cr

.coveragerc

@@ -0,0 +1,2 @@
+[run]
+omit = tests/*

.dockerignore

@@ -1,6 +0,0 @@
-.venv
-.github
-.git
-.mypy_cache
-.pytest_cache
-Dockerfile

.flake8

@@ -1,6 +1,5 @@
 [flake8]
 exclude =
-    venv
     .venv
     __pycache__
     notebooks

.github/workflows/linkcheck.yml

@@ -1,36 +0,0 @@
-name: linkcheck
-
-on:
-  push:
-    branches: [master]
-  pull_request:
-
-env:
-  POETRY_VERSION: "1.3.1"
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.11"
-    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
-        with:
-          python-version: ${{ matrix.python-version }}
-          cache: poetry
-      - name: Install dependencies
-        run: |
-          poetry install --with docs
-      - name: Build the docs
-        run: |
-          make docs_build
-      - name: Analyzing the docs with linkcheck
-        run: |
-          make docs_linkcheck

.github/workflows/release.yml

@@ -1,49 +0,0 @@
-name: release
-
-on:
-  pull_request:
-    types:
-      - closed
-    branches:
-      - master
-    paths:
-      - 'pyproject.toml'
-
-env:
-  POETRY_VERSION: "1.3.1"
-
-jobs:
-  if_release:
-    if: |
-        ${{ github.event.pull_request.merged == true }}
-        && ${{ contains(github.event.pull_request.labels.*.name, 'release') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - name: Install poetry
-        run: pipx install poetry==$POETRY_VERSION
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-          cache: "poetry"
-      - name: Build project for distribution
-        run: poetry build
-      - name: Check Version
-        id: check-version
-        run: |
-          echo version=$(poetry version --short) >> $GITHUB_OUTPUT
-      - name: Create Release
-        uses: ncipollo/release-action@v1
-        with:
-          artifacts: "dist/*"
-          token: ${{ secrets.GITHUB_TOKEN }}
-          draft: false
-          generateReleaseNotes: true
-          tag: v${{ steps.check-version.outputs.version }}
-          commit: master
-      - name: Publish to PyPI
-        env:
-          POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
-        run: | 
-          poetry publish

.github/workflows/test.yml

@@ -31,4 +31,4 @@ jobs:
         run: poetry install
       - name: Run unit tests
         run: |
-          make test
+          make tests

.gitignore

@@ -106,9 +106,7 @@ celerybeat.pid
 
 # Environments
 .env
-.envrc
 .venv
-.venvs
 env/
 venv/
 ENV/
@@ -132,16 +130,3 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
-
-# macOS display setting files
-.DS_Store
-
-# Wandb directory
-wandb/
-
-# asdf tool versions
-.tool-versions
-/.ruff_cache/
-
-*.pkl
-*.bin

CITATION.cff

@@ -1,8 +0,0 @@
-cff-version: 1.2.0
-message: "If you use this software, please cite it as below."
-authors:
-- family-names: "Chase"
-  given-names: "Harrison"
-title: "LangChain"
-date-released: 2022-10-17
-url: "https://github.com/hwchase17/langchain"

CONTRIBUTING.md

@@ -46,8 +46,8 @@ 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/).
+As of now, LangChain has an ad hoc release process: releases are cut with high frequency via by
+a developer and published to [PyPI](https://pypi.org/project/ruff/).
 
 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).
@@ -55,16 +55,12 @@ even patch releases may contain [non-backwards-compatible changes](https://semve
 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
+## 🤖Developer Setup
+
+### 🚀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.
 
-❗Note: If you use `Conda` or `Pyenv` as your environment / package manager, avoid dependency conflicts by doing the following first:
-1. *Before installing Poetry*, create and activate a new Conda env (e.g. `conda create -n langchain python=3.9`)
-2. Install Poetry (see above)
-3. Tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
-4. Continue with the following steps.
-
 To install requirements:
 
 ```bash
@@ -73,15 +69,11 @@ poetry install -E all
 
 This will install all requirements for running the package, examples, linting, formatting, tests, and coverage. Note the `-E all` flag will install all optional dependencies necessary for integration testing.
 
-❗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.
-
-### Code Formatting
+#### Code Formatting
 
 Formatting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/).
 
@@ -91,7 +83,7 @@ To run formatting for this project:
 make format
 ```
 
-### Linting
+#### Linting
 
 Linting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/), [isort](https://pycqa.github.io/isort/), [flake8](https://flake8.pycqa.org/en/latest/), and [mypy](http://mypy-lang.org/).
 
@@ -103,7 +95,7 @@ make lint
 
 We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
 
-### Coverage
+#### Coverage
 
 Code coverage (i.e. the amount of code that is covered by unit tests) helps identify areas of the code that are potentially more or less brittle.
 
@@ -113,20 +105,14 @@ To get a report of current coverage, run the following:
 make coverage
 ```
 
-### Testing
+#### Testing
 
 Unit tests cover modular logic that does not require calls to outside APIs.
 
 To run unit tests:
 
