bump version to 129 (#2281)

Co-authored-by: Marc Päpper <mpaepper@users.noreply.github.com>

.dockerignore

@@ -0,0 +1,2 @@
+.venv
+.github

.github/CONTRIBUTING.md

@@ -73,6 +73,8 @@ poetry install -E all
 
 This will install all requirements for running the package, examples, linting, formatting, tests, and coverage. Note the `-E all` flag will install all optional dependencies necessary for integration testing.
 
+❗Note: If you're running Poetry 1.4.1 and receive a `WheelFileValidationError` for `debugpy` during installation, you can try either downgrading to Poetry 1.4.0 or disabling "modern installation" (`poetry config installer.modern-installation false`) and re-install requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
+
 Now, you should be able to run the common tasks in the following section.
 
 ## ✅Common Tasks

.gitignore

@@ -106,6 +106,7 @@ celerybeat.pid
 
 # Environments
 .env
+.envrc
 .venv
 .venvs
 env/
@@ -134,3 +135,9 @@ dmypy.json
 
 # macOS display setting files
 .DS_Store
+
+# Wandb directory
+wandb/
+
+# asdf tool versions
+.tool-versions

Dockerfile

@@ -0,0 +1,39 @@
+# Use the Python base image
+FROM python:3.11.2-bullseye AS builder
+
+# Print Python version
+RUN echo "Python version:" && python --version && echo ""
+
+# Install Poetry
+RUN echo "Installing Poetry..." && \
+    curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py | python -
+
+# Add Poetry to PATH
+ENV PATH="${PATH}:/root/.local/bin"
+
+# Test if Poetry is added to PATH
+RUN echo "Poetry version:" && poetry --version && echo ""
+
+# Set working directory
+WORKDIR /app
+
+# Use a multi-stage build to install dependencies
+FROM builder AS dependencies
+
+# Copy only the dependency files for installation
+COPY pyproject.toml poetry.lock poetry.toml ./
+
+# Install Poetry dependencies (this layer will be cached as long as the dependencies don't change)
+RUN poetry install --no-interaction --no-ansi
+
+# Use a multi-stage build to run tests
+FROM dependencies AS tests
+
+# Copy the rest of the app source code (this layer will be invalidated and rebuilt whenever the source code changes)
+COPY . .
+
+# Set entrypoint to run tests
+ENTRYPOINT ["poetry", "run", "pytest"]
+
+# Set default command to run all unit tests
+CMD ["tests/unit_tests"]

Makefile

@@ -1,7 +1,7 @@
-.PHONY: all clean format lint test tests test_watch integration_tests help
+.PHONY: all clean format lint test tests test_watch integration_tests docker_tests help
 
 all: help
-	
+
 coverage:
 	poetry run pytest --cov \
 		--cov-config=.coveragerc \
@@ -21,13 +21,12 @@ docs_linkcheck:
 
 format:
 	poetry run black .
-	poetry run isort .
+	poetry run ruff --select I --fix .
 
 lint:
 	poetry run mypy .
 	poetry run black . --check
-	poetry run isort . --check
-	poetry run flake8 .
+	poetry run ruff .
 
 test:
 	poetry run pytest tests/unit_tests
@@ -41,6 +40,10 @@ test_watch:
 integration_tests:
 	poetry run pytest tests/integration_tests
 
+docker_tests:
+	docker build -t my-langchain-image:test .
+	docker run --rm my-langchain-image:test
+
 help:
 	@echo '----'
 	@echo 'coverage            - run unit tests and generate coverage report'
@@ -52,3 +55,4 @@ help:
 	@echo 'test                - run unit tests'
 	@echo 'test_watch          - run unit tests in watch mode'
 	@echo 'integration_tests   - run integration tests'
+	@echo 'docker_tests        - run unit tests in docker'

README.md

@@ -32,7 +32,7 @@ This library is aimed at assisting in the development of those types of applicat
 
 **🤖 Agents**
 
-- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/agents.html)
+- [Documentation](https://langchain.readthedocs.io/en/latest/modules/agents.html)
 - End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
 
 ## 📖 Documentation
@@ -42,7 +42,7 @@ Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documen
 - Getting started (installation, setting up the environment, simple examples)
 - How-To examples (demos, integrations, helper functions)
 - Reference (full API docs)
-  Resources (high-level explanation of core concepts)
+- Resources (high-level explanation of core concepts)
 
 ## 🚀 What can this help with?
 
@@ -79,4 +79,4 @@ For more information on these concepts, please see our [full documentation](http
 
 As an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
 
-For detailed information on how to contribute, see [here](CONTRIBUTING.md).
+For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).

docs/conf.py

@@ -23,13 +23,14 @@ with open("../pyproject.toml") as f:
 # -- Project information -----------------------------------------------------
 
 project = "🦜🔗 LangChain"
-copyright = "2022, Harrison Chase"
+copyright = "2023, Harrison Chase"
 author = "Harrison Chase"
 
 version = data["tool"]["poetry"]["version"]
 release = version
 
 html_title = project + " " + version
+html_last_updated_fmt = "%b %d, %Y"
 
 
 # -- General configuration ---------------------------------------------------
@@ -45,6 +46,7 @@ extensions = [
     "sphinx.ext.viewcode",
     "sphinxcontrib.autodoc_pydantic",
     "myst_nb",
+    "sphinx_copybutton",
     "sphinx_panels",
     "IPython.sphinxext.ipython_console_highlighting",
 ]

docs/deployments.md

@@ -37,3 +37,6 @@ A minimal example on how to run LangChain on Vercel using Flask.
 ## [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.

