Add Patch to stream runs

[RELLM](https://github.com/r2d4/rellm) is a library that wraps local
HuggingFace pipeline models for structured decoding.

RELLM works by generating tokens one at a time. At each step, it masks
tokens that don't conform to the provided partial regular expression.

[JSONFormer](https://github.com/1rgs/jsonformer) is a bit different, where it sequentially adds the keys then decodes each value directly

.devcontainer/Dockerfile

@@ -0,0 +1,42 @@
+# 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

@@ -0,0 +1,33 @@
+// 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

@@ -0,0 +1,31 @@
+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

@@ -2,60 +2,62 @@
 
 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 it be in the form of a new feature, improved infra, or better documentation.
+to contributions, whether they be in the form of new features, improved infra, better documentation, or bug fixes.
+
+## 🗺️ Guidelines
+
+### 👩‍💻 Contributing Code
 
 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.
 
-## 🗺️Contributing Guidelines
+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.
 
 ### 🚩GitHub Issues
 
 Our [issues](https://github.com/hwchase17/langchain/issues) page is kept up to date
-with bugs, improvements, and feature requests. There is a taxonomy of labels to help
-with sorting and discovery of issues of interest. These include:
+with bugs, improvements, and feature requests. 
 
-- 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
+There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help
+organize issues.
 
 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 the two issues are related, or blocking, please link them rather than keep them as one single one.
+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.
 
 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 just let us know.
+If you notice this happening, please let us know.
 
 ### 🙋Getting Help
 
-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.
+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.
 
 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.
 
-### 🏭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
+## 🚀 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.
 
@@ -75,9 +77,9 @@ 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.
+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`.
 
-## ✅Common Tasks
+## ✅ Common Tasks
 
 Type `make` for a list of common tasks.
 
@@ -188,3 +190,17 @@ 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

@@ -0,0 +1,106 @@
+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

@@ -0,0 +1,6 @@
+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

@@ -0,0 +1,19 @@
+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

@@ -0,0 +1,30 @@
+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

@@ -0,0 +1,18 @@
+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

@@ -0,0 +1,46 @@
+# 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

@@ -0,0 +1,64 @@
+# 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: "1.3.1"
+  POETRY_VERSION: "1.4.2"
 
 jobs:
   build:

.github/workflows/lint.yml

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

.github/workflows/release.yml

@@ -10,7 +10,7 @@ on:
       - 'pyproject.toml'
 
 env:
-  POETRY_VERSION: "1.3.1"
+  POETRY_VERSION: "1.4.2"
 
 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: "1.3.1"
+  POETRY_VERSION: "1.4.2"
 
 jobs:
   build:
@@ -18,17 +18,31 @@ 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: actions/setup-python@v4
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
-          cache: "poetry"
-      - name: Install dependencies
-        run: poetry install
-      - name: Run unit tests
+          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 ${{matrix.test_type}} tests
         run: |
-          make test
+          if [ "${{ matrix.test_type }}" == "core" ]; then
+            make test
+          else
+            make extended_tests
+          fi
+        shell: bash

.gitignore

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

.readthedocs.yaml

@@ -0,0 +1,26 @@
+# 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

@@ -1,5 +1,7 @@
 # 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
 
@@ -7,7 +9,7 @@ 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=/opt/poetry
+ARG POETRY_HOME
 
 # Create a Python virtual environment for Poetry and install it
 RUN python3 -m venv ${POETRY_HOME} && \
@@ -23,6 +25,8 @@ 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
+.PHONY: all clean format lint test tests test_watch integration_tests docker_tests help extended_tests
 
 all: help
 
@@ -32,11 +32,16 @@ lint lint_diff:
 	poetry run black $(PYTHON_FILES) --check
 	poetry run ruff .
 
+TEST_FILE ?= tests/unit_tests/
+
 test:
-	poetry run pytest tests/unit_tests
+	poetry run pytest $(TEST_FILE)
 
 tests:
-	poetry run pytest tests/unit_tests
+	poetry run pytest $(TEST_FILE)
+
+extended_tests:
+	poetry run pytest --only-extended tests/unit_tests
 
 test_watch:
 	poetry run ptw --now . -- tests/unit_tests
@@ -50,13 +55,16 @@ 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_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                         - 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'

README.md

@@ -2,7 +2,19 @@
 
 ⚡ 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) [![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)
+[![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).
 
 **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.
@@ -10,15 +22,14 @@ Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set u
 ## Quick Install
 
 `pip install langchain`
+or
+`conda install langchain -c conda-forge`
 
 ## 🤔 What is this?
 
-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.
+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.
 
-This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
+This library aims to assist in the development of those types of applications. Common examples of these applications include:
 
 **❓ Question Answering over specific documents**
 
@@ -51,32 +62,32 @@ These are, in increasing order of complexity:
 
 **📃 LLMs and Prompts:**
 
-This includes prompt management, prompt optimization, generic interface for all LLMs, and common utilities for working with LLMs.
+This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
 
 **🔗 Chains:**
 
-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.
+Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
 
 **📚 Data Augmented Generation:**
 
-Data Augmented Generation involves specific types of chains that first interact with an external 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.
+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.
 
 **🤖 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 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.
+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.
 
 **🧐 Evaluation:**
 
 [BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
 
-For more information on these concepts, please see our [full documentation](https://langchain.readthedocs.io/en/latest/?).
+For more information on these concepts, please see our [full documentation](https://langchain.readthedocs.io/en/latest/).
 
 ## 💁 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 infra, 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 infrastructure, or better documentation.
 
 For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).

docs/_static/css/custom.css

@@ -11,3 +11,7 @@ pre {
     max-width: 2560px !important;
   }
 }
+
+#my-component-root *, #headlessui-portal-root * {
+  z-index: 1000000000000;
+}

docs/_static/js/mendablesearch.js

@@ -0,0 +1,58 @@
+document.addEventListener('DOMContentLoaded', () => {
+  // Load the external dependencies
+  function loadScript(src, onLoadCallback) {
+    const script = document.createElement('script');
+    script.src = src;
+    script.onload = onLoadCallback;
+    document.head.appendChild(script);
+  }
+
+  function createRootElement() {
+    const rootElement = document.createElement('div');
+    rootElement.id = 'my-component-root';
+    document.body.appendChild(rootElement);
+    return rootElement;
+  }
+
+  
+
+  function initializeMendable() {
+    const rootElement = createRootElement();
+    const { MendableFloatingButton } = Mendable;
+    
+
+    const iconSpan1 = React.createElement('span', {
+    }, '🦜');
+
+    const iconSpan2 = React.createElement('span', {
+    }, '🔗');
+
+    const icon = React.createElement('p', {
+      style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
+    }, [iconSpan1, iconSpan2]);
+
+    
+    
+
+    const mendableFloatingButton = React.createElement(
+      MendableFloatingButton,
+      {
+        style: { darkMode: false, accentColor: '#010810' },
+        floatingButtonStyle: { color: '#ffffff', backgroundColor: '#010810' },
+        anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
+        messageSettings: {
+          openSourcesInNewTab: false,
+        },
+        icon: icon,
+      }
+    );
+
+    ReactDOM.render(mendableFloatingButton, rootElement);
+  }
+
+  loadScript('https://unpkg.com/react@17/umd/react.production.min.js', () => {
+    loadScript('https://unpkg.com/react-dom@17/umd/react-dom.production.min.js', () => {
+      loadScript('https://unpkg.com/@mendable/search@0.0.93/dist/umd/mendable.min.js', initializeMendable);
+    });
+  });
+});

docs/conf.py

@@ -103,5 +103,10 @@ html_static_path = ["_static"]
 html_css_files = [
     "css/custom.css",
 ]
+
+html_js_files = [
+    "js/mendablesearch.js",
+]
+
 nb_execution_mode = "off"
 myst_enable_extensions = ["colon_fence"]

docs/deployments.md

@@ -1,14 +1,10 @@
 # Deployments
 
-So you've made a really cool chain - now what? How do you deploy it and make it easily sharable with the world?
+So, you've created a really cool chain - now what? How do you deploy it and make it easily shareable with the world?
 
-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.
+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.
 
-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.
+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.
 
 ## [Streamlit](https://github.com/hwchase17/langchain-streamlit-template)
 
@@ -33,10 +29,34 @@ 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, persistant 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, persistent 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

@@ -3,6 +3,25 @@ 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

@@ -61,7 +61,6 @@
     "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"
    ]
   },
@@ -109,8 +108,8 @@
     "    experiment_name=\"scenario 1: OpenAI LLM\",\n",
     ")\n",
     "\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), aim_callback])\n",
-    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
+    "callbacks = [StdOutCallbackHandler(), aim_callback]\n",
+    "llm = OpenAI(temperature=0, callbacks=callbacks)"
    ]
   },
   {
@@ -177,7 +176,7 @@
     "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, callback_manager=manager)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
     "\n",
     "test_prompts = [\n",
     "    {\"title\": \"documentary about good video games that push the boundary of game design\"},\n",
@@ -249,13 +248,12 @@
    ],
    "source": [
     "# scenario 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
     "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callback_manager=manager,\n",
-    "    verbose=True,\n",
+    "    callbacks=callbacks,\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

@@ -0,0 +1,15 @@
+# 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

@@ -0,0 +1,17 @@
+# 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

@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Getting API Credentials\n",
+    "## Getting API Credentials\n",
     "\n",
     "We'll be using quite some APIs in this notebook, here is a list and where to get them:\n",
     "\n",
@@ -47,7 +47,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Setting Up"
+    "## Setting Up"
    ]
   },
   {
@@ -79,7 +79,6 @@
    "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",
@@ -93,9 +92,9 @@
     "    complexity_metrics=True,\n",
     "    stream_logs=True\n",
     ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), clearml_callback])\n",
+    "callbacks = [StdOutCallbackHandler(), clearml_callback]\n",
     "# Get the OpenAI model ready to go\n",
-    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
+    "llm = OpenAI(temperature=0, callbacks=callbacks)"
    ]
   },
   {
@@ -103,7 +102,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Scenario 1: Just an LLM\n",
+    "## Scenario 1: Just an LLM\n",
     "\n",
     "First, let's just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML"
    ]
@@ -361,7 +360,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Scenario 2: Creating a agent with tools\n",
+    "## Scenario 2: Creating an agent with tools\n",
     "\n",
     "To show a more advanced workflow, let's create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example.\n",
     "\n",
@@ -523,13 +522,12 @@
     "from langchain.agents import AgentType\n",
     "\n",
     "# SCENARIO 2 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
     "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callback_manager=manager,\n",
-    "    verbose=True,\n",
+    "    callbacks=callbacks,\n",
     ")\n",
     "agent.run(\n",
     "    \"Who is the wife of the person who sang summer of 69?\"\n",
@@ -542,7 +540,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tips and Next Steps\n",
+    "## Tips and Next Steps\n",
     "\n",
     "- Make sure you always use a unique `name` argument for the `clearml_callback.flush_tracker` function. If not, the model parameters used for a run will override the previous run!\n",
     "\n",