 ```bash
-make test
-```
-
-To run unit tests in Docker:
-
-```bash
-make docker_tests
+make tests
 ```
 
 If you add new logic, please add a unit test.
@@ -141,7 +127,7 @@ make integration_tests
 
 If you add support for a new external API, please add a new integration test.
 
-### Adding a Jupyter Notebook
+#### Adding a Jupyter Notebook
 
 If you are adding a Jupyter notebook example, you'll want to install the optional `dev` dependencies.
 
@@ -159,32 +145,10 @@ poetry run jupyter notebook
 
 When you run `poetry install`, the `langchain` package is installed as editable in the virtualenv, so your new logic can be imported into the notebook.
 
-## Documentation
-
-### Contribute Documentation
+#### Contribute Documentation
 
 Docs are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code.
 
 For that reason, we ask that you add good documentation to all classes and methods.
 
 Similar to linting, we recognize documentation can be annoying. If you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
-
-### Build Documentation Locally
-
-Before building the documentation, it is always a good idea to clean the build directory:
-
-```bash
-make docs_clean
-```
-
-Next, you can run the linkchecker to make sure all links are valid:
-
-```bash
-make docs_linkcheck
-```
-
-Finally, you can build the documentation as outlined below:
-
-```bash
-make docs_build
-```

Dockerfile

@@ -1,44 +0,0 @@
-# This is a Dockerfile for running unit tests
-
-# Use the Python base image
-FROM python:3.11.2-bullseye AS builder
-
-# Define the version of Poetry to install (default is 1.4.2)
-ARG POETRY_VERSION=1.4.2
-
-# Define the directory to install Poetry to (default is /opt/poetry)
-ARG POETRY_HOME=/opt/poetry
-
-# Create a Python virtual environment for Poetry and install it
-RUN python3 -m venv ${POETRY_HOME} && \
-    $POETRY_HOME/bin/pip install --upgrade pip && \
-    $POETRY_HOME/bin/pip install poetry==${POETRY_VERSION}
-
-# Test if Poetry is installed in the expected path
-RUN echo "Poetry version:" && $POETRY_HOME/bin/poetry --version
-
-# Set the working directory for the app
-WORKDIR /app
-
-# Use a multi-stage build to install dependencies
-FROM builder AS dependencies
-
-# 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_HOME/bin/poetry install --no-interaction --no-ansi --with test
-
-# Use a multi-stage build to run tests
-FROM dependencies AS tests
-
-# Copy the rest of the app source code (this layer will be invalidated and rebuilt whenever the source code changes)
-COPY . .
-
-RUN /opt/poetry/bin/poetry install --no-interaction --no-ansi --with test
-
-# Set the entrypoint to run tests using Poetry
-ENTRYPOINT ["/opt/poetry/bin/poetry", "run", "pytest"]
-
-# Set the default command to run all unit tests
-CMD ["tests/unit_tests"]

Makefile

@@ -1,6 +1,4 @@
-.PHONY: all clean format lint test tests test_watch integration_tests docker_tests help
-
-all: help
+.PHONY: format lint tests integration_tests
 
 coverage:
 	poetry run pytest --cov \
@@ -8,55 +6,18 @@ coverage:
 		--cov-report xml \
 		--cov-report term-missing:skip-covered
 
-clean: docs_clean
-
-docs_build:
-	cd docs && poetry run make html
-
-docs_clean:
-	cd docs && poetry run make clean
-
-docs_linkcheck:
-	poetry run linkchecker docs/_build/html/index.html
-
 format:
 	poetry run black .
-	poetry run ruff --select I --fix .
+	poetry run isort .
 
-PYTHON_FILES=.
-lint: PYTHON_FILES=.
-lint_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d master | grep -E '\.py$$')
-
-lint lint_diff:
-	poetry run mypy $(PYTHON_FILES)
-	poetry run black $(PYTHON_FILES) --check
-	poetry run ruff .
-
-test:
-	poetry run pytest tests/unit_tests
+lint:
+	poetry run mypy .
+	poetry run black . --check
+	poetry run isort . --check
+	poetry run flake8 .
 
 tests:
 	poetry run pytest tests/unit_tests
 
-test_watch:
-	poetry run ptw --now . -- tests/unit_tests
-
 integration_tests:
 	poetry run pytest tests/integration_tests
-
-docker_tests:
-	docker build -t my-langchain-image:test .
-	docker run --rm my-langchain-image:test
-
-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'

README.md

@@ -2,16 +2,11 @@
 
 ⚡ Building applications with LLMs through composability ⚡
 
-[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml) [![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
-
-**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.
+[![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) [![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)
 
 ## Quick Install
 
 `pip install langchain`
-or
-`conda install langchain -c conda-forge`
 
 ## 🤔 What is this?
 
@@ -20,22 +15,7 @@ developers to build applications that they previously could not.
 But using these LLMs in isolation is often not enough to
 create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
 
-This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
-
-**❓ Question Answering over specific documents**
-
-- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/question_answering.html)
-- End-to-end Example: [Question Answering over Notion Database](https://github.com/hwchase17/notion-qa)
-
-**💬 Chatbots**
-
-- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/chatbots.html)
-- End-to-end Example: [Chat-LangChain](https://github.com/hwchase17/chat-langchain)
-
-**🤖 Agents**
-
-- [Documentation](https://langchain.readthedocs.io/en/latest/modules/agents.html)
-- End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
+This library is aimed at assisting in the development of those types of applications.
 
 ## 📖 Documentation
 
@@ -44,11 +24,11 @@ Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documen
 - Getting started (installation, setting up the environment, simple examples)
 - How-To examples (demos, integrations, helper functions)
 - Reference (full API docs)
-- Resources (high-level explanation of core concepts)
+  Resources (high-level explanation of core concepts)
 
 ## 🚀 What can this help with?
 
-There are six main areas that LangChain is designed to help with.
+There are four main areas that LangChain is designed to help with.
 These are, in increasing order of complexity:
 
 **📃 LLMs and Prompts:**
@@ -59,10 +39,6 @@ This includes prompt management, prompt optimization, generic interface for all
 
 Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
 
-**📚 Data Augmented Generation:**
-
-Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.
-
 **🤖 Agents:**
 
 Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
@@ -71,14 +47,11 @@ Agents involve an LLM making decisions about which Actions to take, taking that
 
 Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
 
-**🧐 Evaluation:**
-
-[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 infra, or better documentation.
 
-For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).
+For detailed information on how to contribute, see [here](CONTRIBUTING.md).

docs/Makefile

@@ -3,7 +3,7 @@
 
 # You can set these variables from the command line, and also
 # from the environment for the first two.
-SPHINXOPTS    ?= 
+SPHINXOPTS    ?=
 SPHINXBUILD   ?= sphinx-build
 SPHINXAUTOBUILD   ?= sphinx-autobuild
 SOURCEDIR     = .

docs/_static/css/custom.css

@@ -1,17 +0,0 @@
-pre {
-  white-space: break-spaces;
-}
-
-@media (min-width: 1200px) {
-  .container,
-  .container-lg,
-  .container-md,
-  .container-sm,
-  .container-xl {
-    max-width: 2560px !important;
-  }
-}
-
-#my-component-root *, #headlessui-portal-root * {
-  z-index: 1000000000000;
-}

docs/_static/js/mendablesearch.js

@@ -1,58 +0,0 @@
-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@0.0.83/dist/umd/mendable.min.js', initializeMendable);
-    });
-  });
-});

docs/conf.py

@@ -22,16 +22,13 @@ with open("../pyproject.toml") as f:
 
 # -- Project information -----------------------------------------------------
 
-project = "🦜🔗 LangChain"
-copyright = "2023, Harrison Chase"
+project = "LangChain"
+copyright = "2022, Harrison Chase"
 author = "Harrison Chase"
 
 version = data["tool"]["poetry"]["version"]
 release = version
 
-html_title = project + " " + version
-html_last_updated_fmt = "%b %d, %Y"
-
 
 # -- General configuration ---------------------------------------------------
 
@@ -45,12 +42,11 @@ extensions = [
     "sphinx.ext.napoleon",
     "sphinx.ext.viewcode",
     "sphinxcontrib.autodoc_pydantic",
-    "myst_nb",
-    "sphinx_copybutton",
+    "myst_parser",
+    "nbsphinx",
     "sphinx_panels",
-    "IPython.sphinxext.ipython_console_highlighting",
 ]
-source_suffix = [".ipynb", ".html", ".md", ".rst"]
+
 
 autodoc_pydantic_model_show_json = False
 autodoc_pydantic_field_list_validators = False
@@ -77,13 +73,8 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = "sphinx_book_theme"
-
-html_theme_options = {
-    "path_to_docs": "docs",
-    "repository_url": "https://github.com/hwchase17/langchain",
-    "use_repository_button": True,
-}
+html_theme = "sphinx_rtd_theme"
+# html_theme = "sphinx_typlog_theme"
 
 html_context = {
     "display_github": True,  # Integrate GitHub
@@ -96,17 +87,4 @@ html_context = {
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
-
-# These paths are either relative to html_static_path
-# or fully qualified paths (eg. https://...)
-html_css_files = [
-    "css/custom.css",
-]
-
-html_js_files = [
-    "js/mendablesearch.js",
-]
-
-nb_execution_mode = "off"
-myst_enable_extensions = ["colon_fence"]
+html_static_path: list = []

docs/deployments.md

@@ -1,51 +0,0 @@
-# Deployments
-
-So you've made a really cool chain - now what? How do you deploy it and make it easily sharable with the world?
-
-This section covers several options for that.
-Note that these are meant as quick deployment options for prototypes and demos, and not for production systems.
-If you are looking for help with deployment of a production system, please contact us directly.
-
-What follows is a list of template GitHub repositories aimed that are intended to be
-very easy to fork and modify to use your chain.
-This is far from an exhaustive list of options, and we are EXTREMELY open to contributions here.
-
-## [Streamlit](https://github.com/hwchase17/langchain-streamlit-template)
-
-This repo serves as a template for how to deploy a LangChain with Streamlit.
-It implements a chatbot interface.
-It also contains instructions for how to deploy this app on the Streamlit platform.
-
-## [Gradio (on Hugging Face)](https://github.com/hwchase17/langchain-gradio-template)
-
-This repo serves as a template for how deploy a LangChain with Gradio.
-It implements a chatbot interface, with a "Bring-Your-Own-Token" approach (nice for not wracking up big bills).
-It also contains instructions for how to deploy this app on the Hugging Face platform.
-This is heavily influenced by James Weaver's [excellent examples](https://huggingface.co/JavaFXpert).
-
-## [Beam](https://github.com/slai-labs/get-beam/tree/main/examples/langchain-question-answering)
-
-This repo serves as a template for how deploy a LangChain with [Beam](https://beam.cloud).
-
-It implements a Question Answering app and contains instructions for deploying the app as a serverless REST API.
-
-## [Vercel](https://github.com/homanp/vercel-langchain)
-
-A minimal example on how to run LangChain on Vercel using Flask.
-
-## [Digitalocean App Platform](https://github.com/homanp/digitalocean-langchain)
-
-A minimal example on how to deploy LangChain to DigitalOcean App Platform.
-
-## [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.
-
-## [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.
-
-## [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.

docs/ecosystem.rst

@@ -1,10 +0,0 @@
-LangChain Ecosystem
-===================
-
-Guides for how other companies/products can be used with LangChain
-
-.. toctree::
-   :maxdepth: 1
-   :glob:
-
-   ecosystem/*

docs/ecosystem/ai21.md

@@ -1,16 +0,0 @@
-# AI21 Labs
-
-This page covers how to use the AI21 ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific AI21 wrappers.
-
-## Installation and Setup
-- Get an AI21 api key and set it as an environment variable (`AI21_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an AI21 LLM wrapper, which you can access with 
-```python
-from langchain.llms import AI21
-```

docs/ecosystem/aim_tracking.ipynb

@@ -1,293 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Aim\n",
-    "\n",
-    "Aim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents. \n",
-    "\n",
-    "With Aim, you can easily debug and examine an individual execution:\n",
-    "\n",
-    "![](https://user-images.githubusercontent.com/13848158/227784778-06b806c7-74a1-4d15-ab85-9ece09b458aa.png)\n",
-    "\n",
-    "Additionally, you have the option to compare multiple executions side by side:\n",
-    "\n",
-    "![](https://user-images.githubusercontent.com/13848158/227784994-699b24b7-e69b-48f9-9ffa-e6a6142fd719.png)\n",
-    "\n",
-    "Aim is fully open source, [learn more](https://github.com/aimhubio/aim) about Aim on GitHub.\n",
-    "\n",
-    "Let's move forward and see how to enable and configure Aim callback."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3>Tracking LangChain Executions with Aim</h3>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this notebook we will explore three usage scenarios. To start off, we will install the necessary packages and import certain modules. Subsequently, we will configure two environment variables that can be established either within the Python script or through the terminal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "mf88kuCJhbVu"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install aim\n",
-    "!pip install langchain\n",
-    "!pip install openai\n",
-    "!pip install google-search-results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "g4eTuajwfl6L"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "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"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our examples use a GPT model as the LLM, and OpenAI offers an API for this purpose. You can obtain the key from the following link: https://platform.openai.com/account/api-keys .\n",
-    "\n",
-    "We will use the SerpApi to retrieve search results from Google. To acquire the SerpApi key, please go to https://serpapi.com/manage-api-key ."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "T1bSmKd6V2If"
-   },
-   "outputs": [],
-   "source": [
-    "os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
-    "os.environ[\"SERPAPI_API_KEY\"] = \"...\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "QenUYuBZjIzc"
-   },
-   "source": [
-    "The event methods of `AimCallbackHandler` accept the LangChain module or agent as input and log at least the prompts and generated results, as well as the serialized version of the LangChain module, to the designated Aim run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KAz8weWuUeXF"
-   },
-   "outputs": [],
-   "source": [
-    "session_group = datetime.now().strftime(\"%m.%d.%Y_%H.%M.%S\")\n",
-    "aim_callback = AimCallbackHandler(\n",
-    "    repo=\".\",\n",
-    "    experiment_name=\"scenario 1: OpenAI LLM\",\n",
-    ")\n",
-    "\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), aim_callback])\n",
-    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "b8WfByB4fl6N"
-   },
-   "source": [
-    "The `flush_tracker` function is used to record LangChain assets on Aim. By default, the session is reset rather than being terminated outright."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3>Scenario 1</h3> In the first scenario, we will use OpenAI LLM."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "o_VmneyIUyx8"
-   },
-   "outputs": [],
-   "source": [
-    "# scenario 1 - LLM\n",
-    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
-    "aim_callback.flush_tracker(\n",
-    "    langchain_asset=llm,\n",
-    "    experiment_name=\"scenario 2: Chain with multiple SubChains on multiple generations\",\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3>Scenario 2</h3> Scenario two involves chaining with multiple SubChains across multiple generations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "trxslyb1U28Y"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "uauQk10SUzF6"
-   },
-   "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, callback_manager=manager)\n",
-    "\n",
-    "test_prompts = [\n",
-    "    {\"title\": \"documentary about good video games that push the boundary of game design\"},\n",
-    "    {\"title\": \"the phenomenon behind the remarkable speed of cheetahs\"},\n",
-    "    {\"title\": \"the best in class mlops tooling\"},\n",
-    "]\n",
-    "synopsis_chain.apply(test_prompts)\n",
-    "aim_callback.flush_tracker(\n",
-    "    langchain_asset=synopsis_chain, experiment_name=\"scenario 3: Agent with Tools\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3>Scenario 3</h3> The third scenario involves an agent with tools."
-   ]
-  },
-  {
-   "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": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Gpq4rk6VT9cu",
-    "outputId": "68ae261e-d0a2-4229-83c4-762562263b66"
-   },
-   "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 Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mLeonardo DiCaprio seemed to prove a long-held theory about his love life right after splitting from girlfriend Camila Morrone just months ...\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: \"Camila Morrone age\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
-      "Action: Calculator\n",
-      "Action Input: 25^0.43\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "# scenario 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callback_manager=manager,\n",
-    "    verbose=True,\n",
-    ")\n",
-    "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")\n",
-    "aim_callback.flush_tracker(langchain_asset=agent, reset=False, finish=True)"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "provenance": []
-  },
-  "gpuClass": "standard",
-  "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": 1
-}

docs/ecosystem/apify.md

@@ -1,46 +0,0 @@
-# Apify
-
-This page covers how to use [Apify](https://apify.com) within LangChain.
-
-## Overview
-
-Apify is a cloud platform for web scraping and data extraction,
-which provides an [ecosystem](https://apify.com/store) of more than a thousand
-ready-made apps called *Actors* for various scraping, crawling, and extraction use cases.
-
-[![Apify Actors](../_static/ApifyActors.png)](https://apify.com/store)
-
-This integration enables you run Actors on the Apify platform and load their results into LangChain to feed your vector
-indexes with documents and data from the web, e.g. to generate answers from websites with documentation,
-blogs, or knowledge bases.
-
-
-## Installation and Setup
-
-- Install the Apify API client for Python with `pip install apify-client`
-- Get your [Apify API token](https://console.apify.com/account/integrations) and either set it as
-  an environment variable (`APIFY_API_TOKEN`) or pass it to the `ApifyWrapper` as `apify_api_token` in the constructor.
-
-
-## Wrappers
-
-### Utility
-
-You can use the `ApifyWrapper` to run Actors on the Apify platform.
-
-```python
-from langchain.utilities import ApifyWrapper
-```
-
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/apify.ipynb).
-
-
-### Loader
-
-You can also use our `ApifyDatasetLoader` to get data from Apify dataset.
-
-```python
-from langchain.document_loaders import ApifyDatasetLoader
-```
-
-For a more detailed walkthrough of this loader, see [this notebook](../modules/indexes/document_loaders/examples/apify_dataset.ipynb).

docs/ecosystem/atlas.md

@@ -1,27 +0,0 @@
-# AtlasDB
-
-This page covers how to use Nomic's Atlas ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Atlas wrappers.
-
-## Installation and Setup
-- Install the Python package with `pip install nomic`
-- Nomic is also included in langchains poetry extras `poetry install -E all`
-
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around the Atlas neural database, allowing you to use it as a vectorstore.
-This vectorstore also gives you full access to the underlying AtlasProject object, which will allow you to use the full range of Atlas map interactions, such as bulk tagging and automatic topic modeling.
-Please see [the Atlas docs](https://docs.nomic.ai/atlas_api.html) for more detailed information.
-
-
-
-
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import AtlasDB
-```
-
-For a more detailed walkthrough of the AtlasDB wrapper, see [this notebook](../modules/indexes/vectorstores/examples/atlas.ipynb)

docs/ecosystem/bananadev.md

@@ -1,79 +0,0 @@
-# Banana
-
-This page covers how to use the Banana ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Banana wrappers.
-
-## Installation and Setup
-
-- Install with `pip install banana-dev`
-- Get an Banana api key and set it as an environment variable (`BANANA_API_KEY`)
-
-## Define your Banana Template
-
-If you want to use an available language model template you can find one [here](https://app.banana.dev/templates/conceptofmind/serverless-template-palmyra-base).
-This template uses the Palmyra-Base model by [Writer](https://writer.com/product/api/).
-You can check out an example Banana repository [here](https://github.com/conceptofmind/serverless-template-palmyra-base).
-
-## Build the Banana app
-
-Banana Apps must include the "output" key in the return json. 
-There is a rigid response structure.
-
-```python
-# Return the results as a dictionary
-result = {'output': result}
-```
-
-An example inference function would be:
-
-```python
-def inference(model_inputs:dict) -> dict:
-    global model
-    global tokenizer
-
-    # Parse out your arguments
-    prompt = model_inputs.get('prompt', None)
-    if prompt == None:
-        return {'message': "No prompt provided"}
-
-    # Run the model
-    input_ids = tokenizer.encode(prompt, return_tensors='pt').cuda()
-    output = model.generate(
-        input_ids,
-        max_length=100,
-        do_sample=True,
-        top_k=50,
-        top_p=0.95,
-        num_return_sequences=1,
-        temperature=0.9,
-        early_stopping=True,
-        no_repeat_ngram_size=3,
-        num_beams=5,
-        length_penalty=1.5,
-        repetition_penalty=1.5,
-        bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]]
-        )
-
-    result = tokenizer.decode(output[0], skip_special_tokens=True)
-    # Return the results as a dictionary
-    result = {'output': result}
-    return result
-```
-
-You can find a full example of a Banana app [here](https://github.com/conceptofmind/serverless-template-palmyra-base/blob/main/app.py).
-
-## Wrappers
-
-### LLM
-
-There exists an Banana LLM wrapper, which you can access with
-
-```python
-from langchain.llms import Banana
-```
-
-You need to provide a model key located in the dashboard:
-
-```python
-llm = Banana(model_key="YOUR_MODEL_KEY")
-```

docs/ecosystem/cerebriumai.md

@@ -1,17 +0,0 @@
-# CerebriumAI
-
-This page covers how to use the CerebriumAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers.
-
-## Installation and Setup
-- Install with `pip install cerebrium`
-- Get an CerebriumAI api key and set it as an environment variable (`CEREBRIUMAI_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an CerebriumAI LLM wrapper, which you can access with 
-```python
-from langchain.llms import CerebriumAI
-```

docs/ecosystem/chroma.md

@@ -1,20 +0,0 @@
-# Chroma
-
-This page covers how to use the Chroma ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Chroma wrappers.
-
-## Installation and Setup
-- Install the Python package with `pip install chromadb`
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Chroma vector 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 Chroma
-```
-
-For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/vectorstores/getting_started.ipynb)

docs/ecosystem/clearml_tracking.ipynb

@@ -1,589 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ClearML Integration\n",
-    "\n",
-    "In order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. ClearML is an experiment manager that neatly tracks and organizes all your experiment runs.\n",
-    "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/ecosystem/clearml_tracking.ipynb\">\n",
-    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-    "</a>"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 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",
-    "- ClearML: https://app.clear.ml/settings/workspace-configuration\n",
-    "- OpenAI: https://platform.openai.com/account/api-keys\n",
-    "- SerpAPI (google search): https://serpapi.com/dashboard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"CLEARML_API_ACCESS_KEY\"] = \"\"\n",
-    "os.environ[\"CLEARML_API_SECRET_KEY\"] = \"\"\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
-    "os.environ[\"SERPAPI_API_KEY\"] = \"\""
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting Up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install clearml\n",
-    "!pip install pandas\n",
-    "!pip install textstat\n",
-    "!pip install spacy\n",
-    "!python -m spacy download en_core_web_sm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`.\n"
-     ]
-    }
-   ],
-   "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",
-    "clearml_callback = ClearMLCallbackHandler(\n",
-    "    task_type=\"inference\",\n",
-    "    project_name=\"langchain_callback_demo\",\n",
-    "    task_name=\"llm\",\n",
-    "    tags=[\"test\"],\n",
-    "    # Change the following parameters based on the amount of detail you want tracked\n",
-    "    visualize=True,\n",
-    "    complexity_metrics=True,\n",
-    "    stream_logs=True\n",
-    ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), clearml_callback])\n",
-    "# Get the OpenAI model ready to go\n",
-    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
-      "{'action_records':           action    name  step  starts  ends  errors  text_ctr  chain_starts  \\\n",
-      "0   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "1   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "2   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "3   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "4   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "5   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "6     on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "7     on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "8     on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "9     on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "10    on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "11    on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "12  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "13  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "14  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "15  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "16  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "17  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "18    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "19    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "20    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "21    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "22    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "23    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "\n",
-      "    chain_ends  llm_starts  ...  difficult_words  linsear_write_formula  \\\n",
-      "0            0           1  ...              NaN                    NaN   \n",
-      "1            0           1  ...              NaN                    NaN   \n",