docs/ecosystem/aim_tracking.ipynb

@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Aim\n",
+    "\n",
+    "Aim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents. \n",
+    "\n",
+    "With Aim, you can easily debug and examine an individual execution:\n",
+    "\n",
+    "![](https://user-images.githubusercontent.com/13848158/227784778-06b806c7-74a1-4d15-ab85-9ece09b458aa.png)\n",
+    "\n",
+    "Additionally, you have the option to compare multiple executions side by side:\n",
+    "\n",
+    "![](https://user-images.githubusercontent.com/13848158/227784994-699b24b7-e69b-48f9-9ffa-e6a6142fd719.png)\n",
+    "\n",
+    "Aim is fully open source, [learn more](https://github.com/aimhubio/aim) about Aim on GitHub.\n",
+    "\n",
+    "Let's move forward and see how to enable and configure Aim callback."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3>Tracking LangChain Executions with Aim</h3>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we will explore three usage scenarios. To start off, we will install the necessary packages and import certain modules. Subsequently, we will configure two environment variables that can be established either within the Python script or through the terminal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "mf88kuCJhbVu"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install aim\n",
+    "!pip install langchain\n",
+    "!pip install openai\n",
+    "!pip install google-search-results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "g4eTuajwfl6L"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from datetime import datetime\n",
+    "\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.callbacks.base import CallbackManager\n",
+    "from langchain.callbacks import AimCallbackHandler, StdOutCallbackHandler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our examples use a GPT model as the LLM, and OpenAI offers an API for this purpose. You can obtain the key from the following link: https://platform.openai.com/account/api-keys .\n",
+    "\n",
+    "We will use the SerpApi to retrieve search results from Google. To acquire the SerpApi key, please go to https://serpapi.com/manage-api-key ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "T1bSmKd6V2If"
+   },
+   "outputs": [],
+   "source": [
+    "os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
+    "os.environ[\"SERPAPI_API_KEY\"] = \"...\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QenUYuBZjIzc"
+   },
+   "source": [
+    "The event methods of `AimCallbackHandler` accept the LangChain module or agent as input and log at least the prompts and generated results, as well as the serialized version of the LangChain module, to the designated Aim run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "KAz8weWuUeXF"
+   },
+   "outputs": [],
+   "source": [
+    "session_group = datetime.now().strftime(\"%m.%d.%Y_%H.%M.%S\")\n",
+    "aim_callback = AimCallbackHandler(\n",
+    "    repo=\".\",\n",
+    "    experiment_name=\"scenario 1: OpenAI LLM\",\n",
+    ")\n",
+    "\n",
+    "manager = CallbackManager([StdOutCallbackHandler(), aim_callback])\n",
+    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b8WfByB4fl6N"
+   },
+   "source": [
+    "The `flush_tracker` function is used to record LangChain assets on Aim. By default, the session is reset rather than being terminated outright."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3>Scenario 1</h3> In the first scenario, we will use OpenAI LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "o_VmneyIUyx8"
+   },
+   "outputs": [],
+   "source": [
+    "# scenario 1 - LLM\n",
+    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
+    "aim_callback.flush_tracker(\n",
+    "    langchain_asset=llm,\n",
+    "    experiment_name=\"scenario 2: Chain with multiple SubChains on multiple generations\",\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3>Scenario 2</h3> Scenario two involves chaining with multiple SubChains across multiple generations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "trxslyb1U28Y"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "uauQk10SUzF6"
+   },
+   "outputs": [],
+   "source": [
+    "# scenario 2 - Chain\n",
+    "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
+    "Title: {title}\n",
+    "Playwright: This is a synopsis for the above play:\"\"\"\n",
+    "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
+    "\n",
+    "test_prompts = [\n",
+    "    {\"title\": \"documentary about good video games that push the boundary of game design\"},\n",
+    "    {\"title\": \"the phenomenon behind the remarkable speed of cheetahs\"},\n",
+    "    {\"title\": \"the best in class mlops tooling\"},\n",
+    "]\n",
+    "synopsis_chain.apply(test_prompts)\n",
+    "aim_callback.flush_tracker(\n",
+    "    langchain_asset=synopsis_chain, experiment_name=\"scenario 3: Agent with Tools\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3>Scenario 3</h3> The third scenario involves an agent with tools."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_jN73xcPVEpI"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.agents import initialize_agent, load_tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Gpq4rk6VT9cu",
+    "outputId": "68ae261e-d0a2-4229-83c4-762562263b66"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mLeonardo DiCaprio seemed to prove a long-held theory about his love life right after splitting from girlfriend Camila Morrone just months ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
+      "Action: Search\n",
+      "Action Input: \"Camila Morrone age\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
+      "Action: Calculator\n",
+      "Action Input: 25^0.43\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# scenario 3 - Agent with Tools\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=\"zero-shot-react-description\",\n",
+    "    callback_manager=manager,\n",
+    "    verbose=True,\n",
+    ")\n",
+    "agent.run(\n",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
+    ")\n",
+    "aim_callback.flush_tracker(langchain_asset=agent, reset=False, finish=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/ecosystem/apify.md