docs/ecosystem/comet_tracking.ipynb

@@ -0,0 +1,347 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://user-images.githubusercontent.com/7529846/230328046-a8b18c51-12e3-4617-9b39-97614a571a2d.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with [Comet](https://www.comet.com/site/?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook).  \n",
+    "\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/ecosystem/comet_tracking.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>\n",
+    "\n",
+    "**Example Project:** [Comet with LangChain](https://www.comet.com/examples/comet-example-langchain/view/b5ZThK6OFdhKWVSP3fDfRtrNF/panels?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img width=\"1280\" alt=\"comet-langchain\" src=\"https://user-images.githubusercontent.com/7529846/230326720-a9711435-9c6f-4edb-a707-94b67271ab25.png\">\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install Comet and Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install comet_ml langchain openai google-search-results spacy textstat pandas\n",
+    "\n",
+    "import sys\n",
+    "!{sys.executable} -m spacy download en_core_web_sm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize Comet and Set your Credentials"
+   ]
+  },
+  {
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import comet_ml\n",
+    "\n",
+    "comet_ml.init(project_name=\"comet-example-langchain\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set OpenAI and SerpAPI credentials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will need an [OpenAI API Key](https://platform.openai.com/account/api-keys) and a [SerpAPI API Key](https://serpapi.com/dashboard) to run the following examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
+    "#os.environ[\"OPENAI_ORGANIZATION\"] = \"...\"\n",
+    "os.environ[\"SERPAPI_API_KEY\"] = \"...\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scenario 1: Using just an LLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "comet_callback = CometCallbackHandler(\n",
+    "    project_name=\"comet-example-langchain\",\n",
+    "    complexity_metrics=True,\n",
+    "    stream_logs=True,\n",
+    "    tags=[\"llm\"],\n",
+    "    visualizations=[\"dep\"],\n",
+    ")\n",
+    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
+    "llm = OpenAI(temperature=0.9, callbacks=callbacks, 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",
+    "comet_callback.flush_tracker(llm, finish=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scenario 2: Using an LLM in a Chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "comet_callback = CometCallbackHandler(\n",
+    "    complexity_metrics=True,\n",
+    "    project_name=\"comet-example-langchain\",\n",
+    "    stream_logs=True,\n",
+    "    tags=[\"synopsis-chain\"],\n",
+    ")\n",
+    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
+    "llm = OpenAI(temperature=0.9, callbacks=callbacks)\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",
+    "\n",
+    "test_prompts = [{\"title\": \"Documentary about Bigfoot in Paris\"}]\n",
+    "print(synopsis_chain.apply(test_prompts))\n",
+    "comet_callback.flush_tracker(synopsis_chain, finish=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scenario 3: Using An Agent with Tools "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "comet_callback = CometCallbackHandler(\n",
+    "    project_name=\"comet-example-langchain\",\n",
+    "    complexity_metrics=True,\n",
+    "    stream_logs=True,\n",
+    "    tags=[\"agent\"],\n",
+    ")\n",
+    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
+    "llm = OpenAI(temperature=0.9, callbacks=callbacks)\n",
+    "\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=\"zero-shot-react-description\",\n",
+    "    callbacks=callbacks,\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",
+    "comet_callback.flush_tracker(agent, finish=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scenario 4: Using Custom Evaluation Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `CometCallbackManager` also allows you to define and use Custom Evaluation Metrics to assess generated outputs from your model. Let's take a look at how this works. \n",
+    "\n",
+    "\n",
+    "In the snippet below, we will use the [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge) metric to evaluate the quality of a generated summary of an input prompt. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install rouge-score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rouge_score import rouge_scorer\n",
+    "\n",
+    "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "\n",
+    "class Rouge:\n",
+    "    def __init__(self, reference):\n",
+    "        self.reference = reference\n",
+    "        self.scorer = rouge_scorer.RougeScorer([\"rougeLsum\"], use_stemmer=True)\n",
+    "\n",
+    "    def compute_metric(self, generation, prompt_idx, gen_idx):\n",
+    "        prediction = generation.text\n",
+    "        results = self.scorer.score(target=self.reference, prediction=prediction)\n",
+    "\n",
+    "        return {\n",
+    "            \"rougeLsum_score\": results[\"rougeLsum\"].fmeasure,\n",
+    "            \"reference\": self.reference,\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "reference = \"\"\"\n",
+    "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building.\n",
+    "It was the first structure to reach a height of 300 metres.\n",
+    "\n",
+    "It is now taller than the Chrysler Building in New York City by 5.2 metres (17 ft)\n",
+    "Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France .\n",
+    "\"\"\"\n",
+    "rouge_score = Rouge(reference=reference)\n",
+    "\n",
+    "template = \"\"\"Given the following article, it is your job to write a summary.\n",
+    "Article:\n",
+    "{article}\n",
+    "Summary: This is the summary for the above article:\"\"\"\n",
+    "prompt_template = PromptTemplate(input_variables=[\"article\"], template=template)\n",
+    "\n",
+    "comet_callback = CometCallbackHandler(\n",
+    "    project_name=\"comet-example-langchain\",\n",
+    "    complexity_metrics=False,\n",
+    "    stream_logs=True,\n",
+    "    tags=[\"custom_metrics\"],\n",
+    "    custom_metrics=rouge_score.compute_metric,\n",
+    ")\n",
+    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
+    "llm = OpenAI(temperature=0.9)\n",
+    "\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)\n",
+    "\n",
+    "test_prompts = [\n",
+    "    {\n",
+    "        \"article\": \"\"\"\n",
+    "                 The tower is 324 metres (1,063 ft) tall, about the same height as\n",
+    "                 an 81-storey building, and the tallest structure in Paris. Its base is square,\n",
+    "                 measuring 125 metres (410 ft) on each side.\n",
+    "                 During its construction, the Eiffel Tower surpassed the\n",
+    "                 Washington Monument to become the tallest man-made structure in the world,\n",
+    "                 a title it held for 41 years until the Chrysler Building\n",
+    "                 in New York City was finished in 1930.\n",
+    "\n",
+    "                 It was the first structure to reach a height of 300 metres.\n",
+    "                 Due to the addition of a broadcasting aerial at the top of the tower in 1957,\n",
+    "                 it is now taller than the Chrysler Building by 5.2 metres (17 ft).\n",
+    "\n",
+    "                 Excluding transmitters, the Eiffel Tower is the second tallest\n",
+    "                 free-standing structure in France after the Millau Viaduct.\n",
+    "                 \"\"\"\n",
+    "    }\n",
+    "]\n",
+    "print(synopsis_chain.apply(test_prompts, callbacks=callbacks))\n",
+    "comet_callback.flush_tracker(synopsis_chain, finish=True)"
+   ]
+  }
+ ],
+ "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": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/ecosystem/databerry.md

@@ -0,0 +1,25 @@
+# Databerry
+
+This page covers how to use the [Databerry](https://databerry.ai) within LangChain.
+
+## What is Databerry?
+
+Databerry is an [open source](https://github.com/gmpetrov/databerry) document retrievial platform that helps to connect your personal data with Large Language Models.
+
+![Databerry](../_static/DataberryDashboard.png)
+
+## Quick start
+
+Retrieving documents stored in Databerry from LangChain is very easy!
+
+```python
+from langchain.retrievers import DataberryRetriever
+
+retriever = DataberryRetriever(
+    datastore_url="https://api.databerry.ai/query/clg1xg2h80000l708dymr0fxc",
+    # api_key="DATABERRY_API_KEY", # optional if datastore is public
+    # top_k=10 # optional
+)
+
+docs = retriever.get_relevant_documents("What's Databerry?")
+```

docs/ecosystem/deeplake.md

@@ -8,8 +8,9 @@ This page covers how to use the Deep Lake ecosystem within LangChain.
 
 ## More Resources
 1. [Ultimate Guide to LangChain & Deep Lake: Build ChatGPT to Answer Questions on Your Financial Data](https://www.activeloop.ai/resources/ultimate-guide-to-lang-chain-deep-lake-build-chat-gpt-to-answer-questions-on-your-financial-data/)
-1. Here is [whitepaper](https://www.deeplake.ai/whitepaper) and [academic paper](https://arxiv.org/pdf/2209.10785.pdf) for Deep Lake
-2. Here is a set of additional resources available for review: [Deep Lake](https://github.com/activeloopai/deeplake), [Getting Started](https://docs.activeloop.ai/getting-started) and [Tutorials](https://docs.activeloop.ai/hub-tutorials)
+2. [Twitter the-algorithm codebase analysis with Deep Lake](../use_cases/code/twitter-the-algorithm-analysis-deeplake.ipynb)
+3. Here is [whitepaper](https://www.deeplake.ai/whitepaper) and [academic paper](https://arxiv.org/pdf/2209.10785.pdf) for Deep Lake
+4. Here is a set of additional resources available for review: [Deep Lake](https://github.com/activeloopai/deeplake), [Getting Started](https://docs.activeloop.ai/getting-started) and [Tutorials](https://docs.activeloop.ai/hub-tutorials)
 
 ## Installation and Setup
 - Install the Python package with `pip install deeplake`

docs/ecosystem/gpt4all.md

@@ -1,21 +1,22 @@
 # GPT4All
 
-This page covers how to use the `GPT4All` wrapper within LangChain.
-It is broken into two parts: installation and setup, and then usage with an example.
+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/gpt4all) and place it in your desired directory
+- Download a [GPT4All model](https://github.com/nomic-ai/pyllamacpp#supported-model) and place it in your desired directory
 
 ## Usage
 
 ### GPT4All
 
 To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model's configuration.
+
 ```python
 from langchain.llms import GPT4All
 
-# Instantiate the model
+# Instantiate the model. Callbacks support token-wise streaming
 model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
 
 # Generate text
@@ -24,14 +25,24 @@ response = model("Once upon a time, ")
 
 You can also customize the generation parameters, such as n_predict, temp, top_p, top_k, and others.
 
-Example:
+To stream the model's predictions, add in a CallbackManager.
 
 ```python
-model = GPT4All(model="./models/gpt4all-model.bin", n_predict=55, temp=0)
-response = model("Once upon a time, ")
+from langchain.llms import GPT4All
+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)
+
+# Generate text. Tokens are streamed through the callback manager.
+model("Once upon a time, ", callbacks=callbacks)
 ```
+
 ## Model File
 
-You can find links to model file downloads at the [GPT4all](https://github.com/nomic-ai/gpt4all) repository. They will need to be converted to `ggml` format to work, as specified in the [pyllamacpp](https://github.com/nomic-ai/pyllamacpp) repository.
+You can find links to model file downloads in the [pyllamacpp](https://github.com/nomic-ai/pyllamacpp) repository.
 
-For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/gpt4all.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/gpt4all.ipynb)

docs/ecosystem/graphsignal.md

@@ -1,6 +1,6 @@
 # Graphsignal
 
-This page covers how to use the Graphsignal ecosystem to trace and monitor LangChain.
+This page covers how to use [Graphsignal](https://app.graphsignal.com) to trace and monitor LangChain. Graphsignal enables full visibility into your application. It provides latency breakdowns by chains and tools, exceptions with full context, data monitoring, compute/GPU utilization, OpenAI cost analytics, and more.
 
 ## Installation and Setup
 
@@ -10,7 +10,7 @@ This page covers how to use the Graphsignal ecosystem to trace and monitor LangC
 
 ## Tracing and Monitoring
 
-Graphsignal automatically instruments and starts tracing and monitoring chains. Traces, metrics and errors are then available in your [Graphsignal dashboard](https://app.graphsignal.com/). No prompts or other sensitive data are sent to Graphsignal cloud, only statistics and metadata.
+Graphsignal automatically instruments and starts tracing and monitoring chains. Traces and metrics are then available in your [Graphsignal dashboards](https://app.graphsignal.com).
 
 Initialize the tracer by providing a deployment name:
 
@@ -20,7 +20,13 @@ import graphsignal
 graphsignal.configure(deployment='my-langchain-app-prod')
 ```
 
-In order to trace full runs and see a breakdown by chains and tools, you can wrap the calling routine or use a decorator:
+To additionally trace any function or code, you can use a decorator or a context manager:
+
+```python
+@graphsignal.trace_function
+def handle_request():    
+    chain.run("some initial text")
+```
 
 ```python
 with graphsignal.start_trace('my-chain'):

docs/ecosystem/lancedb.md

@@ -0,0 +1,23 @@
+# 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

@@ -0,0 +1,26 @@
+# 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

@@ -0,0 +1,172 @@
+{
+ "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

@@ -0,0 +1,65 @@
+# 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

@@ -0,0 +1,19 @@
+# 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

@@ -0,0 +1,56 @@
+# 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

@@ -0,0 +1,79 @@
+# 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 [flan-t5 model](https://replicate.com/daanelson/flan-t5), click on the API tab. The model name/version would be: `daanelson/flan-t5:04e422a9b85baed86a4f24981d7f9953e20c5fd82f6103b74ebc431588e1cec8`
+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"`
 
 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="daanelson/flan-t5:04e422a9b85baed86a4f24981d7f9953e20c5fd82f6103b74ebc431588e1cec8")
+llm = Replicate(model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5")
 ```
 