-      "2            0           1  ...              NaN                    NaN   \n",
-      "3            0           1  ...              NaN                    NaN   \n",
-      "4            0           1  ...              NaN                    NaN   \n",
-      "5            0           1  ...              NaN                    NaN   \n",
-      "6            0           1  ...              0.0                    5.5   \n",
-      "7            0           1  ...              2.0                    6.5   \n",
-      "8            0           1  ...              0.0                    5.5   \n",
-      "9            0           1  ...              2.0                    6.5   \n",
-      "10           0           1  ...              0.0                    5.5   \n",
-      "11           0           1  ...              2.0                    6.5   \n",
-      "12           0           2  ...              NaN                    NaN   \n",
-      "13           0           2  ...              NaN                    NaN   \n",
-      "14           0           2  ...              NaN                    NaN   \n",
-      "15           0           2  ...              NaN                    NaN   \n",
-      "16           0           2  ...              NaN                    NaN   \n",
-      "17           0           2  ...              NaN                    NaN   \n",
-      "18           0           2  ...              0.0                    5.5   \n",
-      "19           0           2  ...              2.0                    6.5   \n",
-      "20           0           2  ...              0.0                    5.5   \n",
-      "21           0           2  ...              2.0                    6.5   \n",
-      "22           0           2  ...              0.0                    5.5   \n",
-      "23           0           2  ...              2.0                    6.5   \n",
-      "\n",
-      "    gunning_fog      text_standard  fernandez_huerta szigriszt_pazos  \\\n",
-      "0           NaN                NaN               NaN             NaN   \n",
-      "1           NaN                NaN               NaN             NaN   \n",
-      "2           NaN                NaN               NaN             NaN   \n",
-      "3           NaN                NaN               NaN             NaN   \n",
-      "4           NaN                NaN               NaN             NaN   \n",
-      "5           NaN                NaN               NaN             NaN   \n",
-      "6          5.20  5th and 6th grade            133.58          131.54   \n",
-      "7          8.28  6th and 7th grade            115.58          112.37   \n",
-      "8          5.20  5th and 6th grade            133.58          131.54   \n",
-      "9          8.28  6th and 7th grade            115.58          112.37   \n",
-      "10         5.20  5th and 6th grade            133.58          131.54   \n",
-      "11         8.28  6th and 7th grade            115.58          112.37   \n",
-      "12          NaN                NaN               NaN             NaN   \n",
-      "13          NaN                NaN               NaN             NaN   \n",
-      "14          NaN                NaN               NaN             NaN   \n",
-      "15          NaN                NaN               NaN             NaN   \n",
-      "16          NaN                NaN               NaN             NaN   \n",
-      "17          NaN                NaN               NaN             NaN   \n",
-      "18         5.20  5th and 6th grade            133.58          131.54   \n",
-      "19         8.28  6th and 7th grade            115.58          112.37   \n",
-      "20         5.20  5th and 6th grade            133.58          131.54   \n",
-      "21         8.28  6th and 7th grade            115.58          112.37   \n",
-      "22         5.20  5th and 6th grade            133.58          131.54   \n",
-      "23         8.28  6th and 7th grade            115.58          112.37   \n",
-      "\n",
-      "    gutierrez_polini  crawford  gulpease_index   osman  \n",
-      "0                NaN       NaN             NaN     NaN  \n",
-      "1                NaN       NaN             NaN     NaN  \n",
-      "2                NaN       NaN             NaN     NaN  \n",
-      "3                NaN       NaN             NaN     NaN  \n",
-      "4                NaN       NaN             NaN     NaN  \n",
-      "5                NaN       NaN             NaN     NaN  \n",
-      "6              62.30      -0.2            79.8  116.91  \n",
-      "7              54.83       1.4            72.1  100.17  \n",
-      "8              62.30      -0.2            79.8  116.91  \n",
-      "9              54.83       1.4            72.1  100.17  \n",
-      "10             62.30      -0.2            79.8  116.91  \n",
-      "11             54.83       1.4            72.1  100.17  \n",
-      "12               NaN       NaN             NaN     NaN  \n",
-      "13               NaN       NaN             NaN     NaN  \n",
-      "14               NaN       NaN             NaN     NaN  \n",
-      "15               NaN       NaN             NaN     NaN  \n",
-      "16               NaN       NaN             NaN     NaN  \n",
-      "17               NaN       NaN             NaN     NaN  \n",
-      "18             62.30      -0.2            79.8  116.91  \n",
-      "19             54.83       1.4            72.1  100.17  \n",
-      "20             62.30      -0.2            79.8  116.91  \n",
-      "21             54.83       1.4            72.1  100.17  \n",
-      "22             62.30      -0.2            79.8  116.91  \n",
-      "23             54.83       1.4            72.1  100.17  \n",
-      "\n",
-      "[24 rows x 39 columns], 'session_analysis':     prompt_step         prompts    name  output_step  \\\n",
-      "0             1  Tell me a joke  OpenAI            2   \n",
-      "1             1  Tell me a poem  OpenAI            2   \n",
-      "2             1  Tell me a joke  OpenAI            2   \n",
-      "3             1  Tell me a poem  OpenAI            2   \n",
-      "4             1  Tell me a joke  OpenAI            2   \n",
-      "5             1  Tell me a poem  OpenAI            2   \n",
-      "6             3  Tell me a joke  OpenAI            4   \n",
-      "7             3  Tell me a poem  OpenAI            4   \n",
-      "8             3  Tell me a joke  OpenAI            4   \n",
-      "9             3  Tell me a poem  OpenAI            4   \n",
-      "10            3  Tell me a joke  OpenAI            4   \n",
-      "11            3  Tell me a poem  OpenAI            4   \n",
-      "\n",
-      "                                               output  \\\n",
-      "0   \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "1   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "2   \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "3   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "4   \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "5   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "6   \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "7   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "8   \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "9   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "10  \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "11  \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "\n",
-      "    token_usage_total_tokens  token_usage_prompt_tokens  \\\n",
-      "0                        162                         24   \n",
-      "1                        162                         24   \n",
-      "2                        162                         24   \n",
-      "3                        162                         24   \n",
-      "4                        162                         24   \n",
-      "5                        162                         24   \n",
-      "6                        162                         24   \n",
-      "7                        162                         24   \n",
-      "8                        162                         24   \n",
-      "9                        162                         24   \n",
-      "10                       162                         24   \n",
-      "11                       162                         24   \n",
-      "\n",
-      "    token_usage_completion_tokens  flesch_reading_ease  flesch_kincaid_grade  \\\n",
-      "0                             138               109.04                   1.3   \n",
-      "1                             138                83.66                   4.8   \n",
-      "2                             138               109.04                   1.3   \n",
-      "3                             138                83.66                   4.8   \n",
-      "4                             138               109.04                   1.3   \n",
-      "5                             138                83.66                   4.8   \n",
-      "6                             138               109.04                   1.3   \n",
-      "7                             138                83.66                   4.8   \n",
-      "8                             138               109.04                   1.3   \n",
-      "9                             138                83.66                   4.8   \n",
-      "10                            138               109.04                   1.3   \n",
-      "11                            138                83.66                   4.8   \n",
-      "\n",
-      "    ...  difficult_words  linsear_write_formula  gunning_fog  \\\n",
-      "0   ...                0                    5.5         5.20   \n",
-      "1   ...                2                    6.5         8.28   \n",
-      "2   ...                0                    5.5         5.20   \n",
-      "3   ...                2                    6.5         8.28   \n",
-      "4   ...                0                    5.5         5.20   \n",
-      "5   ...                2                    6.5         8.28   \n",
-      "6   ...                0                    5.5         5.20   \n",
-      "7   ...                2                    6.5         8.28   \n",
-      "8   ...                0                    5.5         5.20   \n",
-      "9   ...                2                    6.5         8.28   \n",
-      "10  ...                0                    5.5         5.20   \n",
-      "11  ...                2                    6.5         8.28   \n",
-      "\n",
-      "        text_standard  fernandez_huerta  szigriszt_pazos  gutierrez_polini  \\\n",
-      "0   5th and 6th grade            133.58           131.54             62.30   \n",
-      "1   6th and 7th grade            115.58           112.37             54.83   \n",
-      "2   5th and 6th grade            133.58           131.54             62.30   \n",
-      "3   6th and 7th grade            115.58           112.37             54.83   \n",
-      "4   5th and 6th grade            133.58           131.54             62.30   \n",
-      "5   6th and 7th grade            115.58           112.37             54.83   \n",
-      "6   5th and 6th grade            133.58           131.54             62.30   \n",
-      "7   6th and 7th grade            115.58           112.37             54.83   \n",
-      "8   5th and 6th grade            133.58           131.54             62.30   \n",
-      "9   6th and 7th grade            115.58           112.37             54.83   \n",
-      "10  5th and 6th grade            133.58           131.54             62.30   \n",
-      "11  6th and 7th grade            115.58           112.37             54.83   \n",
-      "\n",
-      "   crawford  gulpease_index   osman  \n",
-      "0      -0.2            79.8  116.91  \n",
-      "1       1.4            72.1  100.17  \n",
-      "2      -0.2            79.8  116.91  \n",
-      "3       1.4            72.1  100.17  \n",
-      "4      -0.2            79.8  116.91  \n",
-      "5       1.4            72.1  100.17  \n",
-      "6      -0.2            79.8  116.91  \n",
-      "7       1.4            72.1  100.17  \n",
-      "8      -0.2            79.8  116.91  \n",
-      "9       1.4            72.1  100.17  \n",
-      "10     -0.2            79.8  116.91  \n",
-      "11      1.4            72.1  100.17  \n",
-      "\n",
-      "[12 rows x 24 columns]}\n",
-      "2023-03-29 14:00:25,948 - clearml.Task - INFO - Completed model upload to https://files.clear.ml/langchain_callback_demo/llm.988bd727b0e94a29a3ac0ee526813545/models/simple_sequential\n"
-     ]
-    }
-   ],
-   "source": [
-    "# SCENARIO 1 - LLM\n",
-    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
-    "# After every generation run, use flush to make sure all the metrics\n",
-    "# prompts and other output are properly saved separately\n",
-    "clearml_callback.flush_tracker(langchain_asset=llm, name=\"simple_sequential\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At this point you can already go to https://app.clear.ml and take a look at the resulting ClearML Task that was created.\n",
-    "\n",
-    "Among others, you should see that this notebook is saved along with any git information. The model JSON that contains the used parameters is saved as an artifact, there are also console logs and under the plots section, you'll find tables that represent the flow of the chain.\n",
-    "\n",
-    "Finally, if you enabled visualizations, these are stored as HTML files under debug samples."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 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",
-    "You can now also see the use of the `finish=True` keyword, which will fully close the ClearML Task, instead of just resetting the parameters and prompts for a new conversation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "{'action': 'on_chain_start', 'name': 'AgentExecutor', 'step': 1, 'starts': 1, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 0, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'input': 'Who is the wife of the person who sang summer of 69?'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 2, 'starts': 2, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought:'}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 189, 'token_usage_completion_tokens': 34, 'token_usage_total_tokens': 223, 'model_name': 'text-davinci-003', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': ' I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 91.61, 'flesch_kincaid_grade': 3.8, 'smog_index': 0.0, 'coleman_liau_index': 3.41, 'automated_readability_index': 3.5, 'dale_chall_readability_score': 6.06, 'difficult_words': 2, 'linsear_write_formula': 5.75, 'gunning_fog': 5.4, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 121.07, 'szigriszt_pazos': 119.5, 'gutierrez_polini': 54.91, 'crawford': 0.9, 'gulpease_index': 72.7, 'osman': 92.16}\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who sang summer of 69 and then find out who their wife is.\n",
-      "Action: Search\n",
-      "Action Input: \"Who sang summer of 69\"\u001b[0m{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who sang summer of 69', 'log': ' I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"', 'step': 4, 'starts': 3, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 1, 'tool_ends': 0, 'agent_ends': 0}\n",
-      "{'action': 'on_tool_start', 'input_str': 'Who sang summer of 69', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 5, 'starts': 4, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 0, 'agent_ends': 0}\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mBryan Adams - Summer Of 69 (Official Music Video).\u001b[0m\n",
-      "Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams - Summer Of 69 (Official Music Video).', 'step': 6, 'starts': 4, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 7, 'starts': 5, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"\\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\\nThought:'}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'text': ' I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 94.66, 'flesch_kincaid_grade': 2.7, 'smog_index': 0.0, 'coleman_liau_index': 4.73, 'automated_readability_index': 4.0, 'dale_chall_readability_score': 7.16, 'difficult_words': 2, 'linsear_write_formula': 4.25, 'gunning_fog': 4.2, 'text_standard': '4th and 5th grade', 'fernandez_huerta': 124.13, 'szigriszt_pazos': 119.2, 'gutierrez_polini': 52.26, 'crawford': 0.7, 'gulpease_index': 74.7, 'osman': 84.2}\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who Bryan Adams is married to.\n",
-      "Action: Search\n",
-      "Action Input: \"Who is Bryan Adams married to\"\u001b[0m{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"', 'step': 9, 'starts': 6, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 3, 'tool_ends': 1, 'agent_ends': 0}\n",
-      "{'action': 'on_tool_start', 'input_str': 'Who is Bryan Adams married to', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 10, 'starts': 7, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 1, 'agent_ends': 0}\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mBryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\u001b[0m\n",
-      "Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"\\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\\nThought: I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"\\nObservation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\\nThought:'}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'text': ' I now know the final answer.\\nFinal Answer: Bryan Adams has never been married.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 81.29, 'flesch_kincaid_grade': 3.7, 'smog_index': 0.0, 'coleman_liau_index': 5.75, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 7.37, 'difficult_words': 1, 'linsear_write_formula': 2.5, 'gunning_fog': 2.8, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 115.7, 'szigriszt_pazos': 110.84, 'gutierrez_polini': 49.79, 'crawford': 0.7, 'gulpease_index': 85.4, 'osman': 83.14}\n",
-      "\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: Bryan Adams has never been married.\u001b[0m\n",
-      "{'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "{'action': 'on_chain_end', 'outputs': 'Bryan Adams has never been married.', 'step': 15, 'starts': 8, 'ends': 7, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 1, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}\n",
-      "{'action_records':              action    name  step  starts  ends  errors  text_ctr  \\\n",
-      "0      on_llm_start  OpenAI     1       1     0       0         0   \n",
-      "1      on_llm_start  OpenAI     1       1     0       0         0   \n",
-      "2      on_llm_start  OpenAI     1       1     0       0         0   \n",
-      "3      on_llm_start  OpenAI     1       1     0       0         0   \n",
-      "4      on_llm_start  OpenAI     1       1     0       0         0   \n",
-      "..              ...     ...   ...     ...   ...     ...       ...   \n",
-      "66      on_tool_end     NaN    11       7     4       0         0   \n",
-      "67     on_llm_start  OpenAI    12       8     4       0         0   \n",
-      "68       on_llm_end     NaN    13       8     5       0         0   \n",
-      "69  on_agent_finish     NaN    14       8     6       0         0   \n",
-      "70     on_chain_end     NaN    15       8     7       0         0   \n",
-      "\n",
-      "    chain_starts  chain_ends  llm_starts  ...  gulpease_index  osman  input  \\\n",
-      "0              0           0           1  ...             NaN    NaN    NaN   \n",
-      "1              0           0           1  ...             NaN    NaN    NaN   \n",
-      "2              0           0           1  ...             NaN    NaN    NaN   \n",
-      "3              0           0           1  ...             NaN    NaN    NaN   \n",
-      "4              0           0           1  ...             NaN    NaN    NaN   \n",
-      "..           ...         ...         ...  ...             ...    ...    ...   \n",
-      "66             1           0           2  ...             NaN    NaN    NaN   \n",
-      "67             1           0           3  ...             NaN    NaN    NaN   \n",
-      "68             1           0           3  ...            85.4  83.14    NaN   \n",
-      "69             1           0           3  ...             NaN    NaN    NaN   \n",
-      "70             1           1           3  ...             NaN    NaN    NaN   \n",
-      "\n",
-      "    tool  tool_input                                                log  \\\n",
-      "0    NaN         NaN                                                NaN   \n",
-      "1    NaN         NaN                                                NaN   \n",
-      "2    NaN         NaN                                                NaN   \n",
-      "3    NaN         NaN                                                NaN   \n",
-      "4    NaN         NaN                                                NaN   \n",
-      "..   ...         ...                                                ...   \n",
-      "66   NaN         NaN                                                NaN   \n",
-      "67   NaN         NaN                                                NaN   \n",
-      "68   NaN         NaN                                                NaN   \n",
-      "69   NaN         NaN   I now know the final answer.\\nFinal Answer: B...   \n",
-      "70   NaN         NaN                                                NaN   \n",
-      "\n",
-      "    input_str  description                                             output  \\\n",
-      "0         NaN          NaN                                                NaN   \n",
-      "1         NaN          NaN                                                NaN   \n",
-      "2         NaN          NaN                                                NaN   \n",
-      "3         NaN          NaN                                                NaN   \n",
-      "4         NaN          NaN                                                NaN   \n",
-      "..        ...          ...                                                ...   \n",
-      "66        NaN          NaN  Bryan Adams has never married. In the 1990s, h...   \n",
-      "67        NaN          NaN                                                NaN   \n",
-      "68        NaN          NaN                                                NaN   \n",
-      "69        NaN          NaN                Bryan Adams has never been married.   \n",
-      "70        NaN          NaN                                                NaN   \n",
-      "\n",
-      "                                outputs  \n",
-      "0                                   NaN  \n",
-      "1                                   NaN  \n",
-      "2                                   NaN  \n",
-      "3                                   NaN  \n",
-      "4                                   NaN  \n",
-      "..                                  ...  \n",
-      "66                                  NaN  \n",
-      "67                                  NaN  \n",
-      "68                                  NaN  \n",
-      "69                                  NaN  \n",
-      "70  Bryan Adams has never been married.  \n",
-      "\n",
-      "[71 rows x 47 columns], 'session_analysis':    prompt_step                                            prompts    name  \\\n",
-      "0            2  Answer the following questions as best you can...  OpenAI   \n",
-      "1            7  Answer the following questions as best you can...  OpenAI   \n",
-      "2           12  Answer the following questions as best you can...  OpenAI   \n",
-      "\n",
-      "   output_step                                             output  \\\n",
-      "0            3   I need to find out who sang summer of 69 and ...   \n",
-      "1            8   I need to find out who Bryan Adams is married...   \n",
-      "2           13   I now know the final answer.\\nFinal Answer: B...   \n",
-      "\n",
-      "   token_usage_total_tokens  token_usage_prompt_tokens  \\\n",
-      "0                       223                        189   \n",
-      "1                       270                        242   \n",
-      "2                       332                        314   \n",
-      "\n",
-      "   token_usage_completion_tokens  flesch_reading_ease  flesch_kincaid_grade  \\\n",
-      "0                             34                91.61                   3.8   \n",
-      "1                             28                94.66                   2.7   \n",
-      "2                             18                81.29                   3.7   \n",
-      "\n",
-      "   ...  difficult_words  linsear_write_formula  gunning_fog  \\\n",
-      "0  ...                2                   5.75          5.4   \n",
-      "1  ...                2                   4.25          4.2   \n",
-      "2  ...                1                   2.50          2.8   \n",
-      "\n",
-      "       text_standard  fernandez_huerta  szigriszt_pazos  gutierrez_polini  \\\n",
-      "0  3rd and 4th grade            121.07           119.50             54.91   \n",
-      "1  4th and 5th grade            124.13           119.20             52.26   \n",
-      "2  3rd and 4th grade            115.70           110.84             49.79   \n",
-      "\n",
-      "  crawford  gulpease_index  osman  \n",
-      "0      0.9            72.7  92.16  \n",
-      "1      0.7            74.7  84.20  \n",
-      "2      0.7            85.4  83.14  \n",
-      "\n",
-      "[3 rows x 24 columns]}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Could not update last created model in Task 988bd727b0e94a29a3ac0ee526813545, Task status 'completed' cannot be updated\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.agents import initialize_agent, load_tools\n",
-    "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",
-    "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 the wife of the person who sang summer of 69?\"\n",
-    ")\n",
-    "clearml_callback.flush_tracker(langchain_asset=agent, name=\"Agent with Tools\", finish=True)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 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",
-    "- If you close the ClearML Callback using `clearml_callback.flush_tracker(..., finish=True)` the Callback cannot be used anymore. Make a new one if you want to keep logging.\n",
-    "\n",
-    "- Check out the rest of the open source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more!\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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": "3.10.9"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "a53ebf4a859167383b364e7e7521d0add3c2dbbdecce4edf676e8c4634ff3fbb"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/ecosystem/cohere.md

@@ -1,25 +0,0 @@
-# Cohere
-
-This page covers how to use the Cohere ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Cohere wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install cohere`
-- Get an Cohere api key and set it as an environment variable (`COHERE_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an Cohere LLM wrapper, which you can access with 
-```python
-from langchain.llms import Cohere
-```
-
-### Embeddings
-
-There exists an Cohere Embeddings wrapper, which you can access with 
-```python
-from langchain.embeddings import CohereEmbeddings
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/cohere.ipynb)

