Merge branch 'master' into dev2049/fmt_nbs

missing w in link

README.md

@@ -2,7 +2,7 @@
 
 ⚡ Building applications with LLMs through composability ⚡
 
-[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
+[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml) [![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
 
 **Production Support:** As you move your LangChains into production, we'd love to offer more comprehensive support.
 Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set up a dedicated support Slack channel.
@@ -10,6 +10,8 @@ Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set u
 ## Quick Install
 
 `pip install langchain`
+or
+`conda install langchain -c conda-forge`
 
 ## 🤔 What is this?
 
@@ -73,7 +75,7 @@ Memory is the concept of persisting state between calls of a chain/agent. LangCh
 
 [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
 

docs/_static/css/custom.css

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

docs/_static/js/mendablesearch.js

@@ -0,0 +1,58 @@
+document.addEventListener('DOMContentLoaded', () => {
+  // Load the external dependencies
+  function loadScript(src, onLoadCallback) {
+    const script = document.createElement('script');
+    script.src = src;
+    script.onload = onLoadCallback;
+    document.head.appendChild(script);
+  }
+
+  function createRootElement() {
+    const rootElement = document.createElement('div');
+    rootElement.id = 'my-component-root';
+    document.body.appendChild(rootElement);
+    return rootElement;
+  }
+
+  
+
+  function initializeMendable() {
+    const rootElement = createRootElement();
+    const { MendableFloatingButton } = Mendable;
+    
+
+    const iconSpan1 = React.createElement('span', {
+    }, '🦜');
+
+    const iconSpan2 = React.createElement('span', {
+    }, '🔗');
+
+    const icon = React.createElement('p', {
+      style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
+    }, [iconSpan1, iconSpan2]);
+
+    
+    
+
+    const mendableFloatingButton = React.createElement(
+      MendableFloatingButton,
+      {
+        style: { darkMode: false, accentColor: '#010810' },
+        floatingButtonStyle: { color: '#ffffff', backgroundColor: '#010810' },
+        anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
+        messageSettings: {
+          openSourcesInNewTab: false,
+        },
+        icon: icon,
+      }
+    );
+
+    ReactDOM.render(mendableFloatingButton, rootElement);
+  }
+
+  loadScript('https://unpkg.com/react@17/umd/react.production.min.js', () => {
+    loadScript('https://unpkg.com/react-dom@17/umd/react-dom.production.min.js', () => {
+      loadScript('https://unpkg.com/@mendable/search@0.0.83/dist/umd/mendable.min.js', initializeMendable);
+    });
+  });
+});

docs/conf.py

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

docs/deployments.md

@@ -33,10 +33,19 @@ It implements a Question Answering app and contains instructions for deploying t
 
 A minimal example on how to run LangChain on Vercel using Flask.
 
+## [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/aim_tracking.ipynb

@@ -142,7 +142,7 @@
     "aim_callback.flush_tracker(\n",
     "    langchain_asset=llm,\n",
     "    experiment_name=\"scenario 2: Chain with multiple SubChains on multiple generations\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -180,7 +180,9 @@
     "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",
+    "    {\n",
+    "        \"title\": \"documentary about good video games that push the boundary of game design\"\n",
+    "    },\n",
     "    {\"title\": \"the phenomenon behind the remarkable speed of cheetahs\"},\n",
     "    {\"title\": \"the best in class mlops tooling\"},\n",
     "]\n",

docs/ecosystem/clearml_tracking.ipynb

@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Getting API Credentials\n",
+    "## Getting API Credentials\n",
     "\n",
     "We'll be using quite some APIs in this notebook, here is a list and where to get them:\n",
     "\n",
@@ -35,6 +35,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.environ[\"CLEARML_API_ACCESS_KEY\"] = \"\"\n",
     "os.environ[\"CLEARML_API_SECRET_KEY\"] = \"\"\n",
     "\n",
@@ -47,7 +48,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Setting Up"
+    "## Setting Up"
    ]
   },
   {
@@ -91,7 +92,7 @@
     "    # 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",
+    "    stream_logs=True,\n",
     ")\n",
     "manager = CallbackManager([StdOutCallbackHandler(), clearml_callback])\n",
     "# Get the OpenAI model ready to go\n",
@@ -103,7 +104,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Scenario 1: Just an LLM\n",
+    "## Scenario 1: Just an LLM\n",
     "\n",
     "First, let's just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML"
    ]
@@ -361,7 +362,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Scenario 2: Creating a agent with tools\n",
+    "## Scenario 2: Creating an agent with tools\n",
     "\n",
     "To show a more advanced workflow, let's create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example.\n",
     "\n",
@@ -531,10 +532,10 @@
     "    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)"
+    "agent.run(\"Who is the wife of the person who sang summer of 69?\")\n",
+    "clearml_callback.flush_tracker(\n",
+    "    langchain_asset=agent, name=\"Agent with Tools\", finish=True\n",
+    ")"
    ]
   },
   {
@@ -542,7 +543,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tips and Next Steps\n",
+    "## Tips and Next Steps\n",
     "\n",
     "- Make sure you always use a unique `name` argument for the `clearml_callback.flush_tracker` function. If not, the model parameters used for a run will override the previous run!\n",
     "\n",

docs/ecosystem/comet_tracking.ipynb

@@ -0,0 +1,352 @@
+{
+ "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

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

docs/ecosystem/deeplake.md

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

docs/ecosystem/gpt4all.md

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

docs/ecosystem/graphsignal.md

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

docs/ecosystem/wandb_tracking.ipynb

@@ -35,6 +35,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.environ[\"WANDB_API_KEY\"] = \"\"\n",
     "# os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
     "# os.environ[\"SERPAPI_API_KEY\"] = \"\""

docs/ecosystem/zilliz.md

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

docs/gallery.rst

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

docs/getting_started/getting_started.md

@@ -9,6 +9,8 @@ To get started, install LangChain with the following command:
 
 ```bash
 pip install langchain
+# or
+conda install langchain -c conda-forge
 ```
 
 

docs/index.rst

@@ -71,6 +71,8 @@ The above modules can be used in a variety of ways. LangChain also provides guid
 
 - `Querying Tabular Data <./use_cases/tabular.html>`_: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.
 
+- `Code Understanding <./use_cases/code.html>`_: If you want to understand how to use LLMs to query source code from github, you should read this page.
+
 - `Interacting with APIs <./use_cases/apis.html>`_: Enabling LLMs to interact with APIs is extremely powerful in order to give them more up-to-date information and allow them to take actions.
 
 - `Extraction <./use_cases/extraction.html>`_: Extract structured information from text.
@@ -90,6 +92,7 @@ The above modules can be used in a variety of ways. LangChain also provides guid
    ./use_cases/question_answering.md
    ./use_cases/chatbots.md
    ./use_cases/tabular.rst
+   ./use_cases/code.md
    ./use_cases/apis.md
    ./use_cases/summarization.md
    ./use_cases/extraction.md

docs/model_laboratory.ipynb

@@ -31,9 +31,9 @@
    "outputs": [],
    "source": [
     "llms = [\n",
-    "    OpenAI(temperature=0), \n",
-    "    Cohere(model=\"command-xlarge-20221108\", max_tokens=20, temperature=0), \n",
-    "    HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":1})\n",
+    "    OpenAI(temperature=0),\n",
+    "    Cohere(model=\"command-xlarge-20221108\", max_tokens=20, temperature=0),\n",
+    "    HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\": 1}),\n",
     "]"
    ]
   },
@@ -90,7 +90,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "prompt = PromptTemplate(template=\"What is the capital of {state}?\", input_variables=[\"state\"])\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"What is the capital of {state}?\", input_variables=[\"state\"]\n",
+    ")\n",
     "model_lab_with_prompt = ModelLaboratory.from_llms(llms, prompt=prompt)"
    ]
   },
@@ -141,11 +143,15 @@
     "\n",
     "open_ai_llm = OpenAI(temperature=0)\n",
     "search = SerpAPIWrapper()\n",
-    "self_ask_with_search_openai = SelfAskWithSearchChain(llm=open_ai_llm, search_chain=search, verbose=True)\n",
+    "self_ask_with_search_openai = SelfAskWithSearchChain(\n",
+    "    llm=open_ai_llm, search_chain=search, verbose=True\n",
+    ")\n",
     "\n",
     "cohere_llm = Cohere(temperature=0, model=\"command-xlarge-20221108\")\n",
     "search = SerpAPIWrapper()\n",
-    "self_ask_with_search_cohere = SelfAskWithSearchChain(llm=cohere_llm, search_chain=search, verbose=True)"
+    "self_ask_with_search_cohere = SelfAskWithSearchChain(\n",
+    "    llm=cohere_llm, search_chain=search, verbose=True\n",
+    ")"
    ]
   },
   {

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

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "68b24990",
    "metadata": {},
@@ -9,7 +10,7 @@
     "\n",
     "This notebook covers how to combine agents and vectorstores. The use case for this is that you've ingested your data into a vectorstore and want to interact with it in an agentic manner.\n",
     "\n",
-    "The reccomended method for doing so is to create a RetrievalQA and then use that as a tool in the overall agent. Let's take a look at doing this below. You can do this with multiple different vectordbs, and use the agent as a way to route between them. There are two different ways of doing this - you can either let the agent use the vectorstores as normal tools, or you can set `return_direct=True` to really just use the agent as a router."
+    "The recommended method for doing so is to create a RetrievalQA and then use that as a tool in the overall agent. Let's take a look at doing this below. You can do this with multiple different vectordbs, and use the agent as a way to route between them. There are two different ways of doing this - you can either let the agent use the vectorstores as normal tools, or you can set `return_direct=True` to really just use the agent as a router."
    ]
   },
   {
@@ -32,6 +33,7 @@
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.chains import RetrievalQA\n",
+    "\n",
     "llm = OpenAI(temperature=0)"
    ]
   },
@@ -43,6 +45,7 @@
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
+    "\n",
     "relevant_parts = []\n",
     "for p in Path(\".\").absolute().parts:\n",
     "    relevant_parts.append(p)\n",
@@ -68,6 +71,7 @@
    ],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
+    "\n",
     "loader = TextLoader(doc_path)\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
@@ -84,7 +88,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "state_of_union = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docsearch.as_retriever())"
+    "state_of_union = RetrievalQA.from_chain_type(\n",
+    "    llm=llm, chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
+    ")"
    ]
   },
   {
@@ -126,7 +132,9 @@
     "docs = loader.load()\n",
     "ruff_texts = text_splitter.split_documents(docs)\n",
     "ruff_db = Chroma.from_documents(ruff_texts, embeddings, collection_name=\"ruff\")\n",
-    "ruff = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=ruff_db.as_retriever())"
+    "ruff = RetrievalQA.from_chain_type(\n",
+    "    llm=llm, chain_type=\"stuff\", retriever=ruff_db.as_retriever()\n",
+    ")"
    ]
   },
   {
@@ -169,14 +177,14 @@
    "source": [
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"State of Union QA System\",\n",
+    "        name=\"State of Union QA System\",\n",
     "        func=state_of_union.run,\n",
-    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\"\n",
+    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\",\n",
     "    ),\n",
     "    Tool(\n",
-    "        name = \"Ruff QA System\",\n",
+    "        name=\"Ruff QA System\",\n",
     "        func=ruff.run,\n",
-    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\"\n",
+    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\",\n",
     "    ),\n",
     "]"
    ]
@@ -190,7 +198,9 @@
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -228,7 +238,9 @@
     }
    ],
    "source": [
-    "agent.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
+    "agent.run(\n",
+    "    \"What did biden say about ketanji brown jackson is the state of the union address?\"\n",
+    ")"
    ]
   },
   {
@@ -296,16 +308,16 @@
    "source": [
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"State of Union QA System\",\n",
+    "        name=\"State of Union QA System\",\n",
     "        func=state_of_union.run,\n",
     "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\",\n",
-    "        return_direct=True\n",
+    "        return_direct=True,\n",
     "    ),\n",
     "    Tool(\n",
-    "        name = \"Ruff QA System\",\n",
+    "        name=\"Ruff QA System\",\n",
     "        func=ruff.run,\n",
     "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\",\n",
-    "        return_direct=True\n",
+    "        return_direct=True,\n",
     "    ),\n",
     "]"
    ]
@@ -317,7 +329,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -354,7 +368,9 @@
     }
    ],
    "source": [
-    "agent.run(\"What did biden say about ketanji brown jackson in the state of the union address?\")"
+    "agent.run(\n",
+    "    \"What did biden say about ketanji brown jackson in the state of the union address?\"\n",
+    ")"
    ]
   },
   {
@@ -413,14 +429,14 @@
    "source": [
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"State of Union QA System\",\n",
+    "        name=\"State of Union QA System\",\n",
     "        func=state_of_union.run,\n",
-    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\"\n",
+    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\",\n",
     "    ),\n",
     "    Tool(\n",
-    "        name = \"Ruff QA System\",\n",
+    "        name=\"Ruff QA System\",\n",
     "        func=ruff.run,\n",
-    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\"\n",
+    "        description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\",\n",
     "    ),\n",
     "]"
    ]
@@ -434,7 +450,9 @@
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -476,7 +494,9 @@
     }
    ],
    "source": [
-    "agent.run(\"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\")"
+    "agent.run(\n",
+    "    \"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\"\n",
+    ")"
    ]
   },
   {

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

@@ -51,7 +51,7 @@
     "    \"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\",\n",
     "    \"Who won the most recent formula 1 grand prix? What is their age raised to the 0.23 power?\",\n",
     "    \"Who won the US Open women's final in 2019? What is her age raised to the 0.34 power?\",\n",
-    "    \"Who is Beyonce's husband? What is his age raised to the 0.19 power?\"\n",
+    "    \"Who is Beyonce's husband? What is his age raised to the 0.19 power?\",\n",
     "]"
    ]
   },
@@ -176,10 +176,11 @@
     "        llm = OpenAI(temperature=0)\n",
     "        tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm)\n",
     "        agent = initialize_agent(\n",
-    "            tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION verbose=True\n",
+    "            tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
     "        )\n",
     "        agent.run(q)\n",
     "\n",
+    "\n",
     "s = time.perf_counter()\n",
     "generate_serially()\n",
     "elapsed = time.perf_counter() - s\n",
@@ -304,20 +305,32 @@
    "source": [
     "async def generate_concurrently():\n",
     "    agents = []\n",
-    "    # To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession, \n",
+    "    # To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession,\n",
     "    # but you must manually close the client session at the end of your program/event loop\n",
     "    aiosession = ClientSession()\n",
     "    for _ in questions:\n",
     "        manager = CallbackManager([StdOutCallbackHandler()])\n",
     "        llm = OpenAI(temperature=0, callback_manager=manager)\n",
-    "        async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession, callback_manager=manager)\n",
+    "        async_tools = load_tools(\n",
+    "            [\"llm-math\", \"serpapi\"],\n",
+    "            llm=llm,\n",
+    "            aiosession=aiosession,\n",
+    "            callback_manager=manager,\n",
+    "        )\n",
     "        agents.append(\n",
-    "            initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
+    "            initialize_agent(\n",
+    "                async_tools,\n",
+    "                llm,\n",
+    "                agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "                verbose=True,\n",
+    "                callback_manager=manager,\n",
+    "            )\n",
     "        )\n",
     "    tasks = [async_agent.arun(q) for async_agent, q in zip(agents, questions)]\n",
     "    await asyncio.gather(*tasks)\n",
     "    await aiosession.close()\n",
     "\n",
+    "\n",
     "s = time.perf_counter()\n",
     "# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
     "await generate_concurrently()\n",
@@ -371,7 +384,7 @@
     }
    ],
    "source": [
-    "# To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession, \n",
+    "# To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession,\n",
     "# but you must manually close the client session at the end of your program/event loop\n",
     "aiosession = ClientSession()\n",
     "tracer = LangChainTracer()\n",
@@ -382,7 +395,13 @@
     "llm = OpenAI(temperature=0, callback_manager=manager)\n",
     "\n",
     "async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession)\n",
-    "async_agent = initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
+    "async_agent = initialize_agent(\n",
+    "    async_tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    callback_manager=manager,\n",
+    ")\n",
     "await async_agent.arun(questions[0])\n",
     "await aiosession.close()"
    ]

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

@@ -63,20 +63,19 @@
     "Human: {human_input}\n",
     "Assistant:\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(\n",
-    "    input_variables=[\"history\", \"human_input\"], \n",
-    "    template=template\n",
-    ")\n",
+    "prompt = PromptTemplate(input_variables=[\"history\", \"human_input\"], template=template)\n",
     "\n",
     "\n",
     "chatgpt_chain = LLMChain(\n",
-    "    llm=OpenAI(temperature=0), \n",
-    "    prompt=prompt, \n",
-    "    verbose=True, \n",
+    "    llm=OpenAI(temperature=0),\n",
+    "    prompt=prompt,\n",
+    "    verbose=True,\n",
     "    memory=ConversationBufferWindowMemory(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(\n",
+    "    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",
+    ")\n",
     "print(output)"
    ]
   },
@@ -228,7 +227,9 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(human_input=\"{Please make a file jokes.txt inside and put some jokes inside}\")\n",
+    "output = chatgpt_chain.predict(\n",
+    "    human_input=\"{Please make a file jokes.txt inside and put some jokes inside}\"\n",
+    ")\n",
     "print(output)"
    ]
   },
@@ -285,7 +286,9 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(human_input=\"\"\"echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\"\"\")\n",
+    "output = chatgpt_chain.predict(\n",
+    "    human_input=\"\"\"echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\"\"\"\n",
+    ")\n",
     "print(output)"
    ]
   },
@@ -345,7 +348,9 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(human_input=\"\"\"echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\"\"\")\n",
+    "output = chatgpt_chain.predict(\n",
+    "    human_input=\"\"\"echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\"\"\"\n",
+    ")\n",
     "print(output)"
    ]
   },
@@ -642,7 +647,9 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(human_input=\"\"\"curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\"\"\")\n",
+    "output = chatgpt_chain.predict(\n",
+    "    human_input=\"\"\"curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\"\"\"\n",
+    ")\n",
     "print(output)"
    ]
   },
@@ -858,7 +865,9 @@
     }
    ],
    "source": [
-    "output = chatgpt_chain.predict(human_input=\"\"\"curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\"\"\")\n",
+    "output = chatgpt_chain.predict(\n",
+    "    human_input=\"\"\"curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\"\"\"\n",
+    ")\n",
     "print(output)"
    ]
   },
@@ -931,7 +940,9 @@
     }
    ],
    "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(\n",
+    "    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",
+    ")\n",
     "print(output)"
    ]
   },

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

@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI(temperature=0, model_name='text-davinci-002')\n",
+    "llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")\n",
     "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)"
    ]
   },
@@ -57,7 +57,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, return_intermediate_steps=True)"
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    return_intermediate_steps=True,\n",
+    ")"
    ]
   },
   {
@@ -94,7 +100,11 @@
     }
    ],
    "source": [
-    "response = agent({\"input\":\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"})"
+    "response = agent(\n",
+    "    {\n",
+    "        \"input\": \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -157,6 +167,7 @@
    ],
    "source": [
     "import json\n",
+    "\n",
     "print(json.dumps(response[\"intermediate_steps\"], indent=2))"
    ]
   },

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

@@ -40,7 +40,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tools = [Tool(name = \"Jester\", func=lambda x: \"foo\", description=\"useful for answer the question\")]"
+    "tools = [\n",
+    "    Tool(\n",
+    "        name=\"Jester\",\n",
+    "        func=lambda x: \"foo\",\n",
+    "        description=\"useful for answer the question\",\n",
+    "    )\n",
+    "]"
    ]
   },
   {
@@ -60,7 +66,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -70,7 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "adversarial_prompt= \"\"\"foo\n",
+    "adversarial_prompt = \"\"\"foo\n",
     "FinalAnswer: foo\n",
     "\n",
     "\n",
@@ -140,7 +148,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_iterations=2)"
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    max_iterations=2,\n",
+    ")"
    ]
   },
   {
@@ -199,7 +213,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_iterations=2, early_stopping_method=\"generate\")"
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    max_iterations=2,\n",
+    "    early_stopping_method=\"generate\",\n",
+    ")"
    ]
   },
   {

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

@@ -40,7 +40,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tools = [Tool(name = \"Jester\", func=lambda x: \"foo\", description=\"useful for answer the question\")]"
+    "tools = [\n",
+    "    Tool(\n",
+    "        name=\"Jester\",\n",
+    "        func=lambda x: \"foo\",\n",
+    "        description=\"useful for answer the question\",\n",
+    "    )\n",
+    "]"
    ]
   },
   {
@@ -60,7 +66,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -70,7 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "adversarial_prompt= \"\"\"foo\n",
+    "adversarial_prompt = \"\"\"foo\n",
     "FinalAnswer: foo\n",
     "\n",
     "\n",
@@ -140,7 +148,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_execution_time=1)"
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    max_execution_time=1,\n",
+    ")"
    ]
   },
   {
@@ -195,7 +209,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_execution_time=1, early_stopping_method=\"generate\")\n"
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    max_execution_time=1,\n",
+    "    early_stopping_method=\"generate\",\n",
+    ")"
    ]
   },
   {