 And run it:
@@ -40,8 +40,7 @@ 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/self_hosted_examples.ipynb)
+For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/models/llms/integrations/runhouse.ipynb)
 
 ## Self-hosted Embeddings
 There are several ways to use self-hosted embeddings with LangChain via Runhouse.

docs/ecosystem/tair.md

@@ -0,0 +1,22 @@
+# 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

@@ -10,6 +10,10 @@ 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.
@@ -25,6 +29,15 @@ This page is broken into two parts: installation and setup, and then references
       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

@@ -50,7 +50,6 @@
    "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"
    ]
   },
@@ -196,8 +195,8 @@
     "    name=\"llm\",\n",
     "    tags=[\"test\"],\n",
     ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), wandb_callback])\n",
-    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
+    "callbacks = [StdOutCallbackHandler(), wandb_callback]\n",
+    "llm = OpenAI(temperature=0, callbacks=callbacks)"
    ]
   },
   {
@@ -484,7 +483,7 @@
     "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, callback_manager=manager)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
     "\n",
     "test_prompts = [\n",
     "    {\n",
@@ -577,16 +576,15 @@
    ],
    "source": [
     "# SCENARIO 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\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",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\",\n",
+    "    callbacks=callbacks,\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/getting_started.ipynb)
+For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/indexes/vectorstores/examples/weaviate.ipynb)

docs/ecosystem/yeagerai.md

@@ -0,0 +1,43 @@
+# 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/ecosystem/zilliz.md

@@ -0,0 +1,21 @@
+# Zilliz
+
+This page covers how to use the Zilliz Cloud ecosystem within LangChain.
+Zilliz uses the Milvus integration. 
+It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
+
+## Installation and Setup
+- Install the Python SDK with `pip install pymilvus`
+## Wrappers
+
+### VectorStore
+
+There exists a wrapper around Zilliz indexes, allowing you to use it as a vectorstore,
+whether for semantic search or example selection.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import Milvus
+```
+
+For a more detailed walkthrough of the Miluvs wrapper, see [this notebook](../modules/indexes/vectorstores/examples/zilliz.ipynb)

docs/gallery.rst

@@ -1,5 +1,5 @@
 LangChain Gallery
-=============
+=================
 
 Lots of people have built some pretty awesome stuff with LangChain.
 This is a collection of our favorites.
@@ -223,7 +223,7 @@ Open Source
     Answer questions about the documentation of any project    
 
 Misc. Colab Notebooks
-~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 .. panels::
     :body: text-center
@@ -280,6 +280,17 @@ 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
@@ -332,4 +343,12 @@ 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

@@ -9,6 +9,8 @@ To get started, install LangChain with the following command:
 
 ```bash
 pip install langchain
+# or
+conda install langchain -c conda-forge
 ```
 
 
@@ -44,7 +46,7 @@ LangChain provides many modules that can be used to build language model applica
 
 
 
-`````{dropdown} LLMs: Get predictions from a language model
+## 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. 
@@ -75,10 +77,9 @@ 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).
-`````
 
 
-`````{dropdown} Prompt Templates: Manage prompts for LLMs
+## 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.
@@ -113,11 +114,10 @@ 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)
 
-`````
 
 
 
-`````{dropdown} Chains: Combine LLMs and prompts in multi-step workflows
+## 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.
 
@@ -157,10 +157,7 @@ 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)
 
-`````
-
-
-`````{dropdown} Agents: Dynamically Call Chains Based on User Input
+## Agents: Dynamically Call Chains Based on User Input
 
 So far the chains we've looked at run in a predetermined order.
 
@@ -175,9 +172,9 @@ 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/agents.md).
+**Agents**: For a list of supported agents and their specifications, see [here](../modules/agents/getting_started.ipynb).
 
-**Tools**: For a list of predefined tools and their specifications, see [here](../modules/agents/tools.md).
+**Tools**: For a list of predefined tools and their specifications, see [here](../modules/agents/tools/getting_started.md).
 
 For this example, you will also need to install the SerpAPI Python package.
 
@@ -232,10 +229,8 @@ Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .
 ```
 
 
-`````
 
-
-`````{dropdown} Memory: Add State to Chains and Agents
+## 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/).
 
@@ -249,7 +244,8 @@ from langchain import OpenAI, ConversationChain
 llm = OpenAI(temperature=0)
 conversation = ConversationChain(llm=llm, verbose=True)
 
-conversation.predict(input="Hi there!")
+output = conversation.predict(input="Hi there!")
+print(output)
 ```
 
 ```pycon
@@ -267,7 +263,8 @@ AI:
 ```
 
 ```python
-conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
+output = conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
+print(output)
 ```
 
 ```pycon
@@ -285,7 +282,6 @@ AI:
 > Finished chain.
 " That's great! What would you like to talk about?"
 ```
-`````
 
 ## Building a Language Model Application: Chat Models
 
@@ -293,8 +289,8 @@ 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
@@ -320,7 +316,7 @@ 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="Translate this sentence from English to French. I love programming.")
+    HumanMessage(content="I love programming.")
 ]
 chat(messages)
 # -> AIMessage(content="J'aime programmer.", additional_kwargs={})
@@ -331,29 +327,29 @@ 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="Translate this sentence from English to French. I love programming.")
+        HumanMessage(content="I love programming.")
     ],
     [
         SystemMessage(content="You are a helpful assistant that translates English to French."),
-        HumanMessage(content="Translate this sentence from English to French. I love artificial intelligence.")
+        HumanMessage(content="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': 71, 'completion_tokens': 18, 'total_tokens': 89}})
+# -> 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}})
 ```
 
 You can recover things like token usage from this LLMResult:
 ```
 result.llm_output['token_usage']
-# -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
+# -> {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}
 ```
-`````
 
-`````{dropdown} Chat Prompt Templates
+
+## 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 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:
+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:
 
 ```python
 from langchain.chat_models import ChatOpenAI
@@ -365,9 +361,9 @@ 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])
@@ -376,9 +372,8 @@ 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={})
 ```
-`````
 
-`````{dropdown} Chains with Chat Models
+## Chains with Chat Models
 The `LLMChain` discussed in the above section can be used with chat models as well:
 
 ```python
@@ -392,9 +387,9 @@ 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])
 
@@ -402,9 +397,8 @@ chain = LLMChain(llm=chat, prompt=chat_prompt)
 chain.run(input_language="English", output_language="French", text="I love programming.")
 # -> "J'aime programmer."
 ```
-`````
 
-`````{dropdown} Agents with Chat Models
+## 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
@@ -463,9 +457,7 @@ Final Answer: 2.169459462491557
 > Finished chain.
 '2.169459462491557'
 ```
-`````
-
-`````{dropdown} Memory: Add State to Chains and Agents
+## 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
@@ -499,4 +491,4 @@ 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

@@ -44,6 +44,8 @@ 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
@@ -57,12 +59,17 @@ 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.
@@ -71,6 +78,8 @@ The above modules can be used in a variety of ways. LangChain also provides guid
 
 - `Querying Tabular Data <./use_cases/tabular.html>`_: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.
 
+- `Code Understanding <./use_cases/code.html>`_: If you want to understand how to use LLMs to query source code from github, you should read this page.
+
 - `Interacting with APIs <./use_cases/apis.html>`_: Enabling LLMs to interact with APIs is extremely powerful in order to give them more up-to-date information and allow them to take actions.
 
 - `Extraction <./use_cases/extraction.html>`_: Extract structured information from text.
@@ -87,9 +96,12 @@ 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
+   ./use_cases/code.md
    ./use_cases/apis.md
    ./use_cases/summarization.md
    ./use_cases/extraction.md
@@ -150,6 +162,8 @@ 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.
 
 
@@ -166,4 +180,5 @@ 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

@@ -10,6 +10,42 @@ 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::
@@ -23,25 +59,29 @@ We then split the documentation into the following sections:
 
 **Tools**
 
-An overview of the various tools LangChain supports.
+In this section we cover the different types of tools LangChain supports natively.
+We then cover how to add your own tools.
 
 
 **Agents**
 
-An overview of the different agent types.
+In this section we cover the different types of agents LangChain supports natively.
+We then cover how to modify and create your own agents.
 
 
 **Toolkits**
 
-An overview of toolkits, and examples of the different ones LangChain supports.
+In this section we go over the various toolkits that LangChain supports out of the box,
+and how to create an agent from them.
 
 
 **Agent Executor**
 
-An overview of the Agent Executor class and examples of how to use it.
+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.
 
-Go Deeper
----------
+**Go Deeper**
 
 .. toctree::
    :maxdepth: 1
@@ -50,3 +90,23 @@ Go Deeper
    ./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/agent_vectorstore.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "68b24990",
    "metadata": {},
@@ -9,7 +10,7 @@
     "\n",
     "This notebook covers how to combine agents and vectorstores. The use case for this is that you've ingested your data into a vectorstore and want to interact with it in an agentic manner.\n",
     "\n",
-    "The reccomended method for doing so is to create a RetrievalQA and then use that as a tool in the overall agent. Let's take a look at doing this below. You can do this with multiple different vectordbs, and use the agent as a way to route between them. There are two different ways of doing this - you can either let the agent use the vectorstores as normal tools, or you can set `return_direct=True` to really just use the agent as a router."
+    "The recommended method for doing so is to create a RetrievalQA and then use that as a tool in the overall agent. Let's take a look at doing this below. You can do this with multiple different vectordbs, and use the agent as a way to route between them. There are two different ways of doing this - you can either let the agent use the vectorstores as normal tools, or you can set `return_direct=True` to really just use the agent as a router."
    ]
   },
   {

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`: [`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`: [`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",
     "\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",
+    "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",
     "\n",
     "You can use `arun` to call an `AgentExecutor` asynchronously."
    ]
@@ -28,10 +28,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "id": "da5df06c-af6f-4572-b9f5-0ab971c16487",
    "metadata": {
-    "tags": []
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2023-05-04T01:27:22.755025Z",
+     "start_time": "2023-05-04T01:27:22.754041Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -42,7 +46,6 @@
     "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",
@@ -57,10 +60,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "fd4c294e-b1d6-44b8-b32e-2765c017e503",
    "metadata": {
-    "tags": []
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2023-05-04T01:15:35.466212Z",
+     "start_time": "2023-05-04T01:14:05.452245Z"
+    }
    },
    "outputs": [
     {
@@ -69,119 +76,105 @@
      "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",
+      "\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",
       "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",
+      "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",
       "\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: 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",
+      "\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",
       "Action: Calculator\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",
+      "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",
       "\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 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",
+      "\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",
       "Action: Calculator\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",
+      "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",
       "\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: 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",
+      "\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",
       "Action: Calculator\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",
+      "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",
       "\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: 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",
+      "\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",
       "Action: Calculator\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",
+      "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",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Serial executed in 65.11 seconds.\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "Serial executed in 89.97 seconds.\n"
      ]
     }
    ],
    "source": [
-    "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",
+    "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",
     "\n",
     "s = time.perf_counter()\n",
-    "generate_serially()\n",
+    "for q in questions:\n",
+    "    agent.run(q)\n",
     "elapsed = time.perf_counter() - s\n",
     "print(f\"Serial executed in {elapsed:0.2f} seconds.\")"
    ]
@@ -191,7 +184,11 @@
    "execution_count": 4,
    "id": "076d7b85-45ec-465d-8b31-c2ad119c3438",
    "metadata": {
-    "tags": []
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2023-05-04T01:26:59.737657Z",
+     "start_time": "2023-05-04T01:26:42.182078Z"
+    }
    },
    "outputs": [
     {
@@ -200,192 +197,95 @@
      "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: 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",
+      "\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",
       "Thought:\n",
-      "Observation: \u001b[33;1m\u001b[1;3mMax Verstappen\u001b[0m\n",
+      "Observation: \u001B[36;1m\u001B[1;3mJay-Z\u001B[0m\n",
       "Thought:\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",
+      "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:\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: 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: 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: 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: 22^0.34\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.84599359907945\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:\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",
+      "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",
       "Action: Calculator\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",
+      "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: 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: 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: 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: 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",
       "\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",
+      "\u001B[1m> Finished chain.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Observation: \u001B[33;1m\u001B[1;3mAnswer: 2.12624064206896\u001B[0m\n",
+      "Thought:\n",
+      "\u001B[1m> Finished chain.\u001B[0m\n",
       "\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",
+      "\u001B[1m> Finished chain.\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: 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"
+      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "Concurrent executed in 17.52 seconds.\n"
      ]
     }
    ],
    "source": [
-    "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",
+    "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",
     "\n",
     "s = time.perf_counter()\n",
-    "# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
-    "await generate_concurrently()\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",
     "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": {
@@ -404,7 +304,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/agents/agents/custom_agent.ipynb