docs/ecosystem/comet_tracking.ipynb

@@ -1,352 +0,0 @@
-{
- "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 intializing 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.callbacks.base import CallbackManager\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",
-    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
-    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
-    "\n",
-    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\", \"Tell me a fact\"] * 3)\n",
-    "print(\"LLM result\", llm_result)\n",
-    "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.callbacks.base import CallbackManager\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",
-    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
-    "\n",
-    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
-    "\n",
-    "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
-    "Title: {title}\n",
-    "Playwright: This is a synopsis for the above play:\"\"\"\n",
-    "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\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.callbacks.base import CallbackManager\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",
-    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
-    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
-    "\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=\"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",
-    ")\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.callbacks.base import CallbackManager\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",
-    "manager = CallbackManager([StdOutCallbackHandler(), comet_callback])\n",
-    "llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True)\n",
-    "\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\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))\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

@@ -1,25 +0,0 @@
-# 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/deepinfra.md

@@ -1,17 +0,0 @@
-# DeepInfra
-
-This page covers how to use the DeepInfra ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific DeepInfra wrappers.
-
-## Installation and Setup
-- Get your DeepInfra api key from this link [here](https://deepinfra.com/).
-- Get an DeepInfra api key and set it as an environment variable (`DEEPINFRA_API_TOKEN`)
-
-## Wrappers
-
-### LLM
-
-There exists an DeepInfra LLM wrapper, which you can access with
-```python
-from langchain.llms import DeepInfra
-```

docs/ecosystem/deeplake.md

@@ -1,30 +0,0 @@
-# Deep Lake
-This page covers how to use the Deep Lake ecosystem within LangChain.
-
-## Why Deep Lake?
-- More than just a (multi-modal) vector store. You can later use the dataset to fine-tune your own LLM models.
-- Not only stores embeddings, but also the original data with automatic version control.
-- Truly serverless. Doesn't require another service and can be used with major cloud providers (AWS S3, GCS, etc.)
-
-## 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/)
-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`
-
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Deep Lake, a data lake for Deep Learning applications, allowing you to use it as a vector store (for now), whether for semantic search or example selection.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import DeepLake
-```
-
-
-For a more detailed walkthrough of the Deep Lake wrapper, see [this notebook](../modules/indexes/vectorstores/examples/deeplake.ipynb)

docs/ecosystem/forefrontai.md

@@ -1,16 +0,0 @@
-# ForefrontAI
-
-This page covers how to use the ForefrontAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific ForefrontAI wrappers.
-
-## Installation and Setup
-- Get an ForefrontAI api key and set it as an environment variable (`FOREFRONTAI_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an ForefrontAI LLM wrapper, which you can access with 
-```python
-from langchain.llms import ForefrontAI
-```

docs/ecosystem/google_search.md

@@ -1,32 +0,0 @@
-# Google Search Wrapper
-
-This page covers how to use the Google Search API within LangChain.
-It is broken into two parts: installation and setup, and then references to the specific Google Search wrapper.
-
-## Installation and Setup
-- Install requirements with `pip install google-api-python-client`
-- Set up a Custom Search Engine, following [these instructions](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search)
-- Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables `GOOGLE_API_KEY` and `GOOGLE_CSE_ID` respectively
-
-## Wrappers
-
-### Utility
-
-There exists a GoogleSearchAPIWrapper utility which wraps this API. To import this utility:
-
-```python
-from langchain.utilities import GoogleSearchAPIWrapper
-```
-
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/google_search.ipynb).
-
-### Tool
-
-You can also easily load this wrapper as a Tool (to use with an Agent).
-You can do this with:
-```python
-from langchain.agents import load_tools
-tools = load_tools(["google-search"])
-```
-
-For more information on this, see [this page](../modules/agents/tools/getting_started.md)

docs/ecosystem/google_serper.md

@@ -1,73 +0,0 @@
-# Google Serper Wrapper
-
-This page covers how to use the [Serper](https://serper.dev) Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. 
-It is broken into two parts: setup, and then references to the specific Google Serper wrapper.
-
-## Setup
-- Go to [serper.dev](https://serper.dev) to sign up for a free account
-- Get the api key and set it as an environment variable (`SERPER_API_KEY`)
-
-## Wrappers
-
-### Utility
-
-There exists a GoogleSerperAPIWrapper utility which wraps this API. To import this utility:
-
-```python
-from langchain.utilities import GoogleSerperAPIWrapper
-```
-
-You can use it as part of a Self Ask chain:
-
-```python
-from langchain.utilities import GoogleSerperAPIWrapper
-from langchain.llms.openai import OpenAI
-from langchain.agents import initialize_agent, Tool
-from langchain.agents import AgentType
-
-import os
-
-os.environ["SERPER_API_KEY"] = ""
-os.environ['OPENAI_API_KEY'] = ""
-
-llm = OpenAI(temperature=0)
-search = GoogleSerperAPIWrapper()
-tools = [
-    Tool(
-        name="Intermediate Answer",
-        func=search.run,
-        description="useful for when you need to ask with search"
-    )
-]
-
-self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)
-self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
-```
-
-#### Output
-```
-Entering new AgentExecutor chain...
- Yes.
-Follow up: Who is the reigning men's U.S. Open champion?
-Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion.
-Follow up: Where is Carlos Alcaraz from?
-Intermediate answer: El Palmar, Spain
-So the final answer is: El Palmar, Spain
-
-> Finished chain.
-
-'El Palmar, Spain'
-```
-
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/google_serper.ipynb).
-
-### Tool
-
-You can also easily load this wrapper as a Tool (to use with an Agent).
-You can do this with:
-```python
-from langchain.agents import load_tools
-tools = load_tools(["google-serper"])
-```
-
-For more information on this, see [this page](../modules/agents/tools/getting_started.md)

docs/ecosystem/gooseai.md

@@ -1,23 +0,0 @@
-# GooseAI
-
-This page covers how to use the GooseAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific GooseAI wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install openai`
-- Get your GooseAI api key from this link [here](https://goose.ai/).
-- Set the environment variable (`GOOSEAI_API_KEY`).
-
-```python
-import os
-os.environ["GOOSEAI_API_KEY"] = "YOUR_API_KEY"
-```
-
-## Wrappers
-
-### LLM
-
-There exists an GooseAI LLM wrapper, which you can access with: 
-```python
-from langchain.llms import GooseAI
-```

docs/ecosystem/gpt4all.md

@@ -1,49 +0,0 @@
-# GPT4All
-
-This page covers how to use the `GPT4All` wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.
-
-## Installation and Setup
-- Install the Python package with `pip install pyllamacpp`
-- Download a [GPT4All model](https://github.com/nomic-ai/pyllamacpp#supported-model) and place it in your desired directory
-
-## 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. Callbacks support token-wise streaming
-model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
-
-# Generate text
-response = model("Once upon a time, ")
-```
-
-You can also customize the generation parameters, such as n_predict, temp, top_p, top_k, and others.
-
-To stream the model's predictions, add in a CallbackManager.
-
-```python
-from langchain.llms import GPT4All
-from langchain.callbacks.manager import CallbackManager
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-
-# There are many CallbackHandlers supported, such as
-# from langchain.callbacks.streamlit import StreamlitCallbackHandler
-
-callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
-model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8, callback_handler=callback_handler,
-                verbose=True)
-
-# Generate text. Tokens are streamed through the callback manager.
-model("Once upon a time, ")
-```
-
-## Model File
-
-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)

docs/ecosystem/graphsignal.md

@@ -1,44 +0,0 @@
-# Graphsignal
-
-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
-
-- Install the Python library with `pip install graphsignal`
-- Create free Graphsignal account [here](https://graphsignal.com)
-- Get an API key and set it as an environment variable (`GRAPHSIGNAL_API_KEY`)
-
-## Tracing and Monitoring
-
-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:
-
-```python
-import graphsignal
-
-graphsignal.configure(deployment='my-langchain-app-prod')
-```
-
-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'):
-    chain.run("some initial text")
-```
-
-Optionally, enable profiling to record function-level statistics for each trace.
-
-```python
-with graphsignal.start_trace(
-        'my-chain', options=graphsignal.TraceOptions(enable_profiling=True)):
-    chain.run("some initial text")
-```
-
-See the [Quick Start](https://graphsignal.com/docs/guides/quick-start/) guide for complete setup instructions.

docs/ecosystem/hazy_research.md

@@ -1,19 +0,0 @@
-# Hazy Research
-
-This page covers how to use the Hazy Research ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Hazy Research wrappers.
-
-## Installation and Setup
-- To use the `manifest`, install it with `pip install manifest-ml`
-
-## Wrappers
-
-### LLM
-
-There exists an LLM wrapper around Hazy Research's `manifest` library. 
-`manifest` is a python library which is itself a wrapper around many model providers, and adds in caching, history, and more.
-
-To use this wrapper:
-```python
-from langchain.llms.manifest import ManifestWrapper
-```

docs/ecosystem/helicone.md

@@ -1,53 +0,0 @@
-# Helicone
-
-This page covers how to use the [Helicone](https://helicone.ai) ecosystem within LangChain.
-
-## What is Helicone?
-
-Helicone is an [open source](https://github.com/Helicone/helicone) observability platform that proxies your OpenAI traffic and provides you key insights into your spend, latency and usage.
-
-![Helicone](../_static/HeliconeDashboard.png)
-
-## Quick start
-
-With your LangChain environment you can just add the following parameter.
-
-```bash
-export OPENAI_API_BASE="https://oai.hconeai.com/v1"
-```
-
-Now head over to [helicone.ai](https://helicone.ai/onboarding?step=2) to create your account, and add your OpenAI API key within our dashboard to view your logs.
-
-![Helicone](../_static/HeliconeKeys.png)
-
-## How to enable Helicone caching
-
-```python
-from langchain.llms import OpenAI
-import openai
-openai.api_base = "https://oai.hconeai.com/v1"
-
-llm = OpenAI(temperature=0.9, headers={"Helicone-Cache-Enabled": "true"})
-text = "What is a helicone?"
-print(llm(text))
-```
-
-[Helicone caching docs](https://docs.helicone.ai/advanced-usage/caching)
-
-## How to use Helicone custom properties
-
-```python
-from langchain.llms import OpenAI
-import openai
-openai.api_base = "https://oai.hconeai.com/v1"
-
-llm = OpenAI(temperature=0.9, headers={
-        "Helicone-Property-Session": "24",
-        "Helicone-Property-Conversation": "support_issue_2",
-        "Helicone-Property-App": "mobile",
-      })
-text = "What is a helicone?"
-print(llm(text))
-```
-
-[Helicone property docs](https://docs.helicone.ai/advanced-usage/custom-properties)

docs/ecosystem/huggingface.md

@@ -1,69 +0,0 @@
-# Hugging Face
-
-This page covers how to use the Hugging Face ecosystem (including the [Hugging Face Hub](https://huggingface.co)) within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Hugging Face wrappers.
-
-## Installation and Setup
-
-If you want to work with the Hugging Face Hub:
-- Install the Hub client library with `pip install huggingface_hub`
-- Create a Hugging Face account (it's free!)
-- Create an [access token](https://huggingface.co/docs/hub/security-tokens) and set it as an environment variable (`HUGGINGFACEHUB_API_TOKEN`)
-
-If you want work with the Hugging Face Python libraries:
-- Install `pip install transformers` for working with models and tokenizers
-- Install `pip install datasets` for working with datasets
-
-## Wrappers
-
-### LLM
-
-There exists two Hugging Face LLM wrappers, one for a local pipeline and one for a model hosted on Hugging Face Hub.
-Note that these wrappers only work for models that support the following tasks: [`text2text-generation`](https://huggingface.co/models?library=transformers&pipeline_tag=text2text-generation&sort=downloads), [`text-generation`](https://huggingface.co/models?library=transformers&pipeline_tag=text-classification&sort=downloads)
-
-To use the local pipeline wrapper:
-```python
-from langchain.llms import HuggingFacePipeline
-```
-
-To use a the wrapper for a model hosted on Hugging Face Hub:
-```python
-from langchain.llms import HuggingFaceHub
-```
-For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](../modules/models/llms/integrations/huggingface_hub.ipynb)
-
-
-### Embeddings
-
-There exists two Hugging Face Embeddings wrappers, one for a local model and one for a model hosted on Hugging Face Hub.
-Note that these wrappers only work for [`sentence-transformers` models](https://huggingface.co/models?library=sentence-transformers&sort=downloads).
-
-To use the local pipeline wrapper:
-```python
-from langchain.embeddings import HuggingFaceEmbeddings
-```
-
-To use a the wrapper for a model hosted on Hugging Face Hub:
-```python
-from langchain.embeddings import HuggingFaceHubEmbeddings
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/huggingfacehub.ipynb)
-
-### Tokenizer
-
-There are several places you can use tokenizers available through the `transformers` package.
-By default, it is used to count tokens for all LLMs.
-
-You can also use it to count tokens when splitting documents with 
-```python
-from langchain.text_splitter import CharacterTextSplitter
-CharacterTextSplitter.from_huggingface_tokenizer(...)
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/text_splitters/examples/huggingface_length_function.ipynb)
-
-
-### Datasets
-
-The Hugging Face Hub has lots of great [datasets](https://huggingface.co/datasets) that can be used to evaluate your LLM chains.
-
-For a detailed walkthrough of how to use them to do so, see [this notebook](../use_cases/evaluation/huggingface_datasets.ipynb)

docs/ecosystem/jina.md

@@ -1,18 +0,0 @@
-# Jina
-
-This page covers how to use the Jina ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Jina wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install jina`
-- Get a Jina AI Cloud auth token from [here](https://cloud.jina.ai/settings/tokens) and set it as an environment variable (`JINA_AUTH_TOKEN`)
-
-## Wrappers
-
-### Embeddings
-
-There exists a Jina Embeddings wrapper, which you can access with 
-```python
-from langchain.embeddings import JinaEmbeddings
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/jina.ipynb)

docs/ecosystem/llamacpp.md

@@ -1,26 +0,0 @@
-# Llama.cpp
-
-This page covers how to use [llama.cpp](https://github.com/ggerganov/llama.cpp) within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Llama-cpp wrappers.
-
-## Installation and Setup
-- Install the Python package with `pip install llama-cpp-python`
-- Download one of the [supported models](https://github.com/ggerganov/llama.cpp#description) and convert them to the llama.cpp format per the [instructions](https://github.com/ggerganov/llama.cpp)
-
-## Wrappers
-
-### LLM
-
-There exists a LlamaCpp LLM wrapper, which you can access with 
-```python
-from langchain.llms import LlamaCpp
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/llamacpp.ipynb)
-
-### Embeddings
-
-There exists a LlamaCpp Embeddings wrapper, which you can access with 
-```python
-from langchain.embeddings import LlamaCppEmbeddings
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/llamacpp.ipynb)

docs/ecosystem/milvus.md

@@ -1,20 +0,0 @@
-# Milvus
-
-This page covers how to use the Milvus ecosystem within LangChain.
-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 Milvus 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/milvus.ipynb)

docs/ecosystem/modal.md

@@ -1,66 +0,0 @@
-# Modal
-
-This page covers how to use the Modal ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
-
-## Installation and Setup
-- Install with `pip install modal-client`
-- Run `modal token new`
-
-## Define your Modal Functions and Webhooks
-
-You must include a prompt. There is a rigid response structure.
-
-```python
-class Item(BaseModel):
-    prompt: str
-
-@stub.webhook(method="POST")
-def my_webhook(item: Item):
-    return {"prompt": my_function.call(item.prompt)}
-```
-
-An example with GPT2:
-
-```python
-from pydantic import BaseModel
-
-import modal
-
-stub = modal.Stub("example-get-started")
-
-volume = modal.SharedVolume().persist("gpt2_model_vol")
-CACHE_PATH = "/root/model_cache"
-
-@stub.function(
-    gpu="any",
-    image=modal.Image.debian_slim().pip_install(
-        "tokenizers", "transformers", "torch", "accelerate"
-    ),
-    shared_volumes={CACHE_PATH: volume},
-    retries=3,
-)
-def run_gpt2(text: str):
-    from transformers import GPT2Tokenizer, GPT2LMHeadModel
-    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-    model = GPT2LMHeadModel.from_pretrained('gpt2')
-    encoded_input = tokenizer(text, return_tensors='pt').input_ids
-    output = model.generate(encoded_input, max_length=50, do_sample=True)
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-
-class Item(BaseModel):
-    prompt: str
-
-@stub.webhook(method="POST")
-def get_text(item: Item):
-    return {"prompt": run_gpt2.call(item.prompt)}
-```
-
-## Wrappers
-
-### LLM
-
-There exists an Modal LLM wrapper, which you can access with 
-```python
-from langchain.llms import Modal
-```

docs/ecosystem/nlpcloud.md

@@ -1,17 +0,0 @@
-# NLPCloud
-
-This page covers how to use the NLPCloud ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific NLPCloud wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install nlpcloud`
-- Get an NLPCloud api key and set it as an environment variable (`NLPCLOUD_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an NLPCloud LLM wrapper, which you can access with 
-```python
-from langchain.llms import NLPCloud
-```

docs/ecosystem/openai.md

@@ -1,55 +0,0 @@
-# OpenAI
-
-This page covers how to use the OpenAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific OpenAI wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install openai`
-- Get an OpenAI api key and set it as an environment variable (`OPENAI_API_KEY`)
-- If you want to use OpenAI's tokenizer (only available for Python 3.9+), install it with `pip install tiktoken`
-
-## Wrappers
-
-### LLM
-
-There exists an OpenAI LLM wrapper, which you can access with 
-```python
-from langchain.llms import OpenAI
-```
-
-If you are using a model hosted on Azure, you should use different wrapper for that:
-```python
-from langchain.llms import AzureOpenAI
-```
-For a more detailed walkthrough of the Azure wrapper, see [this notebook](../modules/models/llms/integrations/azure_openai_example.ipynb)
-
-
-
-### Embeddings
-
-There exists an OpenAI Embeddings wrapper, which you can access with 
-```python
-from langchain.embeddings import OpenAIEmbeddings
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/openai.ipynb)
-
-
-### Tokenizer
-
-There are several places you can use the `tiktoken` tokenizer. By default, it is used to count tokens
-for OpenAI LLMs.
-
-You can also use it to count tokens when splitting documents with 
-```python
-from langchain.text_splitter import CharacterTextSplitter
-CharacterTextSplitter.from_tiktoken_encoder(...)
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/text_splitters/examples/tiktoken.ipynb)
-
-### Moderation
-You can also access the OpenAI content moderation endpoint with
-
-```python
-from langchain.chains import OpenAIModerationChain
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/chains/examples/moderation.ipynb)

docs/ecosystem/opensearch.md

@@ -1,21 +0,0 @@
-# OpenSearch
-
-This page covers how to use the OpenSearch ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific OpenSearch wrappers.
-
-## Installation and Setup
-- Install the Python package with `pip install opensearch-py`
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around OpenSearch vector databases, allowing you to use it as a vectorstore 
-for semantic search using approximate vector search powered by lucene, nmslib and faiss engines 
-or using painless scripting and script scoring functions for bruteforce vector search.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import OpenSearchVectorSearch
-```
-
-For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](../modules/indexes/vectorstores/examples/opensearch.ipynb)

docs/ecosystem/petals.md

@@ -1,17 +0,0 @@
-# Petals
-
-This page covers how to use the Petals ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Petals wrappers.
-
-## Installation and Setup
-- Install with `pip install petals`
-- Get a Hugging Face api key and set it as an environment variable (`HUGGINGFACE_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an Petals LLM wrapper, which you can access with 
-```python
-from langchain.llms import Petals
-```

docs/ecosystem/pgvector.md

@@ -1,29 +0,0 @@
-# PGVector
-
-This page covers how to use the Postgres [PGVector](https://github.com/pgvector/pgvector) ecosystem within LangChain
-It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.
-
-## Installation
-- Install the Python package with `pip install pgvector`
-
-
-## Setup
-1. The first step is to create a database with the `pgvector` extension installed.
-
-    Follow the steps at [PGVector Installation Steps](https://github.com/pgvector/pgvector#installation) to install the database and the extension. The docker image is the easiest way to get started.
-
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Postgres vector databases, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores.pgvector import PGVector
-```
-
-### Usage
-
-For a more detailed walkthrough of the PGVector Wrapper, see [this notebook](../modules/indexes/vectorstores/examples/pgvector.ipynb)

docs/ecosystem/pinecone.md

@@ -1,20 +0,0 @@
-# Pinecone
-
-This page covers how to use the Pinecone ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install pinecone-client`
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Pinecone 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 Pinecone
-```
-
-For a more detailed walkthrough of the Pinecone wrapper, see [this notebook](../modules/indexes/vectorstores/examples/pinecone.ipynb)

docs/ecosystem/promptlayer.md

@@ -1,49 +0,0 @@
-# PromptLayer
-
-This page covers how to use [PromptLayer](https://www.promptlayer.com) within LangChain.
-It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers.
-
-## Installation and Setup
-
-If you want to work with PromptLayer:
-- Install the promptlayer python library `pip install promptlayer`
-- Create a PromptLayer account
-- Create an api token and set it as an environment variable (`PROMPTLAYER_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an PromptLayer OpenAI LLM wrapper, which you can access with
-```python
-from langchain.llms import PromptLayerOpenAI
-```
-
-To tag your requests, use the argument `pl_tags` when instanializing the LLM
-```python
-from langchain.llms import PromptLayerOpenAI
-llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"])
-```
-
-To get the PromptLayer request id, use the argument `return_pl_id` when instanializing the LLM
-```python
-from langchain.llms import PromptLayerOpenAI
-llm = PromptLayerOpenAI(return_pl_id=True)
-```
-This will add the PromptLayer request ID in the `generation_info` field of the `Generation` returned when using `.generate` or `.agenerate`
-
-For example:
-```python
-llm_results = llm.generate(["hello world"])
-for res in llm_results.generations:
-    print("pl request id: ", res[0].generation_info["pl_request_id"])
-```
-You can use the PromptLayer request ID to add a prompt, score, or other metadata to your request. [Read more about it here](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
-
-This LLM is identical to the [OpenAI LLM](./openai.md), except that
-- all your requests will be logged to your PromptLayer account
-- you can add `pl_tags` when instantializing to tag your requests on PromptLayer
-- you can add `return_pl_id` when instantializing to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
-
-
-PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](../modules/models/chat/integrations/promptlayer_chatopenai.ipynb) and `PromptLayerOpenAIChat`

docs/ecosystem/qdrant.md

@@ -1,20 +0,0 @@
-# Qdrant
-
-This page covers how to use the Qdrant ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install qdrant-client`
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Qdrant 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 Qdrant
-```
-
-For a more detailed walkthrough of the Qdrant wrapper, see [this notebook](../modules/indexes/vectorstores/examples/qdrant.ipynb)

docs/ecosystem/replicate.md

@@ -1,47 +0,0 @@
-# Replicate
-This page covers how to run models on Replicate within LangChain.
-
-## Installation and Setup
-- Create a [Replicate](https://replicate.com) account. Get your API key and set it as an environment variable (`REPLICATE_API_TOKEN`)
-- Install the [Replicate python client](https://github.com/replicate/replicate-python) with `pip install replicate`
-
-## Calling a model
-
-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`
-
-Only the `model` param is required, but any other model parameters can also be passed in with the format `input={model_param: value, ...}`
-
-
-For example, if we were running stable diffusion and wanted to change the image dimensions:
-
-```
-Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions': '512x512'})
-```
-
-*Note that only the first output of a model will be returned.*
-From here, we can initialize our model:
-
-```python
-llm = Replicate(model="daanelson/flan-t5:04e422a9b85baed86a4f24981d7f9953e20c5fd82f6103b74ebc431588e1cec8")
-```
-
-And run it:
-
-```python
-prompt = """
-Answer the following yes/no question by reasoning step by step.
-Can a dog drive a car?
-"""
-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'}
-
-image_output = text2image("A cat riding a motorcycle by Picasso")
-```

docs/ecosystem/runhouse.md

@@ -1,29 +0,0 @@
-# Runhouse
-
-This page covers how to use the [Runhouse](https://github.com/run-house/runhouse) ecosystem within LangChain.
-It is broken into three parts: installation and setup, LLMs, and Embeddings.
-
-## Installation and Setup
-- Install the Python SDK with `pip install runhouse`