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

docs/ecosystem/atlas.md

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

docs/ecosystem/bananadev.md

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

docs/ecosystem/chroma.md

@@ -17,4 +17,4 @@ To import this vectorstore:
 from langchain.vectorstores import Chroma
 ```
 
-For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)
+For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/vectorstores/getting_started.ipynb)

docs/ecosystem/clearml_tracking.ipynb

@@ -0,0 +1,588 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ClearML Integration\n",
+    "\n",
+    "In order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. ClearML is an experiment manager that neatly tracks and organizes all your experiment runs.\n",
+    "\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/ecosystem/clearml_tracking.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Getting API Credentials\n",
+    "\n",
+    "We'll be using quite some APIs in this notebook, here is a list and where to get them:\n",
+    "\n",
+    "- ClearML: https://app.clear.ml/settings/workspace-configuration\n",
+    "- OpenAI: https://platform.openai.com/account/api-keys\n",
+    "- SerpAPI (google search): https://serpapi.com/dashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"CLEARML_API_ACCESS_KEY\"] = \"\"\n",
+    "os.environ[\"CLEARML_API_SECRET_KEY\"] = \"\"\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
+    "os.environ[\"SERPAPI_API_KEY\"] = \"\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setting Up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install clearml\n",
+    "!pip install pandas\n",
+    "!pip install textstat\n",
+    "!pip install spacy\n",
+    "!python -m spacy download en_core_web_sm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datetime import datetime\n",
+    "from langchain.callbacks import ClearMLCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.callbacks.base import CallbackManager\n",
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "# Setup and use the ClearML Callback\n",
+    "clearml_callback = ClearMLCallbackHandler(\n",
+    "    task_type=\"inference\",\n",
+    "    project_name=\"langchain_callback_demo\",\n",
+    "    task_name=\"llm\",\n",
+    "    tags=[\"test\"],\n",
+    "    # Change the following parameters based on the amount of detail you want tracked\n",
+    "    visualize=True,\n",
+    "    complexity_metrics=True,\n",
+    "    stream_logs=True\n",
+    ")\n",
+    "manager = CallbackManager([StdOutCallbackHandler(), clearml_callback])\n",
+    "# Get the OpenAI model ready to go\n",
+    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scenario 1: Just an LLM\n",
+    "\n",
+    "First, let's just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
+      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
+      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
+      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
+      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
+      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
+      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
+      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
+      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
+      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
+      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
+      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
+      "{'action_records':           action    name  step  starts  ends  errors  text_ctr  chain_starts  \\\n",
+      "0   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
+      "1   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
+      "2   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
+      "3   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
+      "4   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
+      "5   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
+      "6     on_llm_end     NaN     2       1     1       0         0             0   \n",
+      "7     on_llm_end     NaN     2       1     1       0         0             0   \n",
+      "8     on_llm_end     NaN     2       1     1       0         0             0   \n",
+      "9     on_llm_end     NaN     2       1     1       0         0             0   \n",
+      "10    on_llm_end     NaN     2       1     1       0         0             0   \n",
+      "11    on_llm_end     NaN     2       1     1       0         0             0   \n",
+      "12  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
+      "13  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
+      "14  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
+      "15  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
+      "16  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
+      "17  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
+      "18    on_llm_end     NaN     4       2     2       0         0             0   \n",
+      "19    on_llm_end     NaN     4       2     2       0         0             0   \n",
+      "20    on_llm_end     NaN     4       2     2       0         0             0   \n",
+      "21    on_llm_end     NaN     4       2     2       0         0             0   \n",
+      "22    on_llm_end     NaN     4       2     2       0         0             0   \n",
+      "23    on_llm_end     NaN     4       2     2       0         0             0   \n",
+      "\n",
+      "    chain_ends  llm_starts  ...  difficult_words  linsear_write_formula  \\\n",
+      "0            0           1  ...              NaN                    NaN   \n",
+      "1            0           1  ...              NaN                    NaN   \n",
+      "2            0           1  ...              NaN                    NaN   \n",
+      "3            0           1  ...              NaN                    NaN   \n",
+      "4            0           1  ...              NaN                    NaN   \n",
+      "5            0           1  ...              NaN                    NaN   \n",
+      "6            0           1  ...              0.0                    5.5   \n",
+      "7            0           1  ...              2.0                    6.5   \n",
+      "8            0           1  ...              0.0                    5.5   \n",
+      "9            0           1  ...              2.0                    6.5   \n",
+      "10           0           1  ...              0.0                    5.5   \n",
+      "11           0           1  ...              2.0                    6.5   \n",
+      "12           0           2  ...              NaN                    NaN   \n",
+      "13           0           2  ...              NaN                    NaN   \n",
+      "14           0           2  ...              NaN                    NaN   \n",
+      "15           0           2  ...              NaN                    NaN   \n",
+      "16           0           2  ...              NaN                    NaN   \n",
+      "17           0           2  ...              NaN                    NaN   \n",
+      "18           0           2  ...              0.0                    5.5   \n",
+      "19           0           2  ...              2.0                    6.5   \n",
+      "20           0           2  ...              0.0                    5.5   \n",
+      "21           0           2  ...              2.0                    6.5   \n",
+      "22           0           2  ...              0.0                    5.5   \n",
+      "23           0           2  ...              2.0                    6.5   \n",
+      "\n",
+      "    gunning_fog      text_standard  fernandez_huerta szigriszt_pazos  \\\n",
+      "0           NaN                NaN               NaN             NaN   \n",
+      "1           NaN                NaN               NaN             NaN   \n",
+      "2           NaN                NaN               NaN             NaN   \n",
+      "3           NaN                NaN               NaN             NaN   \n",
+      "4           NaN                NaN               NaN             NaN   \n",
+      "5           NaN                NaN               NaN             NaN   \n",
+      "6          5.20  5th and 6th grade            133.58          131.54   \n",
+      "7          8.28  6th and 7th grade            115.58          112.37   \n",