@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "a33e2f7e",
    "metadata": {},
    "outputs": [],
@@ -77,7 +77,7 @@
     "        Returns:\n",
     "            Action specifying what tool to use.\n",
     "        \"\"\"\n",
-    "        return AgentAction(tool=\"Search\", tool_input=\"foo\", log=\"\")\n",
+    "        return AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\")\n",
     "\n",
     "    async def aplan(\n",
     "        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
@@ -92,12 +92,12 @@
     "        Returns:\n",
     "            Action specifying what tool to use.\n",
     "        \"\"\"\n",
-    "        return AgentAction(tool=\"Search\", tool_input=\"foo\", log=\"\")"
+    "        return AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "655d72f6",
    "metadata": {},
    "outputs": [],
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "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;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",
+      "\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",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -136,10 +136,10 @@
     {
      "data": {
       "text/plain": [
-       "'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.'"
+       "'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.'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/modules/agents/agents/custom_agent_with_tool_retrieval.ipynb

@@ -315,7 +315,7 @@
     "                log=llm_output,\n",
     "            )\n",
     "        # Parse out the action and action input\n",
-    "        regex = r\"Action: (.*?)[\\n]*Action Input:[\\s]*(.*)\"\n",
+    "        regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
     "        match = re.search(regex, llm_output, re.DOTALL)\n",
     "        if not match:\n",
     "            raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
@@ -373,6 +373,7 @@
    "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_agent.ipynb

@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "id": "9af9734e",
    "metadata": {},
    "outputs": [],
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 2,
    "id": "becda2a1",
    "metadata": {},
    "outputs": [],
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "339b1bb8",
    "metadata": {},
    "outputs": [],
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "id": "fd969d31",
    "metadata": {},
    "outputs": [],
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 5,
    "id": "798ef9fb",
    "metadata": {},
    "outputs": [],
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "id": "7c6fe0d3",
    "metadata": {},
    "outputs": [],
@@ -204,7 +204,7 @@
     "                log=llm_output,\n",
     "            )\n",
     "        # Parse out the action and action input\n",
-    "        regex = r\"Action: (.*?)[\\n]*Action Input:[\\s]*(.*)\"\n",
+    "        regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
     "        match = re.search(regex, llm_output, re.DOTALL)\n",
     "        if not match:\n",
     "            raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "id": "d278706a",
    "metadata": {},
    "outputs": [],
@@ -236,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "id": "f9d4c374",
    "metadata": {},
    "outputs": [],
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 9,
    "id": "9b1cc2a2",
    "metadata": {},
    "outputs": [],
@@ -279,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "id": "e4f5092f",
    "metadata": {},
    "outputs": [],
@@ -305,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 12,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -326,11 +326,12 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction: Search\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to find out the population of Canada in 2023\n",
+      "Action: Search\n",
       "Action Input: Population of Canada in 2023\u001b[0m\n",
       "\n",
-      "Observation:\u001b[36;1m\u001b[1;3m38,648,380\u001b[0m\u001b[32;1m\u001b[1;3m That's a lot of people!\n",
-      "Final Answer: Arrr, there be 38,648,380 people livin' in Canada come 2023!\u001b[0m\n",
+      "Observation:\u001b[36;1m\u001b[1;3mThe current population of Canada is 38,658,314 as of Wednesday, April 12, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Arrr, there be 38,658,314 people livin' in Canada as of 2023!\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -338,10 +339,165 @@
     {
      "data": {
       "text/plain": [
-       "\"Arrr, there be 38,648,380 people livin' in Canada come 2023!\""
+       "\"Arrr, there be 38,658,314 people livin' in Canada as of 2023!\""
       ]
      },
-     "execution_count": 27,
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"How many people live in canada as of 2023?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5b4a078",
+   "metadata": {},
+   "source": [
+    "## Adding Memory\n",
+    "\n",
+    "If you want to add memory to the agent, you'll need to:\n",
+    "\n",
+    "1. Add a place in the custom prompt for the chat_history\n",
+    "2. Add a memory object to the agent executor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "94fffda1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up the base template\n",
+    "template_with_history = \"\"\"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",
+    "Use the following format:\n",
+    "\n",
+    "Question: the input question you must answer\n",
+    "Thought: you should always think about what to do\n",
+    "Action: the action to take, should be one of [{tool_names}]\n",
+    "Action Input: the input to the action\n",
+    "Observation: the result of the action\n",
+    "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+    "Thought: I now know the final answer\n",
+    "Final Answer: the final answer to the original input question\n",
+    "\n",
+    "Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Arg\"s\n",
+    "\n",
+    "Previous conversation history:\n",
+    "{history}\n",
+    "\n",
+    "New question: {input}\n",
+    "{agent_scratchpad}\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "f58488d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_with_history = CustomPromptTemplate(\n",
+    "    template=template_with_history,\n",
+    "    tools=tools,\n",
+    "    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
+    "    # This includes the `intermediate_steps` variable because that is needed\n",
+    "    input_variables=[\"input\", \"intermediate_steps\", \"history\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d28d4b5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(llm=llm, prompt=prompt_with_history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "3e37b32a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tool_names = [tool.name for tool in tools]\n",
+    "agent = LLMSingleActionAgent(\n",
+    "    llm_chain=llm_chain, \n",
+    "    output_parser=output_parser,\n",
+    "    stop=[\"\\nObservation:\"], \n",
+    "    allowed_tools=tool_names\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "97ea1bce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.memory import ConversationBufferWindowMemory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b5ad69ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "memory=ConversationBufferWindowMemory(k=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "b7b5c9b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "5ec4c39b",
+   "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 population of Canada in 2023\n",
+      "Action: Search\n",
+      "Action Input: Population of Canada in 2023\u001b[0m\n",
+      "\n",
+      "Observation:\u001b[36;1m\u001b[1;3mThe current population of Canada is 38,658,314 as of Wednesday, April 12, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Arrr, there be 38,658,314 people livin' in Canada as of 2023!\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Arrr, there be 38,658,314 people livin' in Canada as of 2023!\""
+      ]
+     },
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -350,10 +506,48 @@
     "agent_executor.run(\"How many people live in canada as of 2023?\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "b2ba45bb",
+   "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 live in Mexico.\n",
+      "Action: Search\n",
+      "Action Input: How many people live in Mexico as of 2023?\u001b[0m\n",
+      "\n",
+      "Observation:\u001b[36;1m\u001b[1;3mThe current population of Mexico is 132,679,922 as of Tuesday, April 11, 2023, based on Worldometer elaboration of the latest United Nations data. Mexico 2020 ...\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Arrr, there be 132,679,922 people livin' in Mexico as of 2023!\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Arrr, there be 132,679,922 people livin' in Mexico as of 2023!\""
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"how about in mexico?\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "adefb4c2",
+   "id": "bd820a7a",
    "metadata": {},
    "outputs": [],
    "source": []

docs/modules/agents/agents/custom_llm_chat_agent.ipynb

@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "9af9734e",
    "metadata": {},
    "outputs": [],
@@ -100,13 +100,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "339b1bb8",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Set up the base template\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",
+    "template = \"\"\"Complete the objective as best you can. You have access to the following tools:\n",
     "\n",
     "{tools}\n",
     "\n",
@@ -121,7 +121,11 @@
     "Thought: I now know the final answer\n",
     "Final Answer: the final answer to the original input question\n",
     "\n",
-    "Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Arg\"s\n",
+    "These were previous tasks you completed:\n",
+    "\n",
+    "\n",
+    "\n",
+    "Begin!\n",
     "\n",
     "Question: {input}\n",
     "{agent_scratchpad}\"\"\""
@@ -129,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "id": "fd969d31",
    "metadata": {},
    "outputs": [],
@@ -161,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "id": "798ef9fb",
    "metadata": {},
    "outputs": [],
@@ -189,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "id": "7c6fe0d3",
    "metadata": {},
    "outputs": [],
@@ -206,7 +210,7 @@
     "                log=llm_output,\n",
     "            )\n",
     "        # Parse out the action and action input\n",
-    "        regex = r\"Action: (.*?)[\\n]*Action Input:[\\s]*(.*)\"\n",
+    "        regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
     "        match = re.search(regex, llm_output, re.DOTALL)\n",
     "        if not match:\n",
     "            raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
@@ -218,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
    "id": "d278706a",
    "metadata": {},
    "outputs": [],
@@ -238,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "id": "f9d4c374",
    "metadata": {},
    "outputs": [],
@@ -270,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "id": "9b1cc2a2",
    "metadata": {},
    "outputs": [],
@@ -281,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "id": "e4f5092f",
    "metadata": {},
    "outputs": [],
@@ -307,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
@@ -317,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -328,16 +332,13 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: Wot year be it now? That be important to know the answer.\n",
+      "\u001b[32;1m\u001b[1;3mThought: I should use a reliable search engine to get accurate information.\n",
       "Action: Search\n",
-      "Action Input: \"current population canada 2023\"\u001b[0m\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
       "\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",
+      "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",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -345,16 +346,16 @@
     {
      "data": {
       "text/plain": [
-       "'Arrrr, thar be 38,649,283 scallywags in Canada as of 2023.'"
+       "\"Leo DiCaprio's current girlfriend is Camila Morrone.\""
       ]
      },
-     "execution_count": 16,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_executor.run(\"How many people live in canada as of 2023?\")"
+    "agent_executor.run(\"Search for Leo DiCaprio's girlfriend on the internet.\")"
    ]
   },
   {