-- If you'd like to use on-demand cluster, check your cloud credentials with `sky check`
-
-## Self-hosted LLMs
-For a basic self-hosted LLM, you can use the `SelfHostedHuggingFaceLLM` class. For more
-custom LLMs, you can use the `SelfHostedPipeline` parent class.
-
-```python
-from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
-```
-
-For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/models/llms/integrations/runhouse.ipynb)
-
-## Self-hosted Embeddings
-There are several ways to use self-hosted embeddings with LangChain via Runhouse.
-
-For a basic self-hosted embedding from a Hugging Face Transformers model, you can use 
-the `SelfHostedEmbedding` class.
-```python
-from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
-```
-
-For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/models/text_embedding/examples/self-hosted.ipynb)

docs/ecosystem/rwkv.md

@@ -1,65 +0,0 @@
-# RWKV-4
-
-This page covers how to use the `RWKV-4` wrapper within LangChain.
-It is broken into two parts: installation and setup, and then usage with an example.
-
-## Installation and Setup
-- Install the Python package with `pip install rwkv`
-- Install the tokenizer Python package with `pip install tokenizer`
-- Download a [RWKV model](https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main) and place it in your desired directory
-- Download the [tokens file](https://raw.githubusercontent.com/BlinkDL/ChatRWKV/main/20B_tokenizer.json)
-
-## Usage
-
-### RWKV
-
-To use the RWKV wrapper, you need to provide the path to the pre-trained model file and the tokenizer's configuration.
-```python
-from langchain.llms import RWKV
-
-# Test the model
-
-```python
-
-def generate_prompt(instruction, input=None):
-    if input:
-        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-
-# Instruction:
-{instruction}
-
-# Input:
-{input}
-
-# Response:
-"""
-    else:
-        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-# Instruction:
-{instruction}
-
-# Response:
-"""
-
-
-model = RWKV(model="./models/RWKV-4-Raven-3B-v7-Eng-20230404-ctx4096.pth", strategy="cpu fp32", tokens_path="./rwkv/20B_tokenizer.json")
-response = model(generate_prompt("Once upon a time, "))
-```
-## Model File
-
-You can find links to model file downloads at the [RWKV-4-Raven](https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main) repository.
-
-### Rwkv-4 models -> recommended VRAM
-
-
-```
-RWKV VRAM
-Model | 8bit | bf16/fp16 | fp32
-14B   | 16GB | 28GB      | >50GB
-7B    | 8GB  | 14GB      | 28GB
-3B    | 2.8GB| 6GB       | 12GB
-1b5   | 1.3GB| 3GB       | 6GB
-```
-
-See the [rwkv pip](https://pypi.org/project/rwkv/) page for more information about strategies, including streaming and cuda support.

docs/ecosystem/searx.md

@@ -1,70 +0,0 @@
-# SearxNG Search API
-
-This page covers how to use the SearxNG search API within LangChain.
-It is broken into two parts: installation and setup, and then references to the specific SearxNG API wrapper.
-
-## Installation and Setup
-
-While it is possible to utilize the wrapper in conjunction with  [public searx
-instances](https://searx.space/) these instances frequently do not permit API
-access (see note on output format below) and have limitations on the frequency
-of requests. It is recommended to opt for a self-hosted instance instead.
-
-### Self Hosted Instance:
-
-See [this page](https://searxng.github.io/searxng/admin/installation.html) for installation instructions.
-
-When you install SearxNG, the only active output format by default is the HTML format.
-You need to activate the `json` format to use the API. This can be done by adding the following line to the `settings.yml` file:
-```yaml
-search:
-    formats:
-        - html
-        - json
-```
-You can make sure that the API is working by issuing a curl request to the API endpoint:
-
-`curl -kLX GET --data-urlencode q='langchain' -d format=json http://localhost:8888`
-
-This should return a JSON object with the results.
-
-
-## Wrappers
-
-### Utility
-
-To use the wrapper we need to pass the host of the SearxNG instance to the wrapper with:
-    1. the named parameter `searx_host` when creating the instance.
-    2. exporting the environment variable `SEARXNG_HOST`.
-
-You can use the wrapper to get results from a SearxNG instance. 
-
-```python
-from langchain.utilities import SearxSearchWrapper
-s = SearxSearchWrapper(searx_host="http://localhost:8888")
-s.run("what is a large language model?")
-```
-
-### Tool
-
-You can also load this wrapper as a Tool (to use with an Agent).
-
-You can do this with:
-
-```python
-from langchain.agents import load_tools
-tools = load_tools(["searx-search"],
-                    searx_host="http://localhost:8888",
-                    engines=["github"])
-```
-
-Note that we could _optionally_ pass custom engines to use.
-
-If you want to obtain results with metadata as *json* you can use:
-```python
-tools = load_tools(["searx-search-results-json"],
-                    searx_host="http://localhost:8888",
-                    num_results=5)
-```
-
-For more information on tools, see [this page](../modules/agents/tools/getting_started.md)

docs/ecosystem/serpapi.md

@@ -1,31 +0,0 @@
-# SerpAPI
-
-This page covers how to use the SerpAPI search APIs within LangChain.
-It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper.
-
-## Installation and Setup
-- Install requirements with `pip install google-search-results`
-- Get a SerpAPI api key and either set it as an environment variable (`SERPAPI_API_KEY`)
-
-## Wrappers
-
-### Utility
-
-There exists a SerpAPI utility which wraps this API. To import this utility:
-
-```python
-from langchain.utilities import SerpAPIWrapper
-```
-
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/serpapi.ipynb).
-
-### Tool
-
-You can also easily load this wrapper as a Tool (to use with an Agent).
-You can do this with:
-```python
-from langchain.agents import load_tools
-tools = load_tools(["serpapi"])
-```
-
-For more information on this, see [this page](../modules/agents/tools/getting_started.md)

docs/ecosystem/stochasticai.md

@@ -1,17 +0,0 @@
-# StochasticAI
-
-This page covers how to use the StochasticAI ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific StochasticAI wrappers.
-
-## Installation and Setup
-- Install with `pip install stochasticx`
-- Get an StochasticAI api key and set it as an environment variable (`STOCHASTICAI_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an StochasticAI LLM wrapper, which you can access with 
-```python
-from langchain.llms import StochasticAI
-```

docs/ecosystem/unstructured.md

@@ -1,45 +0,0 @@
-# Unstructured
-
-This page covers how to use the [`unstructured`](https://github.com/Unstructured-IO/unstructured)
-ecosystem within LangChain. The `unstructured` package from
-[Unstructured.IO](https://www.unstructured.io/) extracts clean text from raw source documents like
-PDFs and Word documents.
-
-
-This page is broken into two parts: installation and setup, and then references to specific
-`unstructured` wrappers.
-
-## Installation and Setup
-- 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.
-    - `libmagic-dev` (filetype detection)
-    - `poppler-utils` (images and PDFs)
-    - `tesseract-ocr`(images and PDFs)
-    - `libreoffice` (MS Office docs)
-    - `pandoc` (EPUBs)
-- If you are parsing PDFs using the `"hi_res"` strategy, run the following to install the `detectron2` model, which
-  `unstructured` uses for layout detection:
-    - `pip install "detectron2@git+https://github.com/facebookresearch/detectron2.git@e2ce8dc#egg=detectron2"`
-    - If `detectron2` is not installed, `unstructured` will fallback to processing PDFs
-      using the `"fast"` strategy, which uses `pdfminer` directly and doesn't require
-      `detectron2`.
-
-## Wrappers
-
-### Data Loaders
-
-The primary `unstructured` wrappers within `langchain` are data loaders. The following
-shows how to use the most basic unstructured data loader. There are other file-specific
-data loaders available in the `langchain.document_loaders` module.
-
-```python
-from langchain.document_loaders import UnstructuredFileLoader
-
-loader = UnstructuredFileLoader("state_of_the_union.txt")
-loader.load()
-```
-
-If you instantiate the loader with `UnstructuredFileLoader(mode="elements")`, the loader
-will track additional metadata like the page number and text type (i.e. title, narrative text)
-when that information is available.

docs/ecosystem/wandb_tracking.ipynb

@@ -1,626 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Weights & Biases\n",
-    "\n",
-    "This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see.\n",
-    "\n",
-    "Run in Colab: https://colab.research.google.com/drive/1DXH4beT4HFaRKy_Vm4PoxhXVDRf7Ym8L?usp=sharing\n",
-    "\n",
-    "View Report: https://wandb.ai/a-sh0ts/langchain_callback_demo/reports/Prompt-Engineering-LLMs-with-LangChain-and-W-B--VmlldzozNjk1NTUw#👋-how-to-build-a-callback-in-langchain-for-better-prompt-engineering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install wandb\n",
-    "!pip install pandas\n",
-    "!pip install textstat\n",
-    "!pip install spacy\n",
-    "!python -m spacy download en_core_web_sm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "id": "T1bSmKd6V2If"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"WANDB_API_KEY\"] = \"\"\n",
-    "# os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
-    "# os.environ[\"SERPAPI_API_KEY\"] = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "8WAGnTWpUUnD"
-   },
-   "outputs": [],
-   "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"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```\n",
-    "Callback Handler that logs to Weights and Biases.\n",
-    "\n",
-    "Parameters:\n",
-    "    job_type (str): The type of job.\n",
-    "    project (str): The project to log to.\n",
-    "    entity (str): The entity to log to.\n",
-    "    tags (list): The tags to log.\n",
-    "    group (str): The group to log to.\n",
-    "    name (str): The name of the run.\n",
-    "    notes (str): The notes to log.\n",
-    "    visualize (bool): Whether to visualize the run.\n",
-    "    complexity_metrics (bool): Whether to log complexity metrics.\n",
-    "    stream_logs (bool): Whether to stream callback actions to W&B\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cxBFfZR8d9FC"
-   },
-   "source": [
-    "```\n",
-    "Default values for WandbCallbackHandler(...)\n",
-    "\n",
-    "visualize: bool = False,\n",
-    "complexity_metrics: bool = False,\n",
-    "stream_logs: bool = False,\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "NOTE: For beta workflows we have made the default analysis based on textstat and the visualizations based on spacy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "id": "KAz8weWuUeXF"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mharrison-chase\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "Tracking run with wandb version 0.14.0"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150408-e47j1914</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">llm</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The wandb callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/wandb/wandb/issues with the tag `langchain`.\n"
-     ]
-    }
-   ],
-   "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",
-    "session_group = datetime.now().strftime(\"%m.%d.%Y_%H.%M.%S\")\n",
-    "wandb_callback = WandbCallbackHandler(\n",
-    "    job_type=\"inference\",\n",
-    "    project=\"langchain_callback_demo\",\n",
-    "    group=f\"minimal_{session_group}\",\n",
-    "    name=\"llm\",\n",
-    "    tags=[\"test\"],\n",
-    ")\n",
-    "manager = CallbackManager([StdOutCallbackHandler(), wandb_callback])\n",
-    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Q-65jwrDeK6w"
-   },
-   "source": [
-    "\n",
-    "\n",
-    "```\n",
-    "# Defaults for WandbCallbackHandler.flush_tracker(...)\n",
-    "\n",
-    "reset: bool = True,\n",
-    "finish: bool = False,\n",
-    "```\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `flush_tracker` function is used to log LangChain sessions to Weights & Biases. It takes in the LangChain module or agent, and logs at minimum the prompts and generations alongside the serialized form of the LangChain module to the specified Weights & Biases project. By default we reset the session as opposed to concluding the session outright."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "id": "o_VmneyIUyx8"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run <strong style=\"color:#cdcd00\">llm</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a><br/>Synced 5 W&B file(s), 2 media file(s), 5 artifact file(s) and 0 other file(s)"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Find logs at: <code>./wandb/run-20230318_150408-e47j1914/logs</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0d7b4307ccdb450ea631497174fca2d1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016745895149999985, max=1.0…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Tracking run with wandb version 0.14.0"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150534-jyxma7hu</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">simple_sequential</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# SCENARIO 1 - LLM\n",
-    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
-    "wandb_callback.flush_tracker(llm, name=\"simple_sequential\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "id": "trxslyb1U28Y"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "id": "uauQk10SUzF6"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run <strong style=\"color:#cdcd00\">simple_sequential</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a><br/>Synced 4 W&B file(s), 2 media file(s), 6 artifact file(s) and 0 other file(s)"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Find logs at: <code>./wandb/run-20230318_150534-jyxma7hu/logs</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dbdbf28fb8ed40a3a60218d2e6d1a987",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016736786816666675, max=1.0…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Tracking run with wandb version 0.14.0"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150550-wzy59zjq</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">agent</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "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, callback_manager=manager)\n",
-    "\n",
-    "test_prompts = [\n",
-    "    {\n",
-    "        \"title\": \"documentary about good video games that push the boundary of game design\"\n",
-    "    },\n",
-    "    {\"title\": \"cocaine bear vs heroin wolf\"},\n",
-    "    {\"title\": \"the best in class mlops tooling\"},\n",
-    "]\n",
-    "synopsis_chain.apply(test_prompts)\n",
-    "wandb_callback.flush_tracker(synopsis_chain, name=\"agent\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "id": "_jN73xcPVEpI"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents import initialize_agent, load_tools\n",
-    "from langchain.agents import AgentType"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "id": "Gpq4rk6VT9cu"
-   },
-   "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 Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mDiCaprio had a steady girlfriend in Camila Morrone. He had been with the model turned actress for nearly five years, as they were first said to be dating at the end of 2017. And the now 26-year-old Morrone is no stranger to Hollywood.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate her age raised to the 0.43 power.\n",
-      "Action: Calculator\n",
-      "Action Input: 26^0.43\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.059182145592686\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: Leo DiCaprio's girlfriend is Camila Morrone and her current age raised to the 0.43 power is 4.059182145592686.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       " View run <strong style=\"color:#cdcd00\">agent</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a><br/>Synced 5 W&B file(s), 2 media file(s), 7 artifact file(s) and 0 other file(s)"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Find logs at: <code>./wandb/run-20230318_150550-wzy59zjq/logs</code>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# SCENARIO 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callback_manager=manager,\n",
-    "    verbose=True,\n",
-    ")\n",
-    "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")\n",
-    "wandb_callback.flush_tracker(agent, reset=False, finish=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

docs/ecosystem/weaviate.md

@@ -1,33 +0,0 @@
-# Weaviate
-
-This page covers how to use the Weaviate ecosystem within LangChain.
-
-What is Weaviate?
-
-**Weaviate in a nutshell:**
-- Weaviate is an open-source ​database of the type ​vector search engine.
-- Weaviate allows you to store JSON documents in a class property-like fashion while attaching machine learning vectors to these documents to represent them in vector space.
-- Weaviate can be used stand-alone (aka bring your vectors) or with a variety of modules that can do the vectorization for you and extend the core capabilities.
-- Weaviate has a GraphQL-API to access your data easily.
-- We aim to bring your vector search set up to production to query in mere milliseconds (check our [open source benchmarks](https://weaviate.io/developers/weaviate/current/benchmarks/) to see if Weaviate fits your use case).
-- Get to know Weaviate in the [basics getting started guide](https://weaviate.io/developers/weaviate/current/core-knowledge/basics.html) in under five minutes.
-
-**Weaviate in detail:**
-
-Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), etc. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering and the fault tolerance of a cloud-native database. It is all accessible through GraphQL, REST, and various client-side programming languages.
-
-## Installation and Setup
-- Install the Python SDK with `pip install weaviate-client`
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Weaviate 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 Weaviate
-```
-
-For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/indexes/vectorstores/getting_started.ipynb)

docs/ecosystem/wolfram_alpha.md

@@ -1,34 +0,0 @@
-# Wolfram Alpha Wrapper
-
-This page covers how to use the Wolfram Alpha API within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Wolfram Alpha wrappers.
-
-## Installation and Setup
-- Install requirements with `pip install wolframalpha`
-- Go to wolfram alpha and sign up for a developer account [here](https://developer.wolframalpha.com/)
-- Create an app and get your APP ID
-- Set your APP ID as an environment variable `WOLFRAM_ALPHA_APPID`
-
-
-## Wrappers
-
-### Utility
-
-There exists a WolframAlphaAPIWrapper utility which wraps this API. To import this utility:
-
-```python
-from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
-```
-
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/wolfram_alpha.ipynb).
-
-### Tool
-
-You can also easily load this wrapper as a Tool (to use with an Agent).
-You can do this with:
-```python
-from langchain.agents import load_tools
-tools = load_tools(["wolfram-alpha"])
-```
-
-For more information on this, see [this page](../modules/agents/tools/getting_started.md)

docs/ecosystem/writer.md

@@ -1,16 +0,0 @@
-# Writer
-
-This page covers how to use the Writer ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Writer wrappers.
-
-## Installation and Setup
-- Get an Writer api key and set it as an environment variable (`WRITER_API_KEY`)
-
-## Wrappers
-
-### LLM
-
-There exists an Writer LLM wrapper, which you can access with 
-```python
-from langchain.llms import Writer
-```

docs/ecosystem/yeagerai.md

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

docs/ecosystem/zilliz.md

@@ -1,21 +0,0 @@
-# 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/examples/agents.rst

@@ -0,0 +1,47 @@
+Agents
+======
+
+The examples here are all end-to-end agents for specific applications.
+In all examples there is an Agent with a particular set of tools.
+
+- Tools: A tool can be anything that takes in a string and returns a string. This means that you can use both the primitives AND the chains found in `this <chains.rst>`_ documentation.
+- Agents: An agent uses an LLMChain to determine which tools to use. For a list of all available agent types, see `here <../explanation/agents.md>`_.
+
+**MRKL**
+
+- **Tools used**: Search, SQLDatabaseChain, LLMMathChain
+- **Agent used**: `zero-shot-react-description`
+- `Paper <https://arxiv.org/pdf/2205.00445.pdf>`_
+- **Note**: This is the most general purpose example, so if you are looking to use an agent with arbitrary tools, please start here.
+- `Example Notebook <agents/mrkl.ipynb>`_
+
+**Self-Ask-With-Search**
+
+- **Tools used**: Search
+- **Agent used**: `self-ask-with-search`
+- `Paper <https://ofir.io/self-ask.pdf>`_
+- `Example Notebook <agents/self_ask_with_search.ipynb>`_
+
+**ReAct**
+
+- **Tools used**: Wikipedia Docstore
+- **Agent used**: `react-docstore`
+- `Paper <https://arxiv.org/pdf/2210.03629.pdf>`_
+- `Example Notebook <agents/react.ipynb>`_
+
+
+
+Additionally, we also provide examples for how to do more customizability:
+
+**Custom Agent**
+
+- Purpose: How to create custom agents.
+- `Example Notebook <agents/custom_agent.ipynb>`_
+
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :hidden:
+
+   agents/*

docs/examples/agents/custom_agent.ipynb

@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ba5f8741",
+   "metadata": {},
+   "source": [
+    "# Custom Agent\n",
+    "\n",
+    "This notebook goes through how to create your own custom agent.\n",
+    "\n",
+    "An agent consists of three parts:\n",
+    "    \n",
+    "    - Tools: The tools the agent has available to use.\n",
+    "    - LLMChain: The LLMChain that produces the text that is parsed in a certain way to determine which action to take.\n",
+    "    - The agent class itself: this parses the output of the LLMChain to determin which action to take.\n",
+    "        \n",
+    "        \n",
+    "In this notebook we walk through two types of custom agents. The first type shows how to create a custom LLMChain, but still use an existing agent class to parse the output. The second shows how to create a custom agent class."
+   ]
+  },
+  {
+   "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",
+    "\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. However, besides those instructions, you can customize the prompt as you wish.\n",
+    "\n",
+    "To ensure that the prompt contains the appropriate instructions, we will utilize a helper method on that class. The helper method for the `ZeroShotAgent` takes the following arguments:\n",
+    "\n",
+    "- tools: List of tools the agent will have access to, used to format the prompt.\n",
+    "- prefix: String to put before the list of tools.\n",
+    "- suffix: String to put after the list of tools.\n",
+    "- input_variables: List of input variables the final prompt will expect.\n",
+    "\n",
+    "For this exercise, we will give our agent access to Google Search, and we will customize it in that we will have it answer as a pirate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9af9734e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import ZeroShotAgent, Tool\n",
+    "from langchain import OpenAI, SerpAPIWrapper, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "becda2a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = SerpAPIWrapper()\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",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "339b1bb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefix = \"\"\"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\"\"\"\n",
+    "suffix = \"\"\"Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Args\"\n",
+    "\n",
+    "Question: {input}\"\"\"\n",
+    "\n",
+    "prompt = ZeroShotAgent.create_prompt(\n",
+    "    tools, \n",
+    "    prefix=prefix, \n",
+    "    suffix=suffix, \n",
+    "    input_variables=[\"input\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59db7b58",
+   "metadata": {},
+   "source": [
+    "In case we are curious, we can now take a look at the final prompt template to see what it looks like when its all put together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e21d2098",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\n",
+      "\n",
+      "Search: useful for when you need to answer questions about current events\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 [Search]\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 \"Args\"\n",
+      "\n",
+      "Question: {input}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(prompt.template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9b1cc2a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e4f5092f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "653b1617",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "How many people live in canada?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look this up\n",
+      "Action: Search\n",
+      "Action Input: How many people live in canada\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Arrr, there be 38,533,678 people in Canada\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Arrr, there be 38,533,678 people in Canada'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"How many people live in canada?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90171b2b",
+   "metadata": {},
+   "source": [
+    "### Custom Agent Class\n",
+    "\n",
+    "Coming soon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adefb4c2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/agents/mrkl.ipynb

@@ -27,13 +27,12 @@
    "outputs": [],
    "source": [
     "from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
-    "from langchain.agents import initialize_agent, Tool\n",
-    "from langchain.agents import AgentType"
+    "from langchain.agents import initialize_agent, Tool"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "07e96d99",
    "metadata": {},
    "outputs": [],
@@ -41,7 +40,7 @@
     "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",
@@ -64,17 +63,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "a069c4b6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "mrkl = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "e603cd7d",
    "metadata": {},
    "outputs": [
@@ -84,50 +83,56 @@
      "text": [
       "\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[1m> Entering new ZeroShotAgent chain...\u001b[0m\n",
+      "Thought:\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: \"Who is Leo DiCaprio's girlfriend?\"\u001b[0m\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 Input: \"Who is Olivia Wilde's boyfriend?\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mOlivia Wilde started dating Harry Styles after ending her years-long engagement to Jason Sudeikis — see their relationship timeline.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age.\n",
+      "Action: Search\n",
+      "Action Input: \"How old is Harry Styles?\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 28 raised to the 0.23 power.\n",
       "Action: Calculator\n",
-      "Action Input: 21^0.43\u001b[0m\n",
+      "Action Input: 28^0.23\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "21^0.43\u001b[32;1m\u001b[1;3m\n",
-      "```text\n",
-      "21**0.43\n",
+      "28^0.23\u001b[32;1m\u001b[1;3m\n",
+      "\n",
+      "```python\n",
+      "import math\n",
+      "print(math.pow(28, 0.23))\n",
       "```\n",