+      "8          5.20  5th and 6th grade            133.58          131.54   \n",
+      "9          8.28  6th and 7th grade            115.58          112.37   \n",
+      "10         5.20  5th and 6th grade            133.58          131.54   \n",
+      "11         8.28  6th and 7th grade            115.58          112.37   \n",
+      "12          NaN                NaN               NaN             NaN   \n",
+      "13          NaN                NaN               NaN             NaN   \n",
+      "14          NaN                NaN               NaN             NaN   \n",
+      "15          NaN                NaN               NaN             NaN   \n",
+      "16          NaN                NaN               NaN             NaN   \n",
+      "17          NaN                NaN               NaN             NaN   \n",
+      "18         5.20  5th and 6th grade            133.58          131.54   \n",
+      "19         8.28  6th and 7th grade            115.58          112.37   \n",
+      "20         5.20  5th and 6th grade            133.58          131.54   \n",
+      "21         8.28  6th and 7th grade            115.58          112.37   \n",
+      "22         5.20  5th and 6th grade            133.58          131.54   \n",
+      "23         8.28  6th and 7th grade            115.58          112.37   \n",
+      "\n",
+      "    gutierrez_polini  crawford  gulpease_index   osman  \n",
+      "0                NaN       NaN             NaN     NaN  \n",
+      "1                NaN       NaN             NaN     NaN  \n",
+      "2                NaN       NaN             NaN     NaN  \n",
+      "3                NaN       NaN             NaN     NaN  \n",
+      "4                NaN       NaN             NaN     NaN  \n",
+      "5                NaN       NaN             NaN     NaN  \n",
+      "6              62.30      -0.2            79.8  116.91  \n",
+      "7              54.83       1.4            72.1  100.17  \n",
+      "8              62.30      -0.2            79.8  116.91  \n",
+      "9              54.83       1.4            72.1  100.17  \n",
+      "10             62.30      -0.2            79.8  116.91  \n",
+      "11             54.83       1.4            72.1  100.17  \n",
+      "12               NaN       NaN             NaN     NaN  \n",
+      "13               NaN       NaN             NaN     NaN  \n",
+      "14               NaN       NaN             NaN     NaN  \n",
+      "15               NaN       NaN             NaN     NaN  \n",
+      "16               NaN       NaN             NaN     NaN  \n",
+      "17               NaN       NaN             NaN     NaN  \n",
+      "18             62.30      -0.2            79.8  116.91  \n",
+      "19             54.83       1.4            72.1  100.17  \n",
+      "20             62.30      -0.2            79.8  116.91  \n",
+      "21             54.83       1.4            72.1  100.17  \n",
+      "22             62.30      -0.2            79.8  116.91  \n",
+      "23             54.83       1.4            72.1  100.17  \n",
+      "\n",
+      "[24 rows x 39 columns], 'session_analysis':     prompt_step         prompts    name  output_step  \\\n",
+      "0             1  Tell me a joke  OpenAI            2   \n",
+      "1             1  Tell me a poem  OpenAI            2   \n",
+      "2             1  Tell me a joke  OpenAI            2   \n",
+      "3             1  Tell me a poem  OpenAI            2   \n",
+      "4             1  Tell me a joke  OpenAI            2   \n",
+      "5             1  Tell me a poem  OpenAI            2   \n",
+      "6             3  Tell me a joke  OpenAI            4   \n",
+      "7             3  Tell me a poem  OpenAI            4   \n",
+      "8             3  Tell me a joke  OpenAI            4   \n",
+      "9             3  Tell me a poem  OpenAI            4   \n",
+      "10            3  Tell me a joke  OpenAI            4   \n",
+      "11            3  Tell me a poem  OpenAI            4   \n",
+      "\n",
+      "                                               output  \\\n",
+      "0   \\n\\nQ: What did the fish say when it hit the w...   \n",
+      "1   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
+      "2   \\n\\nQ: What did the fish say when it hit the w...   \n",
+      "3   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
+      "4   \\n\\nQ: What did the fish say when it hit the w...   \n",
+      "5   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
+      "6   \\n\\nQ: What did the fish say when it hit the w...   \n",
+      "7   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
+      "8   \\n\\nQ: What did the fish say when it hit the w...   \n",
+      "9   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
+      "10  \\n\\nQ: What did the fish say when it hit the w...   \n",
+      "11  \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
+      "\n",
+      "    token_usage_total_tokens  token_usage_prompt_tokens  \\\n",
+      "0                        162                         24   \n",
+      "1                        162                         24   \n",
+      "2                        162                         24   \n",
+      "3                        162                         24   \n",
+      "4                        162                         24   \n",
+      "5                        162                         24   \n",
+      "6                        162                         24   \n",
+      "7                        162                         24   \n",
+      "8                        162                         24   \n",
+      "9                        162                         24   \n",
+      "10                       162                         24   \n",
+      "11                       162                         24   \n",
+      "\n",
+      "    token_usage_completion_tokens  flesch_reading_ease  flesch_kincaid_grade  \\\n",
+      "0                             138               109.04                   1.3   \n",
+      "1                             138                83.66                   4.8   \n",
+      "2                             138               109.04                   1.3   \n",
+      "3                             138                83.66                   4.8   \n",
+      "4                             138               109.04                   1.3   \n",
+      "5                             138                83.66                   4.8   \n",
+      "6                             138               109.04                   1.3   \n",
+      "7                             138                83.66                   4.8   \n",
+      "8                             138               109.04                   1.3   \n",
+      "9                             138                83.66                   4.8   \n",
+      "10                            138               109.04                   1.3   \n",
+      "11                            138                83.66                   4.8   \n",
+      "\n",
+      "    ...  difficult_words  linsear_write_formula  gunning_fog  \\\n",
+      "0   ...                0                    5.5         5.20   \n",
+      "1   ...                2                    6.5         8.28   \n",
+      "2   ...                0                    5.5         5.20   \n",
+      "3   ...                2                    6.5         8.28   \n",
+      "4   ...                0                    5.5         5.20   \n",
+      "5   ...                2                    6.5         8.28   \n",
+      "6   ...                0                    5.5         5.20   \n",
+      "7   ...                2                    6.5         8.28   \n",
+      "8   ...                0                    5.5         5.20   \n",
+      "9   ...                2                    6.5         8.28   \n",
+      "10  ...                0                    5.5         5.20   \n",
+      "11  ...                2                    6.5         8.28   \n",
+      "\n",
+      "        text_standard  fernandez_huerta  szigriszt_pazos  gutierrez_polini  \\\n",
+      "0   5th and 6th grade            133.58           131.54             62.30   \n",
+      "1   6th and 7th grade            115.58           112.37             54.83   \n",
+      "2   5th and 6th grade            133.58           131.54             62.30   \n",
