n",
       "- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True\n",
       "\n",
@@ -429,8 +428,7 @@
       "- It is considered the northern branch of the Norwegian Sea. True\n",
       "\"\"\"\n",
       "\n",
-      "Original Summary:\n",
-      "\"\"\"\n",
+      "Original Summary:\"\"\"\n",
       "The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.\n",
       "\"\"\"\n",
       "\n",
@@ -445,7 +443,7 @@
       "\n",
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true or false.\n",
+      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.\n",
       "\n",
       "If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".\n",
       "\n",
@@ -557,8 +555,7 @@
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.\n",
       "\n",
-      "Checked Assertions:\n",
-      "\"\"\"\n",
+      "Checked Assertions:\"\"\"\n",
       "\n",
       "- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True\n",
       "\n",
@@ -577,8 +574,7 @@
       "- It is considered the northern branch of the Norwegian Sea. False - It is considered the northern branch of the Atlantic Ocean.\n",
       "\"\"\"\n",
       "\n",
-      "Original Summary:\n",
-      "\"\"\"\n",
+      "Original Summary:\"\"\"\n",
       "\n",
       "The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.\n",
       "\"\"\"\n",
@@ -587,20 +583,14 @@
       "\n",
       "The output should have the same structure and formatting as the original summary.\n",
       "\n",
-      "Summary:\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Summary:\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "\n",
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true or false.\n",
+      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.\n",
       "\n",
       "If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".\n",
       "\n",
@@ -711,8 +701,7 @@
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.  If the answer is false, a suggestion is given for a correction.\n",
       "\n",
-      "Checked Assertions:\n",
-      "\"\"\"\n",
+      "Checked Assertions:\"\"\"\n",
       "\n",
       "- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True\n",
       "\n",
@@ -729,8 +718,7 @@
       "- It is considered the northern branch of the Atlantic Ocean. False - The Greenland Sea is considered part of the Arctic Ocean, not the Atlantic Ocean.\n",
       "\"\"\"\n",
       "\n",
-      "Original Summary:\n",
-      "\"\"\"\n",
+      "Original Summary:\"\"\"\n",
       "\n",
       "\n",
       "The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Atlantic Ocean.\n",
@@ -747,7 +735,7 @@
       "\n",
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true or false.\n",
+      "\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.\n",
       "\n",
       "If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".\n",
       "\n",
@@ -825,14 +813,14 @@
     "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
-    "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=3)\n",
+    "checker_chain = LLMSummarizationCheckerChain(llm=llm, verbose=True, max_checks=3)\n",
     "text = \"The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.\"\n",
     "checker_chain.run(text)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -1089,7 +1077,7 @@
        "'Birds are not mammals, but they are a class of their own. They lay eggs, unlike mammals which give birth to live young.'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1099,10 +1087,17 @@
     "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
-    "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, max_checks=3, verbose=True)\n",
+    "checker_chain = LLMSummarizationCheckerChain(llm=llm, max_checks=3, verbose=True)\n",
     "text = \"Mammals can lay eggs, birds can lay eggs, therefore birds are mammals.\"\n",
     "checker_chain.run(text)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