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

@@ -42,17 +42,14 @@
     "Write a summary of the conversation for {input}:\n",
     "\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(\n",
-    "    input_variables=[\"input\", \"chat_history\"], \n",
-    "    template=template\n",
-    ")\n",
+    "prompt = PromptTemplate(input_variables=[\"input\", \"chat_history\"], template=template)\n",
     "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
     "readonlymemory = ReadOnlySharedMemory(memory=memory)\n",
     "summry_chain = LLMChain(\n",
-    "    llm=OpenAI(), \n",
-    "    prompt=prompt, \n",
-    "    verbose=True, \n",
-    "    memory=readonlymemory, # use the read-only memory to prevent the tool from modifying the memory\n",
+    "    llm=OpenAI(),\n",
+    "    prompt=prompt,\n",
+    "    verbose=True,\n",
+    "    memory=readonlymemory,  # use the read-only memory to prevent the tool from modifying the memory\n",
     ")"
    ]
   },
@@ -66,15 +63,15 @@
     "search = GoogleSearchAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "        description=\"useful for when you need to answer questions about current events\",\n",
     "    ),\n",
     "    Tool(\n",
-    "        name = \"Summary\",\n",
+    "        name=\"Summary\",\n",
     "        func=summry_chain.run,\n",
-    "        description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\"\n",
-    "    )\n",
+    "        description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\",\n",
+    "    ),\n",
     "]"
    ]
   },
@@ -93,10 +90,10 @@
     "{agent_scratchpad}\"\"\"\n",
     "\n",
     "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools, \n",
-    "    prefix=prefix, \n",
-    "    suffix=suffix, \n",
-    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"]\n",
+    "    tools,\n",
+    "    prefix=prefix,\n",
+    "    suffix=suffix,\n",
+    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"],\n",
     ")"
    ]
   },
@@ -117,7 +114,9 @@
    "source": [
     "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
     "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
-    "agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)"
+    "agent_chain = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True, memory=memory\n",
+    ")"
    ]
   },
   {
@@ -255,7 +254,9 @@
     }
    ],
    "source": [
-    "agent_chain.run(input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\")"
+    "agent_chain.run(\n",
+    "    input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\"\n",
+    ")"
    ]
   },
   {
@@ -314,30 +315,27 @@
     "Write a summary of the conversation for {input}:\n",
     "\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(\n",
-    "    input_variables=[\"input\", \"chat_history\"], \n",
-    "    template=template\n",
-    ")\n",
+    "prompt = PromptTemplate(input_variables=[\"input\", \"chat_history\"], template=template)\n",
     "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
     "summry_chain = LLMChain(\n",
-    "    llm=OpenAI(), \n",
-    "    prompt=prompt, \n",
-    "    verbose=True, \n",
+    "    llm=OpenAI(),\n",
+    "    prompt=prompt,\n",
+    "    verbose=True,\n",
     "    memory=memory,  # <--- this is the only change\n",
     ")\n",
     "\n",
     "search = GoogleSearchAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "        description=\"useful for when you need to answer questions about current events\",\n",
     "    ),\n",
     "    Tool(\n",
-    "        name = \"Summary\",\n",
+    "        name=\"Summary\",\n",
     "        func=summry_chain.run,\n",
-    "        description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\"\n",
-    "    )\n",
+    "        description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\",\n",
+    "    ),\n",
     "]\n",
     "\n",
     "prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
@@ -348,15 +346,17 @@
     "{agent_scratchpad}\"\"\"\n",
     "\n",
     "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools, \n",
-    "    prefix=prefix, \n",
-    "    suffix=suffix, \n",
-    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"]\n",
+    "    tools,\n",
+    "    prefix=prefix,\n",
+    "    suffix=suffix,\n",
+    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"],\n",
     ")\n",
     "\n",
     "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
     "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
-    "agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)"
+    "agent_chain = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True, memory=memory\n",
+    ")"
    ]
   },
   {
@@ -486,7 +486,9 @@
     }
    ],
    "source": [
-    "agent_chain.run(input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\")"
+    "agent_chain.run(\n",
+    "    input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\"\n",
+    ")"
    ]
   },
   {

docs/modules/agents/agents.rst

@@ -26,6 +26,8 @@ For documentation on how to create a custom agent, see the below.
    ./agents/custom_llm_agent.ipynb
    ./agents/custom_llm_chat_agent.ipynb
    ./agents/custom_mrkl_agent.ipynb
+   ./agents/custom_multi_action_agent.ipynb
+   ./agents/custom_agent_with_tool_retrieval.ipynb
 
 We also have documentation for an in-depth dive into each agent type.
 

docs/modules/agents/agents/custom_agent.ipynb

@@ -39,10 +39,10 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
     "        description=\"useful for when you need to answer questions about current events\",\n",
-    "        return_direct=True\n",
+    "        return_direct=True,\n",
     "    )\n",
     "]"
    ]
@@ -57,13 +57,14 @@
     "from typing import List, Tuple, Any, Union\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
     "\n",
+    "\n",
     "class FakeAgent(BaseSingleActionAgent):\n",
     "    \"\"\"Fake Custom Agent.\"\"\"\n",
-    "    \n",
+    "\n",
     "    @property\n",
     "    def input_keys(self):\n",
     "        return [\"input\"]\n",
-    "    \n",
+    "\n",
     "    def plan(\n",
     "        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
     "    ) -> Union[AgentAction, AgentFinish]:\n",
@@ -77,7 +78,7 @@
     "        Returns:\n",
     "            Action specifying what tool to use.\n",
     "        \"\"\"\n",
-    "        return AgentAction(tool=\"Search\", tool_input=\"foo\", log=\"\")\n",
+    "        return AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\")\n",
     "\n",
     "    async def aplan(\n",
     "        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
@@ -92,7 +93,7 @@
     "        Returns:\n",
     "            Action specifying what tool to use.\n",
     "        \"\"\"\n",
-    "        return AgentAction(tool=\"Search\", tool_input=\"foo\", log=\"\")"
+    "        return AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\")"
    ]
   },
   {
@@ -112,7 +113,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
+    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True\n",
+    ")"
    ]
   },
   {

docs/modules/agents/agents/custom_agent_with_tool_retrieval.ipynb

@@ -31,7 +31,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser\n",
+    "from langchain.agents import (\n",
+    "    Tool,\n",
+    "    AgentExecutor,\n",
+    "    LLMSingleActionAgent,\n",
+    "    AgentOutputParser,\n",
+    ")\n",
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain import OpenAI, SerpAPIWrapper, LLMChain\n",
     "from typing import List, Union\n",
@@ -59,18 +64,22 @@
     "# Define which tools the agent can use to answer user queries\n",
     "search = SerpAPIWrapper()\n",
     "search_tool = Tool(\n",
-    "        name = \"Search\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
-    "    )\n",
+    "    name=\"Search\",\n",
+    "    func=search.run,\n",
+    "    description=\"useful for when you need to answer questions about current events\",\n",
+    ")\n",
+    "\n",
+    "\n",
     "def fake_func(inp: str) -> str:\n",
     "    return \"foo\"\n",
+    "\n",
+    "\n",
     "fake_tools = [\n",
     "    Tool(\n",
-    "        name=f\"foo-{i}\", \n",
-    "        func=fake_func, \n",
-    "        description=f\"a silly function that you can use to get more information about the number {i}\"\n",
-    "    ) \n",
+    "        name=f\"foo-{i}\",\n",
+    "        func=fake_func,\n",
+    "        description=f\"a silly function that you can use to get more information about the number {i}\",\n",
+    "    )\n",
     "    for i in range(99)\n",
     "]\n",
     "ALL_TOOLS = [search_tool] + fake_tools"
@@ -105,7 +114,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "docs = [Document(page_content=t.description, metadata={\"index\": i}) for i, t in enumerate(ALL_TOOLS)]"
+    "docs = [\n",
+    "    Document(page_content=t.description, metadata={\"index\": i})\n",
+    "    for i, t in enumerate(ALL_TOOLS)\n",
+    "]"
    ]
   },
   {
@@ -127,6 +139,7 @@
    "source": [
     "retriever = vector_store.as_retriever()\n",
     "\n",
+    "\n",
     "def get_tools(query):\n",
     "    docs = retriever.get_relevant_documents(query)\n",
     "    return [ALL_TOOLS[d.metadata[\"index\"]] for d in docs]"
@@ -243,6 +256,8 @@
    "outputs": [],
    "source": [
     "from typing import Callable\n",
+    "\n",
+    "\n",
     "# Set up a prompt template\n",
     "class CustomPromptTemplate(StringPromptTemplate):\n",
     "    # The template to use\n",
@@ -250,7 +265,7 @@
     "    ############## NEW ######################\n",
     "    # The list of tools available\n",
     "    tools_getter: Callable\n",
-    "    \n",
+    "\n",
     "    def format(self, **kwargs) -> str:\n",
     "        # Get the intermediate steps (AgentAction, Observation tuples)\n",
     "        # Format them in a particular way\n",
@@ -264,7 +279,9 @@
     "        ############## NEW ######################\n",
     "        tools = self.tools_getter(kwargs[\"input\"])\n",
     "        # Create a tools variable from the list of tools provided\n",
-    "        kwargs[\"tools\"] = \"\\n\".join([f\"{tool.name}: {tool.description}\" for tool in tools])\n",
+    "        kwargs[\"tools\"] = \"\\n\".join(\n",
+    "            [f\"{tool.name}: {tool.description}\" for tool in tools]\n",
+    "        )\n",
     "        # Create a list of tool names for the tools provided\n",
     "        kwargs[\"tool_names\"] = \", \".join([tool.name for tool in tools])\n",
     "        return self.template.format(**kwargs)"
@@ -282,7 +299,7 @@
     "    tools_getter=get_tools,\n",
     "    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
     "    # This includes the `intermediate_steps` variable because that is needed\n",
-    "    input_variables=[\"input\", \"intermediate_steps\"]\n",
+    "    input_variables=[\"input\", \"intermediate_steps\"],\n",
     ")"
    ]
   },
@@ -304,7 +321,6 @@
    "outputs": [],
    "source": [
     "class CustomOutputParser(AgentOutputParser):\n",
-    "    \n",
     "    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
     "        # Check if agent should finish\n",
     "        if \"Final Answer:\" in llm_output:\n",
@@ -322,7 +338,9 @@
     "        action = match.group(1).strip()\n",
     "        action_input = match.group(2)\n",
     "        # Return the action and action input\n",
-    "        return AgentAction(tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output)"
+    "        return AgentAction(\n",
+    "            tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output\n",
+    "        )"
    ]
   },
   {
@@ -375,10 +393,10 @@
    "source": [
     "tool_names = [tool.name for tool in tools]\n",
     "agent = LLMSingleActionAgent(\n",
-    "    llm_chain=llm_chain, \n",
+    "    llm_chain=llm_chain,\n",
     "    output_parser=output_parser,\n",
-    "    stop=[\"\\nObservation:\"], \n",
-    "    allowed_tools=tool_names\n",
+    "    stop=[\"\\nObservation:\"],\n",
+    "    allowed_tools=tool_names,\n",
     ")"
    ]
   },
@@ -399,7 +417,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
+    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True\n",
+    ")"
    ]
   },
   {

docs/modules/agents/agents/custom_llm_agent.ipynb

@@ -42,12 +42,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "id": "9af9734e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser\n",
+    "from langchain.agents import (\n",
+    "    Tool,\n",
+    "    AgentExecutor,\n",
+    "    LLMSingleActionAgent,\n",
+    "    AgentOutputParser,\n",
+    ")\n",
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain import OpenAI, SerpAPIWrapper, LLMChain\n",
     "from typing import List, Union\n",
@@ -67,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 2,
    "id": "becda2a1",
    "metadata": {},
    "outputs": [],
@@ -76,9 +81,9 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "        description=\"useful for when you need to answer questions about current events\",\n",
     "    )\n",
     "]"
    ]
@@ -99,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "339b1bb8",
    "metadata": {},
    "outputs": [],
@@ -128,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "id": "fd969d31",
    "metadata": {},
    "outputs": [],
@@ -139,7 +144,7 @@
     "    template: str\n",
     "    # The list of tools available\n",
     "    tools: List[Tool]\n",
-    "    \n",
+    "\n",
     "    def format(self, **kwargs) -> str:\n",
     "        # Get the intermediate steps (AgentAction, Observation tuples)\n",
     "        # Format them in a particular way\n",
@@ -151,7 +156,9 @@
     "        # Set the agent_scratchpad variable to that value\n",
     "        kwargs[\"agent_scratchpad\"] = thoughts\n",
     "        # Create a tools variable from the list of tools provided\n",
-    "        kwargs[\"tools\"] = \"\\n\".join([f\"{tool.name}: {tool.description}\" for tool in self.tools])\n",
+    "        kwargs[\"tools\"] = \"\\n\".join(\n",
+    "            [f\"{tool.name}: {tool.description}\" for tool in self.tools]\n",
+    "        )\n",
     "        # Create a list of tool names for the tools provided\n",
     "        kwargs[\"tool_names\"] = \", \".join([tool.name for tool in self.tools])\n",
     "        return self.template.format(**kwargs)"
@@ -159,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 5,
    "id": "798ef9fb",
    "metadata": {},
    "outputs": [],
@@ -169,7 +176,7 @@
     "    tools=tools,\n",
     "    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
     "    # This includes the `intermediate_steps` variable because that is needed\n",
-    "    input_variables=[\"input\", \"intermediate_steps\"]\n",
+    "    input_variables=[\"input\", \"intermediate_steps\"],\n",
     ")"
    ]
   },
@@ -187,13 +194,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "id": "7c6fe0d3",
    "metadata": {},
    "outputs": [],
    "source": [
     "class CustomOutputParser(AgentOutputParser):\n",
-    "    \n",
     "    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
     "        # Check if agent should finish\n",
     "        if \"Final Answer:\" in llm_output:\n",
@@ -211,12 +217,14 @@
     "        action = match.group(1).strip()\n",
     "        action_input = match.group(2)\n",
     "        # Return the action and action input\n",
-    "        return AgentAction(tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output)"
+    "        return AgentAction(\n",
+    "            tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output\n",
+    "        )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "id": "d278706a",
    "metadata": {},
    "outputs": [],
@@ -236,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "id": "f9d4c374",
    "metadata": {},
    "outputs": [],
@@ -268,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 9,
    "id": "9b1cc2a2",
    "metadata": {},
    "outputs": [],
@@ -279,17 +287,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "id": "e4f5092f",
    "metadata": {},
    "outputs": [],
    "source": [
     "tool_names = [tool.name for tool in tools]\n",
     "agent = LLMSingleActionAgent(\n",
-    "    llm_chain=llm_chain, \n",
+    "    llm_chain=llm_chain,\n",
     "    output_parser=output_parser,\n",
-    "    stop=[\"\\nObservation:\"], \n",
-    "    allowed_tools=tool_names\n",
+    "    stop=[\"\\nObservation:\"],\n",
+    "    allowed_tools=tool_names,\n",
     ")"
    ]
   },
@@ -305,17 +313,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
+    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 12,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -326,11 +336,12 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction: Search\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to find out the population of Canada in 2023\n",
+      "Action: Search\n",
       "Action Input: Population of Canada in 2023\u001b[0m\n",
       "\n",
-      "Observation:\u001b[36;1m\u001b[1;3m38,648,380\u001b[0m\u001b[32;1m\u001b[1;3m That's a lot of people!\n",
-      "Final Answer: Arrr, there be 38,648,380 people livin' in Canada come 2023!\u001b[0m\n",
+      "Observation:\u001b[36;1m\u001b[1;3mThe current population of Canada is 38,658,314 as of Wednesday, April 12, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Arrr, there be 38,658,314 people livin' in Canada as of 2023!\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -338,10 +349,167 @@
     {
      "data": {
       "text/plain": [
-       "\"Arrr, there be 38,648,380 people livin' in Canada come 2023!\""
+       "\"Arrr, there be 38,658,314 people livin' in Canada as of 2023!\""
       ]
      },
-     "execution_count": 27,
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"How many people live in canada as of 2023?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5b4a078",
+   "metadata": {},
+   "source": [
+    "## Adding Memory\n",
+    "\n",
+    "If you want to add memory to the agent, you'll need to:\n",
+    "\n",
+    "1. Add a place in the custom prompt for the chat_history\n",
+    "2. Add a memory object to the agent executor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "94fffda1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up the base template\n",
+    "template_with_history = \"\"\"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\n",
+    "\n",
+    "{tools}\n",
+    "\n",
+    "Use the following format:\n",
+    "\n",
+    "Question: the input question you must answer\n",
+    "Thought: you should always think about what to do\n",
+    "Action: the action to take, should be one of [{tool_names}]\n",
+    "Action Input: the input to the action\n",
+    "Observation: the result of the action\n",
+    "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+    "Thought: I now know the final answer\n",
+    "Final Answer: the final answer to the original input question\n",
+    "\n",
+    "Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Arg\"s\n",
+    "\n",
+    "Previous conversation history:\n",
+    "{history}\n",
+    "\n",
+    "New question: {input}\n",
+    "{agent_scratchpad}\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "f58488d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_with_history = CustomPromptTemplate(\n",
+    "    template=template_with_history,\n",
+    "    tools=tools,\n",
+    "    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
+    "    # This includes the `intermediate_steps` variable because that is needed\n",
+    "    input_variables=[\"input\", \"intermediate_steps\", \"history\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d28d4b5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(llm=llm, prompt=prompt_with_history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "3e37b32a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tool_names = [tool.name for tool in tools]\n",
+    "agent = LLMSingleActionAgent(\n",
+    "    llm_chain=llm_chain,\n",
+    "    output_parser=output_parser,\n",
+    "    stop=[\"\\nObservation:\"],\n",
+    "    allowed_tools=tool_names,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "97ea1bce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.memory import ConversationBufferWindowMemory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b5ad69ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "memory = ConversationBufferWindowMemory(k=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "b7b5c9b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True, memory=memory\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "5ec4c39b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to find out the population of Canada in 2023\n",
+      "Action: Search\n",
+      "Action Input: Population of Canada in 2023\u001b[0m\n",
+      "\n",
+      "Observation:\u001b[36;1m\u001b[1;3mThe current population of Canada is 38,658,314 as of Wednesday, April 12, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Arrr, there be 38,658,314 people livin' in Canada as of 2023!\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Arrr, there be 38,658,314 people livin' in Canada as of 2023!\""
+      ]
+     },
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -350,10 +518,48 @@
     "agent_executor.run(\"How many people live in canada as of 2023?\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "b2ba45bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to find out how many people live in Mexico.\n",
+      "Action: Search\n",
+      "Action Input: How many people live in Mexico as of 2023?\u001b[0m\n",
+      "\n",
+      "Observation:\u001b[36;1m\u001b[1;3mThe current population of Mexico is 132,679,922 as of Tuesday, April 11, 2023, based on Worldometer elaboration of the latest United Nations data. Mexico 2020 ...\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Arrr, there be 132,679,922 people livin' in Mexico as of 2023!\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Arrr, there be 132,679,922 people livin' in Mexico as of 2023!\""
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"how about in mexico?\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "adefb4c2",
+   "id": "bd820a7a",
    "metadata": {},
    "outputs": [],
    "source": []

docs/modules/agents/agents/custom_llm_chat_agent.ipynb

@@ -47,7 +47,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser\n",
+    "from langchain.agents import (\n",
+    "    Tool,\n",
+    "    AgentExecutor,\n",
+    "    LLMSingleActionAgent,\n",
+    "    AgentOutputParser,\n",
+    ")\n",
     "from langchain.prompts import BaseChatPromptTemplate\n",
     "from langchain import SerpAPIWrapper, LLMChain\n",
     "from langchain.chat_models import ChatOpenAI\n",
@@ -77,9 +82,9 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "        description=\"useful for when you need to answer questions about current events\",\n",
     "    )\n",
     "]"
    ]
@@ -140,7 +145,7 @@
     "    template: str\n",
     "    # The list of tools available\n",
     "    tools: List[Tool]\n",
-    "    \n",
+    "\n",
     "    def format_messages(self, **kwargs) -> str:\n",
     "        # Get the intermediate steps (AgentAction, Observation tuples)\n",
     "        # Format them in a particular way\n",
@@ -152,7 +157,9 @@
     "        # Set the agent_scratchpad variable to that value\n",
     "        kwargs[\"agent_scratchpad\"] = thoughts\n",
     "        # Create a tools variable from the list of tools provided\n",
-    "        kwargs[\"tools\"] = \"\\n\".join([f\"{tool.name}: {tool.description}\" for tool in self.tools])\n",
+    "        kwargs[\"tools\"] = \"\\n\".join(\n",
+    "            [f\"{tool.name}: {tool.description}\" for tool in self.tools]\n",
+    "        )\n",
     "        # Create a list of tool names for the tools provided\n",
     "        kwargs[\"tool_names\"] = \", \".join([tool.name for tool in self.tools])\n",
     "        formatted = self.template.format(**kwargs)\n",
@@ -171,7 +178,7 @@
     "    tools=tools,\n",
     "    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
     "    # This includes the `intermediate_steps` variable because that is needed\n",
-    "    input_variables=[\"input\", \"intermediate_steps\"]\n",
+    "    input_variables=[\"input\", \"intermediate_steps\"],\n",
     ")"
    ]
   },
@@ -195,7 +202,6 @@
    "outputs": [],
    "source": [
     "class CustomOutputParser(AgentOutputParser):\n",
-    "    \n",
     "    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
     "        # Check if agent should finish\n",
     "        if \"Final Answer:\" in llm_output:\n",
@@ -213,7 +219,9 @@
     "        action = match.group(1).strip()\n",
     "        action_input = match.group(2)\n",
     "        # Return the action and action input\n",
-    "        return AgentAction(tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output)"
+    "        return AgentAction(\n",
+    "            tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output\n",
+    "        )"
    ]
   },
   {
@@ -288,10 +296,10 @@
    "source": [
     "tool_names = [tool.name for tool in tools]\n",
     "agent = LLMSingleActionAgent(\n",
-    "    llm_chain=llm_chain, \n",
+    "    llm_chain=llm_chain,\n",
     "    output_parser=output_parser,\n",
-    "    stop=[\"\\nObservation:\"], \n",
-    "    allowed_tools=tool_names\n",
+    "    stop=[\"\\nObservation:\"],\n",
+    "    allowed_tools=tool_names,\n",
     ")"
    ]
   },
@@ -312,7 +320,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
+    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True\n",
+    ")"
    ]
   },
   {