-      "...numexpr.evaluate(\"21**0.43\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.7030049853137306\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m2.1520202182226886\n",
+      "\u001b[0m\n",
+      "\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n",
       "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.7030049853137306\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\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 3.7030049853137306.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Final Answer: Harry Styles, Olivia Wilde's boyfriend, is 28 years old and his age raised to the 0.23 power is 2.1520202182226886.\u001b[0m\n",
+      "\u001b[1m> Finished ZeroShotAgent chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.7030049853137306.\""
+       "\"Harry Styles, Olivia Wilde's boyfriend, is 28 years old and his age raised to the 0.23 power is 2.1520202182226886.\""
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mrkl.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "mrkl.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "a5c07010",
    "metadata": {},
    "outputs": [
@@ -137,51 +142,35 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\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",
+      "\u001b[1m> Entering new ZeroShotAgent chain...\u001b[0m\n",
+      "Thought:\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 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",
+      "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",
       "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:"
-     ]
-    },
-    {
-     "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",
+      "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';\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",
+      "Answer:\u001b[32;1m\u001b[1;3m The album 'Jagged Little Pill' by Alanis Morissette is in the FooBar database.\u001b[0m\n",
+      "\u001b[1m> Finished SQLDatabaseChain 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 hers in the FooBar database are Jagged Little Pill.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Observation: \u001b[38;5;200m\u001b[1;3m The album 'Jagged Little Pill' by Alanis Morissette is in the FooBar database.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Alanis Morissette's album 'Jagged Little Pill' is in the FooBar database.\u001b[0m\n",
+      "\u001b[1m> Finished ZeroShotAgent chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "\"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.\""
+       "\"Alanis Morissette's album 'Jagged Little Pill' is in the FooBar database.\""
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -215,7 +204,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

docs/examples/agents/natbot.py

@@ -0,0 +1,88 @@
+"""Run NatBot."""
+import time
+
+from langchain.chains.natbot.base import NatBotChain
+from langchain.chains.natbot.crawler import Crawler  # type: ignore
+
+
+def run_cmd(cmd: str, _crawler: Crawler) -> None:
+    """Run command."""
+    cmd = cmd.split("\n")[0]
+
+    if cmd.startswith("SCROLL UP"):
+        _crawler.scroll("up")
+    elif cmd.startswith("SCROLL DOWN"):
+        _crawler.scroll("down")
+    elif cmd.startswith("CLICK"):
+        commasplit = cmd.split(",")
+        id = commasplit[0].split(" ")[1]
+        _crawler.click(id)
+    elif cmd.startswith("TYPE"):
+        spacesplit = cmd.split(" ")
+        id = spacesplit[1]
+        text_pieces = spacesplit[2:]
+        text = " ".join(text_pieces)
+        # Strip leading and trailing double quotes
+        text = text[1:-1]
+
+        if cmd.startswith("TYPESUBMIT"):
+            text += "\n"
+        _crawler.type(id, text)
+
+    time.sleep(2)
+
+
+if __name__ == "__main__":
+
+    objective = "Make a reservation for 2 at 7pm at bistro vida in menlo park"
+    print("\nWelcome to natbot! What is your objective?")
+    i = input()
+    if len(i) > 0:
+        objective = i
+    quiet = False
+    nat_bot_chain = NatBotChain.from_default(objective)
+    _crawler = Crawler()
+    _crawler.go_to_page("google.com")
+    try:
+        while True:
+            browser_content = "\n".join(_crawler.crawl())
+            llm_command = nat_bot_chain.execute(_crawler.page.url, browser_content)
+            if not quiet:
+                print("URL: " + _crawler.page.url)
+                print("Objective: " + objective)
+                print("----------------\n" + browser_content + "\n----------------\n")
+            if len(llm_command) > 0:
+                print("Suggested command: " + llm_command)
+
+            command = input()
+            if command == "r" or command == "":
+                run_cmd(llm_command, _crawler)
+            elif command == "g":
+                url = input("URL:")
+                _crawler.go_to_page(url)
+            elif command == "u":
+                _crawler.scroll("up")
+                time.sleep(1)
+            elif command == "d":
+                _crawler.scroll("down")
+                time.sleep(1)
+            elif command == "c":
+                id = input("id:")
+                _crawler.click(id)
+                time.sleep(1)
+            elif command == "t":
+                id = input("id:")
+                text = input("text:")
+                _crawler.type(id, text)
+                time.sleep(1)
+            elif command == "o":
+                objective = input("Objective:")
+            else:
+                print(
+                    "(g) to visit url\n(u) scroll up\n(d) scroll down\n(c) to click"
+                    "\n(t) to type\n(h) to view commands again"
+                    "\n(r/enter) to run suggested command\n(o) change objective"
+                )
+    except KeyboardInterrupt:
+        print("\n[!] Ctrl+C detected, exiting gracefully.")
+        exit(0)

docs/examples/agents/react.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "82140df0",
+   "metadata": {},
+   "source": [
+    "# ReAct\n",
+    "\n",
+    "This notebook showcases using an agent to implement the ReAct logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4e272b47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, Wikipedia\n",
+    "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.react.base import DocstoreExplorer\n",
+    "docstore=DocstoreExplorer(Wikipedia())\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name=\"Search\",\n",
+    "        func=docstore.search\n",
+    "    ),\n",
+    "    Tool(\n",
+    "        name=\"Lookup\",\n",
+    "        func=docstore.lookup\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")\n",
+    "react = initialize_agent(tools, llm, agent=\"react-docstore\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8078c8f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ReActDocstoreAgent chain...\u001b[0m\n",
+      "Thought 1:\u001b[32;1m\u001b[1;3m I need to search David Chanoff and find the U.S. Navy admiral he collaborated\n",
+      "with.\n",
+      "Action 1: Search[David Chanoff]\u001b[0m\n",
+      "Observation 1: \u001b[36;1m\u001b[1;3mDavid Chanoff is a noted author of non-fiction work. His work has typically involved collaborations with the principal protagonist of the work concerned. His collaborators have included; Augustus A. White, Joycelyn Elders, Đoàn Văn Toại, William J. Crowe, Ariel Sharon, Kenneth Good and Felix Zandman. He has also written about a wide range of subjects including literary history, education and foreign for The Washington Post, The New Republic and The New York Times Magazine. He has published more than twelve books.\u001b[0m\n",
+      "Thought 2:\u001b[32;1m\u001b[1;3m The U.S. Navy admiral David Chanoff collaborated with is William J. Crowe.\n",
+      "Action 2: Search[William J. Crowe]\u001b[0m\n",
+      "Observation 2: \u001b[36;1m\u001b[1;3mWilliam James Crowe Jr. (January 2, 1925 – October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents Ronald Reagan and George H. W. Bush, and as the ambassador to the United Kingdom and Chair of the Intelligence Oversight Board under President Bill Clinton.\u001b[0m\n",
+      "Thought 3:\u001b[32;1m\u001b[1;3m William J. Crowe served as the ambassador to the United Kingdom under President Bill Clinton.\n",
+      "Action 3: Finish[Bill Clinton]\u001b[0m\n",
+      "\u001b[1m> Finished ReActDocstoreAgent chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Bill Clinton'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?\"\n",
+    "react.run(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ff64e81",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/agents/self_ask_with_search.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "7e3b513e",
    "metadata": {},
    "outputs": [
@@ -22,15 +22,14 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m Yes.\n",
+      "\u001b[1m> Entering new SelfAskWithSearchAgent chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAre follow up questions needed here: 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 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;3mCarlos Alcaraz\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;3mEl Palmar, Spain\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3mSo the final answer is: El Palmar, Spain\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001b[1m> Finished SelfAskWithSearchAgent chain.\u001b[0m\n"
      ]
     },
     {
@@ -39,7 +38,7 @@
        "'El Palmar, Spain'"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -47,26 +46,24 @@
    "source": [
     "from langchain import OpenAI, SerpAPIWrapper\n",
     "from langchain.agents import initialize_agent, Tool\n",
-    "from langchain.agents import AgentType\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
     "        name=\"Intermediate Answer\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to ask with search\"\n",
+    "        func=search.run\n",
     "    )\n",
     "]\n",
     "\n",
-    "self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)\n",
+    "self_ask_with_search = initialize_agent(tools, llm, agent=\"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",
+   "id": "683d69e7",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -88,12 +85,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
-   }
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

docs/examples/chains.rst

@@ -0,0 +1,88 @@
+Chains
+======
+
+The examples here are all end-to-end chains for specific applications.
+A chain is made up of links, which can be either primitives or other chains.
+
+The following primitives exist as options to use for links:
+
+#. `LLM: <../modules/llms.rst>`_ A language model takes text as input and outputs text.
+#. `PromptTemplate: <../modules/prompt.rst>`_ A prompt template takes arbitrary string inputs and returns a final formatted string.
+#. `TextSplitter: <../modules/text_splitter.rst>`_ A text splitter takes a longer document and splits it into smaller chunks.
+#. `Python REPL: <../modules/python.rst>`_ A Python REPL takes a string representing a Python command to run, runs that command, and then returns anything that was printed during that run.
+#. `SQL Database: <../modules/sql_database.rst>`_ A SQL database takes a string representing a SQL command as input and executes that command against the database. If any rows are returned, then those are cast to a string and returned.
+#. `Search: <../modules/serpapi.rst>`_ A search object takes a string as input and executes that against a search object, returning any results.
+#. `Docstore: <../modules/docstore.rst>`_ A docstore object can be used to lookup a document in a database by exact match.
+#. `Vectorstore: <../modules/vectorstore.rst>`_ A vectorstore object uses embeddings stored in a vector database to take in an input string and return documents similar to that string.
+
+With these primitives in mind, the following chains exist:
+
+**LLMChain**
+
+- **Links Used**: PromptTemplate, LLM
+- **Notes**: This chain is the simplest chain, and is widely used by almost every other chain. This chain takes arbitrary user input, creates a prompt with it from the PromptTemplate, passes that to the LLM, and then returns the output of the LLM as the final output.
+- `Example Notebook <chains/llm_chain.ipynb>`_
+
+**LLMMath**
+
+- **Links Used**: Python REPL, LLMChain
+- **Notes**: This chain takes user input (a math question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
+- `Example Notebook <chains/llm_math.ipynb>`_
+
+**PAL**
+
+- **Links Used**: Python REPL, LLMChain
+- **Notes**: This chain takes user input (a reasoning question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
+- `Paper <https://arxiv.org/abs/2211.10435>`_
+- `Example Notebook <chains/pal.ipynb>`_
+
+**Recursive Summarization**
+
+- **Links Used**: TextSplitter, LLMChain
+- **Notes**: This chain splits a document into chunks, runs a first LLMChain over each chunk to summarize it, and then runs a second LLMChain over those results to get a summary of the summaries.
+- `Example Notebook <chains/map_reduce.ipynb>`_
+
+**SQLDatabase Chain**
+
+- **Links Used**: SQLDatabase, LLMChain
+- **Notes**: This chain takes user input (a question), uses a first LLM chain to construct a SQL query to run against the SQL database, and then uses another LLMChain to take the results of that query and use it to answer the original question.
+- `Example Notebook <chains/sqlite.ipynb>`_
+
+
+**Vector Database Question-Answering**
+
+- **Links Used**: Vectorstore, LLMChain
+- **Notes**: This chain takes user input (a question), uses the Vectorstore and semantic search to find relevant documents, and then passes the documents plus the original question to another LLM to generate a final answer.
+- `Example Notebook <chains/vector_db_qa.ipynb>`_
+
+**Vector Database Question-Answering With Sources**
+
+- **Links Used**: Vectorstore, LLMChain
+- **Notes**: This chain takes user input (a question), uses the Vectorstore and semantic search to find relevant documents, and then passes the documents plus the original question to another LLM to generate a final answer with sources.
+- `Example Notebook <chains/vector_db_qa_with_sources.ipynb>`_
+
+**Question-Answering With Sources**
+
+- **Links Used**: LLMChain
+- **Notes**: These types of chains take a question and multiple documents as input, and return an answer plus sources for where that answer came from. There are multiple underlying types of chains to do this, for more information see TODO.
+- `Example Notebook <chains/qa_with_sources.ipynb>`_
+
+**Question-Answering**
+
+- **Links Used**: LLMChain
+- **Notes**: These types of chains take a question and multiple documents as input, and return an answer. There are multiple underlying types of chains to do this, for more information see TODO.
+- `Example Notebook <chains/question_answering.ipynb>`_
+
+**Summarization**
+
+- **Links Used**: LLMChain
+- **Notes**: These types of chains take multiple documents as input, and return a summary of all documents. There are multiple underlying types of chains to do this, for more information see TODO.
+- `Example Notebook <chains/summarize.ipynb>`_
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :caption: Chains
+   :hidden:
+
+   chains/*

docs/examples/chains/chatgpt_clone.ipynb

@@ -5,7 +5,7 @@
    "id": "b253f4d5",
    "metadata": {},
    "source": [
-    "# How to create ChatGPT Clone\n",
+    "# ChatGPT Clone\n",
     "\n",
     "This chain replicates ChatGPT by combining (1) a specific prompt, and (2) the concept of memory.\n",
     "\n",
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 38,
    "id": "a99acd89",
    "metadata": {},
    "outputs": [
@@ -35,20 +35,21 @@
       "Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n",
       "\n",
       "\n",
-      "Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\n",
+      "Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply wiht the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\n",
       "Assistant:\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
       "\n",
       "```\n",
-      "/home/user\n",
+      "$ pwd\n",
+      "/\n",
       "```\n"
      ]
     }
    ],
    "source": [
     "from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate\n",
-    "from langchain.memory import ConversationBufferWindowMemory\n",
+    "from langchain.chains.conversation.memory import ConversationalBufferWindowMemory\n",
     "\n",
     "\n",
     "template = \"\"\"Assistant is a large language model trained by OpenAI.\n",
@@ -73,16 +74,16 @@
     "    llm=OpenAI(temperature=0), \n",
     "    prompt=prompt, \n",
     "    verbose=True, \n",
-    "    memory=ConversationBufferWindowMemory(k=2),\n",
+    "    memory=ConversationalBufferWindowMemory(k=2),\n",
     ")\n",
     "\n",
-    "output = chatgpt_chain.predict(human_input=\"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\")\n",
+    "output = chatgpt_chain.predict(human_input=\"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply wiht the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\")\n",
     "print(output)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 39,
    "id": "4ef711d6",
    "metadata": {},
    "outputs": [
@@ -102,7 +103,7 @@
       "\n",
       "Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n",
       "\n",
-      "Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\n",
+      "Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply wiht the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\n",
       "AI: \n",
       "```\n",
       "$ pwd\n",
@@ -127,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 40,
    "id": "a5d6dac2",
    "metadata": {},
    "outputs": [
@@ -147,7 +148,7 @@
       "\n",
       "Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n",
       "\n",
-      "Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\n",
+      "Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply wiht the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\n",
       "AI: \n",
       "```\n",
       "$ pwd\n",
@@ -179,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 41,
    "id": "b9283077",
    "metadata": {},
    "outputs": [
@@ -234,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 42,
    "id": "570e785e",
    "metadata": {},
    "outputs": [
@@ -291,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 43,
    "id": "cd0a23d9",
    "metadata": {
     "scrolled": true
@@ -351,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 44,
    "id": "90db6eb2",
    "metadata": {},
    "outputs": [
@@ -415,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 45,
    "id": "c3806f89",
    "metadata": {},
    "outputs": [
@@ -491,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 46,
    "id": "f508f597",
    "metadata": {},
    "outputs": [
@@ -573,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 47,
    "id": "cbd607f4",
    "metadata": {},
    "outputs": [
@@ -648,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 48,
    "id": "d33e0e28",
    "metadata": {},
    "outputs": [
@@ -715,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 49,
    "id": "57c2f113",
    "metadata": {},
    "outputs": [
@@ -788,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 50,
    "id": "babadc78",
    "metadata": {},
    "outputs": [
@@ -864,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 51,
    "id": "0954792a",
    "metadata": {},
    "outputs": [
@@ -914,24 +915,26 @@
       "  \"response\": \"Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions) and self-correction. AI is used to develop computer systems that can think and act like humans.\"\n",
       "}\n",
       "```\n",
-      "Human: curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"}' https://chat.openai.com/chat\n",
+      "Human: curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply wiht the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"}' https://chat.openai.com/chat\n",
       "Assistant:\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
       " \n",
       "\n",
       "```\n",
-      "$ curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"}' https://chat.openai.com/chat\n",
+      "$ curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply wiht the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"}' https://chat.openai.com/chat\n",
       "\n",
       "{\n",
-      "  \"response\": \"```\\n/current/working/directory\\n```\"\n",
+      "  \"response\": \"```\n",
+      "/home/user\n",
+      "```\"\n",
       "}\n",
       "```\n"
      ]
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(human_input=\"\"\"curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"}' https://chat.openai.com/chat\"\"\")\n",
+    "output = chatgpt_chain.predict(human_input=\"\"\"curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply wiht the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"}' https://chat.openai.com/chat\"\"\")\n",
     "print(output)"
    ]
   },
@@ -960,7 +963,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/examples/chains/llm_bash.ipynb

@@ -0,0 +1,87 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BashChain\n",
+    "This notebook showcases using LLMs and a bash process to do perform simple filesystem commands."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
+      "Please write a bash script that prints 'Hello World' to the console.\u001b[32;1m\u001b[1;3m\n",
+      "\n",
+      "```bash\n",
+      "echo \"Hello World\"\n",
+      "```\u001b[0m['```bash', 'echo \"Hello World\"', '```']\n",
+      "\n",
+      "Answer: \u001b[33;1m\u001b[1;3mHello World\n",
+      "\u001b[0m\n",
+      "\u001b[1m> Finished LLMBashChain chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Hello World\\n'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.chains import LLMBashChain\n",
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "\n",
+    "text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
+    "\n",
+    "bash_chain = LLMBashChain(llm=llm, verbose=True)\n",
+    "\n",
+    "bash_chain.run(text)"
+   ]
+  },
+  {
+   "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": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/examples/chains/llm_chain.ipynb

@@ -25,14 +25,14 @@
    "id": "06bcb078",
    "metadata": {},
    "source": [
-    "## Single Input\n",
+    "### Single Input\n",
     "\n",
     "First, lets go over an example using a single input"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "51a54c4d",
    "metadata": {},
    "outputs": [
@@ -42,13 +42,13 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001B[32;1m\u001B[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
+      "\u001b[32;1m\u001b[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
       "\n",
-      "Answer: Let's think step by step.\u001B[0m\n",
+      "Answer: Let's think step by step.\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
+      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
      ]
     },
     {
@@ -57,7 +57,7 @@
        "' Justin Bieber was born in 1994, so the NFL team that won the Super Bowl in 1994 was the Dallas Cowboys.'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -79,13 +79,13 @@
    "id": "79c3ec4d",
    "metadata": {},
    "source": [
-    "## Multiple Inputs\n",
+    "### Multiple Inputs\n",
     "Now lets go over an example using multiple inputs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "03dd6918",
    "metadata": {},
    "outputs": [
@@ -95,11 +95,11 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001B[32;1m\u001B[1;3mWrite a sad poem about ducks.\u001B[0m\n",
+      "\u001b[32;1m\u001b[1;3mWrite a sad poem about ducks.\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
+      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
      ]
     },
     {
@@ -108,7 +108,7 @@
        "\"\\n\\nThe ducks swim in the pond,\\nTheir feathers so soft and warm,\\nBut they can't help but feel so forlorn.\\n\\nTheir quacks echo in the air,\\nBut no one is there to hear,\\nFor they have no one to share.\\n\\nThe ducks paddle around in circles,\\nTheir heads hung low in despair,\\nFor they have no one to care.\\n\\nThe ducks look up to the sky,\\nBut no one is there to see,\\nFor they have no one to be.\\n\\nThe ducks drift away in the night,\\nTheir hearts filled with sorrow and pain,\\nFor they have no one to gain.\""
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -121,51 +121,10 @@
     "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "672f59d4",
-   "metadata": {},
-   "source": [
-    "## From string\n",
-    "You can also construct an LLMChain from a string template directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f8bc262e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
-    "llm_chain = LLMChain.from_string(llm=OpenAI(temperature=0), template=template)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cb164a76",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\n\\nThe ducks swim in the pond,\\nTheir feathers so soft and warm,\\nBut they can't help but feel so forlorn.\\n\\nTheir quacks echo in the air,\\nBut no one is there to hear,\\nFor they have no one to share.\\n\\nThe ducks paddle around in circles,\\nTheir heads hung low in despair,\\nFor they have no one to care.\\n\\nThe ducks look up to the sky,\\nBut no one is there to see,\\nFor they have no one to be.\\n\\nThe ducks drift away in the night,\\nTheir hearts filled with sorrow and pain,\\nFor they have no one to gain.\""
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9f0adbc7",
+   "id": "8310cdaa",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -187,7 +146,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

docs/examples/chains/llm_checker.ipynb

@@ -24,16 +24,16 @@
       "\n",
       "\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
       "\u001b[1mChain 0\u001b[0m:\n",
-      "{'statement': '\\nNone. Mammals do not lay eggs.'}\n",
+      "{'statement': '\\nThe largest mammal that lays eggs is the platypus.'}\n",
       "\n",
       "\u001b[1mChain 1\u001b[0m:\n",
-      "{'assertions': '\\n• Mammals reproduce using live birth\\n• Mammals do not lay eggs\\n• Animals that lay eggs are not mammals'}\n",
+      "{'assertions': '\\n• The largest mammal is the platypus.\\n• The platypus lays eggs.\\n• There is no larger mammal than the platypus that lays eggs.'}\n",
       "\n",
       "\u001b[1mChain 2\u001b[0m:\n",
-      "{'checked_assertions': '\\n1. True\\n\\n2. True\\n\\n3. False - Mammals are a class of animals that includes animals that lay eggs, such as monotremes (platypus and echidna).'}\n",
+      "{'checked_assertions': '\\n1. The largest mammal is the platypus. False. The blue whale is the largest mammal.\\n\\n2. The platypus lays eggs. True. The Platypus is one of only two mammals that lay eggs.\\n\\n3. There is no larger mammal than the platypus that lays eggs. False. The echidna is another mammal that lays eggs and is larger than the platypus.'}\n",
       "\n",
       "\u001b[1mChain 3\u001b[0m:\n",
-      "{'revised_statement': ' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'}\n",
+      "{'revised_statement': ' The echidna is the type of mammal that lays the biggest eggs.'}\n",
       "\n",
       "\n",
       "\u001b[1m> Finished SequentialChain chain.\u001b[0m\n",
@@ -44,7 +44,7 @@
     {
      "data": {
       "text/plain": [
-       "' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'"
+       "' The echidna is the type of mammal that lays the biggest eggs.'"
       ]
      },
      "execution_count": 1,
@@ -89,7 +89,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

docs/examples/chains/llm_math.ipynb

@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e71e720f",
+   "metadata": {},
+   "source": [
+    "# LLM Math\n",
+    "\n",