docs/modules/chains/examples/multi_prompt_router.ipynb

@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a5cf6c49",
-   "metadata": {},
-   "source": [
-    "# Router Chains: Selecting from multiple prompts with MultiPromptChain\n",
-    "\n",
-    "This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects the prompt to use for a given input. Specifically we show how to use the `MultiPromptChain` to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "e8d624d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains.router import MultiPromptChain\n",
-    "from langchain.llms import OpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8d11fa5c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "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",
-    "\n",
-    "Here is a question:\n",
-    "{input}\"\"\"\n",
-    "\n",
-    "\n",
-    "math_template = \"\"\"You are a very good mathematician. You are great at answering math questions. \\\n",
-    "You are so good because you are able to break down hard problems into their component parts, \\\n",
-    "answer the component parts, and then put them together to answer the broader question.\n",
-    "\n",
-    "Here is a question:\n",
-    "{input}\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d0b8856e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt_infos = [\n",
-    "    {\n",
-    "        \"name\": \"physics\", \n",
-    "        \"description\": \"Good for answering questions about physics\", \n",
-    "        \"prompt_template\": physics_template\n",
-    "    },\n",
-    "    {\n",
-    "        \"name\": \"math\", \n",
-    "        \"description\": \"Good for answering math questions\", \n",
-    "        \"prompt_template\": math_template\n",
-    "    }\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "db679975",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = MultiPromptChain.from_prompts(OpenAI(), prompt_infos, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "90fd594c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
-      "physics: {'input': 'What is black body radiation?'}\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\n",
-      "Black body radiation is the emission of electromagnetic radiation from a body due to its temperature. It is a type of thermal radiation that is emitted from the surface of all objects that are at a temperature above absolute zero. It is a spectrum of radiation that is influenced by the temperature of the body and is independent of the composition of the emitting material.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain.run(\"What is black body radiation?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b8c83765",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
-      "math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "?\n",
-      "\n",
-      "The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43. To solve this problem, we can break down the question into two parts: finding the first prime number greater than 40, and then finding a number that is divisible by 3. \n",
-      "\n",
-      "The first step is to find the first prime number greater than 40. A prime number is a number that is only divisible by 1 and itself. The next prime number after 40 is 41.\n",
-      "\n",
-      "The second step is to find a number that is divisible by 3. To do this, we can add 1 to 41, which gives us 42. Now, we can check if 42 is divisible by 3. 42 divided by 3 is 14, so 42 is divisible by 3.\n",
-      "\n",
-      "Therefore, the answer to the question is 43.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain.run(\"What is the first prime number greater than 40 such that one plus the prime number is divisible by 3\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "74c6bba7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
-      "None: {'input': 'What is the name of the type of cloud that rains?'}\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "The type of cloud that typically produces rain is called a cumulonimbus cloud. This type of cloud is characterized by its large vertical extent and can produce thunderstorms and heavy precipitation. Is there anything else you'd like to know?\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain.run(\"What is the name of the type of cloud that rins\"))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv",
-   "language": "python",
-   "name": "venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/chains/examples/multi_retrieval_qa_router.ipynb