docs/modules/agents/agents/custom_mrkl_agent.ipynb

@@ -20,13 +20,14 @@
    ]
   },
   {
+   "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 reccomended 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 recommended 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",
@@ -42,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 1,
    "id": "9af9734e",
    "metadata": {},
    "outputs": [],
@@ -53,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "id": "becda2a1",
    "metadata": {},
    "outputs": [],
@@ -70,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 3,
    "id": "339b1bb8",
    "metadata": {},
    "outputs": [],
@@ -99,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 4,
    "id": "e21d2098",
    "metadata": {},
    "outputs": [
@@ -145,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 5,
    "id": "9b1cc2a2",
    "metadata": {},
    "outputs": [],
@@ -155,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 6,
    "id": "e4f5092f",
    "metadata": {},
    "outputs": [],
@@ -166,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 7,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
@@ -176,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 8,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -190,9 +191,9 @@
       "\u001b[32;1m\u001b[1;3mThought: I need to find out the population of Canada\n",
       "Action: Search\n",
       "Action Input: Population of Canada 2023\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,610,447 as of Saturday, February 18, 2023, based on Worldometer elaboration of the latest United Nations data. Canada 2020 population is estimated at 37,742,154 people at mid year according to UN data.\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,661,927 as of Sunday, April 16, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Arrr, Canada be havin' 38,610,447 scallywags livin' there as of 2023!\u001b[0m\n",
+      "Final Answer: Arrr, Canada be havin' 38,661,927 people livin' there as of 2023!\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -200,10 +201,10 @@
     {
      "data": {
       "text/plain": [
-       "\"Arrr, Canada be havin' 38,610,447 scallywags livin' there as of 2023!\""
+       "\"Arrr, Canada be havin' 38,661,927 people livin' there as of 2023!\""
       ]
      },
-     "execution_count": 31,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 9,
    "id": "43dbfa2f",
    "metadata": {},
    "outputs": [],
@@ -244,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "id": "0f087313",
    "metadata": {},
    "outputs": [],
@@ -254,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 11,
    "id": "92c75a10",
    "metadata": {},
    "outputs": [],
@@ -264,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 12,
    "id": "ac5b83bf",
    "metadata": {},
    "outputs": [],
@@ -274,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 13,
    "id": "c960e4ff",
    "metadata": {},
    "outputs": [
@@ -285,12 +286,16 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I need to find out the population of Canada in 2023.\n",
+      "\u001b[32;1m\u001b[1;3mThought: I should look for recent population estimates.\n",
       "Action: Search\n",
-      "Action Input: Population of Canada in 2023\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,610,447 as of Saturday, February 18, 2023, based on Worldometer elaboration of the latest United Nations data. Canada 2020 population is estimated at 37,742,154 people at mid year according to UN data.\u001b[0m\n",
+      "Action Input: Canada population 2023\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m39,566,248\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should double check this number.\n",
+      "Action: Search\n",
+      "Action Input: Canada population estimates 2023\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCanada's population was estimated at 39,566,248 on January 1, 2023, after a record population growth of 1,050,110 people from January 1, 2022, to January 1, 2023.\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: La popolazione del Canada nel 2023 è stimata in 38.610.447 persone.\u001b[0m\n",
+      "Final Answer: La popolazione del Canada è stata stimata a 39.566.248 il 1° gennaio 2023, dopo un record di crescita demografica di 1.050.110 persone dal 1° gennaio 2022 al 1° gennaio 2023.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -298,10 +303,10 @@
     {
      "data": {
       "text/plain": [
-       "'La popolazione del Canada nel 2023 è stimata in 38.610.447 persone.'"
+       "'La popolazione del Canada è stata stimata a 39.566.248 il 1° gennaio 2023, dopo un record di crescita demografica di 1.050.110 persone dal 1° gennaio 2022 al 1° gennaio 2023.'"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/modules/agents/agents/custom_multi_action_agent.ipynb

@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "id": "d7c4ebdc",
    "metadata": {},
    "outputs": [],
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "id": "becda2a1",
    "metadata": {},
    "outputs": [],
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "id": "a33e2f7e",
    "metadata": {},
    "outputs": [],
@@ -96,8 +96,8 @@
     "        \"\"\"\n",
     "        if len(intermediate_steps) == 0:\n",
     "            return [\n",
-    "                AgentAction(tool=\"Search\", tool_input=\"foo\", log=\"\"),\n",
-    "                AgentAction(tool=\"RandomWord\", tool_input=\"foo\", log=\"\"),\n",
+    "                AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\"),\n",
+    "                AgentAction(tool=\"RandomWord\", tool_input=kwargs[\"input\"], 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=\"foo\", log=\"\"),\n",
-    "                AgentAction(tool=\"RandomWord\", tool_input=\"foo\", log=\"\"),\n",
+    "                AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\"),\n",
+    "                AgentAction(tool=\"RandomWord\", tool_input=kwargs[\"input\"], log=\"\"),\n",
     "            ]\n",
     "        else:\n",
     "            return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")"
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 5,
    "id": "655d72f6",
    "metadata": {},
    "outputs": [],
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 6,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "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;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",
+      "\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",
       "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": 26,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/modules/agents/agents/examples/chat_conversation_agent.ipynb

@@ -28,7 +28,15 @@
    "execution_count": 2,
    "id": "f65308ab",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x10a1767c0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain.agents import Tool\n",
     "from langchain.memory import ConversationBufferMemory\n",
@@ -88,7 +96,20 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fab40d0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[32;1m\u001b[1;3m{\n",
       "    \"action\": \"Final Answer\",\n",
       "    \"action_input\": \"Hello Bob! How can I assist you today?\"\n",
@@ -124,7 +145,20 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fab44f0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[32;1m\u001b[1;3m{\n",
       "    \"action\": \"Final Answer\",\n",
       "    \"action_input\": \"Your name is Bob.\"\n",
@@ -167,10 +201,24 @@
       "    \"action\": \"Current Search\",\n",
       "    \"action_input\": \"Thai food dinner recipes\"\n",
       "}\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m{\n",
+      "Observation: \u001b[36;1m\u001b[1;3m59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's Thai Spicy ...\u001b[0m\n",
+      "Thought:"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fae8be0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m{\n",
       "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website.\"\n",
+      "    \"action_input\": \"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and Thai Spicy ... (59 recipes in total).\"\n",
       "}\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -179,7 +227,7 @@
     {
      "data": {
       "text/plain": [
-       "\"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website.\""
+       "'Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and Thai Spicy ... (59 recipes in total).'"
       ]
      },
      "execution_count": 8,
@@ -210,11 +258,25 @@
       "    \"action_input\": \"who won the world cup in 1978\"\n",
       "}\n",
       "```\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe Argentina national football team represents Argentina in men's international football and is administered by the Argentine Football Association, the governing body for football in Argentina. Nicknamed La Albiceleste, they are the reigning world champions, having won the most recent World Cup in 2022.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m```json\n",
+      "Observation: \u001b[36;1m\u001b[1;3mArgentina national football team\u001b[0m\n",
+      "Thought:"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fae86d0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m```json\n",
       "{\n",
       "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978.\"\n",
+      "    \"action_input\": \"The last letter in your name is 'b', and the winner of the 1978 World Cup was the Argentina national football team.\"\n",
       "}\n",
       "```\u001b[0m\n",
       "\n",
@@ -224,7 +286,7 @@
     {
      "data": {
       "text/plain": [
-       "\"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978.\""
+       "\"The last letter in your name is 'b', and the winner of the 1978 World Cup was the Argentina national football team.\""
       ]
      },
      "execution_count": 9,
@@ -253,10 +315,24 @@
       "    \"action\": \"Current Search\",\n",
       "    \"action_input\": \"weather in pomfret\"\n",
       "}\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mMostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers possible. High near 40F. Winds NNW at 20 to 30 mph.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m{\n",
+      "Observation: \u001b[36;1m\u001b[1;3m10 Day Weather-Pomfret, CT ; Sun 16. 64° · 50°. 24% · NE 7 mph ; Mon 17. 58° · 45°. 70% · ESE 8 mph ; Tue 18. 57° · 37°. 8% · WSW 15 mph.\u001b[0m\n",
+      "Thought:"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fa9d7f0>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m{\n",
       "    \"action\": \"Final Answer\",\n",
-      "    \"action_input\": \"The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.\"\n",
+      "    \"action_input\": \"The weather in Pomfret, CT for the next 10 days is as follows: Sun 16. 64° · 50°. 24% · NE 7 mph ; Mon 17. 58° · 45°. 70% · ESE 8 mph ; Tue 18. 57° · 37°. 8% · WSW 15 mph.\"\n",
       "}\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -265,7 +341,7 @@
     {
      "data": {
       "text/plain": [
-       "'The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.'"
+       "'The weather in Pomfret, CT for the next 10 days is as follows: Sun 16. 64° · 50°. 24% · NE 7 mph ; Mon 17. 58° · 45°. 70% · ESE 8 mph ; Tue 18. 57° · 37°. 8% · WSW 15 mph.'"
       ]
      },
      "execution_count": 10,

docs/modules/agents/agents/examples/conversational_agent.ipynb

@@ -23,7 +23,7 @@
     "from langchain.agents import AgentType\n",
     "from langchain.memory import ConversationBufferMemory\n",
     "from langchain import OpenAI\n",
-    "from langchain.utilities import GoogleSearchAPIWrapper\n",
+    "from langchain.utilities import SerpAPIWrapper\n",
     "from langchain.agents import initialize_agent"
    ]
   },
@@ -34,7 +34,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "search = GoogleSearchAPIWrapper()\n",
+    "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
     "        name = \"Current Search\",\n",
@@ -149,8 +149,12 @@
       "\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? No\n",
-      "AI: If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!\u001b[0m\n",
+      "Thought: Do I need to use a tool? Yes\n",
+      "Action: Current Search\n",
+      "Action Input: Thai food dinner recipes\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's Thai Spicy ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
+      "AI: Here are some great Thai dinner recipes you can try this week: Marion Grasby's Thai Spicy Chilli and Basil Fried Rice, Thai Curry Noodle Soup, Thai Green Curry with Coconut Rice, Thai Red Curry with Vegetables, and Thai Coconut Soup. I hope you enjoy them!\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -158,7 +162,7 @@
     {
      "data": {
       "text/plain": [
-       "'If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!'"
+       "\"Here are some great Thai dinner recipes you can try this week: Marion Grasby's Thai Spicy Chilli and Basil Fried Rice, Thai Curry Noodle Soup, Thai Green Curry with Coconut Rice, Thai Red Curry with Vegetables, and Thai Coconut Soup. I hope you enjoy them!\""
       ]
      },
      "execution_count": 7,
@@ -187,9 +191,9 @@
       "Thought: Do I need to use a tool? Yes\n",
       "Action: Current Search\n",
       "Action Input: Who won the World Cup in 1978\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe Cup was won by the host nation, Argentina, who defeated the Netherlands 3–1 in the final, after extra time. The final was held at River Plate's home stadium ... Amid Argentina's celebrations, there was sympathy for the Netherlands, runners-up for the second tournament running, following a 3-1 final defeat at the Estadio ... The match was won by the Argentine squad in extra time by a score of 3–1. Mario Kempes, who finished as the tournament's top scorer, was named the man of the ... May 21, 2022 ... Argentina won the World Cup for the first time in their history, beating Netherlands 3-1 in the final. This edition of the World Cup was full of ... The adidas Golden Ball is presented to the best player at each FIFA World Cup finals. Those who finish as runners-up in the vote receive the adidas Silver ... Holders West Germany failed to beat Holland and Italy and were eliminated when Berti Vogts' own goal gave Austria a 3-2 victory. Holland thrashed the Austrians ... Jun 14, 2018 ... On a clear afternoon on 1 June 1978 at the revamped El Monumental stadium in Buenos Aires' Belgrano barrio, several hundred children in white ... Dec 15, 2022 ... The tournament couldn't have gone better for the ruling junta. Argentina went on to win the championship, defeating the Netherlands, 3-1, in the ... Nov 9, 2022 ... Host: Argentina Teams: 16. Format: Group stage, second round, third-place playoff, final. Matches: 38. Goals: 102. Winner: Argentina Feb 19, 2009 ... Argentina sealed their first World Cup win on home soil when they defeated the Netherlands in an exciting final that went to extra-time. For the ...\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mArgentina national football team\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
-      "AI: The last letter in your name is 'b'. Argentina won the World Cup in 1978.\u001b[0m\n",
+      "AI: The last letter in your name is \"b\" and the winner of the 1978 World Cup was the Argentina national football team.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -197,7 +201,7 @@
     {
      "data": {
       "text/plain": [
-       "\"The last letter in your name is 'b'. Argentina won the World Cup in 1978.\""
+       "'The last letter in your name is \"b\" and the winner of the 1978 World Cup was the Argentina national football team.'"
       ]
      },
      "execution_count": 8,
@@ -226,9 +230,9 @@
       "Thought: Do I need to use a tool? Yes\n",
       "Action: Current Search\n",
       "Action Input: Current temperature in Pomfret\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mA mixture of rain and snow showers. High 39F. Winds NNW at 5 to 10 mph. Chance of precip 50%. Snow accumulations less than one inch. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Pomfret Center Weather Forecasts. ... Pomfret Center, CT Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be COOLER than today. It is 46 degrees fahrenheit, or 8 degrees celsius and feels like 46 degrees fahrenheit. The barometric pressure is 29.78 - measured by inch of mercury units - ... Pomfret Weather Forecasts. ... Pomfret, MD Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be MUCH COOLER than today. Additional Headlines. En Español · Share |. Current conditions at ... Pomfret CT. Tonight ... Past Weather Information · Interactive Forecast Map. Pomfret MD detailed current weather report for 20675 in Charles county, Maryland. ... Pomfret, MD weather condition is Mostly Cloudy and 43°F. Mostly Cloudy. Hazardous Weather Conditions. Hazardous Weather Outlook · En Español · Share |. Current conditions at ... South Pomfret VT. Tonight. Pomfret Center, CT Weather. Current Report for Thu Jan 5 2023. As of 2:00 PM EST. 5-Day Forecast | Road Conditions. 45°F 7°c. Feels Like 44°F. Pomfret Center CT. Today. Today: Areas of fog before 9am. Otherwise, cloudy, with a ... Otherwise, cloudy, with a temperature falling to around 33 by 5pm.\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mPartly cloudy skies. High around 70F. Winds W at 5 to 10 mph. Humidity41%.\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
-      "AI: The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.\u001b[0m\n",
+      "AI: The current temperature in Pomfret is around 70F with partly cloudy skies and winds W at 5 to 10 mph. The humidity is 41%.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -236,7 +240,7 @@
     {
      "data": {
       "text/plain": [
-       "'The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.'"
+       "'The current temperature in Pomfret is around 70F with partly cloudy skies and winds W at 5 to 10 mph. The humidity is 41%.'"
       ]
      },
      "execution_count": 9,