+      "3   6th and 7th grade            115.58           112.37             54.83   \n",
+      "4   5th and 6th grade            133.58           131.54             62.30   \n",
+      "5   6th and 7th grade            115.58           112.37             54.83   \n",
+      "6   5th and 6th grade            133.58           131.54             62.30   \n",
+      "7   6th and 7th grade            115.58           112.37             54.83   \n",
+      "8   5th and 6th grade            133.58           131.54             62.30   \n",
+      "9   6th and 7th grade            115.58           112.37             54.83   \n",
+      "10  5th and 6th grade            133.58           131.54             62.30   \n",
+      "11  6th and 7th grade            115.58           112.37             54.83   \n",
+      "\n",
+      "   crawford  gulpease_index   osman  \n",
+      "0      -0.2            79.8  116.91  \n",
+      "1       1.4            72.1  100.17  \n",
+      "2      -0.2            79.8  116.91  \n",
+      "3       1.4            72.1  100.17  \n",
+      "4      -0.2            79.8  116.91  \n",
+      "5       1.4            72.1  100.17  \n",
+      "6      -0.2            79.8  116.91  \n",
+      "7       1.4            72.1  100.17  \n",
+      "8      -0.2            79.8  116.91  \n",
+      "9       1.4            72.1  100.17  \n",
+      "10     -0.2            79.8  116.91  \n",
+      "11      1.4            72.1  100.17  \n",
+      "\n",
+      "[12 rows x 24 columns]}\n",
+      "2023-03-29 14:00:25,948 - clearml.Task - INFO - Completed model upload to https://files.clear.ml/langchain_callback_demo/llm.988bd727b0e94a29a3ac0ee526813545/models/simple_sequential\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SCENARIO 1 - LLM\n",
+    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
+    "# After every generation run, use flush to make sure all the metrics\n",
+    "# prompts and other output are properly saved separately\n",
+    "clearml_callback.flush_tracker(langchain_asset=llm, name=\"simple_sequential\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this point you can already go to https://app.clear.ml and take a look at the resulting ClearML Task that was created.\n",
+    "\n",
+    "Among others, you should see that this notebook is saved along with any git information. The model JSON that contains the used parameters is saved as an artifact, there are also console logs and under the plots section, you'll find tables that represent the flow of the chain.\n",
+    "\n",
+    "Finally, if you enabled visualizations, these are stored as HTML files under debug samples."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scenario 2: Creating a agent with tools\n",
+    "\n",
+    "To show a more advanced workflow, let's create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example.\n",
+    "\n",
+    "You can now also see the use of the `finish=True` keyword, which will fully close the ClearML Task, instead of just resetting the parameters and prompts for a new conversation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "{'action': 'on_chain_start', 'name': 'AgentExecutor', 'step': 1, 'starts': 1, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 0, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'input': 'Who is the wife of the person who sang summer of 69?'}\n",
+      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 2, 'starts': 2, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought:'}\n",
+      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 189, 'token_usage_completion_tokens': 34, 'token_usage_total_tokens': 223, 'model_name': 'text-davinci-003', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': ' I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 91.61, 'flesch_kincaid_grade': 3.8, 'smog_index': 0.0, 'coleman_liau_index': 3.41, 'automated_readability_index': 3.5, 'dale_chall_readability_score': 6.06, 'difficult_words': 2, 'linsear_write_formula': 5.75, 'gunning_fog': 5.4, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 121.07, 'szigriszt_pazos': 119.5, 'gutierrez_polini': 54.91, 'crawford': 0.9, 'gulpease_index': 72.7, 'osman': 92.16}\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who sang summer of 69 and then find out who their wife is.\n",
+      "Action: Search\n",
+      "Action Input: \"Who sang summer of 69\"\u001b[0m{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who sang summer of 69', 'log': ' I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"', 'step': 4, 'starts': 3, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 1, 'tool_ends': 0, 'agent_ends': 0}\n",
+      "{'action': 'on_tool_start', 'input_str': 'Who sang summer of 69', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 5, 'starts': 4, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 0, 'agent_ends': 0}\n",
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3mBryan Adams - Summer Of 69 (Official Music Video).\u001b[0m\n",
+      "Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams - Summer Of 69 (Official Music Video).', 'step': 6, 'starts': 4, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0}\n",
+      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 7, 'starts': 5, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"\\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\\nThought:'}\n",
+      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'text': ' I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 94.66, 'flesch_kincaid_grade': 2.7, 'smog_index': 0.0, 'coleman_liau_index': 4.73, 'automated_readability_index': 4.0, 'dale_chall_readability_score': 7.16, 'difficult_words': 2, 'linsear_write_formula': 4.25, 'gunning_fog': 4.2, 'text_standard': '4th and 5th grade', 'fernandez_huerta': 124.13, 'szigriszt_pazos': 119.2, 'gutierrez_polini': 52.26, 'crawford': 0.7, 'gulpease_index': 74.7, 'osman': 84.2}\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Bryan Adams is married to.\n",
+      "Action: Search\n",
+      "Action Input: \"Who is Bryan Adams married to\"\u001b[0m{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"', 'step': 9, 'starts': 6, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 3, 'tool_ends': 1, 'agent_ends': 0}\n",
+      "{'action': 'on_tool_start', 'input_str': 'Who is Bryan Adams married to', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 10, 'starts': 7, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 1, 'agent_ends': 0}\n",
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3mBryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\u001b[0m\n",
+      "Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0}\n",
+      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"\\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\\nThought: I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"\\nObservation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\\nThought:'}\n",
+      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'text': ' I now know the final answer.\\nFinal Answer: Bryan Adams has never been married.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 81.29, 'flesch_kincaid_grade': 3.7, 'smog_index': 0.0, 'coleman_liau_index': 5.75, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 7.37, 'difficult_words': 1, 'linsear_write_formula': 2.5, 'gunning_fog': 2.8, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 115.7, 'szigriszt_pazos': 110.84, 'gutierrez_polini': 49.79, 'crawford': 0.7, 'gulpease_index': 85.4, 'osman': 83.14}\n",
+      "\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Bryan Adams has never been married.\u001b[0m\n",
+      "{'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "{'action': 'on_chain_end', 'outputs': 'Bryan Adams has never been married.', 'step': 15, 'starts': 8, 'ends': 7, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 1, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}\n",
+      "{'action_records':              action    name  step  starts  ends  errors  text_ctr  \\\n",
+      "0      on_llm_start  OpenAI     1       1     0       0         0   \n",
+      "1      on_llm_start  OpenAI     1       1     0       0         0   \n",
+      "2      on_llm_start  OpenAI     1       1     0       0         0   \n",
+      "3      on_llm_start  OpenAI     1       1     0       0         0   \n",
+      "4      on_llm_start  OpenAI     1       1     0       0         0   \n",