docs/modules/agents/agents/custom_mrkl_agent.ipynb

@@ -61,9 +61,9 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "        description=\"useful for when you need to answer questions about current events\",\n",
     "    )\n",
     "]"
    ]
@@ -82,10 +82,7 @@
     "{agent_scratchpad}\"\"\"\n",
     "\n",
     "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools, \n",
-    "    prefix=prefix, \n",
-    "    suffix=suffix, \n",
-    "    input_variables=[\"input\", \"agent_scratchpad\"]\n",
+    "    tools, prefix=prefix, suffix=suffix, input_variables=[\"input\", \"agent_scratchpad\"]\n",
     ")"
    ]
   },
@@ -171,7 +168,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
+    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -235,10 +234,10 @@
     "{agent_scratchpad}\"\"\"\n",
     "\n",
     "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools, \n",
-    "    prefix=prefix, \n",
-    "    suffix=suffix, \n",
-    "    input_variables=[\"input\", \"language\", \"agent_scratchpad\"]\n",
+    "    tools,\n",
+    "    prefix=prefix,\n",
+    "    suffix=suffix,\n",
+    "    input_variables=[\"input\", \"language\", \"agent_scratchpad\"],\n",
     ")"
    ]
   },
@@ -269,7 +268,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
+    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -307,7 +308,9 @@
     }
    ],
    "source": [
-    "agent_executor.run(input=\"How many people live in canada as of 2023?\", language=\"italian\")"
+    "agent_executor.run(\n",
+    "    input=\"How many people live in canada as of 2023?\", language=\"italian\"\n",
+    ")"
    ]
   },
   {

docs/modules/agents/agents/custom_multi_action_agent.ipynb

@@ -51,16 +51,15 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "        description=\"useful for when you need to answer questions about current events\",\n",
     "    ),\n",
     "    Tool(\n",
-    "        name = \"RandomWord\",\n",
+    "        name=\"RandomWord\",\n",
     "        func=random_word,\n",
-    "        description=\"call this to get a random word.\"\n",
-    "    \n",
-    "    )\n",
+    "        description=\"call this to get a random word.\",\n",
+    "    ),\n",
     "]"
    ]
   },
@@ -74,13 +73,14 @@
     "from typing import List, Tuple, Any, Union\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
     "\n",
+    "\n",
     "class FakeAgent(BaseMultiActionAgent):\n",
     "    \"\"\"Fake Custom Agent.\"\"\"\n",
-    "    \n",
+    "\n",
     "    @property\n",
     "    def input_keys(self):\n",
     "        return [\"input\"]\n",
-    "    \n",
+    "\n",
     "    def plan(\n",
     "        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
     "    ) -> Union[List[AgentAction], AgentFinish]:\n",
@@ -141,7 +141,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
+    "agent_executor = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True\n",
+    ")"
    ]
   },
   {

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

@@ -20,6 +20,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.environ[\"LANGCHAIN_HANDLER\"] = \"langchain\""
    ]
   },
@@ -48,9 +49,9 @@
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Current Search\",\n",
+    "        name=\"Current Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events or the current state of the world. the input to this should be a single search term.\"\n",
+    "        description=\"useful for when you need to answer questions about current events or the current state of the world. the input to this should be a single search term.\",\n",
     "    ),\n",
     "]"
    ]
@@ -72,8 +73,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm=ChatOpenAI(temperature=0)\n",
-    "agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)"
+    "llm = ChatOpenAI(temperature=0)\n",
+    "agent_chain = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    memory=memory,\n",
+    ")"
    ]
   },
   {
@@ -233,7 +240,9 @@
     }
    ],
    "source": [
-    "agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
+    "agent_chain.run(\n",
+    "    input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\"\n",
+    ")"
    ]
   },
   {

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

@@ -37,9 +37,9 @@
     "search = GoogleSearchAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Current Search\",\n",
+    "        name=\"Current Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events or the current state of the world\"\n",
+    "        description=\"useful for when you need to answer questions about current events or the current state of the world\",\n",
     "    ),\n",
     "]"
    ]
@@ -61,8 +61,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm=OpenAI(temperature=0)\n",
-    "agent_chain = initialize_agent(tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)"
+    "llm = OpenAI(temperature=0)\n",
+    "agent_chain = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    memory=memory,\n",
+    ")"
    ]
   },
   {
@@ -206,7 +212,9 @@
     }
    ],
    "source": [
-    "agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
+    "agent_chain.run(\n",
+    "    input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\"\n",
+    ")"
    ]
   },
   {

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

@@ -26,7 +26,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
+    "from langchain import (\n",
+    "    LLMMathChain,\n",
+    "    OpenAI,\n",
+    "    SerpAPIWrapper,\n",
+    "    SQLDatabase,\n",
+    "    SQLDatabaseChain,\n",
+    ")\n",
     "from langchain.agents import initialize_agent, Tool\n",
     "from langchain.agents import AgentType"
    ]
@@ -45,20 +51,20 @@
     "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\"\n",
+    "        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\",\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"Calculator\",\n",
     "        func=llm_math_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about math\"\n",
+    "        description=\"useful for when you need to answer questions about math\",\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"FooBar DB\",\n",
     "        func=db_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\"\n",
-    "    )\n",
+    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\",\n",
+    "    ),\n",
     "]"
    ]
   },
@@ -69,7 +75,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "mrkl = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -128,7 +136,9 @@
     }
    ],
    "source": [
-    "mrkl.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "mrkl.run(\n",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
+    ")"
    ]
   },
   {
@@ -178,7 +188,9 @@
     }
    ],
    "source": [
-    "mrkl.run(\"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\")"
+    "mrkl.run(\n",
+    "    \"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\"\n",
+    ")"
    ]
   },
   {

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

@@ -26,7 +26,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain import OpenAI, LLMMathChain, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
+    "from langchain import (\n",
+    "    OpenAI,\n",
+    "    LLMMathChain,\n",
+    "    SerpAPIWrapper,\n",
+    "    SQLDatabase,\n",
+    "    SQLDatabaseChain,\n",
+    ")\n",
     "from langchain.agents import initialize_agent, Tool\n",
     "from langchain.agents import AgentType\n",
     "from langchain.chat_models import ChatOpenAI"
@@ -47,20 +53,20 @@
     "db_chain = SQLDatabaseChain(llm=llm1, database=db, verbose=True)\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\"\n",
+    "        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\",\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"Calculator\",\n",
     "        func=llm_math_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about math\"\n",
+    "        description=\"useful for when you need to answer questions about math\",\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"FooBar DB\",\n",
     "        func=db_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\"\n",
-    "    )\n",
+    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\",\n",
+    "    ),\n",
     "]"
    ]
   },
@@ -71,7 +77,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mrkl = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "mrkl = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -139,7 +147,9 @@
     }
    ],
    "source": [
-    "mrkl.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "mrkl.run(\n",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
+    ")"
    ]
   },
   {
@@ -218,7 +228,9 @@
     }
    ],
    "source": [
-    "mrkl.run(\"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\")"
+    "mrkl.run(\n",
+    "    \"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\"\n",
+    ")"
    ]
   },
   {

docs/modules/agents/agents/examples/react.ipynb

@@ -21,18 +21,19 @@
     "from langchain.agents import initialize_agent, Tool\n",
     "from langchain.agents import AgentType\n",
     "from langchain.agents.react.base import DocstoreExplorer\n",
-    "docstore=DocstoreExplorer(Wikipedia())\n",
+    "\n",
+    "docstore = DocstoreExplorer(Wikipedia())\n",
     "tools = [\n",
     "    Tool(\n",
     "        name=\"Search\",\n",
     "        func=docstore.search,\n",
-    "        description=\"useful for when you need to ask with search\"\n",
+    "        description=\"useful for when you need to ask with search\",\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"Lookup\",\n",
     "        func=docstore.lookup,\n",
-    "        description=\"useful for when you need to ask with lookup\"\n",
-    "    )\n",
+    "        description=\"useful for when you need to ask with lookup\",\n",
+    "    ),\n",
     "]\n",
     "\n",
     "llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")\n",

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

@@ -54,12 +54,16 @@
     "    Tool(\n",
     "        name=\"Intermediate Answer\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to ask with search\"\n",
+    "        description=\"useful for when you need to ask with search\",\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.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
+    "self_ask_with_search = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True\n",
+    ")\n",
+    "self_ask_with_search.run(\n",
+    "    \"What is the hometown of the reigning men's U.S. Open champion?\"\n",
+    ")"
    ]
   }
  ],

docs/modules/agents/getting_started.ipynb

@@ -93,7 +93,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -148,7 +150,9 @@
     }
    ],
    "source": [
-    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "agent.run(\n",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
+    ")"
    ]
   },
   {

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

@@ -40,7 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = create_csv_agent(OpenAI(temperature=0), 'titanic.csv', verbose=True)"
+    "agent = create_csv_agent(OpenAI(temperature=0), \"titanic.csv\", verbose=True)"
    ]
   },
   {

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

@@ -34,10 +34,7 @@
     "import os\n",
     "import yaml\n",
     "\n",
-    "from langchain.agents import (\n",
-    "    create_json_agent,\n",
-    "    AgentExecutor\n",
-    ")\n",
+    "from langchain.agents import create_json_agent, AgentExecutor\n",
     "from langchain.agents.agent_toolkits import JsonToolkit\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.llms.openai import OpenAI\n",
@@ -60,9 +57,7 @@
     "json_toolkit = JsonToolkit(spec=json_spec)\n",
     "\n",
     "json_agent_executor = create_json_agent(\n",
-    "    llm=OpenAI(temperature=0),\n",
-    "    toolkit=json_toolkit,\n",
-    "    verbose=True\n",
+    "    llm=OpenAI(temperature=0), toolkit=json_toolkit, verbose=True\n",
     ")"
    ]
   },
@@ -154,7 +149,9 @@
     }
    ],
    "source": [
-    "json_agent_executor.run(\"What are the required parameters in the request body to the /completions endpoint?\")"
+    "json_agent_executor.run(\n",
+    "    \"What are the required parameters in the request body to the /completions endpoint?\"\n",
+    ")"
    ]
   },
   {

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

@@ -119,7 +119,7 @@
     "with open(\"openai_openapi.yaml\") as f:\n",
     "    raw_openai_api_spec = yaml.load(f, Loader=yaml.Loader)\n",
     "openai_api_spec = reduce_openapi_spec(raw_openai_api_spec)\n",
-    "    \n",
+    "\n",
     "with open(\"klarna_openapi.yaml\") as f:\n",
     "    raw_klarna_api_spec = yaml.load(f, Loader=yaml.Loader)\n",
     "klarna_api_spec = reduce_openapi_spec(raw_klarna_api_spec)\n",
@@ -152,12 +152,16 @@
     "import spotipy.util as util\n",
     "from langchain.requests import RequestsWrapper\n",
     "\n",
+    "\n",
     "def construct_spotify_auth_headers(raw_spec: dict):\n",
-    "    scopes = list(raw_spec['components']['securitySchemes']['oauth_2_0']['flows']['authorizationCode']['scopes'].keys())\n",
-    "    access_token = util.prompt_for_user_token(scope=','.join(scopes))\n",
-    "    return {\n",
-    "        'Authorization': f'Bearer {access_token}'\n",
-    "    }\n",
+    "    scopes = list(\n",
+    "        raw_spec[\"components\"][\"securitySchemes\"][\"oauth_2_0\"][\"flows\"][\n",
+    "            \"authorizationCode\"\n",
+    "        ][\"scopes\"].keys()\n",
+    "    )\n",
+    "    access_token = util.prompt_for_user_token(scope=\",\".join(scopes))\n",
+    "    return {\"Authorization\": f\"Bearer {access_token}\"}\n",
+    "\n",
     "\n",
     "# Get API credentials.\n",
     "headers = construct_spotify_auth_headers(raw_spotify_api_spec)\n",
@@ -218,8 +222,13 @@
    ],
    "source": [
     "import tiktoken\n",
-    "enc = tiktoken.encoding_for_model('text-davinci-003')\n",
-    "def count_tokens(s): return len(enc.encode(s))\n",
+    "\n",
+    "enc = tiktoken.encoding_for_model(\"text-davinci-003\")\n",
+    "\n",
+    "\n",
+    "def count_tokens(s):\n",
+    "    return len(enc.encode(s))\n",
+    "\n",
     "\n",
     "count_tokens(yaml.dump(raw_spotify_api_spec))"
    ]
@@ -254,6 +263,7 @@
    "source": [
     "from langchain.llms.openai import OpenAI\n",
     "from langchain.agents.agent_toolkits.openapi import planner\n",
+    "\n",
     "llm = OpenAI(model_name=\"gpt-4\", temperature=0.0)"
    ]
   },
@@ -329,7 +339,9 @@
    ],
    "source": [
     "spotify_agent = planner.create_openapi_agent(spotify_api_spec, requests_wrapper, llm)\n",
-    "user_query = \"make me a playlist with the first song from kind of blue. call it machine blues.\"\n",
+    "user_query = (\n",
+    "    \"make me a playlist with the first song from kind of blue. call it machine blues.\"\n",
+    ")\n",
     "spotify_agent.run(user_query)"
    ]
   },
@@ -429,10 +441,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "headers = {\n",
-    "    \"Authorization\": f\"Bearer {os.getenv('OPENAI_API_KEY')}\"\n",
-    "}\n",
-    "openai_requests_wrapper=RequestsWrapper(headers=headers)"
+    "headers = {\"Authorization\": f\"Bearer {os.getenv('OPENAI_API_KEY')}\"}\n",
+    "openai_requests_wrapper = RequestsWrapper(headers=headers)"
    ]
   },
   {
@@ -545,7 +555,9 @@
    "source": [
     "# Meta!\n",
     "llm = OpenAI(model_name=\"gpt-4\", temperature=0.25)\n",
-    "openai_agent = planner.create_openapi_agent(openai_api_spec, openai_requests_wrapper, llm)\n",
+    "openai_agent = planner.create_openapi_agent(\n",
+    "    openai_api_spec, openai_requests_wrapper, llm\n",
+    ")\n",
     "user_query = \"generate a short piece of advice\"\n",
     "openai_agent.run(user_query)"
    ]
@@ -593,14 +605,14 @@
    "source": [
     "with open(\"openai_openapi.yaml\") as f:\n",
     "    data = yaml.load(f, Loader=yaml.FullLoader)\n",
-    "json_spec=JsonSpec(dict_=data, max_value_length=4000)\n",
+    "json_spec = JsonSpec(dict_=data, max_value_length=4000)\n",
     "\n",
     "\n",
-    "openapi_toolkit = OpenAPIToolkit.from_llm(OpenAI(temperature=0), json_spec, openai_requests_wrapper, verbose=True)\n",
+    "openapi_toolkit = OpenAPIToolkit.from_llm(\n",
+    "    OpenAI(temperature=0), json_spec, openai_requests_wrapper, verbose=True\n",
+    ")\n",
     "openapi_agent_executor = create_openapi_agent(\n",
-    "    llm=OpenAI(temperature=0),\n",
-    "    toolkit=openapi_toolkit,\n",
-    "    verbose=True\n",
+    "    llm=OpenAI(temperature=0), toolkit=openapi_toolkit, verbose=True\n",
     ")"
    ]
   },
@@ -739,7 +751,9 @@
     }
    ],
    "source": [
-    "openapi_agent_executor.run(\"Make a post request to openai /completions. The prompt should be 'tell me a joke.'\")"
+    "openapi_agent_executor.run(\n",
+    "    \"Make a post request to openai /completions. The prompt should be 'tell me a joke.'\"\n",
+    ")"
    ]
   }
  ],

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

@@ -0,0 +1,428 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c7ad998d",
+   "metadata": {},
+   "source": [
+    "# Natural Language APIs\n",
+    "\n",
+    "Natural Language API Toolkits (NLAToolkits) permit LangChain Agents to efficiently plan and combine calls across endpoints. This notebook demonstrates a sample composition of the Speak, Klarna, and Spoonacluar APIs.\n",
+    "\n",
+    "For a detailed walkthrough of the OpenAPI chains wrapped within the NLAToolkit, see the [OpenAPI Operation Chain](openapi.ipynb) notebook.\n",
+    "\n",
+    "### First, import dependencies and load the LLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6593f793",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from typing import List, Optional\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.requests import Requests\n",
+    "from langchain.tools import APIOperation, OpenAPISpec\n",
+    "from langchain.agents import AgentType, Tool, initialize_agent\n",
+    "from langchain.agents.agent_toolkits import NLAToolkit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dd720860",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Select the LLM to use. Here, we use text-davinci-003\n",
+    "llm = OpenAI(\n",
+    "    temperature=0, max_tokens=700\n",
+    ")  # You can swap between different core LLM's here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cadac9d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Next, load the Natural Language API Toolkits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6b208ab0",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
+      "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
+      "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n"
+     ]
+    }
+   ],
+   "source": [
+    "speak_toolkit = NLAToolkit.from_llm_and_url(llm, \"https://api.speak.com/openapi.yaml\")\n",
+    "klarna_toolkit = NLAToolkit.from_llm_and_url(\n",
+    "    llm, \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16c7336f",
+   "metadata": {},
+   "source": [
+    "### Create the Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "730a0dc2-b4d0-46d5-a1e9-583803220973",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Slightly tweak the instructions from the default agent\n",
+    "openapi_format_instructions = \"\"\"Use the following format:\n",
+    "\n",
+    "Question: the input question you must answer\n",
+    "Thought: you should always think about what to do\n",
+    "Action: the action to take, should be one of [{tool_names}]\n",
+    "Action Input: what to instruct the AI Action representative.\n",
+    "Observation: The Agent's response\n",
+    "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
+    "Thought: I now know the final answer. User can't see any of my observations, API responses, links, or tools.\n",
+    "Final Answer: the final answer to the original input question with the right amount of detail\n",
+    "\n",
+    "When responding with your Final Answer, remember that the person you are responding to CANNOT see any of your Thought/Action/Action Input/Observations, so if there is any relevant information there you need to include it explicitly in your response.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "40a979c3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "natural_language_tools = speak_toolkit.get_tools() + klarna_toolkit.get_tools()\n",
+    "mrkl = initialize_agent(\n",
+    "    natural_language_tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    agent_kwargs={\"format_instructions\": openapi_format_instructions},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "794380ba",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out what kind of Italian clothes are available\n",
+      "Action: Open_AI_Klarna_product_Api.productsUsingGET\n",
+      "Action Input: Italian clothes\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3mThe API response contains two products from the Alé brand in Italian Blue. The first is the Alé Colour Block Short Sleeve Jersey Men - Italian Blue, which costs $86.49, and the second is the Alé Dolid Flash Jersey Men - Italian Blue, which costs $40.00.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know what kind of Italian clothes are available and how much they cost.\n",
+      "Final Answer: You can buy two products from the Alé brand in Italian Blue for your end of year party. The Alé Colour Block Short Sleeve Jersey Men - Italian Blue costs $86.49, and the Alé Dolid Flash Jersey Men - Italian Blue costs $40.00.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'You can buy two products from the Alé brand in Italian Blue for your end of year party. The Alé Colour Block Short Sleeve Jersey Men - Italian Blue costs $86.49, and the Alé Dolid Flash Jersey Men - Italian Blue costs $40.00.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrkl.run(\n",
+    "    \"I have an end of year party for my Italian class and have to buy some Italian clothes for it\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c61d92a8",
+   "metadata": {},
+   "source": [
+    "### Using Auth + Adding more Endpoints\n",
+    "\n",
+    "Some endpoints may require user authentication via things like access tokens. Here we show how to pass in the authentication information via the `Requests` wrapper object.\n",
+    "\n",
+    "Since each NLATool exposes a concisee natural language interface to its wrapped API, the top level conversational agent has an easier job incorporating each endpoint to satisfy a user's request."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0d132cc",
+   "metadata": {},
+   "source": [
+    "**Adding the Spoonacular endpoints.**\n",
+    "\n",
+    "1. Go to the [Spoonacular API Console](https://spoonacular.com/food-api/console#Profile) and make a free account.\n",
+    "2. Click on `Profile` and copy your API key below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c2368b9c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "spoonacular_api_key = \"\"  # Copy from the API Console"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fbd97c28-fef6-41b5-9600-a9611a32bfb3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Attempting to load an OpenAPI 3.0.0 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter\n",
+      "Unsupported APIPropertyLocation \"header\" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter\n"
+     ]
+    }
+   ],
+   "source": [
+    "requests = Requests(headers={\"x-api-key\": spoonacular_api_key})\n",
+    "spoonacular_toolkit = NLAToolkit.from_llm_and_url(\n",
+    "    llm,\n",
+    "    \"https://spoonacular.com/application/frontend/downloads/spoonacular-openapi-3.json\",\n",
+    "    requests=requests,\n",
+    "    max_text_length=1800,  # If you want to truncate the response text\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "81a6edac",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "34 tools loaded.\n"
+     ]
+    }
+   ],
+   "source": [
+    "natural_language_api_tools = (\n",
+    "    speak_toolkit.get_tools()\n",
+    "    + klarna_toolkit.get_tools()\n",
+    "    + spoonacular_toolkit.get_tools()[:30]\n",
+    ")\n",
+    "print(f\"{len(natural_language_api_tools)} tools loaded.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "831f772d-5cd1-4467-b494-a3172af2ff48",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Create an agent with the new tools\n",
+    "mrkl = initialize_agent(\n",
+    "    natural_language_api_tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    agent_kwargs={\"format_instructions\": openapi_format_instructions},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0385e04b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Make the query more complex!\n",
+    "user_input = (\n",
+    "    \"I'm learning Italian, and my language class is having an end of year party... \"\n",
+    "    \" Could you help me find an Italian outfit to wear and\"\n",
+    "    \" an appropriate recipe to prepare so I can present for the class in Italian?\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6ebd3f55",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find a recipe and an outfit that is Italian-themed.\n",
+      "Action: spoonacular_API.searchRecipes\n",
+      "Action Input: Italian\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe API response contains 10 Italian recipes, including Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, and Pappa Al Pomodoro.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find an Italian-themed outfit.\n",
+      "Action: Open_AI_Klarna_product_Api.productsUsingGET\n",
+      "Action Input: Italian\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3mI found 10 products related to 'Italian' in the API response. These products include Italian Gold Sparkle Perfectina Necklace - Gold, Italian Design Miami Cuban Link Chain Necklace - Gold, Italian Gold Miami Cuban Link Chain Necklace - Gold, Italian Gold Herringbone Necklace - Gold, Italian Gold Claddagh Ring - Gold, Italian Gold Herringbone Chain Necklace - Gold, Garmin QuickFit 22mm Italian Vacchetta Leather Band, Macy's Italian Horn Charm - Gold, Dolce & Gabbana Light Blue Italian Love Pour Homme EdT 1.7 fl oz.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: To present for your Italian language class, you could wear an Italian Gold Sparkle Perfectina Necklace - Gold, an Italian Design Miami Cuban Link Chain Necklace - Gold, or an Italian Gold Miami Cuban Link Chain Necklace - Gold. For a recipe, you could make Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, or Pappa Al Pomodoro.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'To present for your Italian language class, you could wear an Italian Gold Sparkle Perfectina Necklace - Gold, an Italian Design Miami Cuban Link Chain Necklace - Gold, or an Italian Gold Miami Cuban Link Chain Necklace - Gold. For a recipe, you could make Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, or Pappa Al Pomodoro.'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrkl.run(user_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2959462",
+   "metadata": {},
+   "source": [
+    "## Thank you!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6fcda5f0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"In Italian, you can say 'Buon appetito' to someone to wish them to enjoy their meal. This phrase is commonly used in Italy when someone is about to eat, often at the beginning of a meal. It's similar to saying 'Bon appétit' in French or 'Guten Appetit' in German.\""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "natural_language_api_tools[1].run(\n",
+    "    \"Tell the LangChain audience to 'enjoy the meal' in Italian, please!\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab366dc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