docs/modules/agents/agents/examples/mrkl.ipynb

@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "07e96d99",
    "metadata": {},
    "outputs": [],
@@ -41,7 +41,7 @@
     "llm = OpenAI(temperature=0)\n",
     "search = SerpAPIWrapper()\n",
     "llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
-    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")\n",
     "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
     "tools = [\n",
     "    Tool(\n",
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "a069c4b6",
    "metadata": {},
    "outputs": [],
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "e603cd7d",
    "metadata": {},
    "outputs": [
@@ -88,30 +88,24 @@
       "\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: \"Who is Leo DiCaprio's girlfriend?\"\u001b[0m\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: Search\n",
-      "Action Input: \"How old is Camila Morrone?\"\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",
+      "Observation: \u001b[36;1m\u001b[1;3mDiCaprio met actor Camila Morrone in December 2017, when she was 20 and he was 43. They were spotted at Coachella and went on multiple vacations together. Some reports suggested that DiCaprio was ready to ask Morrone to marry him. The couple made their red carpet debut at the 2020 Academy Awards.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate Camila Morrone's age raised to the 0.43 power.\n",
       "Action: Calculator\n",
-      "Action Input: 25^0.43\u001b[0m\n",
+      "Action Input: 21^0.43\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "25^0.43\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(25, 0.43))\n",
+      "21^0.43\u001b[32;1m\u001b[1;3m\n",
+      "```text\n",
+      "21**0.43\n",
       "```\n",
+      "...numexpr.evaluate(\"21**0.43\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\n",
-      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.7030049853137306\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\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 25 years old and her age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.7030049853137306\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 3.7030049853137306.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -119,10 +113,10 @@
     {
      "data": {
       "text/plain": [
-       "'Camila Morrone is 25 years old and her age raised to the 0.43 power is 3.991298452658078.'"
+       "\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.7030049853137306.\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -133,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "a5c07010",
    "metadata": {},
    "outputs": [
@@ -147,21 +141,36 @@
       "\u001b[32;1m\u001b[1;3m I need to find out the artist's full name and then search the FooBar database for their albums.\n",
       "Action: Search\n",
       "Action Input: \"The Storm Before the Calm\" artist\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe Storm Before the Calm (stylized in all lowercase) is the tenth (and eighth international) studio album by Canadian-American singer-songwriter Alanis ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now need to search the FooBar database for Alanis Morissette's albums\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe Storm Before the Calm (stylized in all lowercase) is the tenth (and eighth international) studio album by Canadian-American singer-songwriter Alanis Morissette, released June 17, 2022, via Epiphany Music and Thirty Tigers, as well as by RCA Records in Europe.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to search the FooBar database for Alanis Morissette's albums.\n",
       "Action: FooBar DB\n",
       "Action Input: What albums by Alanis Morissette are in the FooBar database?\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
-      "What albums by Alanis Morissette are in the FooBar database? \n",
-      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Title FROM Album INNER JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Artist.Name = 'Alanis Morissette' LIMIT 5;\u001b[0m\n",
+      "What albums by Alanis Morissette are in the FooBar database?\n",
+      "SQLQuery:"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:191: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
+      "  sample_rows = connection.execute(command)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m SELECT \"Title\" FROM \"Album\" INNER JOIN \"Artist\" ON \"Album\".\"ArtistId\" = \"Artist\".\"ArtistId\" WHERE \"Name\" = 'Alanis Morissette' LIMIT 5;\u001b[0m\n",
       "SQLResult: \u001b[33;1m\u001b[1;3m[('Jagged Little Pill',)]\u001b[0m\n",
       "Answer:\u001b[32;1m\u001b[1;3m The albums by Alanis Morissette in the FooBar database are Jagged Little Pill.\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "Observation: \u001b[38;5;200m\u001b[1;3m The albums by Alanis Morissette in the FooBar database are Jagged Little Pill.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: The artist who released the album The Storm Before the Calm is Alanis Morissette and the albums of theirs in the FooBar database are Jagged Little Pill.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: The artist who released the album 'The Storm Before the Calm' is Alanis Morissette and the albums of hers in the FooBar database are Jagged Little Pill.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -169,10 +178,10 @@
     {
      "data": {
       "text/plain": [
-       "'The artist who released the album The Storm Before the Calm is Alanis Morissette and the albums of theirs in the FooBar database are Jagged Little Pill.'"
+       "\"The artist who released the album 'The Storm Before the Calm' is Alanis Morissette and the albums of hers in the FooBar database are Jagged Little Pill.\""
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/modules/agents/agents/examples/mrkl_chat.ipynb

@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "ac561cc4",
    "metadata": {},
    "outputs": [],
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "id": "07e96d99",
    "metadata": {},
    "outputs": [],
@@ -43,7 +43,7 @@
     "llm1 = OpenAI(temperature=0)\n",
     "search = SerpAPIWrapper()\n",
     "llm_math_chain = LLMMathChain(llm=llm1, verbose=True)\n",
-    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")\n",
     "db_chain = SQLDatabaseChain(llm=llm1, database=db, verbose=True)\n",
     "tools = [\n",
     "    Tool(\n",
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "id": "a069c4b6",
    "metadata": {},
    "outputs": [],
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "id": "e603cd7d",
    "metadata": {},
    "outputs": [
@@ -92,37 +92,34 @@
       "```\n",
       "{\n",
       "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"Who is Leo DiCaprio's girlfriend?\"\n",
+      "  \"action_input\": \"Leo DiCaprio girlfriend\"\n",
       "}\n",
       "```\n",
       "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mFor the second question, I need to use the calculator tool to raise her current age to the 0.43 power.\n",
+      "Observation: \u001b[36;1m\u001b[1;3mGigi Hadid: 2022 Leo and Gigi were first linked back in September 2022, when a source told Us Weekly that Leo had his “sights set\" on her (alarming way to put it, but okay).\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mFor the second question, I need to calculate the age raised to the 0.43 power. I will use the calculator tool.\n",
       "Action:\n",
       "```\n",
       "{\n",
       "  \"action\": \"Calculator\",\n",
-      "  \"action_input\": \"22.0^(0.43)\"\n",
+      "  \"action_input\": \"((2022-1995)^0.43)\"\n",
       "}\n",
       "```\n",
-      "\n",
       "\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "22.0^(0.43)\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(22.0, 0.43))\n",
+      "((2022-1995)^0.43)\u001b[32;1m\u001b[1;3m\n",
+      "```text\n",
+      "(2022-1995)**0.43\n",
       "```\n",
+      "...numexpr.evaluate(\"(2022-1995)**0.43\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
-      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m4.125593352125936\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
-      "\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.125593352125936\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
-      "Final Answer: Camila Morrone, 3.777824273683966.\u001b[0m\n",
+      "Final Answer: Gigi Hadid is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is approximately 4.13.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -130,10 +127,10 @@
     {
      "data": {
       "text/plain": [
-       "'Camila Morrone, 3.777824273683966.'"
+       "\"Gigi Hadid is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is approximately 4.13.\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -144,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "id": "a5c07010",
    "metadata": {},
    "outputs": [
@@ -156,7 +153,7 @@
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3mQuestion: What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\n",
-      "Thought: I should use the Search tool to find the answer to the first part of the question and then use the FooBar DB tool to find the answer to the second part of the question.\n",
+      "Thought: I should use the Search tool to find the answer to the first part of the question and then use the FooBar DB tool to find the answer to the second part.\n",
       "Action:\n",
       "```\n",
       "{\n",
@@ -166,7 +163,7 @@
       "```\n",
       "\u001b[0m\n",
       "Observation: \u001b[36;1m\u001b[1;3mAlanis Morissette\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mNow that I have the name of the artist, I can use the FooBar DB tool to find their albums in the database.\n",
+      "Thought:\u001b[32;1m\u001b[1;3mNow that I know the artist's name, I can use the FooBar DB tool to find out if they are in the database and what albums of theirs are in it.\n",
       "Action:\n",
       "```\n",
       "{\n",
@@ -178,7 +175,7 @@
       "\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
-      "What albums does Alanis Morissette have in the database? \n",
+      "What albums does Alanis Morissette have in the database?\n",
       "SQLQuery:"
      ]
     },
@@ -186,7 +183,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:141: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
+      "/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:191: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
       "  sample_rows = connection.execute(command)\n"
      ]
     },
@@ -194,14 +191,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32;1m\u001b[1;3m SELECT Title FROM Album WHERE ArtistId IN (SELECT ArtistId FROM Artist WHERE Name = 'Alanis Morissette') LIMIT 5;\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m SELECT \"Title\" FROM \"Album\" WHERE \"ArtistId\" IN (SELECT \"ArtistId\" FROM \"Artist\" WHERE \"Name\" = 'Alanis Morissette') LIMIT 5;\u001b[0m\n",
       "SQLResult: \u001b[33;1m\u001b[1;3m[('Jagged Little Pill',)]\u001b[0m\n",
-      "Answer:\u001b[32;1m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m Alanis Morissette has the album Jagged Little Pill in the database.\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[38;5;200m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI have found the answer to both parts of the question.\n",
-      "Final Answer: The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3m Alanis Morissette has the album Jagged Little Pill in the database.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mThe artist Alanis Morissette is in the FooBar database and has the album Jagged Little Pill in it.\n",
+      "Final Answer: Alanis Morissette is in the FooBar database and has the album Jagged Little Pill in it.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -209,10 +206,10 @@
     {
      "data": {
       "text/plain": [
-       "\"The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\""
+       "'Alanis Morissette is in the FooBar database and has the album Jagged Little Pill in it.'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/modules/agents/agents/examples/self_ask_with_search.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "7e3b513e",
    "metadata": {},
    "outputs": [
@@ -25,11 +25,12 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m Yes.\n",
       "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
-      "Intermediate answer: \u001b[36;1m\u001b[1;3mCarlos Alcaraz won the 2022 Men's single title while Poland's Iga Swiatek won the Women's single title defeating Tunisian's Ons Jabeur.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mFollow up: Where is Carlos Alcaraz from?\u001b[0m\n",
+      "Intermediate answer: \u001b[36;1m\u001b[1;3mCarlos Alcaraz Garfia\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mFollow up: Where is Carlos Alcaraz Garfia from?\u001b[0m\n",
       "Intermediate answer: \u001b[36;1m\u001b[1;3mEl Palmar, Spain\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3mSo the final answer is: El Palmar, Spain\u001b[0m\n",
-      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
@@ -38,7 +39,7 @@
        "'El Palmar, Spain'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -61,6 +62,14 @@
     "self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)\n",
     "self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2e4d6bc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -79,7 +88,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {

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

@@ -0,0 +1,424 @@
+{
+ "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

@@ -0,0 +1,362 @@
+{
+ "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

@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "id": "16c4dc59",
    "metadata": {},
    "outputs": [],
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "id": "46b9489d",
    "metadata": {},
    "outputs": [
@@ -72,7 +72,7 @@
        "'There are 891 rows in the dataframe.'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "a96309be",
    "metadata": {},
    "outputs": [
@@ -110,18 +110,18 @@
        "'30 people have more than 3 siblings.'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent.run(\"how many people have more than 3 sibligngs\")"
+    "agent.run(\"how many people have more than 3 siblings\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "964a09f7",
    "metadata": {},
    "outputs": [
@@ -143,7 +143,7 @@
       "Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
       "Action: python_repl_ast\n",
       "Action Input: import math\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
       "Action: python_repl_ast\n",
       "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
@@ -160,7 +160,7 @@
        "'5.449689683556195'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }

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

@@ -0,0 +1,232 @@
+{
+ "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

@@ -0,0 +1,167 @@
+{
+ "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": "3.9.0"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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

@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c7ad998d",
+   "metadata": {},
+   "source": [
+    "# Natural Language APIs\n",
+    "\n",
+    "Natural Language API Toolkits (NLAToolkits) permit LangChain Agents to efficiently plan and combine calls across endpoints. This notebook demonstrates a sample composition of the Speak, Klarna, and Spoonacluar APIs.\n",
+    "\n",
+    "For a detailed walkthrough of the OpenAPI chains wrapped within the NLAToolkit, see the [OpenAPI Operation Chain](openapi.ipynb) notebook.\n",
+    "\n",
+    "### First, import dependencies and load the LLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6593f793",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from typing import List, Optional\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.requests import Requests\n",
+    "from langchain.tools import APIOperation, OpenAPISpec\n",
+    "from langchain.agents import AgentType, Tool, initialize_agent\n",
+    "from langchain.agents.agent_toolkits import NLAToolkit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dd720860",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Select the LLM to use. Here, we use text-davinci-003\n",
+    "llm = OpenAI(temperature=0, max_tokens=700) # You can swap between different core LLM's here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cadac9d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Next, load the Natural Language API Toolkits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6b208ab0",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
+      "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
+      "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n"
+     ]
+    }
+   ],
+   "source": [
+    "speak_toolkit = NLAToolkit.from_llm_and_url(llm, \"https://api.speak.com/openapi.yaml\")\n",
+    "klarna_toolkit = NLAToolkit.from_llm_and_url(llm, \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16c7336f",
+   "metadata": {},
+   "source": [
+    "### Create the Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "730a0dc2-b4d0-46d5-a1e9-583803220973",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Slightly tweak the instructions from the default agent\n",
+    "openapi_format_instructions = \"\"\"Use the following format:\n",
+    "\n",
+    "Question: the input question you must answer\n",
+    "Thought: you should always think about what to do\n",
+    "Action: the action to take, should be one of [{tool_names}]\n",
+    "Action Input: what to instruct the AI Action representative.\n",
+    "Observation: The Agent's response\n",
+    "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+    "Thought: I now know the final answer. User can't see any of my observations, API responses, links, or tools.\n",
+    "Final Answer: the final answer to the original input question with the right amount of detail\n",
+    "\n",
+    "When responding with your Final Answer, remember that the person you are responding to CANNOT see any of your Thought/Action/Action Input/Observations, so if there is any relevant information there you need to include it explicitly in your response.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "40a979c3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "natural_language_tools = speak_toolkit.get_tools() + klarna_toolkit.get_tools()\n",
+    "mrkl = initialize_agent(natural_language_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, \n",
+    "                        verbose=True, agent_kwargs={\"format_instructions\":openapi_format_instructions})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "794380ba",
+   "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 what kind of Italian clothes are available\n",
+      "Action: Open_AI_Klarna_product_Api.productsUsingGET\n",
+      "Action Input: Italian clothes\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3mThe API response contains two products from the Alé brand in Italian Blue. The first is the Alé Colour Block Short Sleeve Jersey Men - Italian Blue, which costs $86.49, and the second is the Alé Dolid Flash Jersey Men - Italian Blue, which costs $40.00.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know what kind of Italian clothes are available and how much they cost.\n",
+      "Final Answer: You can buy two products from the Alé brand in Italian Blue for your end of year party. The Alé Colour Block Short Sleeve Jersey Men - Italian Blue costs $86.49, and the Alé Dolid Flash Jersey Men - Italian Blue costs $40.00.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'You can buy two products from the Alé brand in Italian Blue for your end of year party. The Alé Colour Block Short Sleeve Jersey Men - Italian Blue costs $86.49, and the Alé Dolid Flash Jersey Men - Italian Blue costs $40.00.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrkl.run(\"I have an end of year party for my Italian class and have to buy some Italian clothes for it\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c61d92a8",
+   "metadata": {},
+   "source": [
+    "### Using Auth + Adding more Endpoints\n",
+    "\n",
+    "Some endpoints may require user authentication via things like access tokens. Here we show how to pass in the authentication information via the `Requests` wrapper object.\n",
+    "\n",
+    "Since each NLATool exposes a concisee natural language interface to its wrapped API, the top level conversational agent has an easier job incorporating each endpoint to satisfy a user's request."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0d132cc",
+   "metadata": {},
+   "source": [
+    "**Adding the Spoonacular endpoints.**\n",
+    "\n",
+    "1. Go to the [Spoonacular API Console](https://spoonacular.com/food-api/console#Profile) and make a free account.\n",
+    "2. Click on `Profile` and copy your API key below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c2368b9c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "spoonacular_api_key = \"\" # Copy from the API Console"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fbd97c28-fef6-41b5-9600-a9611a32bfb3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Attempting to load an OpenAPI 3.0.0 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n"
+     ]
+    }
+   ],
+   "source": [
+    "requests = Requests(headers={\"x-api-key\": spoonacular_api_key})\n",
+    "spoonacular_toolkit = NLAToolkit.from_llm_and_url(\n",
+    "    llm, \n",
+    "    \"https://spoonacular.com/application/frontend/downloads/spoonacular-openapi-3.json\",\n",
+    "    requests=requests,\n",
+    "    max_text_length=1800, # If you want to truncate the response text\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "81a6edac",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "34 tools loaded.\n"
+     ]
+    }
+   ],
+   "source": [
+    "natural_language_api_tools = (speak_toolkit.get_tools() \n",
+    "                              + klarna_toolkit.get_tools() \n",
+    "                              + spoonacular_toolkit.get_tools()[:30]\n",
+    "                             )\n",
+    "print(f\"{len(natural_language_api_tools)} tools loaded.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "831f772d-5cd1-4467-b494-a3172af2ff48",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Create an agent with the new tools\n",
+    "mrkl = initialize_agent(natural_language_api_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, \n",
+    "                        verbose=True, agent_kwargs={\"format_instructions\":openapi_format_instructions})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0385e04b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Make the query more complex!\n",
+    "user_input = (\n",
+    "    \"I'm learning Italian, and my language class is having an end of year party... \"\n",
+    "    \" Could you help me find an Italian outfit to wear and\"\n",
+    "    \" an appropriate recipe to prepare so I can present for the class in Italian?\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6ebd3f55",
+   "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 a recipe and an outfit that is Italian-themed.\n",
+      "Action: spoonacular_API.searchRecipes\n",
+      "Action Input: Italian\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe API response contains 10 Italian recipes, including Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, and Pappa Al Pomodoro.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find an Italian-themed outfit.\n",
+      "Action: Open_AI_Klarna_product_Api.productsUsingGET\n",
+      "Action Input: Italian\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3mI found 10 products related to 'Italian' in the API response. These products include Italian Gold Sparkle Perfectina Necklace - Gold, Italian Design Miami Cuban Link Chain Necklace - Gold, Italian Gold Miami Cuban Link Chain Necklace - Gold, Italian Gold Herringbone Necklace - Gold, Italian Gold Claddagh Ring - Gold, Italian Gold Herringbone Chain Necklace - Gold, Garmin QuickFit 22mm Italian Vacchetta Leather Band, Macy's Italian Horn Charm - Gold, Dolce & Gabbana Light Blue Italian Love Pour Homme EdT 1.7 fl oz.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: To present for your Italian language class, you could wear an Italian Gold Sparkle Perfectina Necklace - Gold, an Italian Design Miami Cuban Link Chain Necklace - Gold, or an Italian Gold Miami Cuban Link Chain Necklace - Gold. For a recipe, you could make Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, or Pappa Al Pomodoro.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'To present for your Italian language class, you could wear an Italian Gold Sparkle Perfectina Necklace - Gold, an Italian Design Miami Cuban Link Chain Necklace - Gold, or an Italian Gold Miami Cuban Link Chain Necklace - Gold. For a recipe, you could make Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, or Pappa Al Pomodoro.'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrkl.run(user_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2959462",
+   "metadata": {},
+   "source": [
+    "## Thank you!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6fcda5f0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"In Italian, you can say 'Buon appetito' to someone to wish them to enjoy their meal. This phrase is commonly used in Italy when someone is about to eat, often at the beginning of a meal. It's similar to saying 'Bon appétit' in French or 'Guten Appetit' in German.\""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "natural_language_api_tools[1].run(\"Tell the LangChain audience to 'enjoy the meal' in Italian, please!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab366dc0",
+   "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/toolkits/examples/pandas.ipynb

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

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

@@ -0,0 +1,219 @@
+{
+ "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": 1,
+   "execution_count": 4,
    "id": "f98e9c90-5c37-4fb9-af3e-d09693af8543",
    "metadata": {
     "tags": []
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "cc422f53-c51c-4694-a834-72ecd1e68363",
    "metadata": {
     "tags": []
@@ -206,9 +206,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "LangChain",
    "language": "python",
-   "name": "python3"
+   "name": "langchain"
   },
   "language_info": {
    "codemirror_mode": {
@@ -220,7 +220,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,