+      "..              ...     ...   ...     ...   ...     ...       ...   \n",
+      "66      on_tool_end     NaN    11       7     4       0         0   \n",
+      "67     on_llm_start  OpenAI    12       8     4       0         0   \n",
+      "68       on_llm_end     NaN    13       8     5       0         0   \n",
+      "69  on_agent_finish     NaN    14       8     6       0         0   \n",
+      "70     on_chain_end     NaN    15       8     7       0         0   \n",
+      "\n",
+      "    chain_starts  chain_ends  llm_starts  ...  gulpease_index  osman  input  \\\n",
+      "0              0           0           1  ...             NaN    NaN    NaN   \n",
+      "1              0           0           1  ...             NaN    NaN    NaN   \n",
+      "2              0           0           1  ...             NaN    NaN    NaN   \n",
+      "3              0           0           1  ...             NaN    NaN    NaN   \n",
+      "4              0           0           1  ...             NaN    NaN    NaN   \n",
+      "..           ...         ...         ...  ...             ...    ...    ...   \n",
+      "66             1           0           2  ...             NaN    NaN    NaN   \n",
+      "67             1           0           3  ...             NaN    NaN    NaN   \n",
+      "68             1           0           3  ...            85.4  83.14    NaN   \n",
+      "69             1           0           3  ...             NaN    NaN    NaN   \n",
+      "70             1           1           3  ...             NaN    NaN    NaN   \n",
+      "\n",
+      "    tool  tool_input                                                log  \\\n",
+      "0    NaN         NaN                                                NaN   \n",
+      "1    NaN         NaN                                                NaN   \n",
+      "2    NaN         NaN                                                NaN   \n",
+      "3    NaN         NaN                                                NaN   \n",
+      "4    NaN         NaN                                                NaN   \n",
+      "..   ...         ...                                                ...   \n",
+      "66   NaN         NaN                                                NaN   \n",
+      "67   NaN         NaN                                                NaN   \n",
+      "68   NaN         NaN                                                NaN   \n",
+      "69   NaN         NaN   I now know the final answer.\\nFinal Answer: B...   \n",
+      "70   NaN         NaN                                                NaN   \n",
+      "\n",
+      "    input_str  description                                             output  \\\n",
+      "0         NaN          NaN                                                NaN   \n",
+      "1         NaN          NaN                                                NaN   \n",
+      "2         NaN          NaN                                                NaN   \n",
+      "3         NaN          NaN                                                NaN   \n",
+      "4         NaN          NaN                                                NaN   \n",
+      "..        ...          ...                                                ...   \n",
+      "66        NaN          NaN  Bryan Adams has never married. In the 1990s, h...   \n",
+      "67        NaN          NaN                                                NaN   \n",
+      "68        NaN          NaN                                                NaN   \n",
+      "69        NaN          NaN                Bryan Adams has never been married.   \n",
+      "70        NaN          NaN                                                NaN   \n",
+      "\n",
+      "                                outputs  \n",
+      "0                                   NaN  \n",
+      "1                                   NaN  \n",
+      "2                                   NaN  \n",
+      "3                                   NaN  \n",
+      "4                                   NaN  \n",
+      "..                                  ...  \n",
+      "66                                  NaN  \n",
+      "67                                  NaN  \n",
+      "68                                  NaN  \n",
+      "69                                  NaN  \n",
+      "70  Bryan Adams has never been married.  \n",
+      "\n",
+      "[71 rows x 47 columns], 'session_analysis':    prompt_step                                            prompts    name  \\\n",
+      "0            2  Answer the following questions as best you can...  OpenAI   \n",
+      "1            7  Answer the following questions as best you can...  OpenAI   \n",
+      "2           12  Answer the following questions as best you can...  OpenAI   \n",
+      "\n",
+      "   output_step                                             output  \\\n",
+      "0            3   I need to find out who sang summer of 69 and ...   \n",
+      "1            8   I need to find out who Bryan Adams is married...   \n",
+      "2           13   I now know the final answer.\\nFinal Answer: B...   \n",
+      "\n",
+      "   token_usage_total_tokens  token_usage_prompt_tokens  \\\n",
+      "0                       223                        189   \n",
+      "1                       270                        242   \n",
+      "2                       332                        314   \n",
+      "\n",
+      "   token_usage_completion_tokens  flesch_reading_ease  flesch_kincaid_grade  \\\n",
+      "0                             34                91.61                   3.8   \n",
+      "1                             28                94.66                   2.7   \n",
+      "2                             18                81.29                   3.7   \n",
+      "\n",
+      "   ...  difficult_words  linsear_write_formula  gunning_fog  \\\n",
+      "0  ...                2                   5.75          5.4   \n",
+      "1  ...                2                   4.25          4.2   \n",
+      "2  ...                1                   2.50          2.8   \n",
+      "\n",
+      "       text_standard  fernandez_huerta  szigriszt_pazos  gutierrez_polini  \\\n",
+      "0  3rd and 4th grade            121.07           119.50             54.91   \n",
+      "1  4th and 5th grade            124.13           119.20             52.26   \n",
+      "2  3rd and 4th grade            115.70           110.84             49.79   \n",
+      "\n",
+      "  crawford  gulpease_index  osman  \n",
+      "0      0.9            72.7  92.16  \n",
+      "1      0.7            74.7  84.20  \n",
+      "2      0.7            85.4  83.14  \n",
+      "\n",
+      "[3 rows x 24 columns]}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Could not update last created model in Task 988bd727b0e94a29a3ac0ee526813545, Task status 'completed' cannot be updated\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.agents import initialize_agent, load_tools\n",
+    "\n",
+    "# SCENARIO 2 - Agent with Tools\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=\"zero-shot-react-description\",\n",
+    "    callback_manager=manager,\n",
+    "    verbose=True,\n",
+    ")\n",
+    "agent.run(\n",
+    "    \"Who is the wife of the person who sang summer of 69?\"\n",
+    ")\n",
+    "clearml_callback.flush_tracker(langchain_asset=agent, name=\"Agent with Tools\", finish=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tips and Next Steps\n",
+    "\n",
+    "- Make sure you always use a unique `name` argument for the `clearml_callback.flush_tracker` function. If not, the model parameters used for a run will override the previous run!\n",
+    "\n",
+    "- If you close the ClearML Callback using `clearml_callback.flush_tracker(..., finish=True)` the Callback cannot be used anymore. Make a new one if you want to keep logging.\n",
+    "\n",
+    "- Check out the rest of the open source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "a53ebf4a859167383b364e7e7521d0add3c2dbbdecce4edf676e8c4634ff3fbb"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/ecosystem/cohere.md

@@ -22,4 +22,4 @@ There exists an Cohere Embeddings wrapper, which you can access with
 ```python
 from langchain.embeddings import CohereEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/cohere.ipynb)