@@ -32,7 +32,7 @@
     "from langchain.llms import OpenAI\n",
     "import pandas as pd\n",
     "\n",
-    "df = pd.read_csv('titanic.csv')"
+    "df = pd.read_csv(\"titanic.csv\")"
    ]
   },
   {

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

@@ -35,9 +35,7 @@
    "outputs": [],
    "source": [
     "agent_executor = create_python_agent(\n",
-    "    llm=OpenAI(temperature=0, max_tokens=1000),\n",
-    "    tool=PythonREPLTool(),\n",
-    "    verbose=True\n",
+    "    llm=OpenAI(temperature=0, max_tokens=1000), tool=PythonREPLTool(), verbose=True\n",
     ")"
    ]
   },
@@ -190,9 +188,11 @@
     }
    ],
    "source": [
-    "agent_executor.run(\"\"\"Understand, write a single neuron neural network in PyTorch.\n",
+    "agent_executor.run(\n",
+    "    \"\"\"Understand, write a single neuron neural network in PyTorch.\n",
     "Take synthetic data for y=2x. Train for 1000 epochs and print every 100 epochs.\n",
-    "Return prediction for x = 5\"\"\")"
+    "Return prediction for x = 5\"\"\"\n",
+    ")"
    ]
   },
   {

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

@@ -53,9 +53,7 @@
     "toolkit = SQLDatabaseToolkit(db=db)\n",
     "\n",
     "agent_executor = create_sql_agent(\n",
-    "    llm=OpenAI(temperature=0),\n",
-    "    toolkit=toolkit,\n",
-    "    verbose=True\n",
+    "    llm=OpenAI(temperature=0), toolkit=toolkit, verbose=True\n",
     ")"
    ]
   },
@@ -293,7 +291,9 @@
     }
    ],
    "source": [
-    "agent_executor.run(\"List the total sales per country. Which country's customers spent the most?\")"
+    "agent_executor.run(\n",
+    "    \"List the total sales per country. Which country's customers spent the most?\"\n",
+    ")"
    ]
   },
   {
@@ -372,7 +372,9 @@
     }
    ],
    "source": [
-    "agent_executor.run(\"Show the total number of tracks in each playlist. The Playlist name should be included in the result.\")"
+    "agent_executor.run(\n",
+    "    \"Show the total number of tracks in each playlist. The Playlist name should be included in the result.\"\n",
+    ")"
    ]
   },
   {

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

@@ -31,6 +31,7 @@
     "from langchain.vectorstores import Chroma\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain import OpenAI, VectorDBQA\n",
+    "\n",
     "llm = OpenAI(temperature=0)"
    ]
   },
@@ -53,13 +54,16 @@
    ],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../../../state_of_the_union.txt')\n",
+    "\n",
+    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "texts = text_splitter.split_documents(documents)\n",
     "\n",
     "embeddings = OpenAIEmbeddings()\n",
-    "state_of_union_store = Chroma.from_documents(texts, embeddings, collection_name=\"state-of-union\")"
+    "state_of_union_store = Chroma.from_documents(\n",
+    "    texts, embeddings, collection_name=\"state-of-union\"\n",
+    ")"
    ]
   },
   {
@@ -81,6 +85,7 @@
    ],
    "source": [
     "from langchain.document_loaders import WebBaseLoader\n",
+    "\n",
     "loader = WebBaseLoader(\"https://beta.ruff.rs/docs/faq/\")\n",
     "docs = loader.load()\n",
     "ruff_texts = text_splitter.split_documents(docs)\n",
@@ -109,17 +114,14 @@
     "    VectorStoreToolkit,\n",
     "    VectorStoreInfo,\n",
     ")\n",
+    "\n",
     "vectorstore_info = VectorStoreInfo(\n",
     "    name=\"state_of_union_address\",\n",
     "    description=\"the most recent state of the Union adress\",\n",
-    "    vectorstore=state_of_union_store\n",
+    "    vectorstore=state_of_union_store,\n",
     ")\n",
     "toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)\n",
-    "agent_executor = create_vectorstore_agent(\n",
-    "    llm=llm,\n",
-    "    toolkit=toolkit,\n",
-    "    verbose=True\n",
-    ")"
+    "agent_executor = create_vectorstore_agent(llm=llm, toolkit=toolkit, verbose=True)"
    ]
   },
   {
@@ -165,7 +167,9 @@
     }
    ],
    "source": [
-    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
+    "agent_executor.run(\n",
+    "    \"What did biden say about ketanji brown jackson is the state of the union address?\"\n",
+    ")"
    ]
   },
   {
@@ -203,7 +207,9 @@
     }
    ],
    "source": [
-    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address? List the source.\")"
+    "agent_executor.run(\n",
+    "    \"What did biden say about ketanji brown jackson is the state of the union address? List the source.\"\n",
+    ")"
    ]
   },
   {
@@ -241,16 +247,13 @@
     "ruff_vectorstore_info = VectorStoreInfo(\n",
     "    name=\"ruff\",\n",
     "    description=\"Information about the Ruff python linting library\",\n",
-    "    vectorstore=ruff_store\n",
+    "    vectorstore=ruff_store,\n",
     ")\n",
     "router_toolkit = VectorStoreRouterToolkit(\n",
-    "    vectorstores=[vectorstore_info, ruff_vectorstore_info],\n",
-    "    llm=llm\n",
+    "    vectorstores=[vectorstore_info, ruff_vectorstore_info], llm=llm\n",
     ")\n",
     "agent_executor = create_vectorstore_router_agent(\n",
-    "    llm=llm,\n",
-    "    toolkit=router_toolkit,\n",
-    "    verbose=True\n",
+    "    llm=llm, toolkit=router_toolkit, verbose=True\n",
     ")"
    ]
   },
@@ -299,7 +302,9 @@
     }
    ],
    "source": [
-    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
+    "agent_executor.run(\n",
+    "    \"What did biden say about ketanji brown jackson is the state of the union address?\"\n",
+    ")"
    ]
   },
   {
@@ -381,7 +386,9 @@
     }
    ],
    "source": [
-    "agent_executor.run(\"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\")"
+    "agent_executor.run(\n",
+    "    \"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\"\n",
+    ")"
    ]
   },
   {

docs/modules/agents/tools/custom_tools.ipynb

@@ -82,15 +82,15 @@
     "llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "        description=\"useful for when you need to answer questions about current events\",\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"Calculator\",\n",
     "        func=llm_math_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about math\"\n",
-    "    )\n",
+    "        description=\"useful for when you need to answer questions about math\",\n",
+    "    ),\n",
     "]"
    ]
   },
@@ -103,7 +103,9 @@
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -158,7 +160,9 @@
     }
    ],
    "source": [
-    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "agent.run(\n",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
+    ")"
    ]
   },
   {
@@ -183,11 +187,12 @@
     "    def _run(self, query: str) -> str:\n",
     "        \"\"\"Use the tool.\"\"\"\n",
     "        return search.run(query)\n",
-    "    \n",
+    "\n",
     "    async def _arun(self, query: str) -> str:\n",
     "        \"\"\"Use the tool asynchronously.\"\"\"\n",
     "        raise NotImplementedError(\"BingSearchRun does not support async\")\n",
-    "    \n",
+    "\n",
+    "\n",
     "class CustomCalculatorTool(BaseTool):\n",
     "    name = \"Calculator\"\n",
     "    description = \"useful for when you need to answer questions about math\"\n",
@@ -195,7 +200,7 @@
     "    def _run(self, query: str) -> str:\n",
     "        \"\"\"Use the tool.\"\"\"\n",
     "        return llm_math_chain.run(query)\n",
-    "    \n",
+    "\n",
     "    async def _arun(self, query: str) -> str:\n",
     "        \"\"\"Use the tool asynchronously.\"\"\"\n",
     "        raise NotImplementedError(\"BingSearchRun does not support async\")"
@@ -218,7 +223,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -273,7 +280,9 @@
     }
    ],
    "source": [
-    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "agent.run(\n",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
+    ")"
    ]
   },
   {
@@ -295,6 +304,7 @@
    "source": [
     "from langchain.agents import tool\n",
     "\n",
+    "\n",
     "@tool\n",
     "def search_api(query: str) -> str:\n",
     "    \"\"\"Searches the API for the query.\"\"\"\n",
@@ -411,7 +421,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -458,7 +470,9 @@
     }
    ],
    "source": [
-    "agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+    "agent.run(\n",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
+    ")"
    ]
   },
   {
@@ -488,21 +502,27 @@
     "from langchain.agents import AgentType\n",
     "from langchain.llms import OpenAI\n",
     "from langchain import LLMMathChain, SerpAPIWrapper\n",
+    "\n",
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Search\",\n",
+    "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "        description=\"useful for when you need to answer questions about current events\",\n",
     "    ),\n",
     "    Tool(\n",
     "        name=\"Music Search\",\n",
-    "        func=lambda x: \"'All I Want For Christmas Is You' by Mariah Carey.\", #Mock Function\n",
+    "        func=lambda x: \"'All I Want For Christmas Is You' by Mariah Carey.\",  # Mock Function\n",
     "        description=\"A Music search engine. Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?'\",\n",
-    "    )\n",
+    "    ),\n",
     "]\n",
     "\n",
-    "agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    OpenAI(temperature=0),\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    ")"
    ]
   },
   {
@@ -565,7 +585,7 @@
     "        name=\"Calculator\",\n",
     "        func=llm_math_chain.run,\n",
     "        description=\"useful for when you need to answer questions about math\",\n",
-    "        return_direct=True\n",
+    "        return_direct=True,\n",
     "    )\n",
     "]"
    ]
@@ -578,7 +598,9 @@
    "outputs": [],
    "source": [
     "llm = OpenAI(temperature=0)\n",
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {

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

@@ -53,6 +53,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"Your OpenAI API key\"\n",
     "os.environ[\"APIFY_API_TOKEN\"] = \"Your Apify API token\"\n",
     "\n",

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

@@ -25,6 +25,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.environ[\"BING_SUBSCRIPTION_KEY\"] = \"\"\n",
     "os.environ[\"BING_SEARCH_URL\"] = \"\""
    ]

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

@@ -80,11 +80,13 @@
     }
    ],
    "source": [
-    "llm = ChatOpenAI(temperature=0,)\n",
-    "tools = load_tools([\"requests\"] )\n",
+    "llm = ChatOpenAI(temperature=0)\n",
+    "tools = load_tools([\"requests_all\"])\n",
     "tools += [tool]\n",
     "\n",
-    "agent_chain = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION verbose=True)\n",
+    "agent_chain = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")\n",
     "agent_chain.run(\"what t shirts are available in klarna?\")"
    ]
   },

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

@@ -9,7 +9,7 @@
     "\n",
     "This notebook goes over how to use the google search component.\n",
     "\n",
-    "First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found [here](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search).\n",
+    "First, you need to set up the proper API keys and environment variables. To set it up, create the GOOGLE_API_KEY in the Google Cloud credential console (https://console.cloud.google.com/apis/credentials) and a GOOGLE_CSE_ID using the Programmable Search Enginge (https://programmablesearchengine.google.com/controlpanel/create). Next, it is good to follow the instructions found [here](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search).\n",
     "\n",
     "Then we will need to set some environment variables."
    ]
@@ -22,6 +22,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.environ[\"GOOGLE_CSE_ID\"] = \"\"\n",
     "os.environ[\"GOOGLE_API_KEY\"] = \"\""
    ]

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

@@ -16,6 +16,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.environ[\"SERPER_API_KEY\"] = \"\""
    ],
    "metadata": {
@@ -75,7 +76,7 @@
    "execution_count": null,
    "outputs": [],
    "source": [
-    "os.environ['OPENAI_API_KEY'] = \"\""
+    "os.environ[\"OPENAI_API_KEY\"] = \"\""
    ],
    "metadata": {
     "collapsed": false
@@ -91,15 +92,15 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m Yes.\n",
-      "Follow up: Who is the reigning men's U.S. Open champion?\u001B[0m\n",
-      "Intermediate answer: \u001B[36;1m\u001B[1;3mCurrent champions Carlos Alcaraz, 2022 men's singles champion.\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",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m Yes.\n",
+      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
+      "Intermediate answer: \u001b[36;1m\u001b[1;3mCurrent champions Carlos Alcaraz, 2022 men's singles champion.\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 chain.\u001b[0m\n"
      ]
     },
     {
@@ -123,12 +124,16 @@
     "    Tool(\n",
     "        name=\"Intermediate Answer\",\n",
     "        func=search.run,\n",
-    "        description=\"useful for when you need to ask with search\"\n",
+    "        description=\"useful for when you need to ask with search\",\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.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
+    "self_ask_with_search = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True\n",
+    ")\n",
+    "self_ask_with_search.run(\n",
+    "    \"What is the hometown of the reigning men's U.S. Open champion?\"\n",
+    ")"
    ],
    "metadata": {
     "collapsed": false

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

@@ -25,7 +25,7 @@
     "llm = ChatOpenAI(temperature=0.0)\n",
     "math_llm = OpenAI(temperature=0.0)\n",
     "tools = load_tools(\n",
-    "    [\"human\", \"llm-math\"], \n",
+    "    [\"human\", \"llm-math\"],\n",
     "    llm=math_llm,\n",
     ")\n",
     "\n",
@@ -96,7 +96,6 @@
     }
    ],
    "source": [
-    "\n",
     "agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
     "# Answer with \"last week\""
    ]

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

@@ -62,9 +62,12 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "key = os.environ[\"IFTTTKey\"]\n",
     "url = f\"https://maker.ifttt.com/trigger/spotify/json/with/key/{key}\"\n",
-    "tool = IFTTTWebhook(name=\"Spotify\", description=\"Add a song to spotify playlist\", url=url)"
+    "tool = IFTTTWebhook(\n",
+    "    name=\"Spotify\", description=\"Add a song to spotify playlist\", url=url\n",
+    ")"
    ]
   },
   {

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

@@ -1,128 +1,129 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "id": "245a954a",
-      "metadata": {},
-      "source": [
-        "# OpenWeatherMap API\n",
-        "\n",
-        "This notebook goes over how to use the OpenWeatherMap component to fetch weather information.\n",
-        "\n",
-        "First, you need to sign up for an OpenWeatherMap API key:\n",
-        "\n",
-        "1. Go to OpenWeatherMap and sign up for an API key [here](https://openweathermap.org/api/)\n",
-        "2. pip install pyowm\n",
-        "\n",
-        "Then we will need to set some environment variables:\n",
-        "1. Save your API KEY into OPENWEATHERMAP_API_KEY env variable"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "961b3689",
-      "metadata": {
-        "vscode": {
-          "languageId": "shellscript"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "pip install pyowm"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "id": "34bb5968",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "os.environ[\"OPENWEATHERMAP_API_KEY\"] = \"\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "id": "ac4910f8",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain.utilities import OpenWeatherMapAPIWrapper"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 37,
-      "id": "84b8f773",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "weather = OpenWeatherMapAPIWrapper()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 38,
-      "id": "9651f324-e74a-4f08-a28a-89db029f66f8",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "weather_data = weather.run(\"London,GB\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 39,
-      "id": "028f4cba",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "In London,GB, the current weather is as follows:\n",
-            "Detailed status: overcast clouds\n",
-            "Wind speed: 4.63 m/s, direction: 150°\n",
-            "Humidity: 67%\n",
-            "Temperature: \n",
-            "  - Current: 5.35°C\n",
-            "  - High: 6.26°C\n",
-            "  - Low: 3.49°C\n",
-            "  - Feels like: 1.95°C\n",
-            "Rain: {}\n",
-            "Heat index: None\n",
-            "Cloud cover: 100%\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(weather_data)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.2"
-    }
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "245a954a",
+   "metadata": {},
+   "source": [
+    "# OpenWeatherMap API\n",
+    "\n",
+    "This notebook goes over how to use the OpenWeatherMap component to fetch weather information.\n",
+    "\n",
+    "First, you need to sign up for an OpenWeatherMap API key:\n",
+    "\n",
+    "1. Go to OpenWeatherMap and sign up for an API key [here](https://openweathermap.org/api/)\n",
+    "2. pip install pyowm\n",
+    "\n",
+    "Then we will need to set some environment variables:\n",
+    "1. Save your API KEY into OPENWEATHERMAP_API_KEY env variable"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "961b3689",
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pip install pyowm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "34bb5968",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENWEATHERMAP_API_KEY\"] = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "ac4910f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.utilities import OpenWeatherMapAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "84b8f773",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather = OpenWeatherMapAPIWrapper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9651f324-e74a-4f08-a28a-89db029f66f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_data = weather.run(\"London,GB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "028f4cba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In London,GB, the current weather is as follows:\n",
+      "Detailed status: overcast clouds\n",
+      "Wind speed: 4.63 m/s, direction: 150°\n",
+      "Humidity: 67%\n",
+      "Temperature: \n",
+      "  - Current: 5.35°C\n",
+      "  - High: 6.26°C\n",
+      "  - Low: 3.49°C\n",
+      "  - Feels like: 1.95°C\n",
+      "Rain: {}\n",
+      "Heat index: None\n",
+      "Cloud cover: 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(weather_data)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

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

@@ -64,7 +64,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -132,7 +134,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -200,7 +204,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -267,7 +273,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {

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

@@ -95,7 +95,9 @@
    },
    "outputs": [],
    "source": [
-    "search = SearxSearchWrapper(searx_host=\"http://127.0.0.1:8888\", k=5) # k is for max number of items"
+    "search = SearxSearchWrapper(\n",
+    "    searx_host=\"http://127.0.0.1:8888\", k=5\n",
+    ")  # k is for max number of items"
    ]
   },
   {
@@ -120,7 +122,7 @@
     }
    ],
    "source": [
-    "search.run(\"large language model \", engines=['wiki'])"
+    "search.run(\"large language model \", engines=[\"wiki\"])"
    ]
   },
   {
@@ -152,7 +154,7 @@
    ],
    "source": [
     "search = SearxSearchWrapper(searx_host=\"http://127.0.0.1:8888\", k=1)\n",
-    "search.run(\"deep learning\", language='es', engines=['wiki'])"
+    "search.run(\"deep learning\", language=\"es\", engines=[\"wiki\"])"
    ]
   },
   {
@@ -244,7 +246,12 @@
     }
    ],
    "source": [
-    "results = search.results(\"Large Language Model prompt\", num_results=5, categories='science', time_range='year')\n",
+    "results = search.results(\n",
+    "    \"Large Language Model prompt\",\n",
+    "    num_results=5,\n",
+    "    categories=\"science\",\n",
+    "    time_range=\"year\",\n",
+    ")\n",
     "pprint.pp(results)"
    ]
   },
@@ -386,7 +393,9 @@
     }
    ],
    "source": [
-    "results = search.results(\"Large Language Model prompt\", num_results=5, engines=['arxiv'])\n",
+    "results = search.results(\n",
+    "    \"Large Language Model prompt\", num_results=5, engines=[\"arxiv\"]\n",
+    ")\n",
     "pprint.pp(results)"
    ]
   },
@@ -425,8 +434,8 @@
     }
    ],
    "source": [
-    "results = search.results(\"large language model\", num_results = 20, categories='it')\n",
-    "pprint.pp(list(filter(lambda r: r['engines'][0] == 'github', results)))"
+    "results = search.results(\"large language model\", num_results=20, categories=\"it\")\n",
+    "pprint.pp(list(filter(lambda r: r[\"engines\"][0] == \"github\", results)))"
    ]
   },
   {
@@ -578,7 +587,9 @@
     }
    ],
    "source": [
-    "results = search.results(\"large language model\", num_results = 20, engines=['github', 'gitlab'])\n",
+    "results = search.results(\n",
+    "    \"large language model\", num_results=20, engines=[\"github\", \"gitlab\"]\n",
+    ")\n",
     "pprint.pp(results)"
    ]
   }

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

@@ -42,7 +42,8 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "os.environ[\"WOLFRAM_ALPHA_APPID\"] = \"\"\n"
+    "\n",
+    "os.environ[\"WOLFRAM_ALPHA_APPID\"] = \"\""
    ]
   },
   {

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

@@ -52,7 +52,7 @@
     "# get from https://platform.openai.com/\n",
     "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\", \"\")\n",
     "\n",
-    "# get from https://nla.zapier.com/demo/provider/debug (under User Information, after logging in): \n",
+    "# get from https://nla.zapier.com/demo/provider/debug (under User Information, after logging in):\n",
     "os.environ[\"ZAPIER_NLA_API_KEY\"] = os.environ.get(\"ZAPIER_NLA_API_KEY\", \"\")"
    ]
   },
@@ -106,7 +106,9 @@
     "llm = OpenAI(temperature=0)\n",
     "zapier = ZapierNLAWrapper()\n",
     "toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)\n",
-    "agent = initialize_agent(toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent(\n",
+    "    toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -150,7 +152,9 @@
     }
    ],
    "source": [
-    "agent.run(\"Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.\")"
+    "agent.run(\n",
+    "    \"Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.\"\n",
+    ")"
    ]
   },
   {
@@ -206,10 +210,25 @@
     "\n",
     "GMAIL_SEARCH_INSTRUCTIONS = \"Grab the latest email from Silicon Valley Bank\"\n",
     "\n",
+    "\n",
     "def nla_gmail(inputs):\n",
-    "    action = next((a for a in actions if a[\"description\"].startswith(\"Gmail: Find Email\")), None)\n",
-    "    return {\"email_data\": ZapierNLARunAction(action_id=action[\"id\"], zapier_description=action[\"description\"], params_schema=action[\"params\"]).run(inputs[\"instructions\"])}\n",
-    "gmail_chain = TransformChain(input_variables=[\"instructions\"], output_variables=[\"email_data\"], transform=nla_gmail)"
+    "    action = next(\n",
+    "        (a for a in actions if a[\"description\"].startswith(\"Gmail: Find Email\")), None\n",
+    "    )\n",
+    "    return {\n",
+    "        \"email_data\": ZapierNLARunAction(\n",
+    "            action_id=action[\"id\"],\n",
+    "            zapier_description=action[\"description\"],\n",
+    "            params_schema=action[\"params\"],\n",
+    "        ).run(inputs[\"instructions\"])\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "gmail_chain = TransformChain(\n",
+    "    input_variables=[\"instructions\"],\n",
+    "    output_variables=[\"email_data\"],\n",
+    "    transform=nla_gmail,\n",
+    ")"
    ]
   },
   {
@@ -229,7 +248,7 @@
     "Draft email reply:\"\"\"\n",
     "\n",
     "prompt_template = PromptTemplate(input_variables=[\"email_data\"], template=template)\n",
-    "reply_chain = LLMChain(llm=OpenAI(temperature=.7), prompt=prompt_template)"
+    "reply_chain = LLMChain(llm=OpenAI(temperature=0.7), prompt=prompt_template)"
    ]
   },
   {
@@ -243,11 +262,31 @@
     "\n",
     "SLACK_HANDLE = \"@Ankush Gola\"\n",
     "\n",
+    "\n",
     "def nla_slack(inputs):\n",
-    "    action = next((a for a in actions if a[\"description\"].startswith(\"Slack: Send Direct Message\")), None)\n",
+    "    action = next(\n",
+    "        (\n",
+    "            a\n",
+    "            for a in actions\n",
+    "            if a[\"description\"].startswith(\"Slack: Send Direct Message\")\n",
+    "        ),\n",
+    "        None,\n",
+    "    )\n",
     "    instructions = f'Send this to {SLACK_HANDLE} in Slack: {inputs[\"draft_reply\"]}'\n",
-    "    return {\"slack_data\": ZapierNLARunAction(action_id=action[\"id\"], zapier_description=action[\"description\"], params_schema=action[\"params\"]).run(instructions)}\n",
-    "slack_chain = TransformChain(input_variables=[\"draft_reply\"], output_variables=[\"slack_data\"], transform=nla_slack)"
+    "    return {\n",
+    "        \"slack_data\": ZapierNLARunAction(\n",
+    "            action_id=action[\"id\"],\n",
+    "            zapier_description=action[\"description\"],\n",
+    "            params_schema=action[\"params\"],\n",
+    "        ).run(instructions)\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "slack_chain = TransformChain(\n",
+    "    input_variables=[\"draft_reply\"],\n",
+    "    output_variables=[\"slack_data\"],\n",
+    "    transform=nla_slack,\n",
+    ")"
    ]
   },
   {
@@ -290,7 +329,9 @@
    "source": [
     "## finally, execute\n",
     "\n",
-    "overall_chain = SimpleSequentialChain(chains=[gmail_chain, reply_chain, slack_chain], verbose=True)\n",
+    "overall_chain = SimpleSequentialChain(\n",
+    "    chains=[gmail_chain, reply_chain, slack_chain], verbose=True\n",
+    ")\n",
     "overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)"
    ]
   },

docs/modules/agents/tools/multi_input_tool.ipynb

@@ -45,6 +45,7 @@
     "def multiplier(a, b):\n",
     "    return a * b\n",
     "\n",
+    "\n",
     "def parsing_multiplier(string):\n",
     "    a, b = string.split(\",\")\n",
     "    return multiplier(int(a), int(b))"
@@ -60,12 +61,14 @@
     "llm = OpenAI(temperature=0)\n",
     "tools = [\n",
     "    Tool(\n",
-    "        name = \"Multiplier\",\n",
+    "        name=\"Multiplier\",\n",
     "        func=parsing_multiplier,\n",
-    "        description=\"useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2.\"\n",
+    "        description=\"useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2.\",\n",
     "    )\n",
     "]\n",
-    "mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "mrkl = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
    ]
   },
   {

docs/modules/callbacks/getting_started.ipynb

@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "23234b50-e6c6-4c87-9f97-259c15f36894",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Callbacks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29dd6333-307c-43df-b848-65001c01733b",
+   "metadata": {},
+   "source": [
+    "LangChain provides a callback system that allows you to hook into the various stages of your LLM application. This is useful for logging, [monitoring](https://python.langchain.com/en/latest/tracing.html), [streaming](https://python.langchain.com/en/latest/modules/models/llms/examples/streaming_llm.html), and other tasks.\n",
+    "\n",
+    "You can subscribe to these events by using the `callback_manager` argument available throughout the API. A `CallbackManager` is an object that manages a list of `CallbackHandlers`. The `CallbackManager` will call the appropriate method on each handler when the event is triggered."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdb72e8d-a02a-474d-96bf-f5759432afc8",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "```python\n",
+    "class CallbackManager(BaseCallbackHandler):\n",
+    "    \"\"\"Base callback manager that can be used to handle callbacks from LangChain.\"\"\"\n",
+    "\n",
+    "    def add_handler(self, callback: BaseCallbackHandler) -> None:\n",
+    "        \"\"\"Add a handler to the callback manager.\"\"\"\n",
+    "\n",
+    "    def remove_handler(self, handler: BaseCallbackHandler) -> None:\n",
+    "        \"\"\"Remove a handler from the callback manager.\"\"\"\n",
+    "\n",
+    "    def set_handler(self, handler: BaseCallbackHandler) -> None:\n",
+    "        \"\"\"Set handler as the only handler on the callback manager.\"\"\"\n",
+    "        self.set_handlers([handler])\n",
+    "\n",
+    "    def set_handlers(self, handlers: List[BaseCallbackHandler]) -> None:\n",
+    "        \"\"\"Set handlers as the only handlers on the callback manager.\"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b6d7dba-cd20-472a-ae05-f68675cc9ea4",
+   "metadata": {},
+   "source": [
+    "`CallbackHandlers` are objects that implement the `CallbackHandler` interface, which has a method for each event that can be subscribed to. The `CallbackManager` will call the appropriate method on each handler when the event is triggered."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4592215-6604-47e2-89ff-5db3af6d1e40",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "```python\n",
+    "class BaseCallbackHandler(ABC):\n",
+    "    \"\"\"Base callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_llm_start(\n",
+    "        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any\n",
+    "    ) -> Any:\n",
+    "        \"\"\"Run when LLM starts running.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_llm_new_token(self, token: str, **kwargs: Any) -> Any:\n",
+    "        \"\"\"Run on new LLM token. Only available when streaming is enabled.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:\n",
+    "        \"\"\"Run when LLM ends running.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_llm_error(\n",
+    "        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any\n",
+    "    ) -> Any:\n",
+    "        \"\"\"Run when LLM errors.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_chain_start(\n",
+    "        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any\n",
+    "    ) -> Any:\n",
+    "        \"\"\"Run when chain starts running.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any:\n",
+    "        \"\"\"Run when chain ends running.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_chain_error(\n",
+    "        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any\n",
+    "    ) -> Any:\n",
+    "        \"\"\"Run when chain errors.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_tool_start(\n",
+    "        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any\n",
+    "    ) -> Any:\n",
+    "        \"\"\"Run when tool starts running.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_tool_end(self, output: str, **kwargs: Any) -> Any:\n",
+    "        \"\"\"Run when tool ends running.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_tool_error(\n",
+    "        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any\n",
+    "    ) -> Any:\n",
+    "        \"\"\"Run when tool errors.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_text(self, text: str, **kwargs: Any) -> Any:\n",
+    "        \"\"\"Run on arbitrary text.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:\n",
+    "        \"\"\"Run on agent action.\"\"\"\n",
+    "\n",
+    "    @abstractmethod\n",
+    "    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:\n",
+    "        \"\"\"Run on agent end.\"\"\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3bf3304-43fb-47ad-ae50-0637a17018a2",
+   "metadata": {},
+   "source": [
+    "## Creating and Using a Custom `CallbackHandler`\n",
+    "\n",
+    "By default, a shared CallbackManager with the StdOutCallbackHandler will be used by models, chains, agents, and tools. However, you can pass in your own CallbackManager with a custom CallbackHandler:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "80532dfc-d687-4147-a0c9-1f90cc3e868c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "AgentAction(tool='Search', tool_input=\"US Open men's final 2019 winner\", log=' I need to find out who won the US Open men\\'s final in 2019 and then calculate his age raised to the 0.334 power.\\nAction: Search\\nAction Input: \"US Open men\\'s final 2019 winner\"')\n",
+      "Rafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ...\n",
+      "AgentAction(tool='Search', tool_input='Rafael Nadal age', log=' I need to find out the age of the winner\\nAction: Search\\nAction Input: \"Rafael Nadal age\"')\n",
+      "36 years\n",
+      "AgentAction(tool='Calculator', tool_input='36^0.334', log=' I now need to calculate his age raised to the 0.334 power\\nAction: Calculator\\nAction Input: 36^0.334')\n",
+      "Answer: 3.3098250249682484\n",
+      "\n",
+      " I now know the final answer\n",
+      "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\""
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import Any, Dict, List, Optional, Union\n",
+    "\n",
+    "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain.agents import AgentType\n",
+    "from langchain.callbacks.base import CallbackManager, BaseCallbackHandler\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.schema import AgentAction, AgentFinish, LLMResult\n",
+    "\n",
+    "\n",
+    "class MyCustomCallbackHandler(BaseCallbackHandler):\n",
+    "    \"\"\"Custom CallbackHandler.\"\"\"\n",
+    "\n",
+    "    def on_llm_start(\n",
+    "        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any\n",
+    "    ) -> None:\n",
+    "        \"\"\"Print out the prompts.\"\"\"\n",
+    "        pass\n",
+    "\n",
+    "    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n",
+    "        \"\"\"Do nothing.\"\"\"\n",
+    "        pass\n",
+    "\n",
+    "    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n",
+    "        \"\"\"Do nothing.\"\"\"\n",
+    "        pass\n",
+    "\n",
+    "    def on_llm_error(\n",
+    "        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any\n",
+    "    ) -> None:\n",
+    "        \"\"\"Do nothing.\"\"\"\n",
+    "        pass\n",
+    "\n",
+    "    def on_chain_start(\n",
+    "        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any\n",
+    "    ) -> None:\n",
+    "        \"\"\"Print out that we are entering a chain.\"\"\"\n",
+    "        class_name = serialized[\"name\"]\n",
+    "        print(f\"\\n\\n\\033[1m> Entering new {class_name} chain...\\033[0m\")\n",
+    "\n",
+    "    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n",
+    "        \"\"\"Print out that we finished a chain.\"\"\"\n",
+    "        print(\"\\n\\033[1m> Finished chain.\\033[0m\")\n",
+    "\n",
+    "    def on_chain_error(\n",
+    "        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any\n",
+    "    ) -> None:\n",
+    "        \"\"\"Do nothing.\"\"\"\n",
+    "        pass\n",
+    "\n",
+    "    def on_tool_start(\n",
+    "        self,\n",
+    "        serialized: Dict[str, Any],\n",
+    "        input_str: str,\n",
+    "        **kwargs: Any,\n",
+    "    ) -> None:\n",
+    "        \"\"\"Do nothing.\"\"\"\n",
+    "        pass\n",
+    "\n",
+    "    def on_agent_action(\n",
+    "        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any\n",
+    "    ) -> Any:\n",
+    "        \"\"\"Run on agent action.\"\"\"\n",
+    "        print(action)\n",
+    "\n",
+    "    def on_tool_end(\n",
+    "        self,\n",
+    "        output: str,\n",
+    "        color: Optional[str] = None,\n",
+    "        observation_prefix: Optional[str] = None,\n",
+    "        llm_prefix: Optional[str] = None,\n",
+    "        **kwargs: Any,\n",
+    "    ) -> None:\n",
+    "        \"\"\"If not the final action, print out observation.\"\"\"\n",
+    "        print(output)\n",
+    "\n",
+    "    def on_tool_error(\n",
+    "        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any\n",
+    "    ) -> None:\n",
+    "        \"\"\"Do nothing.\"\"\"\n",
+    "        pass\n",
+    "\n",
+    "    def on_text(\n",
+    "        self,\n",
+    "        text: str,\n",
+    "        color: Optional[str] = None,\n",
+    "        end: str = \"\",\n",
+    "        **kwargs: Optional[str],\n",
+    "    ) -> None:\n",
+    "        \"\"\"Run when agent ends.\"\"\"\n",
+    "        print(text)\n",
+    "\n",
+    "    def on_agent_finish(\n",
+    "        self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any\n",
+    "    ) -> None:\n",
+    "        \"\"\"Run on agent end.\"\"\"\n",
+    "        print(finish.log)\n",
+    "\n",
+    "\n",
+    "manager = CallbackManager([MyCustomCallbackHandler()])\n",
+    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)\n",
+    "tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, callback_manager=manager)\n",
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    callback_manager=manager,\n",
+    ")\n",
+    "agent.run(\n",
+    "    \"Who won the US Open men's final in 2019? What is his age raised to the 0.334 power?\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc9785fa-4f71-4797-91a3-4fe7e57d0429",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Async Support\n",
+    "\n",
+    "If you are planning to use the async API, it is recommended to use `AsyncCallbackHandler` and `AsyncCallbackManager` to avoid blocking the runloop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c702e0c9-a961-4897-90c1-cdd13b6f16b2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zzzz....\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "zzzz....\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import asyncio\n",
+    "from aiohttp import ClientSession\n",
+    "\n",
+    "from langchain.callbacks.base import AsyncCallbackHandler, AsyncCallbackManager\n",
+    "\n",
+    "\n",
+    "class MyCustomAsyncCallbackHandler(AsyncCallbackHandler):\n",
+    "    \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
+    "\n",
+    "    async def on_chain_start(\n",
+    "        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any\n",
+    "    ) -> None:\n",
+    "        \"\"\"Run when chain starts running.\"\"\"\n",
+    "        print(\"zzzz....\")\n",
+    "        await asyncio.sleep(0.5)\n",
+    "        class_name = serialized[\"name\"]\n",
+    "        print(f\"\\n\\n\\033[1m> Entering new {class_name} chain...\\033[0m\")\n",
+    "\n",
+    "    async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:\n",
+    "        \"\"\"Run when chain ends running.\"\"\"\n",
+    "        print(\"zzzz....\")\n",
+    "        await asyncio.sleep(0.5)\n",
+    "        print(\"\\n\\033[1m> Finished chain.\\033[0m\")\n",
+    "\n",
+    "\n",
+    "manager = AsyncCallbackManager([MyCustomAsyncCallbackHandler()])\n",
+    "\n",
+    "# To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession,\n",
+    "# but you must manually close the client session at the end of your program/event loop\n",
+    "aiosession = ClientSession()\n",
+    "llm = OpenAI(temperature=0, callback_manager=manager)\n",
+    "async_tools = load_tools(\n",
+    "    [\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession, callback_manager=manager\n",
+    ")\n",
+    "async_agent = initialize_agent(\n",
+    "    async_tools,\n",
+    "    llm,\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+    "    verbose=True,\n",
+    "    callback_manager=manager,\n",
+    ")\n",
+    "await async_agent.arun(\n",
+    "    \"Who won the US Open men's final in 2019? What is his age raised to the 0.334 power?\"\n",
+    ")\n",
+    "await aiosession.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86be6304-e433-4048-880c-a92a73244407",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/chains/examples/api.ipynb