+    "This notebook showcases using LLMs and Python REPLs to do complex word math problems."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "44e9ba31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "How many of the integers between 0 and 99 inclusive are divisible by 8?\u001b[102m\n",
+      "\n",
+      "```python\n",
+      "count = 0\n",
+      "for i in range(100):\n",
+      "    if i % 8 == 0:\n",
+      "        count += 1\n",
+      "print(count)\n",
+      "```\n",
+      "\u001b[0m\n",
+      "Answer: \u001b[103m13\n",
+      "\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Answer: 13\\n'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import OpenAI, LLMMathChain\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "llm_math = LLMMathChain(llm=llm, verbose=True)\n",
+    "\n",
+    "llm_math.run(\"How many of the integers between 0 and 99 inclusive are divisible by 8?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f62f0c75",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/llm_requests.ipynb

@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "2ea81168",
    "metadata": {},
    "outputs": [
@@ -78,10 +78,10 @@
       "text/plain": [
        "{'query': 'What are the Three (3) biggest countries, and their respective sizes?',\n",
        " 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',\n",
-       " 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), United States (9,826,675 km²)'}"
+       " 'output': ' Russia (17,098,242 sq km), Canada (9,984,670 sq km), China (9,706,961 sq km)'}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -115,7 +115,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

docs/examples/chains/moderation.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "b83e61ed",
    "metadata": {},
@@ -14,12 +13,12 @@
     "In this notebook, we will show:\n",
     "\n",
     "1. How to run any piece of text through a moderation chain.\n",
-    "2. How to append a Moderation chain to an LLMChain."
+    "2. How to append a Moderation chain to a LLMChain."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 13,
    "id": "b7aa1ff2",
    "metadata": {},
    "outputs": [],
@@ -132,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "954f3da2",
    "metadata": {},
    "outputs": [
@@ -143,11 +142,11 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmoderation_chain_error\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mI will kill you\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/workplace/langchain/langchain/chains/base.py:138\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    137\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`run` supports only one positional argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 138\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(kwargs)[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n",
-      "File \u001b[0;32m~/workplace/langchain/langchain/chains/base.py:112\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n\u001b[1;32m    109\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m    110\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[1m> Entering new \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m chain...\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    111\u001b[0m     )\n\u001b[0;32m--> 112\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[1m> Finished \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m chain.\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/workplace/langchain/langchain/chains/moderation.py:81\u001b[0m, in \u001b[0;36mOpenAIModerationChain._call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m     79\u001b[0m text \u001b[38;5;241m=\u001b[39m inputs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_key]\n\u001b[1;32m     80\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient\u001b[38;5;241m.\u001b[39mcreate(text)\n\u001b[0;32m---> 81\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_moderate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresults\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_key: output}\n",
-      "File \u001b[0;32m~/workplace/langchain/langchain/chains/moderation.py:73\u001b[0m, in \u001b[0;36mOpenAIModerationChain._moderate\u001b[0;34m(self, text, results)\u001b[0m\n\u001b[1;32m     71\u001b[0m error_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mText was found that violates OpenAI\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms content policy.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39merror:\n\u001b[0;32m---> 73\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(error_str)\n\u001b[1;32m     74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     75\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m error_str\n",
+      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmoderation_chain_error\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mI will kill you\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/workplace/third_party/langchain/langchain/chains/base.py:114\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    110\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    111\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`run` not supported when there is not exactly \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    112\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mone output key, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    113\u001b[0m     )\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minput_keys\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n",
+      "File \u001b[0;32m~/workplace/third_party/langchain/langchain/chains/base.py:87\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m     85\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[1m> Entering new \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m chain...\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     86\u001b[0m     )\n\u001b[0;32m---> 87\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[1m> Finished \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m chain.\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/workplace/third_party/langchain/langchain/chains/moderation.py:79\u001b[0m, in \u001b[0;36mOpenAIModerationChain._call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m     77\u001b[0m text \u001b[38;5;241m=\u001b[39m inputs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_key]\n\u001b[1;32m     78\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient\u001b[38;5;241m.\u001b[39mcreate(text)\n\u001b[0;32m---> 79\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_moderate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresults\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     80\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_key: output}\n",
+      "File \u001b[0;32m~/workplace/third_party/langchain/langchain/chains/moderation.py:71\u001b[0m, in \u001b[0;36mOpenAIModerationChain._moderate\u001b[0;34m(self, text, results)\u001b[0m\n\u001b[1;32m     69\u001b[0m error_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mText was found that violates OpenAI\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms content policy.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39merror:\n\u001b[0;32m---> 71\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(error_str)\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     73\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m error_str\n",
       "\u001b[0;31mValueError\u001b[0m: Text was found that violates OpenAI's content policy."
      ]
     }
@@ -166,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "3960e985",
    "metadata": {},
    "outputs": [],
@@ -184,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "1152ec11",
    "metadata": {},
    "outputs": [
@@ -194,7 +193,7 @@
        "'This is okay'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -205,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "973257bf",
    "metadata": {},
    "outputs": [
@@ -215,7 +214,7 @@
        "\"The following text was found that violates OpenAI's content policy: I will kill you\""
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -238,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "id": "0d129333",
    "metadata": {},
    "outputs": [],
@@ -249,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "id": "a557c531",
    "metadata": {},
    "outputs": [
@@ -259,7 +258,7 @@
        "' I will kill you'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "id": "d4d10f1c",
    "metadata": {},
    "outputs": [],
@@ -290,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "id": "02f37985",
    "metadata": {},
    "outputs": [
@@ -300,7 +299,7 @@
        "\"Text was found that violates OpenAI's content policy.\""
       ]
      },
-     "execution_count": 14,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -319,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 22,
    "id": "7118ec36",
    "metadata": {},
    "outputs": [],
@@ -330,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 26,
    "id": "003bdfce",
    "metadata": {},
    "outputs": [
@@ -340,7 +339,7 @@
        "{'text': ' I will kill you'}"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -362,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 28,
    "id": "77b64228",
    "metadata": {},
    "outputs": [],
@@ -374,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 31,
    "id": "998a95be",
    "metadata": {},
    "outputs": [],
@@ -384,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 33,
    "id": "9c97a136",
    "metadata": {},
    "outputs": [
@@ -394,7 +393,7 @@
        "{'sanitized_text': \"Text was found that violates OpenAI's content policy.\"}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -428,7 +427,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/examples/chains/pal.ipynb

@@ -21,24 +21,6 @@
     "from langchain import OpenAI"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9a58e15e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "095adc76",
-   "metadata": {},
-   "source": [
-    "## Math Prompt"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -46,6 +28,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
     "pal_chain = PALChain.from_math_prompt(llm, verbose=True)"
    ]
   },
@@ -71,17 +54,17 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3mdef solution():\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mdef solution():\n",
       "    \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
       "    cindy_pets = 4\n",
       "    marcia_pets = cindy_pets + 2\n",
       "    jan_pets = marcia_pets * 3\n",
       "    total_pets = cindy_pets + marcia_pets + jan_pets\n",
       "    result = total_pets\n",
-      "    return result\u001B[0m\n",
+      "    return result\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
@@ -99,14 +82,6 @@
     "pal_chain.run(question)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "0269d20a",
-   "metadata": {},
-   "source": [
-    "## Colored Objects"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -114,6 +89,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
     "pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True)"
    ]
   },
@@ -139,8 +115,8 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
       "objects = []\n",
       "objects += [('booklet', 'blue')] * 2\n",
       "objects += [('booklet', 'purple')] * 2\n",
@@ -151,9 +127,9 @@
       "\n",
       "# Count number of purple objects\n",
       "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
-      "answer = num_purple\u001B[0m\n",
+      "answer = num_purple\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished PALChain chain.\u001B[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
@@ -171,94 +147,10 @@
     "pal_chain.run(question)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "fc3d7f10",
-   "metadata": {},
-   "source": [
-    "## Intermediate Steps\n",
-    "You can also use the intermediate steps flag to return the code executed that generates the answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9d2d9c61",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True, return_intermediate_steps=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b29b971b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a2c40c28",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
-      "objects = []\n",
-      "objects += [('booklet', 'blue')] * 2\n",
-      "objects += [('booklet', 'purple')] * 2\n",
-      "objects += [('sunglasses', 'yellow')] * 2\n",
-      "\n",
-      "# Remove all pairs of sunglasses\n",
-      "objects = [object for object in objects if object[0] != 'sunglasses']\n",
-      "\n",
-      "# Count number of purple objects\n",
-      "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
-      "answer = num_purple\u001B[0m\n",
-      "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = pal_chain({\"question\": question})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "efddd033",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"# Put objects into a list to record ordering\\nobjects = []\\nobjects += [('booklet', 'blue')] * 2\\nobjects += [('booklet', 'purple')] * 2\\nobjects += [('sunglasses', 'yellow')] * 2\\n\\n# Remove all pairs of sunglasses\\nobjects = [object for object in objects if object[0] != 'sunglasses']\\n\\n# Count number of purple objects\\nnum_purple = len([object for object in objects if object[1] == 'purple'])\\nanswer = num_purple\""
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result['intermediate_steps']"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dfd88594",
+   "id": "4ab20fec",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -280,7 +172,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.8.7"
   }
  },
  "nbformat": 4,

docs/examples/chains/qa_with_sources.ipynb

@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "74148cee",
+   "metadata": {},
+   "source": [
+    "# Question Answering with Sources\n",
+    "\n",
+    "This notebook walks through how to use LangChain for question answering with sources over a list of documents. It covers three different chain types: `stuff`, `map_reduce`, and `refine`. For a more in depth explanation of what these chain types are, see [here](../../explanation/combine_docs.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca2f0efc",
+   "metadata": {},
+   "source": [
+    "### Prepare Data\n",
+    "First we prepare the data. For this example we do similarity search over a vector database, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "78f28130",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.embeddings.cohere import CohereEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
+    "from langchain.docstore.document import Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4da195a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5ec2b55b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch = FAISS.from_texts(texts, embeddings, metadatas=[{\"source\": i} for i in range(len(texts))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5286f58f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Justice Breyer\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "005a47e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d82f899a",
+   "metadata": {},
+   "source": [
+    "### The `stuff` Chain\n",
+    "\n",
+    "This sections shows results of using the `stuff` Chain to do question answering with sources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fc1a5ed6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e239964b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = [Document(page_content=t, metadata={\"source\": i}) for i, t in enumerate(texts[:3])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7d766417",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_text': ' The president did not mention Justice Breyer.\\nSOURCES: 0-pl, 1-pl, 2-pl'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Justice Breyer\"\n",
+    "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5dbb304",
+   "metadata": {},
+   "source": [
+    "### The `map_reduce` Chain\n",
+    "\n",
+    "This sections shows results of using the `map_reduce` Chain to do question answering with sources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "921db0a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_reduce\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e417926a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n",
+      "Token indices sequence length is longer than the specified maximum sequence length for this model (1546 > 1024). Running this sequence through the model will result in indexing errors\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'output_text': ' The president did not mention Justice Breyer.\\nSOURCES: 0, 1, 2'}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Justice Breyer\"\n",
+    "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bf0e1ab",
+   "metadata": {},
+   "source": [
+    "### The `refine` Chain\n",
+    "\n",
+    "This sections shows results of using the `refine` Chain to do question answering with sources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "904835c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"refine\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f60875c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_text': \"\\n\\nThe president did not mention Justice Breyer in his speech to the European Parliament, which focused on building a coalition of freedom-loving nations to confront Putin, unifying European allies, countering Russia's lies with truth, and enforcing powerful economic sanctions. Source: 2\"}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Justice Breyer\"\n",
+    "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "929620d0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/question_answering.ipynb

@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "05859721",
+   "metadata": {},
+   "source": [
+    "# Question Answering\n",
+    "\n",
+    "This notebook walks through how to use LangChain for question answering over a list of documents. It covers three different types of chaings: `stuff`, `map_reduce`, and `refine`. For a more in depth explanation of what these chain types are, see [here](../../explanation/combine_docs.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "726f4996",
+   "metadata": {},
+   "source": [
+    "### Prepare Data\n",
+    "First we prepare the data. For this example we do similarity search over a vector database, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "17fcbc0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
+    "from langchain.docstore.document import Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "291f0117",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fd9666a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch = FAISS.from_texts(texts, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d1eaf6e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Justice Breyer\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a16e3453",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.question_answering import load_qa_chain\n",
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f78787a0",
+   "metadata": {},
+   "source": [
+    "### The `stuff` Chain\n",
+    "\n",
+    "This sections shows results of using the `stuff` Chain to do question answering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "180fd4c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d145ae31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = [Document(page_content=t) for t in texts[:3]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "77fdf1aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_text': ' The president did not mention Justice Breyer.'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Justice Breyer\"\n",
+    "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91522e29",
+   "metadata": {},
+   "source": [
+    "### The `map_reduce` Chain\n",
+    "\n",
+    "This sections shows results of using the `map_reduce` Chain to do question answering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b0060f51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fbdb9137",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_text': ' The president did not mention Justice Breyer.'}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Justice Breyer\"\n",
+    "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ea50ad0",
+   "metadata": {},
+   "source": [
+    "### The `refine` Chain\n",
+    "\n",
+    "This sections shows results of using the `refine` Chain to do question answering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fb167057",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d8b5286e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_text': \"\\n\\nThe president did not mention Justice Breyer in his speech to the European Parliament about building a coalition of freedom-loving nations to confront Putin, unifying European allies, countering Russia's lies with truth, and enforcing powerful economic sanctions.\"}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Justice Breyer\"\n",
+    "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49e9c6d7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/sqlite.ipynb

@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0ed6aab1",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# SQLite example\n",
+    "\n",
+    "This example showcases hooking up an LLM to answer questions over a database."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2f66479",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "This uses the example Chinook database.\n",
+    "To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d0e27d88",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, SQLDatabase, SQLDatabaseChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "72ede462",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../notebooks/Chinook.db\")\n",
+    "llm = OpenAI(temperature=0)\n",
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "15ff81df",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "How many employees are there?\n",
+      "SQLQuery:\u001b[102m SELECT COUNT(*) FROM Employee\u001b[0m\n",
+      "SQLResult: \u001b[103m[(8,)]\u001b[0m\n",
+      "Answer:\u001b[102m 8\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' 8'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db_chain.run(\"How many employees are there?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61d91b85",
+   "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/summarize.ipynb

@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d9a0131f",
+   "metadata": {},
+   "source": [
+    "# Summarization\n",
+    "\n",
+    "This notebook walks through how to use LangChain for summarization over a list of documents. It covers three different chain types: `stuff`, `map_reduce`, and `refine`. For a more in depth explanation of what these chain types are, see [here](../../explanation/combine_docs.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b5660bf",
+   "metadata": {},
+   "source": [
+    "### Prepare Data\n",
+    "First we prepare the data. For this example we create multiple documents from one long one, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e9db25f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, PromptTemplate, LLMChain\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.chains.mapreduce import MapReduceChain\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "\n",
+    "\n",
+    "text_splitter = CharacterTextSplitter()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "99bbe19b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "texts = text_splitter.split_text(state_of_the_union)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "baa6e808",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8dff4f43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = [Document(page_content=t) for t in texts[:3]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "27989fc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.summarize import load_summarize_chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea2d5c99",
+   "metadata": {},
+   "source": [
+    "### The `stuff` Chain\n",
+    "\n",
+    "This sections shows results of using the `stuff` Chain to do summarization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f01f3196",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_summarize_chain(llm, chain_type=\"stuff\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "da4d9801",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' In his speech, President Biden addressed the ongoing conflict between Russia and Ukraine, and the need for the United States and its allies to stand with Ukraine. He also discussed the American Rescue Plan, the Bipartisan Infrastructure Law, and the Bipartisan Innovation Act, which will help to create jobs, modernize infrastructure, and level the playing field with China. He also emphasized the importance of buying American products to support American jobs.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c868e86",
+   "metadata": {},
+   "source": [
+    "### The `map_reduce` Chain\n",
+    "\n",
+    "This sections shows results of using the `map_reduce` Chain to do summarization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef28e1d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_summarize_chain(llm, chain_type=\"map_reduce\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f82c5f9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" In response to Vladimir Putin's aggression in Ukraine, the US and its allies have taken action to hold him accountable, including economic sanctions, cutting off access to technology, and seizing the assets of Russian oligarchs. They are also providing military, economic, and humanitarian assistance to the Ukrainians, and releasing 60 million barrels of oil from reserves around the world. President Biden has passed several laws to provide economic relief to Americans and create jobs, and is making sure taxpayer dollars support American jobs and businesses.\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f61350f9",
+   "metadata": {},
+   "source": [
+    "### The `refine` Chain\n",
+    "\n",
+    "This sections shows results of using the `refine` Chain to do summarization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3bcbe31e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_summarize_chain(llm, chain_type=\"refine\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c8cad866",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nIn this speech, the speaker addresses the American people and their allies, discussing the recent aggression of Russia's Vladimir Putin in Ukraine. The speaker outlines the actions taken by the United States and its allies to hold Putin accountable, including economic sanctions, cutting off access to technology, and seizing the assets of Russian oligarchs. The speaker also announces the closing of American airspace to Russian flights, further isolating Russia and adding an additional squeeze on their economy. The Russian stock market has lost 40% of its value and trading remains suspended. Together with our allies, the United States is providing military, economic, and humanitarian assistance to Ukraine, and has mobilized forces to protect NATO countries. The speaker also announces the release of 60 million barrels of oil from reserves around the world, with the United States releasing 30 million barrels from its own Strategic Petroleum Reserve. The speaker emphasizes that the United States and its allies will defend every inch of NATO territory and that Putin will pay a high price for his aggression. The speaker also acknowledges the hardships faced by the American people due to the pandemic and the American Rescue Plan, which has provided immediate economic relief for tens of millions of Americans, helped put food on their table, keep a roof over their heads, and cut the cost of health insurance. The speaker\""