docs/ecosystem/deepinfra.md

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

docs/ecosystem/deeplake.md

@@ -0,0 +1,25 @@
+# Deep Lake
+
+This page covers how to use the Deep Lake ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Deep Lake wrappers. For more information.
+
+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)
+
+## Installation and Setup
+- Install the Python package with `pip install deeplake`
+
+## Wrappers
+
+### VectorStore
+
+There exists a wrapper around Deep Lake, a data lake for Deep Learning applications, allowing you to use it as a vectorstore (for now), whether for semantic search or example selection.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import DeepLake
+```
+
+
+For a more detailed walkthrough of the Deep Lake wrapper, see [this notebook](../modules/indexes/vectorstores/examples/deeplake.ipynb)

docs/ecosystem/google_search.md

@@ -18,7 +18,7 @@ There exists a GoogleSearchAPIWrapper utility which wraps this API. To import th
 from langchain.utilities import GoogleSearchAPIWrapper
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/google_search.ipynb).
+For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/google_search.ipynb).
 
 ### Tool
 
@@ -29,4 +29,4 @@ from langchain.agents import load_tools
 tools = load_tools(["google-search"])
 ```
 
-For more information on this, see [this page](../modules/agents/tools.md)
+For more information on this, see [this page](../modules/agents/tools/getting_started.md)

docs/ecosystem/google_serper.md

@@ -34,7 +34,8 @@ search = GoogleSerperAPIWrapper()
 tools = [
     Tool(
         name="Intermediate Answer",
-        func=search.run
+        func=search.run,
+        description="useful for when you need to ask with search"
     )
 ]
 
@@ -57,7 +58,7 @@ So the final answer is: El Palmar, Spain
 'El Palmar, Spain'
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/google_serper.ipynb).
+For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/google_serper.ipynb).
 
 ### Tool
 
@@ -68,4 +69,4 @@ from langchain.agents import load_tools
 tools = load_tools(["google-serper"])
 ```
 
-For more information on this, see [this page](../modules/agents/tools.md)
+For more information on this, see [this page](../modules/agents/tools/getting_started.md)

docs/ecosystem/graphsignal.md

@@ -0,0 +1,38 @@
+# Graphsignal
+
+This page covers how to use the Graphsignal ecosystem to trace and monitor LangChain.
+
+## Installation and Setup
+
+- Install the Python library with `pip install graphsignal`
+- Create free Graphsignal account [here](https://graphsignal.com)
+- Get an API key and set it as an environment variable (`GRAPHSIGNAL_API_KEY`)
+
+## Tracing and Monitoring
+
+Graphsignal automatically instruments and starts tracing and monitoring chains. Traces, 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.
+
+Initialize the tracer by providing a deployment name:
+
+```python
+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:
+
+```python
+with graphsignal.start_trace('my-chain'):
+    chain.run("some initial text")
+```
+
+Optionally, enable profiling to record function-level statistics for each trace.
+
+```python
+with graphsignal.start_trace(
+        'my-chain', options=graphsignal.TraceOptions(enable_profiling=True)):
+    chain.run("some initial text")
+```
+
+See the [Quick Start](https://graphsignal.com/docs/guides/quick-start/) guide for complete setup instructions.

docs/ecosystem/helicone.md

@@ -1,6 +1,6 @@
 # Helicone
 
-This page covers how to use the [Helicone](https://helicone.ai) within LangChain.
+This page covers how to use the [Helicone](https://helicone.ai) ecosystem within LangChain.
 
 ## What is Helicone?
 
@@ -19,3 +19,35 @@ export OPENAI_API_BASE="https://oai.hconeai.com/v1"
 Now head over to [helicone.ai](https://helicone.ai/onboarding?step=2) to create your account, and add your OpenAI API key within our dashboard to view your logs.
 
 ![Helicone](../_static/HeliconeKeys.png)
+
+## How to enable Helicone caching
+
+```python
+from langchain.llms import OpenAI
+import openai
+openai.api_base = "https://oai.hconeai.com/v1"
+
+llm = OpenAI(temperature=0.9, headers={"Helicone-Cache-Enabled": "true"})
+text = "What is a helicone?"
+print(llm(text))
+```
+
+[Helicone caching docs](https://docs.helicone.ai/advanced-usage/caching)
+
+## How to use Helicone custom properties
+
+```python
+from langchain.llms import OpenAI
+import openai
+openai.api_base = "https://oai.hconeai.com/v1"
+
+llm = OpenAI(temperature=0.9, headers={
+        "Helicone-Property-Session": "24",
+        "Helicone-Property-Conversation": "support_issue_2",
+        "Helicone-Property-App": "mobile",
+      })
+text = "What is a helicone?"
+print(llm(text))
+```
+
+[Helicone property docs](https://docs.helicone.ai/advanced-usage/custom-properties)

docs/ecosystem/huggingface.md

@@ -30,7 +30,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub:
 ```python
 from langchain.llms import HuggingFaceHub
 ```
-For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](../modules/llms/integrations/huggingface_hub.ipynb)
+For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](../modules/models/llms/integrations/huggingface_hub.ipynb)
 
 
 ### Embeddings
@@ -47,7 +47,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub:
 ```python
 from langchain.embeddings import HuggingFaceHubEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/huggingfacehub.ipynb)
 
 ### Tokenizer
 
@@ -59,7 +59,7 @@ You can also use it to count tokens when splitting documents with
 from langchain.text_splitter import CharacterTextSplitter
 CharacterTextSplitter.from_huggingface_tokenizer(...)
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/textsplitter.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/indexes/text_splitters/examples/huggingface_length_function.ipynb)
 
 
 ### Datasets

docs/ecosystem/jina.md

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

docs/ecosystem/milvus.md

@@ -0,0 +1,20 @@
+# Milvus
+
+This page covers how to use the Milvus ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
+
+## Installation and Setup
+- Install the Python SDK with `pip install pymilvus`
+## Wrappers
+
+### VectorStore
+
+There exists a wrapper around Milvus indexes, allowing you to use it as a vectorstore,
+whether for semantic search or example selection.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import Milvus
+```
+
+For a more detailed walkthrough of the Miluvs wrapper, see [this notebook](../modules/indexes/vectorstores/examples/milvus.ipynb)

docs/ecosystem/modal.md

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

docs/ecosystem/openai.md

@@ -21,7 +21,7 @@ If you are using a model hosted on Azure, you should use different wrapper for t
 ```python
 from langchain.llms import AzureOpenAI
 ```
-For a more detailed walkthrough of the Azure wrapper, see [this notebook](../modules/llms/integrations/azure_openai_example.ipynb)
+For a more detailed walkthrough of the Azure wrapper, see [this notebook](../modules/models/llms/integrations/azure_openai_example.ipynb)
 
 
 
@@ -31,7 +31,7 @@ There exists an OpenAI Embeddings wrapper, which you can access with
 ```python
 from langchain.embeddings import OpenAIEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/openai.ipynb)
 
 
 ### Tokenizer
@@ -44,7 +44,7 @@ You can also use it to count tokens when splitting documents with
 from langchain.text_splitter import CharacterTextSplitter
 CharacterTextSplitter.from_tiktoken_encoder(...)
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/textsplitter.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/indexes/text_splitters/examples/tiktoken.ipynb)
 