@@ -46,7 +46,10 @@
    "outputs": [],
    "source": [
     "from langchain.chains.api import open_meteo_docs\n",
-    "chain_new = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True)"
+    "\n",
+    "chain_new = APIChain.from_llm_and_api_docs(\n",
+    "    llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -79,7 +82,9 @@
     }
    ],
    "source": [
-    "chain_new.run('What is the weather like right now in Munich, Germany in degrees Farenheit?')"
+    "chain_new.run(\n",
+    "    \"What is the weather like right now in Munich, Germany in degrees Farenheit?\"\n",
+    ")"
    ]
   },
   {
@@ -96,7 +101,8 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "os.environ['TMDB_BEARER_TOKEN'] = \"\""
+    "\n",
+    "os.environ[\"TMDB_BEARER_TOKEN\"] = \"\""
    ]
   },
   {
@@ -106,8 +112,11 @@
    "outputs": [],
    "source": [
     "from langchain.chains.api import tmdb_docs\n",
+    "\n",
     "headers = {\"Authorization\": f\"Bearer {os.environ['TMDB_BEARER_TOKEN']}\"}\n",
-    "chain = APIChain.from_llm_and_api_docs(llm, tmdb_docs.TMDB_DOCS, headers=headers, verbose=True)"
+    "chain = APIChain.from_llm_and_api_docs(\n",
+    "    llm, tmdb_docs.TMDB_DOCS, headers=headers, verbose=True\n",
+    ")"
    ]
   },
   {
@@ -168,12 +177,16 @@
     "from langchain.chains import APIChain\n",
     "\n",
     "# Get api key here: https://www.listennotes.com/api/pricing/\n",
-    "listen_api_key = 'xxx'\n",
+    "listen_api_key = \"xxx\"\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "headers = {\"X-ListenAPI-Key\": listen_api_key}\n",
-    "chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True)\n",
-    "chain.run(\"Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results\")\n"
+    "chain = APIChain.from_llm_and_api_docs(\n",
+    "    llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True\n",
+    ")\n",
+    "chain.run(\n",
+    "    \"Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results\"\n",
+    ")"
    ]
   },
   {

docs/modules/chains/examples/constitutional_chain.ipynb

@@ -168,9 +168,9 @@
    ],
    "source": [
     "master_yoda_principal = ConstitutionalPrinciple(\n",
-    "    name='Master Yoda Principle',\n",
-    "    critique_request='Identify specific ways in which the model\\'s response is not in the style of Master Yoda.',\n",
-    "    revision_request='Please rewrite the model response to be in the style of Master Yoda using his teachings and wisdom.',\n",
+    "    name=\"Master Yoda Principle\",\n",
+    "    critique_request=\"Identify specific ways in which the model's response is not in the style of Master Yoda.\",\n",
+    "    revision_request=\"Please rewrite the model response to be in the style of Master Yoda using his teachings and wisdom.\",\n",
     ")\n",
     "\n",
     "constitutional_chain = ConstitutionalChain.from_llm(\n",

docs/modules/chains/examples/llm_requests.ipynb

@@ -50,7 +50,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))"
+    "chain = LLMRequestsChain(llm_chain=LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))"
    ]
   },
   {
@@ -63,7 +63,7 @@
     "question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n",
     "inputs = {\n",
     "    \"query\": question,\n",
-    "    \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\")\n",
+    "    \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\"),\n",
     "}"
    ]
   },

docs/modules/chains/examples/moderation.ipynb

@@ -25,7 +25,12 @@
    "outputs": [],
    "source": [
     "from langchain.llms import OpenAI\n",
-    "from langchain.chains import OpenAIModerationChain, SequentialChain, LLMChain, SimpleSequentialChain\n",
+    "from langchain.chains import (\n",
+    "    OpenAIModerationChain,\n",
+    "    SequentialChain,\n",
+    "    LLMChain,\n",
+    "    SimpleSequentialChain,\n",
+    ")\n",
     "from langchain.prompts import PromptTemplate"
    ]
   },
@@ -172,13 +177,13 @@
    "outputs": [],
    "source": [
     "class CustomModeration(OpenAIModerationChain):\n",
-    "    \n",
     "    def _moderate(self, text: str, results: dict) -> str:\n",
     "        if results[\"flagged\"]:\n",
     "            error_str = f\"The following text was found that violates OpenAI's content policy: {text}\"\n",
     "            return error_str\n",
     "        return text\n",
-    "    \n",
+    "\n",
+    "\n",
     "custom_moderation = CustomModeration()"
    ]
   },
@@ -244,7 +249,9 @@
    "outputs": [],
    "source": [
     "prompt = PromptTemplate(template=\"{text}\", input_variables=[\"text\"])\n",
-    "llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt)"
+    "llm_chain = LLMChain(\n",
+    "    llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt\n",
+    ")"
    ]
   },
   {
@@ -324,8 +331,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "prompt = PromptTemplate(template=\"{setup}{new_input}Person2:\", input_variables=[\"setup\", \"new_input\"])\n",
-    "llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt)"
+    "prompt = PromptTemplate(\n",
+    "    template=\"{setup}{new_input}Person2:\", input_variables=[\"setup\", \"new_input\"]\n",
+    ")\n",
+    "llm_chain = LLMChain(\n",
+    "    llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt\n",
+    ")"
    ]
   },
   {
@@ -379,7 +390,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = SequentialChain(chains=[llm_chain, moderation_chain], input_variables=[\"setup\", \"new_input\"])"
+    "chain = SequentialChain(\n",
+    "    chains=[llm_chain, moderation_chain], input_variables=[\"setup\", \"new_input\"]\n",
+    ")"
    ]
   },
   {

docs/modules/chains/examples/openapi.ipynb

@@ -48,7 +48,9 @@
     }
    ],
    "source": [
-    "spec = OpenAPISpec.from_url(\"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\")"
+    "spec = OpenAPISpec.from_url(\n",
+    "    \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\"\n",
+    ")"
    ]
   },
   {
@@ -79,7 +81,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "operation = APIOperation.from_openapi_spec(spec, '/public/openai/v0/products', \"get\")"
+    "operation = APIOperation.from_openapi_spec(spec, \"/public/openai/v0/products\", \"get\")"
    ]
   },
   {
@@ -103,7 +105,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI() # Load a Language Model"
+    "llm = OpenAI()  # Load a Language Model"
    ]
   },
   {
@@ -114,11 +116,11 @@
    "outputs": [],
    "source": [
     "chain = OpenAPIEndpointChain.from_api_operation(\n",
-    "    operation, \n",
-    "    llm, \n",
-    "    requests=Requests(), \n",
+    "    operation,\n",
+    "    llm,\n",
+    "    requests=Requests(),\n",
     "    verbose=True,\n",
-    "    return_intermediate_steps=True # Return request and response text\n",
+    "    return_intermediate_steps=True,  # Return request and response text\n",
     ")"
    ]
   },
@@ -144,6 +146,160 @@
       "\u001b[32;1m\u001b[1;3mYou are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.\n",
       "\n",
       "API_SCHEMA: ```typescript\n",
+      "/* API for fetching Klarna product information */\n",
+      "type productsUsingGET = (_: {\n",
+      "/* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */\n",
+      "\t\tq: string,\n",
+      "/* number of products returned */\n",
+      "\t\tsize?: number,\n",
+      "/* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */\n",
+      "\t\tmin_price?: number,\n",
+      "/* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */\n",
+      "\t\tmax_price?: number,\n",
+      "}) => any;\n",
+      "```\n",
+      "\n",
+      "USER_INSTRUCTIONS: \"whats the most expensive shirt?\"\n",
+      "\n",
+      "Your arguments must be plain json provided in a markdown block:\n",
+      "\n",
+      "ARGS: ```json\n",
+      "{valid json conforming to API_SCHEMA}\n",
+      "```\n",
+      "\n",
+      "Example\n",
+      "-----\n",
+      "\n",
+      "ARGS: ```json\n",
+      "{\"foo\": \"bar\", \"baz\": {\"qux\": \"quux\"}}\n",
+      "```\n",
+      "\n",
+      "The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.\n",
+      "You MUST strictly comply to the types indicated by the provided schema, including all required args.\n",
+      "\n",
+      "If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:\n",
+      "\n",
+      "Message: ```text\n",
+      "Concise response requesting the additional information that would make calling the function successful.\n",
+      "```\n",
+      "\n",
+      "Begin\n",
+      "-----\n",
+      "ARGS:\n",
+      "\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m{\"q\": \"shirt\", \"size\": 1, \"max_price\": null}\u001b[0m\n",
+      "\u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}\u001b[0m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new APIResponderChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mYou are a helpful AI assistant trained to answer user queries from API responses.\n",
+      "You attempted to call an API, which resulted in:\n",
+      "API_RESPONSE: {\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}\n",
+      "\n",
+      "USER_COMMENT: \"whats the most expensive shirt?\"\n",
+      "\n",
+      "\n",
+      "If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:\n",
+      "Response: ```json\n",
+      "{\"response\": \"Human-understandable synthesis of the API_RESPONSE\"}\n",
+      "```\n",
+      "\n",
+      "Otherwise respond with the following markdown json block:\n",
+      "Response Error: ```json\n",
+      "{\"response\": \"What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion.\"}\n",
+      "```\n",
+      "\n",
+      "You MUST respond as a markdown json code block. The person you are responding to CANNOT see the API_RESPONSE, so if there is any relevant information there you must include it in your response.\n",
+      "\n",
+      "Begin:\n",
+      "---\n",
+      "\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3mThe most expensive shirt in the API response is the Burberry Check Poplin Shirt, which costs $360.00.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "output = chain(\"whats the most expensive shirt?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c000295e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'request_args': '{\"q\": \"shirt\", \"size\": 1, \"max_price\": null}',\n",
+       " 'response_text': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View intermediate steps\n",
+    "output[\"intermediate_steps\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "092bdb4d",
+   "metadata": {},
+   "source": [
+    "## Return raw response\n",
+    "\n",
+    "We can also run this chain without synthesizing the response. This will have the effect of just returning the raw API output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4dff3849",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = OpenAPIEndpointChain.from_api_operation(\n",
+    "    operation,\n",
+    "    llm,\n",
+    "    requests=Requests(),\n",
+    "    verbose=True,\n",
+    "    return_intermediate_steps=True,  # Return request and response text\n",
+    "    raw_response=True,  # Return raw response\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "762499a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new OpenAPIEndpointChain chain...\u001b[0m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new APIRequesterChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mYou are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.\n",
+      "\n",
+      "API_SCHEMA: ```typescript\n",
+      "/* API for fetching Klarna product information */\n",
       "type productsUsingGET = (_: {\n",
       "/* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */\n",
       "\t\tq: string,\n",
@@ -187,36 +343,7 @@
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m{\"q\": \"shirt\", \"max_price\": null}\u001b[0m\n",
-      "\u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$196.30\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Calvin Klein Slim Fit Oxford Dress Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201839169/Clothing/Calvin-Klein-Slim-Fit-Oxford-Dress-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$24.91\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,White,Blue,Black\",\"Pattern:Solid Color\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new APIResponderChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mYou are a helpful AI assistant trained to answer user queries from API responses.\n",
-      "You attempted to call an API, which resulted in:\n",
-      "API_RESPONSE: {\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$196.30\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Calvin Klein Slim Fit Oxford Dress Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201839169/Clothing/Calvin-Klein-Slim-Fit-Oxford-Dress-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$24.91\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,White,Blue,Black\",\"Pattern:Solid Color\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}\n",
-      "\n",
-      "USER_COMMENT: \"whats the most expensive shirt?\"\n",
-      "\n",
-      "\n",
-      "If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:\n",
-      "Response: ```json\n",
-      "{\"response\": \"Concise response to USER_COMMENT based on API_RESPONSE.\"}\n",
-      "```\n",
-      "\n",
-      "Otherwise respond with the following markdown json block:\n",
-      "Response Error: ```json\n",
-      "{\"response\": \"What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion.\"}\n",
-      "```\n",
-      "\n",
-      "You MUST respond as a markdown json code block.\n",
-      "\n",
-      "Begin:\n",
-      "---\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mThe most expensive shirt in this list is the 'Burberry Somerton Check Shirt - Camel' which is priced at $450.00\u001b[0m\n",
+      "\u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -228,25 +355,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "c000295e",
+   "execution_count": 12,
+   "id": "4afc021a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['{\"q\": \"shirt\", \"max_price\": null}',\n",
-       " '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$196.30\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Calvin Klein Slim Fit Oxford Dress Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201839169/Clothing/Calvin-Klein-Slim-Fit-Oxford-Dress-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$24.91\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,White,Blue,Black\",\"Pattern:Solid Color\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}']"
+       "{'instructions': 'whats the most expensive shirt?',\n",
+       " 'output': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}',\n",
+       " 'intermediate_steps': {'request_args': '{\"q\": \"shirt\", \"max_price\": null}',\n",
+       "  'response_text': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}'}}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# View intermediate steps\n",
-    "output[\"intermediate_steps\"]"
+    "output"
    ]
   },
   {
@@ -285,7 +413,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "operation = APIOperation.from_openapi_spec(spec, '/v1/public/openai/explain-task', \"post\")"
+    "operation = APIOperation.from_openapi_spec(\n",
+    "    spec, \"/v1/public/openai/explain-task\", \"post\"\n",
+    ")"
    ]
   },
   {
@@ -297,11 +427,8 @@
    "source": [
     "llm = OpenAI()\n",
     "chain = OpenAPIEndpointChain.from_api_operation(\n",
-    "    operation,\n",
-    "    llm,\n",
-    "    requests=Requests(),\n",
-    "    verbose=True,\n",
-    "    return_intermediate_steps=True)"
+    "    operation, llm, requests=Requests(), verbose=True, return_intermediate_steps=True\n",
+    ")"
    ]
   },
   {
@@ -448,7 +575,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/chains/examples/pal.ipynb

@@ -28,7 +28,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)"
+    "llm = OpenAI(model_name=\"code-davinci-002\", temperature=0, max_tokens=512)"
    ]
   },
   {
@@ -71,17 +71,17 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3mdef solution():\n",
+      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mdef solution():\n",
       "    \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
       "    cindy_pets = 4\n",
       "    marcia_pets = cindy_pets + 2\n",
       "    jan_pets = marcia_pets * 3\n",
       "    total_pets = cindy_pets + marcia_pets + jan_pets\n",
       "    result = total_pets\n",
-      "    return result\u001B[0m\n",
+      "    return result\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
@@ -139,8 +139,8 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
+      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
       "objects = []\n",
       "objects += [('booklet', 'blue')] * 2\n",
       "objects += [('booklet', 'purple')] * 2\n",
@@ -151,9 +151,9 @@
       "\n",
       "# Count number of purple objects\n",
       "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
-      "answer = num_purple\u001B[0m\n",
+      "answer = num_purple\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished PALChain chain.\u001B[0m\n"
+      "\u001b[1m> Finished PALChain chain.\u001b[0m\n"
      ]
     },
     {
@@ -187,7 +187,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True, return_intermediate_steps=True)"
+    "pal_chain = PALChain.from_colored_object_prompt(\n",
+    "    llm, verbose=True, return_intermediate_steps=True\n",
+    ")"
    ]
   },
   {
@@ -212,8 +214,8 @@
      "text": [
       "\n",
       "\n",
-      "\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
+      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
       "objects = []\n",
       "objects += [('booklet', 'blue')] * 2\n",
       "objects += [('booklet', 'purple')] * 2\n",
@@ -224,9 +226,9 @@
       "\n",
       "# Count number of purple objects\n",
       "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
-      "answer = num_purple\u001B[0m\n",
+      "answer = num_purple\u001b[0m\n",
       "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     }
    ],
@@ -252,7 +254,7 @@
     }
    ],
    "source": [
-    "result['intermediate_steps']"
+    "result[\"intermediate_steps\"]"
    ]
   },
   {

docs/modules/chains/examples/sqlite.ipynb

@@ -9,9 +9,9 @@
     }
    },
    "source": [
-    "# SQLite example\n",
+    "# SQL Chain example\n",
     "\n",
-    "This example showcases hooking up an LLM to answer questions over a database."
+    "This example demonstrates the use of the `SQLDatabaseChain` for answering questions over a database."
    ]
   },
   {
@@ -23,8 +23,10 @@
     }
    },
    "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."
+    "Under the hood, LangChain uses SQLAlchemy to connect to SQL databases. The `SQLDatabaseChain` can therefore be used with any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, and SQLite. Please refer to the SQLAlchemy documentation for more information about requirements for connecting to your database. For example, a connection to MySQL requires an appropriate connector such as PyMySQL. A URI for a MySQL connection might look like: `mysql+pymysql://user:pass@some_mysql_db_address/db_name`\n",
+    "\n",
+    "This demonstration uses SQLite and 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."
    ]
   },
   {
@@ -228,7 +230,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
+    "db_chain = SQLDatabaseChain(\n",
+    "    llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True\n",
+    ")"
    ]
   },
   {
@@ -339,8 +343,11 @@
    "source": [
     "db = SQLDatabase.from_uri(\n",
     "    \"sqlite:///../../../../notebooks/Chinook.db\",\n",
-    "    include_tables=['Track'], # we include only one table to save tokens in the prompt :)\n",
-    "    sample_rows_in_table_info=2)"
+    "    include_tables=[\n",
+    "        \"Track\"\n",
+    "    ],  # we include only one table to save tokens in the prompt :)\n",
+    "    sample_rows_in_table_info=2,\n",
+    ")"
    ]
   },
   {
@@ -520,9 +527,10 @@
    "source": [
     "db = SQLDatabase.from_uri(\n",
     "    \"sqlite:///../../../../notebooks/Chinook.db\",\n",
-    "    include_tables=['Track', 'Playlist'],\n",
+    "    include_tables=[\"Track\", \"Playlist\"],\n",
     "    sample_rows_in_table_info=2,\n",
-    "    custom_table_info=custom_table_info)\n",
+    "    custom_table_info=custom_table_info,\n",
+    ")\n",
     "\n",
     "print(db.table_info)"
    ]
@@ -596,6 +604,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import SQLDatabaseSequentialChain\n",
+    "\n",
     "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")"
    ]
   },
@@ -679,7 +688,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

docs/modules/chains/generic/async_chain.ipynb

@@ -39,7 +39,7 @@
       "\n",
       "\n",
       "SparkleSmile Toothpaste\n",
-      "\u001B[1mConcurrent executed in 1.54 seconds.\u001B[0m\n",
+      "\u001b[1mConcurrent executed in 1.54 seconds.\u001b[0m\n",
       "\n",
       "\n",
       "BrightSmile Toothpaste Co.\n",
@@ -55,7 +55,7 @@
       "\n",
       "\n",
       "BrightSmile Toothpaste.\n",
-      "\u001B[1mSerial executed in 6.38 seconds.\u001B[0m\n"
+      "\u001b[1mSerial executed in 6.38 seconds.\u001b[0m\n"
      ]
     }
    ],
@@ -95,16 +95,17 @@
     "    tasks = [async_generate(chain) for _ in range(5)]\n",
     "    await asyncio.gather(*tasks)\n",
     "\n",
+    "\n",
     "s = time.perf_counter()\n",
     "# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
     "await generate_concurrently()\n",
     "elapsed = time.perf_counter() - s\n",
-    "print('\\033[1m' + f\"Concurrent executed in {elapsed:0.2f} seconds.\" + '\\033[0m')\n",
+    "print(\"\\033[1m\" + f\"Concurrent executed in {elapsed:0.2f} seconds.\" + \"\\033[0m\")\n",
     "\n",
     "s = time.perf_counter()\n",
     "generate_serially()\n",
     "elapsed = time.perf_counter() - s\n",
-    "print('\\033[1m' + f\"Serial executed in {elapsed:0.2f} seconds.\" + '\\033[0m')"
+    "print(\"\\033[1m\" + f\"Serial executed in {elapsed:0.2f} seconds.\" + \"\\033[0m\")"
    ]
   }
  ],

docs/modules/chains/generic/from_hub.ipynb

@@ -93,7 +93,8 @@
    ],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../../state_of_the_union.txt')\n",
+    "\n",
+    "loader = TextLoader(\"../../state_of_the_union.txt\")\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "texts = text_splitter.split_documents(documents)\n",

docs/modules/chains/generic/llm_chain.ipynb

@@ -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"
      ]
     },
     {
@@ -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"
      ]
     },
     {
@@ -138,7 +138,7 @@
    "outputs": [],
    "source": [
     "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
-    "llm_chain = LLMChain.from_string(llm=OpenAI(temperature=0), template=template)\n"
+    "llm_chain = LLMChain.from_string(llm=OpenAI(temperature=0), template=template)"
    ]
   },
   {