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

@@ -0,0 +1,398 @@
+{
+ "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

@@ -24,6 +24,7 @@ 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

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "5436020b",
    "metadata": {},
@@ -9,28 +10,29 @@
     "\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\n",
-    "- description (str), is optional\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",
     "- 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 ways to define a tool, we will cover both in the example below."
+    "There are two main ways to define a tool, we will cover both in the example below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "1aaba18c",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Import things that are needed generically\n",
-    "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"
+    "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"
    ]
   },
   {
@@ -43,63 +45,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "36ed392e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "llm = OpenAI(temperature=0)"
+    "llm = ChatOpenAI(temperature=0)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f8bc72c2",
    "metadata": {},
    "source": [
-    "## Completely New Tools \n",
-    "First, we show how to create completely new tools from scratch.\n",
+    "## 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",
     "\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"
+    "### Tool dataclass\n",
+    "\n",
+    "The 'Tool' dataclass wraps functions that accept a single string input and returns a string output."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "56ff7670",
-   "metadata": {},
-   "outputs": [],
+   "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"
+     ]
+    }
+   ],
    "source": [
     "# Load the tool configs that are needed.\n",
     "search = SerpAPIWrapper()\n",
     "llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
     "tools = [\n",
-    "    Tool(\n",
-    "        name = \"Search\",\n",
+    "    Tool.from_function(\n",
     "        func=search.run,\n",
+    "        name = \"Search\",\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": "5b93047d",
+   "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": []
+   },
+   "outputs": [],
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
@@ -108,9 +159,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "6f96a891",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -119,29 +172,34 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\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",
+      "\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age\n",
       "Action: Search\n",
       "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\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",
+      "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",
       "Action: Calculator\n",
-      "Action Input: 22^0.43\u001b[0m\n",
+      "Action Input: 25^(0.43)\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "22^0.43\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(22, 0.43))\n",
+      "25^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
+      "25**(0.43)\n",
       "```\n",
+      "...numexpr.evaluate(\"25**(0.43)\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
-      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\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",
+      "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",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -149,10 +207,10 @@
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
+       "\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,70 +220,75 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6f12eaf0",
    "metadata": {},
    "source": [
-    "### Subclassing the BaseTool class"
+    "### 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\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "c58a7c40",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "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": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
+    "tools = [CustomSearchTool(), CustomCalculatorTool()]\n",
     "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "6a2cebbf",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -234,29 +297,30 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\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",
+      "\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",
       "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\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",
+      "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",
       "Action: Calculator\n",
-      "Action Input: 22^0.43\u001b[0m\n",
+      "Action Input: 19 ^ 0.43\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "22^0.43\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(22, 0.43))\n",
+      "19 ^ 0.43\u001b[32;1m\u001b[1;3m```text\n",
+      "19 ** 0.43\n",
       "```\n",
+      "...numexpr.evaluate(\"19 ** 0.43\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
-      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.547023357958959\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\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",
+      "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",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -264,10 +328,10 @@
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
+       "'3.547023357958959'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -288,37 +352,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "8f15307d",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "from langchain.agents import tool\n",
+    "from langchain.tools import tool\n",
     "\n",
     "@tool\n",
     "def search_api(query: str) -> str:\n",
     "    \"\"\"Searches the API for the query.\"\"\"\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": [
+    "    return f\"Results for query {query}\"\n",
+    "\n",
     "search_api"
    ]
   },
@@ -332,9 +379,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "28cdf04d",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "@tool(\"search\", return_direct=True)\n",
@@ -345,17 +394,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "id": "1085a4bd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "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)"
+       "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)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -365,18 +414,194 @@
    ]
   },
   {
+   "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 just modify them. 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 modify them directly. 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": 8,
+   "execution_count": 13,
    "id": "79213f40",
    "metadata": {},
    "outputs": [],
@@ -386,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "id": "e1067dcb",
    "metadata": {},
    "outputs": [],
@@ -396,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "id": "6c66ffe8",
    "metadata": {},
    "outputs": [],
@@ -406,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "id": "f45b5bc3",
    "metadata": {},
    "outputs": [],
@@ -416,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "id": "565e2b9b",
    "metadata": {},
    "outputs": [
@@ -427,21 +652,20 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\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",
+      "\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age.\n",
       "Action: Google Search\n",
       "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\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",
+      "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",
       "Action: Google 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;3m I need to calculate 25 raised to the 0.43 power\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: Calculator\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",
+      "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",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -449,10 +673,10 @@
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\""
+       "\"The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55.\""
       ]
      },
-     "execution_count": 12,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -478,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "id": "3450512e",
    "metadata": {},
    "outputs": [],
@@ -507,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "id": "4b9a7849",
    "metadata": {},
    "outputs": [
@@ -520,9 +744,7 @@
       "\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\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",
+      "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",
       "Final Answer: 'All I Want For Christmas Is You' by Mariah Carey.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -534,7 +756,7 @@
        "\"'All I Want For Christmas Is You' by Mariah Carey.\""
       ]
      },
-     "execution_count": 14,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -554,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "id": "3bb6185f",
    "metadata": {},
    "outputs": [],
@@ -572,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "id": "113ddb84",
    "metadata": {},
    "outputs": [],
@@ -583,9 +805,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "id": "582439a6",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -596,9 +820,7 @@
       "\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\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.2599210498948732\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\u001b[0m\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",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -606,10 +828,10 @@
     {
      "data": {
       "text/plain": [
-       "'Answer: 1.2599210498948732'"
+       "'Answer: 1.086734862526058'"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -617,14 +839,6 @@
    "source": [
     "agent.run(\"whats 2**.12\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "537bc628",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -643,7 +857,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.2"
   },
   "vscode": {
    "interpreter": {

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

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

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

@@ -0,0 +1,259 @@
+{
+ "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

@@ -0,0 +1,119 @@
+{
+ "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

@@ -5,57 +5,158 @@
       "id": "8f210ec3",
       "metadata": {},
       "source": [
-        "# 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."
+        "# 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."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 1,
       "id": "f7b3767b",
-      "metadata": {},
+      "metadata": {
+        "tags": []
+      },
       "outputs": [],
       "source": [
-        "from langchain.utilities import BashProcess"
+        "from langchain.tools import ShellTool\n",
+        "\n",
+        "shell_tool = ShellTool()"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 2,
-      "id": "cf1c92f0",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "bash = BashProcess()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "2fa952fc",
-      "metadata": {},
+      "id": "c92ac832-556b-4f66-baa4-b78f965dfba0",
+      "metadata": {
+        "tags": []
+      },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "bash.ipynb\n",
-            "google_search.ipynb\n",
-            "python.ipynb\n",
-            "requests.ipynb\n",
-            "serpapi.ipynb\n",
+            "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(bash.run(\"ls\"))"
+        "print(shell_tool.run({\"commands\": [\"echo 'Hello World!'\", \"time\"]}))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2fa952fc",
+      "metadata": {},
+      "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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "851fee9f",
+      "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;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"
+          ]
+        },
+        {
+          "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.\")"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "id": "851fee9f",
+      "id": "8d0ea3ac-0890-4e39-9cec-74bd80b4b8b8",
       "metadata": {},
       "outputs": [],
       "source": []
@@ -77,7 +178,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.9"
+      "version": "3.8.16"
     }
   },
   "nbformat": 4,

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

@@ -80,11 +80,11 @@
     }
    ],
    "source": [
-    "llm = ChatOpenAI(temperature=0,)\n",
-    "tools = load_tools([\"requests\"] )\n",
+    "llm = ChatOpenAI(temperature=0)\n",
+    "tools = load_tools([\"requests_all\"] )\n",
     "tools += [tool]\n",
     "\n",
-    "agent_chain = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION verbose=True)\n",
+    "agent_chain = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n",
     "agent_chain.run(\"what t shirts are available in klarna?\")"
    ]
   },

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

@@ -0,0 +1,91 @@
+{
+ "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

@@ -0,0 +1,190 @@
+{
+ "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

@@ -0,0 +1,105 @@
+{
+ "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

@@ -9,7 +9,7 @@
     "\n",
     "This notebook goes over how to use the google search component.\n",
     "\n",
-    "First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found [here](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search).\n",
+    "First, you need to set up the proper API keys and environment variables. To set it up, create the GOOGLE_API_KEY in the Google Cloud credential console (https://console.cloud.google.com/apis/credentials) and a GOOGLE_CSE_ID using the Programmable Search Enginge (https://programmablesearchengine.google.com/controlpanel/create). Next, it is good to follow the instructions found [here](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search).\n",
     "\n",
     "Then we will need to set some environment variables."
    ]
@@ -33,7 +33,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.utilities import GoogleSearchAPIWrapper"
+    "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",
+    ")"
    ]
   },
   {
@@ -41,30 +50,20 @@
    "execution_count": 3,
    "id": "84b8f773",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "search = GoogleSearchAPIWrapper()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "068991a6",
-   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'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...'"
+       "\"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...\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "search.run(\"Obama's first name?\")"
+    "tool.run(\"Obama's first name?\")"
    ]
   },
   {
@@ -78,17 +77,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "5083fbdd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "search = GoogleSearchAPIWrapper(k=1)"
+    "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",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "77aaa857",
    "metadata": {},
    "outputs": [
@@ -98,13 +103,13 @@
        "'The official home of the Python Programming Language.'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "search.run(\"python\")"
+    "tool.run(\"python\")"
    ]
   },
   {
@@ -137,48 +142,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "028f4cba",
    "metadata": {},
    "outputs": [],
    "source": [
-    "search = GoogleSearchAPIWrapper()"
+    "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",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "4d8f734f",
+   "execution_count": null,
+   "id": "4d7f92e1",
    "metadata": {},
-   "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)"
-   ]
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -197,7 +184,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.2"
   },
   "vscode": {
    "interpreter": {

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

@@ -12,21 +12,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "outputs": [],
    "source": [
     "import os\n",
+    "import pprint\n",
     "os.environ[\"SERPER_API_KEY\"] = \"\""
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "pycharm": {
+     "is_executing": true
+    },
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:56:29.336521Z",
+     "start_time": "2023-05-04T00:56:29.334173Z"
+    }
    }
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "54bf5afd",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:54:07.676293Z",
+     "start_time": "2023-05-04T00:54:06.665742Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain.utilities import GoogleSerperAPIWrapper"
@@ -36,7 +49,12 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "31f8f382",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:54:08.324245Z",
+     "start_time": "2023-05-04T00:54:08.321577Z"
+    }
+   },
    "outputs": [],
    "source": [
     "search = GoogleSerperAPIWrapper()"
@@ -46,7 +64,12 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "25ce0225",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:54:11.399847Z",
+     "start_time": "2023-05-04T00:54:09.335597Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -72,13 +95,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "outputs": [],
    "source": [
     "os.environ['OPENAI_API_KEY'] = \"\""
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:54:14.311773Z",
+     "start_time": "2023-05-04T00:54:14.304389Z"
+    }
    }
   },
   {
@@ -133,6 +160,693 @@
    "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

@@ -0,0 +1,102 @@
+{
+ "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

@@ -13,10 +13,11 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "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",
@@ -42,13 +43,15 @@
    "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."
+    "You can customize `prompt_func` and `input_func` according to your need (as shown below)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -57,29 +60,28 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI don't know Eric Zhu, so I should ask a human for guidance.\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: \"Do you know when Eric Zhu's birthday is?\"\u001b[0m\n",
+      "Action Input: \"What is Eric's surname?\"\u001b[0m\n",
       "\n",
-      "Do you know when Eric Zhu's birthday is?\n",
-      "last week\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;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: \"Do you know the specific date of Eric Zhu's birthday?\"\u001b[0m\n",
-      "\n",
-      "Do you know the specific date of Eric Zhu's birthday?\n",
-      "august 1st\n",
-      "\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",
+      "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"
      ]
@@ -87,18 +89,175 @@
     {
      "data": {
       "text/plain": [
-       "\"Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\""
+       "\"Eric's surname is Zhu.\""
       ]
      },
-     "execution_count": 3,
+     "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",
+   "metadata": {},
+   "source": [
+    "## Configuring the Input Function\n",
     "\n",
-    "agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
-    "# Answer with \"last week\""
+    "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",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI should ask a human for guidance\n",
+      "Action: Human\n",
+      "Action Input: \"Can you help me attribute a quote?\"\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": [
+      "\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",
+      "Action: Human\n",
+      "Action Input: \"The quote is 'Veni, vidi, vici'\"\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": [
+      "\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",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Julius Caesar said the quote \"Veni, vidi, vici\" which means \"I came, I saw, I conquered\".'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(\"I need help attributing a quote\")"
    ]
   },
   {
@@ -125,9 +284,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

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

@@ -0,0 +1,246 @@
+{
+ "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

@@ -19,6 +19,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.agents import Tool\n",
     "from langchain.utilities import PythonREPL"
    ]
   },
@@ -59,7 +60,14 @@
    "id": "54fc1f03",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "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",
+    ")"
+   ]
   }
  ],
  "metadata": {

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

@@ -0,0 +1,139 @@
+{
+ "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

@@ -102,7 +102,15 @@
    "id": "e0a1dc1c",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "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",
+    ")"
+   ]
   }
  ],
  "metadata": {