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0da92750",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/transformation.ipynb

@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "id": "bbbb4330",
    "metadata": {},
    "outputs": [],
@@ -26,18 +26,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "id": "8ae5937c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open(\"../../state_of_the_union.txt\") as f:\n",
+    "with open('../state_of_the_union.txt') as f:\n",
     "    state_of_the_union = f.read()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "98739592",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "e9397934",
    "metadata": {},
    "outputs": [],
@@ -78,17 +78,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "f7caa1ee",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'"
+       "' This speech addresses the American people and acknowledges the difficulties of last year due to COVID-19. It emphasizes the importance of coming together regardless of political affiliation and encourages a sense of unity as Americans.'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,7 +122,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

docs/examples/chains/vector_db_qa.ipynb

@@ -0,0 +1,104 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "07c1e3b9",
+   "metadata": {},
+   "source": [
+    "# Vector DB Question/Answering\n",
+    "\n",
+    "This example showcases question answering over a vector database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "82525493",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain import OpenAI, VectorDBQA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5c7049db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "docsearch = FAISS.from_texts(texts, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3018f865",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qa = VectorDBQA.from_llm(llm=OpenAI(), vectorstore=docsearch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "032a47f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator and federal public defender, and from a family of public school educators and police officers. He also said that she has received a broad range of support since she was nominated, from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "qa.run(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f056f6fd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/chains/vector_db_qa_with_sources.ipynb

@@ -0,0 +1,146 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "efc5be67",
+   "metadata": {},
+   "source": [
+    "# VectorDB Question Ansering with Sources\n",
+    "\n",
+    "This notebook goes over how to do question-answering with sources. It does this in a few different ways - first showing how you can use the `QAWithSourcesChain` to take in documents and use those, and next showing the `VectorDBQAWithSourcesChain`, which also does the lookup of the documents from a vector database. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1c613960",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.embeddings.cohere import CohereEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
+    "from langchain.vectorstores.faiss import FAISS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "17d1306e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0e745d99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch = FAISS.from_texts(texts, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f42d79dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add in a fake source information\n",
+    "for i, d in enumerate(docsearch.docstore._dict.values()):\n",
+    "    d.metadata = {'source': f\"{i}-pl\"}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6fc81de",
+   "metadata": {},
+   "source": [
+    "### VectorDBQAWithSourcesChain\n",
+    "\n",
+    "This shows how to use the `VectorDBQAWithSourcesChain`, which uses a vector database to look up relevant documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8aa571ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import VectorDBQAWithSourcesChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "aa859d4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = VectorDBQAWithSourcesChain.from_llm(OpenAI(temperature=0), vectorstore=docsearch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8ba36fa7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': ' The president thanked Justice Breyer for his service.',\n",
+       " 'sources': '27-pl'}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "980fae3b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/integrations.rst

@@ -0,0 +1,10 @@
+Integrations
+============
+
+The examples here all highlight a specific type of integration.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   integrations/*

docs/examples/integrations/embeddings.ipynb

@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7ef4d402-6662-4a26-b612-35b542066487",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "#  Embeddings & VectorStores\n",
+    "\n",
+    "This notebook show cases how to use embeddings to create a VectorStore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "965eecee",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
+    "from langchain.vectorstores.faiss import FAISS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "68481687",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "015f4ff5",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "docsearch = FAISS.from_texts(texts, embeddings)\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "67baf32e",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eea6e627",
+   "metadata": {},
+   "source": [
+    "## Requires having ElasticSearch setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4906b8a3",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "docsearch = ElasticVectorSearch.from_texts(texts, embeddings, elasticsearch_url=\"http://localhost:9200\")\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "95f9eee9",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n",
+      "\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  }
+ ],
+ "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": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/integrations/huggingface_hub.ipynb

@@ -0,0 +1,71 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "959300d4",
+   "metadata": {},
+   "source": [
+    "# HuggingFace Hub\n",
+    "\n",
+    "This example showcases how to connect to the HuggingFace Hub."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3acf0069",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Seattle Seahawks won the Super Bowl in 2010. Justin Beiber was born in 2010. The\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain import PromptTemplate, HuggingFaceHub, LLMChain\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":1e-10}))\n",
+    "\n",
+    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "\n",
+    "print(llm_chain.run(question))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae4559c7",
+   "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": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/integrations/manifest.ipynb

@@ -15,30 +15,14 @@
    "id": "59fcaebc",
    "metadata": {},
    "source": [
-    "For more detailed information on `manifest`, and how to use it with local hugginface models like in this example, see https://github.com/HazyResearch/manifest\n",
-    "\n",
-    "Another example of [using Manifest with Langchain](https://github.com/HazyResearch/manifest/blob/main/examples/langchain_chatgpt.ipynb)."
+    "For more detailed information on `manifest`, and how to use it with local hugginface models like in this example, see https://github.com/HazyResearch/manifest"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1205d1e4-e6da-4d67-a0c7-b7e8fd1e98d5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install manifest-ml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "04a0170a",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from manifest import Manifest\n",
@@ -47,12 +31,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "de250a6a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'model_name': 'bigscience/T0_3B', 'model_path': 'bigscience/T0_3B'}\n"
+     ]
+    }
+   ],
    "source": [
     "manifest = Manifest(\n",
     "    client_name = \"huggingface\",\n",
@@ -116,7 +106,7 @@
     }
    ],
    "source": [
-    "with open('../../../state_of_the_union.txt') as f:\n",
+    "with open('../state_of_the_union.txt') as f:\n",
     "    state_of_the_union = f.read()\n",
     "mp_chain.run(state_of_the_union)"
    ]
@@ -212,7 +202,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.7"
   },
   "vscode": {
    "interpreter": {

docs/examples/integrations/textsplitter.ipynb

@@ -0,0 +1,304 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b118c9dc",
+   "metadata": {},
+   "source": [
+    "# Text Splitter\n",
+    "\n",
+    "When you want to deal wit long pieces of text, it is necessary to split up that text into chunks.\n",
+    "This notebook showcases several ways to do that.\n",
+    "\n",
+    "At a high level, text splitters work as following:\n",
+    "\n",
+    "1. Split the text up into small, semantically meaningful chunks (often sentences).\n",
+    "2. Start combining these small chunks into a larger chunk until you reach a certain size (as measured by some function).\n",
+    "3. Once you reach that size, make that chunk its own piece of text and then start creating a new chunk of text with some overlap (to keep context between chunks)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e82c4685",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import CharacterTextSplitter, NLTKTextSplitter, SpacyTextSplitter\n",
+    "# This is a long document we can split up.\n",
+    "with open('../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c461b26",
+   "metadata": {},
+   "source": [
+    "## Character Text Splitting\n",
+    "\n",
+    "Let's start with the most simple method: let's split based on characters (by default \"\\n\\n\") and measure chunk length by number of characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79ff6737",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_splitter = CharacterTextSplitter(        \n",
+    "    separator = \"\\n\\n\",\n",
+    "    chunk_size = 1000,\n",
+    "    chunk_overlap  = 200,\n",
+    "    length_function = len,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "38547666",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \\n\\nLast year COVID-19 kept us apart. This year we are finally together again. \\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \\n\\nWith a duty to one another to the American people to the Constitution. \\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \\n\\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \\n\\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \\n\\nHe met the Ukrainian people. \\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \\n\\nGroups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. '"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "texts[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13dc0983",
+   "metadata": {},
+   "source": [
+    "## HuggingFace Length Function\n",
+    "Most LLMs are constrained by the number of tokens that you can pass in, which is not the same as the number of characters. In order to get a more accurate estimate, we can use HuggingFace tokenizers to count the text length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a8ce51d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import GPT2TokenizerFast\n",
+    "\n",
+    "tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ca5e72c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(tokenizer, chunk_size=100, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "37cdfbeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n",
+      "\n",
+      "Last year COVID-19 kept us apart. This year we are finally together again. \n",
+      "\n",
+      "Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
+      "\n",
+      "With a duty to one another to the American people to the Constitution. \n",
+      "\n",
+      "And with an unwavering resolve that freedom will always triumph over tyranny. \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(texts[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7683b36a",
+   "metadata": {},
+   "source": [
+    "## tiktoken (OpenAI) Length Function\n",
+    "You can also use tiktoken, a open source tokenizer package from OpenAI to estimate tokens used. Will probably be ore accurate for their models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "825f7c0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=100, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae35d165",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n",
+      "\n",
+      "Last year COVID-19 kept us apart. This year we are finally together again. \n",
+      "\n",
+      "Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
+      "\n",
+      "With a duty to one another to the American people to the Constitution. \n",
+      "\n",
+      "And with an unwavering resolve that freedom will always triumph over tyranny. \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(texts[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea2973ac",
+   "metadata": {},
+   "source": [
+    "## NLTK Text Splitter\n",
+    "Rather than just splitting on \"\\n\\n\", we can use NLTK to split based on tokenizers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "20fa9c23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_splitter = NLTKTextSplitter(chunk_size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5ea10835",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.\\n\\nMembers of Congress and the Cabinet.\\n\\nJustices of the Supreme Court.\\n\\nMy fellow Americans.\\n\\nLast year COVID-19 kept us apart.\\n\\nThis year we are finally together again.\\n\\nTonight, we meet as Democrats Republicans and Independents.\\n\\nBut most importantly as Americans.\\n\\nWith a duty to one another to the American people to the Constitution.\\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny.\\n\\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.\\n\\nBut he badly miscalculated.\\n\\nHe thought he could roll into Ukraine and the world would roll over.\\n\\nInstead he met a wall of strength he never imagined.\\n\\nHe met the Ukrainian people.\\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.\\n\\nGroups of citizens blocking tanks with their bodies.\\n\\nEveryone from students to retirees teachers turned soldiers defending their homeland.'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "texts[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dab86b60",
+   "metadata": {},
+   "source": [
+    "## Spacy Text Splitter\n",
+    "Another alternative to NLTK is to use Spacy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f9cc9dfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_splitter = SpacyTextSplitter(chunk_size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cef2b29e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.\\n\\nMembers of Congress and the Cabinet.\\n\\nJustices of the Supreme Court.\\n\\nMy fellow Americans.  \\n\\n\\n\\nLast year COVID-19 kept us apart.\\n\\nThis year we are finally together again.\\n\\n\\n\\n\\n\\nTonight, we meet as Democrats Republicans and Independents.\\n\\nBut most importantly as Americans.\\n\\n\\n\\n\\n\\nWith a duty to one another to the American people to the Constitution. \\n\\n\\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny.\\n\\n\\n\\n\\n\\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.\\n\\nBut he badly miscalculated.\\n\\n\\n\\n\\n\\nHe thought he could roll into Ukraine and the world would roll over.\\n\\nInstead he met a wall of strength he never imagined.\\n\\n\\n\\n\\n\\nHe met the Ukrainian people.\\n\\n\\n\\n\\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.\\n\\n\\n\\n\\n\\nGroups of citizens blocking tanks with their bodies.\\n\\nEveryone from students to retirees teachers turned soldiers defending their homeland.'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "texts[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1a118b1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/memory.rst

@@ -0,0 +1,11 @@
+Memory
+======
+
+The examples here are all related to working with the concept of Memory in LangChain.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :caption: Memory
+
+   memory/*

docs/examples/memory/adding_memory.ipynb

@@ -5,7 +5,7 @@
    "id": "00695447",
    "metadata": {},
    "source": [
-    "# How to add Memory to an LLMChain\n",
+    "# Adding Memory To an LLMChain\n",
     "\n",
     "This notebook goes over how to use the Memory class with an LLMChain. For the purposes of this walkthrough, we will add  the `ConversationBufferMemory` class, although this can be any memory class."
    ]
@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
     "from langchain import OpenAI, LLMChain, PromptTemplate"
    ]
   },
@@ -76,7 +76,7 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
       "\n",
@@ -84,13 +84,13 @@
       "Human: Hi there my friend\n",
       "Chatbot:\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "' Hi there, how are you doing today?'"
+       "' Hi there!'"
       ]
      },
      "execution_count": 4,
@@ -114,23 +114,23 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
       "\n",
       "\n",
       "Human: Hi there my friend\n",
-      "AI:  Hi there, how are you doing today?\n",
+      "AI:  Hi there!\n",
       "Human: Not to bad - how are you?\n",
       "Chatbot:\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "\" I'm doing great, thank you for asking!\""
+       "\"\\n\\nI'm doing well, thanks for asking. How about you?\""
       ]
      },
      "execution_count": 5,
@@ -139,7 +139,7 @@
     }
    ],
    "source": [
-    "llm_chain.predict(human_input=\"Not too bad - how are you?\")"
+    "llm_chain.predict(human_input=\"Not to bad - how are you?\")"
    ]
   },
   {
@@ -167,7 +167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

docs/examples/memory/agent_with_memory.ipynb

@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fa6802ac",
+   "metadata": {},
+   "source": [
+    "# Adding Memory to an Agent\n",
+    "\n",
+    "This notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:\n",
+    "\n",
+    "- [Adding memory to an LLM Chain](adding_memory.ipynb)\n",
+    "- [Custom Agents](../agents/custom_agent.ipynb)\n",
+    "\n",
+    "In order to add a memory to an agent we are going to the the following steps:\n",
+    "\n",
+    "1. We are going to create an LLMChain with memory.\n",
+    "2. We are going to use that LLMChain to create a custom Agent.\n",
+    "\n",
+    "For the purposes of this exercise, we are going to create a simple custom Agent that has access to a search tool and utilizes the `ConversationBufferMemory` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8db95912",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import ZeroShotAgent, Tool\n",
+    "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
+    "from langchain import OpenAI, SerpAPIWrapper, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "97ad8467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = SerpAPIWrapper()\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",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ad2e708",
+   "metadata": {},
+   "source": [
+    "Notice the usage of the `chat_history` variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e3439cd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
+    "suffix = \"\"\"Begin!\"\n",
+    "\n",
+    "{chat_history}\n",
+    "Question: {input}\"\"\"\n",
+    "\n",
+    "prompt = ZeroShotAgent.create_prompt(\n",
+    "    tools, \n",
+    "    prefix=prefix, \n",
+    "    suffix=suffix, \n",
+    "    input_variables=[\"input\", \"chat_history\"]\n",
+    ")\n",
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0021675b",
+   "metadata": {},
+   "source": [
+    "We can now construct the LLMChain, with the Memory object, and then create the agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c56a0e73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt, memory=memory)\n",
+    "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ca4bc1fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "How many people live in canada?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look up how many people live in canada\n",
+      "Action: Search\n",
+      "Action Input: \"How many people live in canada?\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"How many people live in canada?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45627664",
+   "metadata": {},
+   "source": [
+    "To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "eecc0462",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "what is their national anthem called?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m\n",
+      "AI:  I should look up the name of Canada's national anthem\n",
+      "Action: Search\n",
+      "Action Input: \"What is the name of Canada's national anthem?\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAfter 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m\n",
+      "AI:  I now know the final answer\n",
+      "Final Answer: After 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa Lavallée.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"After 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa Lavallée.\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"what is their national anthem called?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc3d0aa4",
+   "metadata": {},
+   "source": [
+    "We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name of Canada's national anthem was.\n",
+    "\n",
+    "For fun, let's compare this to an agent that does NOT have memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3359d043",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
+    "suffix = \"\"\"Begin!\"\n",
+    "\n",
+    "Question: {input}\"\"\"\n",
+    "\n",
+    "prompt = ZeroShotAgent.create_prompt(\n",
+    "    tools, \n",
+    "    prefix=prefix, \n",
+    "    suffix=suffix, \n",
+    "    input_variables=[\"input\"]\n",
+    ")\n",
+    "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
+    "agent_without_memory = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "970d23df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "How many people live in canada?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look up how many people live in canada\n",
+      "Action: Search\n",
+      "Action Input: \"How many people live in canada?\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The current population of Canada is 38,533,678\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The current population of Canada is 38,533,678'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_without_memory.run(\"How many people live in canada?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d9ea82f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "what is their national anthem called?\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should probably look this up\n",
+      "Action: Search\n",
+      "Action Input: \"What is the national anthem of [country]\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mMost nation states have an anthem, defined as \"a song, as of praise, devotion, or patriotism\"; most anthems are either marches or hymns in style.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The national anthem is called \"the national anthem.\"\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The national anthem is called \"the national anthem.\"'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_without_memory.run(\"what is their national anthem called?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b1f9223",
+   "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/examples/memory/custom_memory.ipynb

@@ -5,7 +5,7 @@
    "id": "94e33ebe",
    "metadata": {},
    "source": [
-    "# How to create a custom Memory class\n",
+    "# Custom Memory\n",
     "Although there are a few predefined types of memory in LangChain, it is highly possible you will want to add your own type of memory that is optimal for your application. This notebook covers how to do that."
    ]
   },
@@ -25,7 +25,7 @@
    "outputs": [],
    "source": [
     "from langchain import OpenAI, ConversationChain\n",
-    "from langchain.schema import BaseMemory\n",
+    "from langchain.chains.base import Memory\n",
     "from pydantic import BaseModel\n",
     "from typing import List, Dict, Any"
    ]
@@ -44,8 +44,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "48a5dd13",
+   "execution_count": 2,
+   "id": "12bbed4e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "ff065f58",
    "metadata": {},
    "outputs": [],
@@ -66,21 +66,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "1d45d429",
    "metadata": {},
    "outputs": [],
    "source": [
-    "class SpacyEntityMemory(BaseMemory, BaseModel):\n",
+    "class SpacyEntityMemory(Memory, BaseModel):\n",
     "    \"\"\"Memory class for storing information about entities.\"\"\"\n",
     "\n",
     "    # Define dictionary to store information about entities.\n",
     "    entities: dict = {}\n",
     "    # Define key to pass information about entities into prompt.\n",
     "    memory_key: str = \"entities\"\n",
-    "        \n",
-    "    def clear(self):\n",
-    "        self.entities = {}\n",
     "\n",
     "    @property\n",
     "    def memory_variables(self) -> List[str]:\n",
@@ -120,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "c05159b6",
    "metadata": {},
    "outputs": [],
@@ -150,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "f08dc8ed",
    "metadata": {},
    "outputs": [],
@@ -169,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "5b96e836",
    "metadata": {},
    "outputs": [
@@ -179,7 +176,7 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.\n",
       "\n",
@@ -190,16 +187,16 @@
       "Human: Harrison likes machine learning\n",
       "AI:\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "\" That's great to hear! Machine learning is a fascinating field of study. It involves using algorithms to analyze data and make predictions. Have you ever studied machine learning, Harrison?\""
+       "\"\\n\\nThat's really interesting! I'm sure he has a lot of fun with it.\""
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -218,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "4bca7070",
    "metadata": {},
    "outputs": [
@@ -228,7 +225,7 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.\n",
       "\n",
@@ -239,16 +236,16 @@
       "Human: What do you think Harrison's favorite subject in college was?\n",
       "AI:\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "' From what I know about Harrison, I believe his favorite subject in college was machine learning. He has expressed a strong interest in the subject and has mentioned it often.'"
+       "\" Harrison's favorite subject in college was machine learning.\""
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -290,7 +287,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,