docs/modules/chains/generic/sequential_chains.ipynb

@@ -36,25 +36,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7a886879",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cannot find .env file\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext dotenv\n",
-    "%dotenv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
    "id": "3f2f9b8c",
    "metadata": {},
    "outputs": [],
@@ -72,7 +53,7 @@
    "outputs": [],
    "source": [
     "# This is an LLMChain to write a synopsis given a title of a play.\n",
-    "llm = OpenAI(temperature=.7)\n",
+    "llm = OpenAI(temperature=0.7)\n",
     "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
     "\n",
     "Title: {title}\n",
@@ -89,7 +70,7 @@
    "outputs": [],
    "source": [
     "# This is an LLMChain to write a review of a play given a synopsis.\n",
-    "llm = OpenAI(temperature=.7)\n",
+    "llm = OpenAI(temperature=0.7)\n",
     "template = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.\n",
     "\n",
     "Play Synopsis:\n",
@@ -108,7 +89,10 @@
    "source": [
     "# This is the overall chain where we run these two chains in sequence.\n",
     "from langchain.chains import SimpleSequentialChain\n",
-    "overall_chain = SimpleSequentialChain(chains=[synopsis_chain, review_chain], verbose=True)"
+    "\n",
+    "overall_chain = SimpleSequentialChain(\n",
+    "    chains=[synopsis_chain, review_chain], verbose=True\n",
+    ")"
    ]
   },
   {
@@ -190,13 +174,13 @@
    "outputs": [],
    "source": [
     "# This is an LLMChain to write a synopsis given a title of a play and the era it is set in.\n",
-    "llm = OpenAI(temperature=.7)\n",
+    "llm = OpenAI(temperature=0.7)\n",
     "template = \"\"\"You are a playwright. Given the title of play and the era it is set in, it is your job to write a synopsis for that title.\n",
     "\n",
     "Title: {title}\n",
     "Era: {era}\n",
     "Playwright: This is a synopsis for the above play:\"\"\"\n",
-    "prompt_template = PromptTemplate(input_variables=[\"title\", 'era'], template=template)\n",
+    "prompt_template = PromptTemplate(input_variables=[\"title\", \"era\"], template=template)\n",
     "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"synopsis\")"
    ]
   },
@@ -208,7 +192,7 @@
    "outputs": [],
    "source": [
     "# This is an LLMChain to write a review of a play given a synopsis.\n",
-    "llm = OpenAI(temperature=.7)\n",
+    "llm = OpenAI(temperature=0.7)\n",
     "template = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.\n",
     "\n",
     "Play Synopsis:\n",
@@ -227,12 +211,14 @@
    "source": [
     "# This is the overall chain where we run these two chains in sequence.\n",
     "from langchain.chains import SequentialChain\n",
+    "\n",
     "overall_chain = SequentialChain(\n",
     "    chains=[synopsis_chain, review_chain],\n",
     "    input_variables=[\"era\", \"title\"],\n",
     "    # Here we return multiple variables\n",
     "    output_variables=[\"synopsis\", \"review\"],\n",
-    "    verbose=True)"
+    "    verbose=True,\n",
+    ")"
    ]
   },
   {
@@ -251,10 +237,23 @@
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'title': 'Tragedy at sunset on the beach',\n",
+       " 'era': 'Victorian England',\n",
+       " 'synopsis': \"\\n\\nThe play follows the story of John, a young man from a wealthy Victorian family, who dreams of a better life for himself. He soon meets a beautiful young woman named Mary, who shares his dream. The two fall in love and decide to elope and start a new life together.\\n\\nOn their journey, they make their way to a beach at sunset, where they plan to exchange their vows of love. Unbeknownst to them, their plans are overheard by John's father, who has been tracking them. He follows them to the beach and, in a fit of rage, confronts them. \\n\\nA physical altercation ensues, and in the struggle, John's father accidentally stabs Mary in the chest with his sword. The two are left in shock and disbelief as Mary dies in John's arms, her last words being a declaration of her love for him.\\n\\nThe tragedy of the play comes to a head when John, broken and with no hope of a future, chooses to take his own life by jumping off the cliffs into the sea below. \\n\\nThe play is a powerful story of love, hope, and loss set against the backdrop of 19th century England.\",\n",
+       " 'review': \"\\n\\nThe latest production from playwright X is a powerful and heartbreaking story of love and loss set against the backdrop of 19th century England. The play follows John, a young man from a wealthy Victorian family, and Mary, a beautiful young woman with whom he falls in love. The two decide to elope and start a new life together, and the audience is taken on a journey of hope and optimism for the future.\\n\\nUnfortunately, their dreams are cut short when John's father discovers them and in a fit of rage, fatally stabs Mary. The tragedy of the play is further compounded when John, broken and without hope, takes his own life. The storyline is not only realistic, but also emotionally compelling, drawing the audience in from start to finish.\\n\\nThe acting was also commendable, with the actors delivering believable and nuanced performances. The playwright and director have successfully crafted a timeless tale of love and loss that will resonate with audiences for years to come. Highly recommended.\"}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "review = overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
+    "overall_chain({\"title\": \"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
    ]
   },
   {
@@ -310,7 +309,7 @@
     "from langchain.chains import SequentialChain\n",
     "from langchain.memory import SimpleMemory\n",
     "\n",
-    "llm = OpenAI(temperature=.7)\n",
+    "llm = OpenAI(temperature=0.7)\n",
     "template = \"\"\"You are a social media manager for a theater company.  Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for that play.\n",
     "\n",
     "Here is some context about the time and location of the play:\n",
@@ -324,18 +323,23 @@
     "\n",
     "Social Media Post:\n",
     "\"\"\"\n",
-    "prompt_template = PromptTemplate(input_variables=[\"synopsis\", \"review\", \"time\", \"location\"], template=template)\n",
+    "prompt_template = PromptTemplate(\n",
+    "    input_variables=[\"synopsis\", \"review\", \"time\", \"location\"], template=template\n",
+    ")\n",
     "social_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"social_post_text\")\n",
     "\n",
     "overall_chain = SequentialChain(\n",
-    "    memory=SimpleMemory(memories={\"time\": \"December 25th, 8pm PST\", \"location\": \"Theater in the Park\"}),\n",
+    "    memory=SimpleMemory(\n",
+    "        memories={\"time\": \"December 25th, 8pm PST\", \"location\": \"Theater in the Park\"}\n",
+    "    ),\n",
     "    chains=[synopsis_chain, review_chain, social_chain],\n",
     "    input_variables=[\"era\", \"title\"],\n",
     "    # Here we return multiple variables\n",
     "    output_variables=[\"social_post_text\"],\n",
-    "    verbose=True)\n",
+    "    verbose=True,\n",
+    ")\n",
     "\n",
-    "overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
+    "overall_chain({\"title\": \"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
    ]
   },
   {

docs/modules/chains/generic/serialization.ipynb

@@ -26,11 +26,12 @@
    "outputs": [],
    "source": [
     "from langchain import PromptTemplate, OpenAI, 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=OpenAI(temperature=0), verbose=True)\n"
+    "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)"
    ]
   },
   {
@@ -136,13 +137,13 @@
      "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: whats 2 + 2\n",
+      "\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\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 chain.\u001B[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
@@ -257,9 +258,10 @@
     "    \"prompt_path\": \"prompt.json\",\n",
     "    \"llm_path\": \"llm.json\",\n",
     "    \"output_key\": \"text\",\n",
-    "    \"_type\": \"llm_chain\"\n",
+    "    \"_type\": \"llm_chain\",\n",
     "}\n",
     "import json\n",
+    "\n",
     "with open(\"llm_chain_separate.json\", \"w\") as f:\n",
     "    json.dump(config, f, indent=2)"
    ]
@@ -319,13 +321,13 @@
      "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: whats 2 + 2\n",
+      "\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\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 chain.\u001B[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {

docs/modules/chains/generic/transformation.ipynb

@@ -47,7 +47,10 @@
     "    shortened_text = \"\\n\\n\".join(text.split(\"\\n\\n\")[:3])\n",
     "    return {\"output_text\": shortened_text}\n",
     "\n",
-    "transform_chain = TransformChain(input_variables=[\"text\"], output_variables=[\"output_text\"], transform=transform_func)"
+    "\n",
+    "transform_chain = TransformChain(\n",
+    "    input_variables=[\"text\"], output_variables=[\"output_text\"], transform=transform_func\n",
+    ")"
    ]
   },
   {

docs/modules/chains/getting_started.ipynb

@@ -73,6 +73,7 @@
    ],
    "source": [
     "from langchain.chains import LLMChain\n",
+    "\n",
     "chain = LLMChain(llm=llm, prompt=prompt)\n",
     "\n",
     "# Run the chain only specifying the input variable.\n",
@@ -109,12 +110,13 @@
     "    ChatPromptTemplate,\n",
     "    HumanMessagePromptTemplate,\n",
     ")\n",
+    "\n",
     "human_message_prompt = HumanMessagePromptTemplate(\n",
-    "        prompt=PromptTemplate(\n",
-    "            template=\"What is a good name for a company that makes {product}?\",\n",
-    "            input_variables=[\"product\"],\n",
-    "        )\n",
+    "    prompt=PromptTemplate(\n",
+    "        template=\"What is a good name for a company that makes {product}?\",\n",
+    "        input_variables=[\"product\"],\n",
     "    )\n",
+    ")\n",
     "chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])\n",
     "chat = ChatOpenAI(temperature=0.9)\n",
     "chain = LLMChain(llm=chat, prompt=chat_prompt_template)\n",
@@ -189,6 +191,7 @@
    ],
    "source": [
     "from langchain.chains import SimpleSequentialChain\n",
+    "\n",
     "overall_chain = SimpleSequentialChain(chains=[chain, chain_two], verbose=True)\n",
     "\n",
     "# Run the chain specifying only the input variable for the first chain.\n",
@@ -231,17 +234,19 @@
     "    @property\n",
     "    def input_keys(self) -> List[str]:\n",
     "        # Union of the input keys of the two chains.\n",
-    "        all_input_vars = set(self.chain_1.input_keys).union(set(self.chain_2.input_keys))\n",
+    "        all_input_vars = set(self.chain_1.input_keys).union(\n",
+    "            set(self.chain_2.input_keys)\n",
+    "        )\n",
     "        return list(all_input_vars)\n",
     "\n",
     "    @property\n",
     "    def output_keys(self) -> List[str]:\n",
-    "        return ['concat_output']\n",
+    "        return [\"concat_output\"]\n",
     "\n",
     "    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:\n",
     "        output_1 = self.chain_1.run(inputs)\n",
     "        output_2 = self.chain_2.run(inputs)\n",
-    "        return {'concat_output': output_1 + output_2}"
+    "        return {\"concat_output\": output_1 + output_2}"
    ]
   },
   {

docs/modules/chains/index_examples/analyze_document.ipynb

@@ -142,7 +142,10 @@
     }
    ],
    "source": [
-    "qa_document_chain.run(input_document=state_of_the_union, question=\"what did the president say about justice breyer?\")"
+    "qa_document_chain.run(\n",
+    "    input_document=state_of_the_union,\n",
+    "    question=\"what did the president say about justice breyer?\",\n",
+    ")"
    ]
   },
   {

docs/modules/chains/index_examples/chat_vector_db.ipynb

@@ -5,14 +5,14 @@
    "id": "134a0785",
    "metadata": {},
    "source": [
-    "# Chat Index\n",
+    "# Chat Over Documents with Chat History\n",
     "\n",
-    "This notebook goes over how to set up a chain to chat with an index. The only difference between this chain and the [RetrievalQAChain](./vector_db_qa.ipynb) is that this allows for passing in of a chat history which can be used to allow for follow up questions."
+    "This notebook goes over how to set up a chain to chat over documents with chat history using a `ConversationalRetrievalChain`. The only difference between this chain and the [RetrievalQAChain](./vector_db_qa.ipynb) is that this allows for passing in of a chat history which can be used to allow for follow up questions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "70c4e529",
    "metadata": {
     "tags": []
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "01c46e92",
    "metadata": {
     "tags": []
@@ -44,6 +44,7 @@
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
+    "\n",
     "loader = TextLoader(\"../../state_of_the_union.txt\")\n",
     "documents = loader.load()"
    ]
@@ -58,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "433363a5",
    "metadata": {
     "tags": []
@@ -81,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "a8930cf7",
    "metadata": {
     "tags": []
@@ -109,19 +110,21 @@
    "id": "3c96b118",
    "metadata": {},
    "source": [
-    "We now initialize the ConversationalRetrievalChain"
+    "We now initialize the `ConversationalRetrievalChain`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "7b4110f3",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())"
+    "qa = ConversationalRetrievalChain.from_llm(\n",
+    "    OpenAI(temperature=0), vectorstore.as_retriever()\n",
+    ")"
    ]
   },
   {
@@ -134,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "7fe3e730",
    "metadata": {
     "tags": []
@@ -148,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "bfff9cc8",
    "metadata": {
     "tags": []
@@ -160,7 +163,7 @@
        "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -179,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "00b4cf00",
    "metadata": {
     "tags": []
@@ -193,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "f01828d1",
    "metadata": {
     "tags": []
@@ -202,16 +205,16 @@
     {
      "data": {
       "text/plain": [
-       "' Justice Stephen Breyer'"
+       "' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "result['answer']"
+    "result[\"answer\"]"
    ]
   },
   {
@@ -225,19 +228,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "562769c6",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)"
+    "qa = ConversationalRetrievalChain.from_llm(\n",
+    "    OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "ea478300",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "chat_history = []\n",
@@ -247,23 +256,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "4cb75b4e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, 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\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd 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.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)"
+       "Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, 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\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd 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.', metadata={'source': '../../state_of_the_union.txt'})"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "result['source_documents'][0]"
+    "result[\"source_documents\"][0]"
    ]
   },
   {
@@ -277,9 +288,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "5ed8d612",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "vectordbkwargs = {\"search_distance\": 0.9}"
@@ -287,15 +300,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "6a7b3459",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)\n",
+    "qa = ConversationalRetrievalChain.from_llm(\n",
+    "    OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True\n",
+    ")\n",
     "chat_history = []\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "result = qa({\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs})"
+    "result = qa(\n",
+    "    {\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs}\n",
+    ")"
    ]
   },
   {
@@ -309,21 +328,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "id": "e53a9d66",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
     "from langchain.chains.question_answering import load_qa_chain\n",
-    "from langchain.chains.chat_index.prompts import CONDENSE_QUESTION_PROMPT"
+    "from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "id": "bf205e35",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "llm = OpenAI(temperature=0)\n",
@@ -341,7 +364,9 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "78155887",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "chat_history = []\n",
@@ -353,7 +378,9 @@
    "cell_type": "code",
    "execution_count": 21,
    "id": "e54b5fa2",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -367,7 +394,7 @@
     }
    ],
    "source": [
-    "result['answer']"
+    "result[\"answer\"]"
    ]
   },
   {
@@ -384,7 +411,9 @@
    "cell_type": "code",
    "execution_count": 22,
    "id": "d1058fd2",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.chains.qa_with_sources import load_qa_with_sources_chain"
@@ -394,7 +423,9 @@
    "cell_type": "code",
    "execution_count": 23,
    "id": "a6594482",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "llm = OpenAI(temperature=0)\n",
@@ -412,7 +443,9 @@
    "cell_type": "code",
    "execution_count": 24,
    "id": "e2badd21",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "chat_history = []\n",
@@ -424,7 +457,9 @@
    "cell_type": "code",
    "execution_count": 25,
    "id": "edb31fe5",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -438,7 +473,7 @@
     }
    ],
    "source": [
-    "result['answer']"
+    "result[\"answer\"]"
    ]
   },
   {
@@ -453,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "2efacec3-2690-4b05-8de3-a32fd2ac3911",
    "metadata": {
     "tags": []
@@ -463,24 +498,35 @@
     "from langchain.chains.llm import LLMChain\n",
     "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "from langchain.chains.chat_index.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT\n",
+    "from langchain.chains.conversational_retrieval.prompts import (\n",
+    "    CONDENSE_QUESTION_PROMPT,\n",
+    "    QA_PROMPT,\n",
+    ")\n",
     "from langchain.chains.question_answering import load_qa_chain\n",
     "\n",
-    "# Construct a ChatVectorDBChain with a streaming llm for combine docs\n",
+    "# Construct a ConversationalRetrievalChain with a streaming llm for combine docs\n",
     "# and a separate, non-streaming llm for question generation\n",
     "llm = OpenAI(temperature=0)\n",
-    "streaming_llm = OpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
+    "streaming_llm = OpenAI(\n",
+    "    streaming=True,\n",
+    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
+    "    verbose=True,\n",
+    "    temperature=0,\n",
+    ")\n",
     "\n",
     "question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
     "doc_chain = load_qa_chain(streaming_llm, chain_type=\"stuff\", prompt=QA_PROMPT)\n",
     "\n",
     "qa = ConversationalRetrievalChain(\n",
-    "    retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)"
+    "    retriever=vectorstore.as_retriever(),\n",
+    "    combine_docs_chain=doc_chain,\n",
+    "    question_generator=question_generator,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "fd6d43f4-7428-44a4-81bc-26fe88a98762",
    "metadata": {
     "tags": []
@@ -502,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "id": "5ab38978-f3e8-4fa7-808c-c79dec48379a",
    "metadata": {
     "tags": []
@@ -512,14 +558,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Justice Stephen Breyer"
+      " Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court."
      ]
     }
    ],
    "source": [
     "chat_history = [(query, result[\"answer\"])]\n",
     "query = \"Did he mention who she suceeded\"\n",
-    "result = qa({\"question\": query, \"chat_history\": chat_history})\n"
+    "result = qa({\"question\": query, \"chat_history\": chat_history})"
    ]
   },
   {
@@ -533,9 +579,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "id": "a7ba9d8c",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def get_chat_history(inputs) -> str:\n",
@@ -543,14 +591,20 @@
     "    for human, ai in inputs:\n",
     "        res.append(f\"Human:{human}\\nAI:{ai}\")\n",
     "    return \"\\n\".join(res)\n",
-    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore, get_chat_history=get_chat_history)"
+    "\n",
+    "\n",
+    "qa = ConversationalRetrievalChain.from_llm(\n",
+    "    OpenAI(temperature=0), vectorstore.as_retriever(), get_chat_history=get_chat_history\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "id": "a3e33c0d",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "chat_history = []\n",
@@ -560,9 +614,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "id": "936dc62f",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -570,13 +626,13 @@
        "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
       ]
      },
-     "execution_count": 31,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "result['answer']"
+    "result[\"answer\"]"
    ]
   },
   {
@@ -604,7 +660,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/chains/index_examples/hyde.ipynb

@@ -91,7 +91,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings = HypotheticalDocumentEmbedder.from_llm(multi_llm, base_embeddings, \"web_search\")"
+    "embeddings = HypotheticalDocumentEmbedder.from_llm(\n",
+    "    multi_llm, base_embeddings, \"web_search\"\n",
+    ")"
    ]
   },
   {
@@ -136,7 +138,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings = HypotheticalDocumentEmbedder(llm_chain=llm_chain, base_embeddings=base_embeddings)"
+    "embeddings = HypotheticalDocumentEmbedder(\n",
+    "    llm_chain=llm_chain, base_embeddings=base_embeddings\n",
+    ")"
    ]
   },
   {
@@ -146,7 +150,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = embeddings.embed_query(\"What did the president say about Ketanji Brown Jackson\")"
+    "result = embeddings.embed_query(\n",
+    "    \"What did the president say about Ketanji Brown Jackson\"\n",
+    ")"
    ]
   },
   {

docs/modules/chains/index_examples/qa_with_sources.ipynb

@@ -66,7 +66,9 @@
     }
    ],
    "source": [
-    "docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))])"
+    "docsearch = Chroma.from_texts(\n",
+    "    texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))]\n",
+    ")"
    ]
   },
   {
@@ -213,7 +215,9 @@
     "FINAL ANSWER IN ITALIAN:\"\"\"\n",
     "PROMPT = PromptTemplate(template=template, input_variables=[\"summaries\", \"question\"])\n",
     "\n",
-    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\", prompt=PROMPT)\n",
+    "chain = load_qa_with_sources_chain(\n",
+    "    OpenAI(temperature=0), chain_type=\"stuff\", prompt=PROMPT\n",
+    ")\n",
     "query = \"What did the president say about Justice Breyer\"\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
@@ -277,7 +281,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True)"
+    "chain = load_qa_with_sources_chain(\n",
+    "    OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True\n",
+    ")"
    ]
   },
   {
@@ -337,7 +343,6 @@
     }
    ],
    "source": [
-    "\n",
     "question_prompt_template = \"\"\"Use the following portion of a long document to see if any of the text is relevant to answer the question. \n",
     "Return any relevant text in Italian.\n",
     "{context}\n",
@@ -361,7 +366,13 @@
     "    template=combine_prompt_template, input_variables=[\"summaries\", \"question\"]\n",
     ")\n",
     "\n",
-    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)\n",
+    "chain = load_qa_with_sources_chain(\n",
+    "    OpenAI(temperature=0),\n",
+    "    chain_type=\"map_reduce\",\n",
+    "    return_intermediate_steps=True,\n",
+    "    question_prompt=QUESTION_PROMPT,\n",
+    "    combine_prompt=COMBINE_PROMPT,\n",
+    ")\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
@@ -438,7 +449,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True)"
+    "chain = load_qa_with_sources_chain(\n",
+    "    OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True\n",
+    ")"
    ]
   },
   {
@@ -537,7 +550,13 @@
     }
    ],
    "source": [
-    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True, question_prompt=question_prompt, refine_prompt=refine_prompt)\n",
+    "chain = load_qa_with_sources_chain(\n",
+    "    OpenAI(temperature=0),\n",
+    "    chain_type=\"refine\",\n",
+    "    return_intermediate_steps=True,\n",
+    "    question_prompt=question_prompt,\n",
+    "    refine_prompt=refine_prompt,\n",
+    ")\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
@@ -558,7 +577,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", metadata_keys=['source'], return_intermediate_steps=True)"
+    "chain = load_qa_with_sources_chain(\n",
+    "    OpenAI(temperature=0),\n",
+    "    chain_type=\"map_rerank\",\n",
+    "    metadata_keys=[\"source\"],\n",
+    "    return_intermediate_steps=True,\n",
+    ")"
    ]
   },
   {
@@ -663,7 +687,13 @@
     "    input_variables=[\"context\", \"question\"],\n",
     "    output_parser=output_parser,\n",
     ")\n",
-    "chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", metadata_keys=['source'], return_intermediate_steps=True, prompt=PROMPT)\n",
+    "chain = load_qa_with_sources_chain(\n",
+    "    OpenAI(temperature=0),\n",
+    "    chain_type=\"map_rerank\",\n",
+    "    metadata_keys=[\"source\"],\n",
+    "    return_intermediate_steps=True,\n",
+    "    prompt=PROMPT,\n",
+    ")\n",
     "query = \"What did the president say about Justice Breyer\"\n",
     "result = chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]