@@ -1,209 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "782ffcf1",
-   "metadata": {},
-   "source": [
-    "# Router Chains: Selecting from multiple prompts with MultiRetrievalQAChain\n",
-    "\n",
-    "This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects which Retrieval system to use. Specifically we show how to use the `MultiRetrievalQAChain` to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then answers the question using it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b6aeec07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains.router import MultiRetrievalQAChain\n",
-    "from langchain.llms import OpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "3c42f051",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.document_loaders import TextLoader\n",
-    "from langchain.vectorstores import FAISS\n",
-    "\n",
-    "sou_docs = TextLoader('../../state_of_the_union.txt').load_and_split()\n",
-    "sou_retriever = FAISS.from_documents(sou_docs, OpenAIEmbeddings()).as_retriever()\n",
-    "\n",
-    "pg_docs = TextLoader('../../paul_graham_essay.txt').load_and_split()\n",
-    "pg_retriever = FAISS.from_documents(pg_docs, OpenAIEmbeddings()).as_retriever()\n",
-    "\n",
-    "personal_texts = [\n",
-    "    \"I love apple pie\",\n",
-    "    \"My favorite color is fuchsia\",\n",
-    "    \"My dream is to become a professional dancer\",\n",
-    "    \"I broke my arm when I was 12\",\n",
-    "    \"My parents are from Peru\",\n",
-    "]\n",
-    "personal_retriever = FAISS.from_texts(personal_texts, OpenAIEmbeddings()).as_retriever()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "783d6bcd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever_infos = [\n",
-    "    {\n",
-    "        \"name\": \"state of the union\", \n",
-    "        \"description\": \"Good for answering questions about the 2023 State of the Union address\", \n",
-    "        \"retriever\": sou_retriever\n",
-    "    },\n",
-    "    {\n",
-    "        \"name\": \"pg essay\", \n",
-    "        \"description\": \"Good for answer quesitons about Paul Graham's essay on his career\", \n",
-    "        \"retriever\": pg_retriever\n",
-    "    },\n",
-    "    {\n",
-    "        \"name\": \"personal\", \n",
-    "        \"description\": \"Good for answering questions about me\", \n",
-    "        \"retriever\": personal_retriever\n",
-    "    }\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "5b671ac5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = MultiRetrievalQAChain.from_retrievers(OpenAI(), retriever_infos, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "7db5814f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new MultiRetrievalQAChain chain...\u001b[0m\n",
-      "state of the union: {'query': 'What did the president say about the economy in the 2023 State of the Union address?'}\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      " The president said that the economy was stronger than it had been a year prior, and that the American Rescue Plan helped create record job growth and fuel economic relief for millions of Americans. He also proposed a plan to fight inflation and lower costs for families, including cutting the cost of prescription drugs and energy, providing investments and tax credits for energy efficiency, and increasing access to child care and Pre-K.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain.run(\"What did the president say about the economy?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "bbcdbe82",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new MultiRetrievalQAChain chain...\u001b[0m\n",
-      "pg essay: {'query': 'What is something Paul Graham regrets about his work?'}\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      " Paul Graham regrets that he did not take a vacation after selling his company, instead of immediately starting to paint.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain.run(\"What is something Paul Graham regrets about his work?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "37c88a27",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new MultiRetrievalQAChain chain...\u001b[0m\n",
-      "personal: {'query': 'What is my background?'}\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      " Your background is Peruvian.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain.run(\"What is my background?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "de8519b2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new MultiRetrievalQAChain chain...\u001b[0m\n",
-      "None: {'query': 'What year was the Internet created in?'}\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "The Internet was created in 1969 through a project called ARPANET, which was funded by the United States Department of Defense. However, the World Wide Web, which is often confused with the Internet, was created in 1989 by British computer scientist Tim Berners-Lee.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain.run(\"What year was the Internet created in?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e50a0227",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv",
-   "language": "python",
-   "name": "venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/chains/examples/openapi.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# OpenAPI Chain\n",
     "\n",
-    "This notebook shows an example of using an OpenAPI chain to call an endpoint in natural language, and get back a response in natural language."
+    "This notebook shows an example of using an OpenAPI chain to call an endpoint in natural language, and get back a response in natural language"
    ]
   },
   {

docs/modules/chains/examples/pal.ipynb

@@ -28,7 +28,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI(temperature=0, max_tokens=512)"
+    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)"
    ]
   },
   {
@@ -63,9 +63,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "3ef64b27",
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -73,17 +71,17 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mdef solution():\n",
+      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3mdef solution():\n",
       "    \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
       "    cindy_pets = 4\n",
       "    marcia_pets = cindy_pets + 2\n",
       "    jan_pets = marcia_pets * 3\n",
       "    total_pets = cindy_pets + marcia_pets + jan_pets\n",
       "    result = total_pets\n",
-      "    return result\u001b[0m\n",
+      "    return result\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -141,8 +139,8 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
+      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
       "objects = []\n",
       "objects += [('booklet', 'blue')] * 2\n",
       "objects += [('booklet', 'purple')] * 2\n",
@@ -153,9 +151,9 @@
       "\n",
       "# Count number of purple objects\n",
       "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
-      "answer = num_purple\u001b[0m\n",
+      "answer = num_purple\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished PALChain chain.\u001b[0m\n"
+      "\u001B[1m> Finished PALChain chain.\u001B[0m\n"
      ]
     },
     {
@@ -214,8 +212,8 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
+      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
       "objects = []\n",
       "objects += [('booklet', 'blue')] * 2\n",
       "objects += [('booklet', 'purple')] * 2\n",
@@ -226,9 +224,9 @@
       "\n",
       "# Count number of purple objects\n",
       "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
-      "answer = num_purple\u001b[0m\n",
+      "answer = num_purple\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     }
    ],
@@ -282,7 +280,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,