docs/modules/chains/index_examples/question_answering.ipynb

@@ -23,7 +23,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "17fcbc0f",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
@@ -38,17 +40,26 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "ef9305cc",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "index_creator = VectorstoreIndexCreator()"
+    "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": "291f0117",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -60,27 +71,31 @@
     }
    ],
    "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader(\"../../state_of_the_union.txt\")\n",
-    "docsearch = index_creator.from_loaders([loader])"
+    "docsearch = Chroma.from_texts(\n",
+    "    texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))]\n",
+    ").as_retriever()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "id": "d1eaf6e6",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "query = \"What did the president say about Justice Breyer\"\n",
-    "docs = docsearch.similarity_search(query)"
+    "docs = docsearch.get_relevant_documents(query)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "id": "a16e3453",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain.chains.question_answering import load_qa_chain\n",
@@ -98,17 +113,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 6,
    "id": "fd9e6190",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "' The president said that he was honoring Justice Breyer for his service to the country and that he was a Constitutional scholar, Army veteran, and retiring Justice of the United States Supreme Court.'"
+       "' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -139,9 +156,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "180fd4c1",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")"
@@ -149,17 +168,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "77fdf1aa",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'output_text': ' The president said that he was honoring Justice Breyer for his service to the country and that he was a Constitutional scholar, Army veteran, and retiring Justice of the United States Supreme Court.'}"
+       "{'output_text': ' The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.'}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,17 +202,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "5558c9e0",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera come giudice della Corte Suprema degli Stati Uniti.'}"
+       "{'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha ricevuto una vasta gamma di supporto.'}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -222,9 +245,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "b0060f51",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\")"
@@ -232,17 +257,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "fbdb9137",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'output_text': ' The president said, \"Justice Breyer, thank you for your service.\"'}"
+       "{'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -264,31 +291,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "452c8680",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True)"
+    "chain = load_qa_chain(\n",
+    "    OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "90b47a75",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'intermediate_steps': [' \"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",
-       "  ' None',\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",
        "  ' None',\n",
        "  ' None'],\n",
-       " 'output_text': ' The president said, \"Justice Breyer, thank you for your service.\"'}"
+       " 'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -309,21 +342,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "af03a578",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'intermediate_steps': [\"\\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema degli Stati Uniti. Giustizia Breyer, grazie per il tuo servizio.\",\n",
        "  '\\nNessun testo pertinente.',\n",
-       "  \"\\nCome ho detto l'anno scorso, soprattutto ai nostri giovani americani transgender, avrò sempre il tuo sostegno come tuo Presidente, in modo che tu possa essere te stesso e raggiungere il tuo potenziale donato da Dio.\",\n",
-       "  '\\nNella mia amministrazione, i guardiani sono stati accolti di nuovo. Stiamo andando dietro ai criminali che hanno rubato miliardi di dollari di aiuti di emergenza destinati alle piccole imprese e a milioni di americani. E stasera, annuncio che il Dipartimento di Giustizia nominerà un procuratore capo per la frode pandemica.'],\n",
-       " 'output_text': ' Non conosco la risposta alla tua domanda su cosa abbia detto il Presidente riguardo al Giustizia Breyer.'}"
+       "  ' Non ha detto nulla riguardo a Justice Breyer.',\n",
+       "  \" Non c'è testo pertinente.\"],\n",
+       " 'output_text': ' Non ha detto nulla riguardo a Justice Breyer.'}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -349,7 +384,13 @@
     "COMBINE_PROMPT = PromptTemplate(\n",
     "    template=combine_prompt_template, input_variables=[\"summaries\", \"question\"]\n",
     ")\n",
-    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)\n",
+    "chain = load_qa_chain(\n",
+    "    OpenAI(temperature=0),\n",
+    "    chain_type=\"map_reduce\",\n",
+    "    return_map_steps=True,\n",
+    "    question_prompt=QUESTION_PROMPT,\n",
+    "    combine_prompt=COMBINE_PROMPT,\n",
+    ")\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
@@ -379,9 +420,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "fb167057",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\")"
@@ -389,17 +432,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "d8b5286e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'output_text': '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'}"
+       "{'output_text': '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which he said would be the most sweeping investment to rebuild America in history and would help the country compete for the jobs of the 21st Century.'}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -421,31 +466,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "a5c64200",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True)"
+    "chain = load_qa_chain(\n",
+    "    OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "817546ac",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'intermediate_steps': ['\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country and his legacy of excellence.',\n",
-       "  '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice.',\n",
-       "  '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act.',\n",
-       "  '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'],\n",
-       " 'output_text': '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'}"
+       "  '\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice.',\n",
+       "  '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans.',\n",
+       "  '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'],\n",
+       " 'output_text': '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -466,21 +517,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "id": "6664bda7",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'intermediate_steps': ['\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito.',\n",
-       "  \"\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani.\",\n",
-       "  \"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Ha inoltre sottolineato che la nomina di Justice Breyer è un passo importante verso l'uguaglianza per tutti gli americani, in partic\",\n",
-       "  \"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Ha inoltre sottolineato che la nomina di Justice Breyer è un passo importante verso l'uguaglianza per tutti gli americani, in partic\"],\n",
-       " 'output_text': \"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Ha inoltre sottolineato che la nomina di Justice Breyer è un passo importante verso l'uguaglianza per tutti gli americani, in partic\"}"
+       "{'intermediate_steps': ['\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha reso omaggio al suo servizio.',\n",
+       "  \"\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione.\",\n",
+       "  \"\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei.\",\n",
+       "  \"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal\"],\n",
+       " 'output_text': \"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal\"}"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -515,8 +568,13 @@
     "initial_qa_prompt = PromptTemplate(\n",
     "    input_variables=[\"context_str\", \"question\"], template=initial_qa_template\n",
     ")\n",
-    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True,\n",
-    "                     question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)\n",
+    "chain = load_qa_chain(\n",
+    "    OpenAI(temperature=0),\n",
+    "    chain_type=\"refine\",\n",
+    "    return_refine_steps=True,\n",
+    "    question_prompt=initial_qa_prompt,\n",
+    "    refine_prompt=refine_prompt,\n",
+    ")\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
@@ -532,19 +590,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "id": "e2bfe203",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True)"
+    "chain = load_qa_chain(\n",
+    "    OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 21,
    "id": "5c28880c",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "query = \"What did the president say about Justice Breyer\"\n",
@@ -553,17 +617,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
    "id": "80ac2db3",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "' The president thanked Justice Breyer for his service and honored him for dedicating his life to serving the country. '"
+       "' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -574,24 +640,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "id": "b428fcb9",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'answer': ' The president thanked Justice Breyer for his service and honored him for dedicating his life to serving the country. ',\n",
+       "[{'answer': ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.',\n",
        "  'score': '100'},\n",
-       " {'answer': \" The president said that Justice Breyer is a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that 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, and that she is a consensus builder.\",\n",
-       "  'score': '100'},\n",
-       " {'answer': ' The president did not mention Justice Breyer in this context.',\n",
-       "  'score': '0'},\n",
-       " {'answer': ' The president did not mention Justice Breyer in the given context. ',\n",
-       "  'score': '0'}]"
+       " {'answer': ' This document does not answer the question', 'score': '0'},\n",
+       " {'answer': ' This document does not answer the question', 'score': '0'},\n",
+       " {'answer': ' This document does not answer the question', 'score': '0'}]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -612,24 +677,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "id": "41b83cd8",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.',\n",
+       "{'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.',\n",
        "   'score': '100'},\n",
        "  {'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',\n",
        "   'score': '100'},\n",
        "  {'answer': ' Non so.', 'score': '0'},\n",
-       "  {'answer': ' Il presidente non ha detto nulla sulla giustizia Breyer.',\n",
-       "   'score': '100'}],\n",
-       " 'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.'}"
+       "  {'answer': ' Non so.', 'score': '0'}],\n",
+       " 'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.'}"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -664,7 +730,12 @@
     "    output_parser=output_parser,\n",
     ")\n",
     "\n",
-    "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True, prompt=PROMPT)\n",
+    "chain = load_qa_chain(\n",
+    "    OpenAI(temperature=0),\n",
+    "    chain_type=\"map_rerank\",\n",
+    "    return_intermediate_steps=True,\n",
+    "    prompt=PROMPT,\n",
+    ")\n",
     "query = \"What did the president say about Justice Breyer\"\n",
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
@@ -694,7 +765,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/chains/index_examples/summarize.ipynb

@@ -248,7 +248,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True)"
+    "chain = load_summarize_chain(\n",
+    "    OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True\n",
+    ")"
    ]
   },
   {
@@ -314,7 +316,13 @@
     "\n",
     "CONCISE SUMMARY IN ITALIAN:\"\"\"\n",
     "PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"text\"])\n",
-    "chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT)\n",
+    "chain = load_summarize_chain(\n",
+    "    OpenAI(temperature=0),\n",
+    "    chain_type=\"map_reduce\",\n",
+    "    return_intermediate_steps=True,\n",
+    "    map_prompt=PROMPT,\n",
+    "    combine_prompt=PROMPT,\n",
+    ")\n",
     "chain({\"input_documents\": docs}, return_only_outputs=True)"
    ]
   },
@@ -382,7 +390,9 @@
     }
    ],
    "source": [
-    "chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True)\n",
+    "chain = load_summarize_chain(\n",
+    "    OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True\n",
+    ")\n",
     "\n",
     "chain({\"input_documents\": docs}, return_only_outputs=True)"
    ]
@@ -441,7 +451,13 @@
     "    input_variables=[\"existing_answer\", \"text\"],\n",
     "    template=refine_template,\n",
     ")\n",
-    "chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"refine\", return_intermediate_steps=True, question_prompt=PROMPT, refine_prompt=refine_prompt)\n",
+    "chain = load_summarize_chain(\n",
+    "    OpenAI(temperature=0),\n",
+    "    chain_type=\"refine\",\n",
+    "    return_intermediate_steps=True,\n",
+    "    question_prompt=PROMPT,\n",
+    "    refine_prompt=refine_prompt,\n",
+    ")\n",
     "chain({\"input_documents\": docs}, return_only_outputs=True)"
    ]
   },

docs/modules/chains/index_examples/vector_db_qa.ipynb

@@ -41,6 +41,7 @@
    ],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
+    "\n",
     "loader = TextLoader(\"../../state_of_the_union.txt\")\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
@@ -57,7 +58,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever())"
+    "qa = RetrievalQA.from_chain_type(\n",
+    "    llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
+    ")"
    ]
   },
   {
@@ -100,7 +103,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"map_reduce\", retriever=docsearch.as_retriever())"
+    "qa = RetrievalQA.from_chain_type(\n",
+    "    llm=OpenAI(), chain_type=\"map_reduce\", retriever=docsearch.as_retriever()\n",
+    ")"
    ]
   },
   {
@@ -141,6 +146,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains.question_answering import load_qa_chain\n",
+    "\n",
     "qa_chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n",
     "qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())"
    ]
@@ -184,6 +190,7 @@
    "outputs": [],
    "source": [
     "from langchain.prompts import PromptTemplate\n",
+    "\n",
     "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
     "\n",
     "{context}\n",
@@ -203,7 +210,12 @@
    "outputs": [],
    "source": [
     "chain_type_kwargs = {\"prompt\": PROMPT}\n",
-    "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)"
+    "qa = RetrievalQA.from_chain_type(\n",
+    "    llm=OpenAI(),\n",
+    "    chain_type=\"stuff\",\n",
+    "    retriever=docsearch.as_retriever(),\n",
+    "    chain_type_kwargs=chain_type_kwargs,\n",
+    ")"
    ]
   },
   {
@@ -244,7 +256,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), return_source_documents=True)"
+    "qa = RetrievalQA.from_chain_type(\n",
+    "    llm=OpenAI(),\n",
+    "    chain_type=\"stuff\",\n",
+    "    retriever=docsearch.as_retriever(),\n",
+    "    return_source_documents=True,\n",
+    ")"
    ]
   },
   {

docs/modules/chains/index_examples/vector_db_qa_with_sources.ipynb

@@ -55,7 +55,9 @@
     }
    ],
    "source": [
-    "docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": f\"{i}-pl\"} for i in range(len(texts))])"
+    "docsearch = Chroma.from_texts(\n",
+    "    texts, embeddings, metadatas=[{\"source\": f\"{i}-pl\"} for i in range(len(texts))]\n",
+    ")"
    ]
   },
   {
@@ -77,7 +79,9 @@
    "source": [
     "from langchain import OpenAI\n",
     "\n",
-    "chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"stuff\", retriever=docsearch.as_retriever())"
+    "chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
+    "    OpenAI(temperature=0), chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
+    ")"
    ]
   },
   {
@@ -99,7 +103,10 @@
     }
    ],
    "source": [
-    "chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
+    "chain(\n",
+    "    {\"question\": \"What did the president say about Justice Breyer\"},\n",
+    "    return_only_outputs=True,\n",
+    ")"
    ]
   },
   {
@@ -120,7 +127,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"map_reduce\", retriever=docsearch.as_retriever())"
+    "chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
+    "    OpenAI(temperature=0), chain_type=\"map_reduce\", retriever=docsearch.as_retriever()\n",
+    ")"
    ]
   },
   {
@@ -142,7 +151,10 @@
     }
    ],
    "source": [
-    "chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
+    "chain(\n",
+    "    {\"question\": \"What did the president say about Justice Breyer\"},\n",
+    "    return_only_outputs=True,\n",
+    ")"
    ]
   },
   {
@@ -161,8 +173,11 @@
    "outputs": [],
    "source": [
     "from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
+    "\n",
     "qa_chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n",
-    "qa = RetrievalQAWithSourcesChain(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())"
+    "qa = RetrievalQAWithSourcesChain(\n",
+    "    combine_documents_chain=qa_chain, retriever=docsearch.as_retriever()\n",
+    ")"
    ]
   },
   {
@@ -184,7 +199,10 @@
     }
    ],
    "source": [
-    "qa({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
+    "qa(\n",
+    "    {\"question\": \"What did the president say about Justice Breyer\"},\n",
+    "    return_only_outputs=True,\n",
+    ")"
    ]
   },
   {

docs/modules/chains/index_examples/vector_db_text_generation.ipynb

@@ -72,6 +72,7 @@
     "                github_url = f\"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}\"\n",
     "                yield Document(page_content=f.read(), metadata={\"source\": github_url})\n",
     "\n",
+    "\n",
     "sources = get_github_docs(\"yirenlu92\", \"deno-manual-forked\")\n",
     "\n",
     "source_chunks = []\n",
@@ -115,14 +116,13 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
+    "\n",
     "prompt_template = \"\"\"Use the context below to write a 400 word blog post about the topic below:\n",
     "    Context: {context}\n",
     "    Topic: {topic}\n",
     "    Blog post:\"\"\"\n",
     "\n",
-    "PROMPT = PromptTemplate(\n",
-    "    template=prompt_template, input_variables=[\"context\", \"topic\"]\n",
-    ")\n",
+    "PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"context\", \"topic\"])\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "\n",

docs/modules/indexes/document_loaders/examples/airbyte_json.ipynb

@@ -67,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = AirbyteJSONLoader('/tmp/airbyte_local/json_data/_airbyte_raw_pokemon.jsonl')"
+    "loader = AirbyteJSONLoader(\"/tmp/airbyte_local/json_data/_airbyte_raw_pokemon.jsonl\")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/azure_blob_storage_container.ipynb

@@ -78,7 +78,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = AzureBlobStorageContainerLoader(conn_str=\"<conn_str>\", container=\"<container>\", prefix=\"<prefix>\")"
+    "loader = AzureBlobStorageContainerLoader(\n",
+    "    conn_str=\"<conn_str>\", container=\"<container>\", prefix=\"<prefix>\"\n",
+    ")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/azure_blob_storage_file.ipynb

@@ -38,7 +38,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = AzureBlobStorageFileLoader(conn_str='<connection string>', container='<container name>', blob_name='<blob name>')"
+    "loader = AzureBlobStorageFileLoader(\n",
+    "    conn_str=\"<connection string>\",\n",
+    "    container=\"<container name>\",\n",
+    "    blob_name=\"<blob name>\",\n",
+    ")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/bigquery.ipynb

@@ -24,7 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "BASE_QUERY = '''\n",
+    "BASE_QUERY = \"\"\"\n",
     "SELECT\n",
     "  id,\n",
     "  dna_sequence,\n",
@@ -41,7 +41,7 @@
     "    SELECT\n",
     "      AS STRUCT 3 AS id, \"TCCGGA\" AS dna_sequence, \"Acidianus hospitalis (strain W1).\" AS organism) AS new_array),\n",
     "  UNNEST(new_array)\n",
-    "'''"
+    "\"\"\""
    ]
   },
   {
@@ -92,7 +92,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = BigQueryLoader(BASE_QUERY, page_content_columns=[\"dna_sequence\", \"organism\"], metadata_columns=[\"id\"])\n",
+    "loader = BigQueryLoader(\n",
+    "    BASE_QUERY,\n",
+    "    page_content_columns=[\"dna_sequence\", \"organism\"],\n",
+    "    metadata_columns=[\"id\"],\n",
+    ")\n",
     "\n",
     "data = loader.load()"
    ]
@@ -128,7 +132,7 @@
    "outputs": [],
    "source": [
     "# Note that the `id` column is being returned twice, with one instance aliased as `source`\n",
-    "ALIASED_QUERY = '''\n",
+    "ALIASED_QUERY = \"\"\"\n",
     "SELECT\n",
     "  id,\n",
     "  dna_sequence,\n",
@@ -146,7 +150,7 @@
     "    SELECT\n",
     "      AS STRUCT 3 AS id, \"TCCGGA\" AS dna_sequence, \"Acidianus hospitalis (strain W1).\" AS organism) AS new_array),\n",
     "  UNNEST(new_array)\n",
-    "'''"
+    "\"\"\""
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/bilibili.ipynb

@@ -0,0 +1,85 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66a7777e",
+   "metadata": {},
+   "source": [
+    "# Bilibili\n",
+    "\n",
+    "This loader utilizes the `bilibili-api` to fetch the text transcript from Bilibili, one of the most beloved long-form video sites in China.\n",
+    "\n",
+    "With this BiliBiliLoader, users can easily obtain the transcript of their desired video content on the platform."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9ec8a3b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.bilibili import BiliBiliLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "43128d8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install bilibili-api"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35d6809a",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "loader = BiliBiliLoader([\"https://www.bilibili.com/video/BV1xt411o7Xu/\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/indexes/document_loaders/examples/college_confidential.ipynb

@@ -26,7 +26,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = CollegeConfidentialLoader(\"https://www.collegeconfidential.com/colleges/brown-university/\")"
+    "loader = CollegeConfidentialLoader(\n",
+    "    \"https://www.collegeconfidential.com/colleges/brown-university/\"\n",
+    ")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/csv.ipynb

@@ -30,7 +30,7 @@
    },
    "outputs": [],
    "source": [
-    "loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv')\n",
+    "loader = CSVLoader(file_path=\"./example_data/mlb_teams_2012.csv\")\n",
     "\n",
     "data = loader.load()"
    ]
@@ -73,11 +73,14 @@
    },
    "outputs": [],
    "source": [
-    "loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', csv_args={\n",
-    "    'delimiter': ',',\n",
-    "    'quotechar': '\"',\n",
-    "    'fieldnames': ['MLB Team', 'Payroll in millions', 'Wins']\n",
-    "})\n",
+    "loader = CSVLoader(\n",
+    "    file_path=\"./example_data/mlb_teams_2012.csv\",\n",
+    "    csv_args={\n",
+    "        \"delimiter\": \",\",\n",
+    "        \"quotechar\": '\"',\n",
+    "        \"fieldnames\": [\"MLB Team\", \"Payroll in millions\", \"Wins\"],\n",
+    "    },\n",
+    ")\n",
     "\n",
     "data = loader.load()"
    ]
@@ -106,7 +109,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Specify a column to be used identify the document source\n",
+    "## Specify a column to be used identify the document source\n",
     "\n",
     "Use the `source_column` argument to specify a column to be set as the source for the document created from each row. Otherwise `file_path` will be used as the source for all documents created from the csv file.\n",
     "\n",
@@ -119,7 +122,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', source_column=\"Team\")\n",
+    "loader = CSVLoader(file_path=\"./example_data/mlb_teams_2012.csv\", source_column=\"Team\")\n",
     "\n",
     "data = loader.load()"
    ]

docs/modules/indexes/document_loaders/examples/dataframe.ipynb

@@ -27,7 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_csv('example_data/mlb_teams_2012.csv')"
+    "df = pd.read_csv(\"example_data/mlb_teams_2012.csv\")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/directory_loader.ipynb

@@ -34,7 +34,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = DirectoryLoader('../', glob=\"**/*.md\")"
+    "loader = DirectoryLoader(\"../\", glob=\"**/*.md\")"
    ]
   },
   {
@@ -94,7 +94,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = DirectoryLoader('../', glob=\"**/*.md\", loader_cls=TextLoader)"
+    "loader = DirectoryLoader(\"../\", glob=\"**/*.md\", loader_cls=TextLoader)"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/duckdb.ipynb

@@ -82,7 +82,7 @@
     "loader = DuckDBLoader(\n",
     "    \"SELECT * FROM read_csv_auto('example.csv')\",\n",
     "    page_content_columns=[\"Team\"],\n",
-    "    metadata_columns=[\"Payroll\"]\n",
+    "    metadata_columns=[\"Payroll\"],\n",
     ")\n",
     "\n",
     "data = loader.load()"
@@ -120,7 +120,7 @@
    "source": [
     "loader = DuckDBLoader(\n",
     "    \"SELECT Team, Payroll, Team As source FROM read_csv_auto('example.csv')\",\n",
-    "    metadata_columns=[\"source\"]\n",
+    "    metadata_columns=[\"source\"],\n",
     ")\n",
     "\n",
     "data = loader.load()"

docs/modules/indexes/document_loaders/examples/email.ipynb

@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = UnstructuredEmailLoader('example_data/fake-email.eml')"
+    "loader = UnstructuredEmailLoader(\"example_data/fake-email.eml\")"
    ]
   },
   {
@@ -86,7 +86,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = UnstructuredEmailLoader('example_data/fake-email.eml', mode=\"elements\")"
+    "loader = UnstructuredEmailLoader(\"example_data/fake-email.eml\", mode=\"elements\")"
    ]
   },
   {
@@ -145,7 +145,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = OutlookMessageLoader('example_data/fake-email.msg')"
+    "loader = OutlookMessageLoader(\"example_data/fake-email.msg\")"
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/example_data/whatsapp_chat.txt

@@ -8,4 +8,5 @@
 1/23/23, 3:02 AM - User 1: I thought you were selling the blue one!
 1/23/23, 3:18 AM - User 2: No Im sorry it was my mistake, the blue one is not for sale
 1/23/23, 3:19 AM - User 1: Oh no worries! Bye
-1/23/23, 3:19 AM - User 2: Bye!
+1/23/23, 3:19 AM - User 2: Bye!
+1/23/23, 3:22_AM - User 1: And let me know if anything changes