 ### Moderation
 You can also access the OpenAI content moderation endpoint with

docs/ecosystem/opensearch.md

@@ -18,4 +18,4 @@ To import this vectorstore:
 from langchain.vectorstores import OpenSearchVectorSearch
 ```
 
-For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](../modules/indexes/vectorstore_examples/opensearch.ipynb)
+For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](../modules/indexes/vectorstores/examples/opensearch.ipynb)

docs/ecosystem/petals.md

@@ -5,7 +5,7 @@ It is broken into two parts: installation and setup, and then references to spec
 
 ## Installation and Setup
 - Install with `pip install petals`
-- Get an Huggingface api key and set it as an environment variable (`HUGGINGFACE_API_KEY`)
+- Get a Hugging Face api key and set it as an environment variable (`HUGGINGFACE_API_KEY`)
 
 ## Wrappers
 
@@ -14,4 +14,4 @@ It is broken into two parts: installation and setup, and then references to spec
 There exists an Petals LLM wrapper, which you can access with 
 ```python
 from langchain.llms import Petals
-```
+```

docs/ecosystem/pgvector.md

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

docs/ecosystem/pinecone.md

@@ -17,4 +17,4 @@ To import this vectorstore:
 from langchain.vectorstores import Pinecone
 ```
 
-For a more detailed walkthrough of the Pinecone wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)
+For a more detailed walkthrough of the Pinecone wrapper, see [this notebook](../modules/indexes/vectorstores/examples/pinecone.ipynb)

docs/ecosystem/promptlayer.md

@@ -25,7 +25,25 @@ from langchain.llms import PromptLayerOpenAI
 llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"])
 ```
 
-This LLM is identical to the [OpenAI LLM](./openai), except that
+To get the PromptLayer request id, use the argument `return_pl_id` when instanializing the LLM
+```python
+from langchain.llms import PromptLayerOpenAI
+llm = PromptLayerOpenAI(return_pl_id=True)
+```
+This will add the PromptLayer request ID in the `generation_info` field of the `Generation` returned when using `.generate` or `.agenerate`
+
+For example:
+```python
+llm_results = llm.generate(["hello world"])
+for res in llm_results.generations:
+    print("pl request id: ", res[0].generation_info["pl_request_id"])
+```
+You can use the PromptLayer request ID to add a prompt, score, or other metadata to your request. [Read more about it here](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
+
+This LLM is identical to the [OpenAI LLM](./openai.md), except that
 - all your requests will be logged to your PromptLayer account
 - you can add `pl_tags` when instantializing to tag your requests on PromptLayer
+- you can add `return_pl_id` when instantializing to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
 
+
+PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](../modules/models/chat/integrations/promptlayer_chatopenai.ipynb) and `PromptLayerOpenAIChat`

docs/ecosystem/qdrant.md

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

docs/ecosystem/replicate.md

@@ -0,0 +1,47 @@
+# Replicate
+This page covers how to run models on Replicate within LangChain.
+
+## Installation and Setup
+- Create a [Replicate](https://replicate.com) account. Get your API key and set it as an environment variable (`REPLICATE_API_TOKEN`)
+- Install the [Replicate python client](https://github.com/replicate/replicate-python) with `pip install replicate`
+
+## Calling a model
+
+Find a model on the [Replicate explore page](https://replicate.com/explore), and then paste in the model name and version in this format: `owner-name/model-name:version`
+
+For example, for this [flan-t5 model](https://replicate.com/daanelson/flan-t5), click on the API tab. The model name/version would be: `daanelson/flan-t5:04e422a9b85baed86a4f24981d7f9953e20c5fd82f6103b74ebc431588e1cec8`
+
+Only the `model` param is required, but any other model parameters can also be passed in with the format `input={model_param: value, ...}`
+
+
+For example, if we were running stable diffusion and wanted to change the image dimensions:
+
+```
+Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions': '512x512'})
+```
+
+*Note that only the first output of a model will be returned.*
+From here, we can initialize our model:
+
+```python
+llm = Replicate(model="daanelson/flan-t5:04e422a9b85baed86a4f24981d7f9953e20c5fd82f6103b74ebc431588e1cec8")
+```
+
+And run it:
+
+```python
+prompt = """
+Answer the following yes/no question by reasoning step by step.
+Can a dog drive a car?
+"""
+llm(prompt)
+```
+
+We can call any Replicate model (not just LLMs) using this syntax. For example, we can call [Stable Diffusion](https://replicate.com/stability-ai/stable-diffusion):
+
+```python
+text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
+                       input={'image_dimensions'='512x512'}
+
+image_output = text2image("A cat riding a motorcycle by Picasso")
+```

docs/ecosystem/runhouse.md

@@ -15,7 +15,7 @@ custom LLMs, you can use the `SelfHostedPipeline` parent class.
 from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
 ```
 
-For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/llms/integrations/self_hosted_examples.ipynb)
+For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/models/llms/integrations/self_hosted_examples.ipynb)
 
 ## Self-hosted Embeddings
 There are several ways to use self-hosted embeddings with LangChain via Runhouse.
@@ -26,6 +26,4 @@ the `SelfHostedEmbedding` class.
 from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
 ```
 
-For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
-
-##
+For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/models/text_embedding/examples/self-hosted.ipynb)

docs/ecosystem/searx.md

@@ -5,31 +5,66 @@ It is broken into two parts: installation and setup, and then references to the
 
 ## Installation and Setup
 
-- You can find a list of public SearxNG instances [here](https://searx.space/). 
-- It recommended to use a self-hosted instance to avoid abuse on the public instances. Also note that public instances often have a limit on the number of requests.
-- To run a self-hosted instance see [this page](https://searxng.github.io/searxng/admin/installation.html) for more information.
-- To use the tool you need to provide the searx host url by:
-    1. passing the named parameter `searx_host` when creating the instance.
-    2. exporting the environment variable `SEARXNG_HOST`. 
+While it is possible to utilize the wrapper in conjunction with  [public searx
+instances](https://searx.space/) these instances frequently do not permit API
+access (see note on output format below) and have limitations on the frequency
+of requests. It is recommended to opt for a self-hosted instance instead.
+
+### Self Hosted Instance:
+
+See [this page](https://searxng.github.io/searxng/admin/installation.html) for installation instructions.
+
+When you install SearxNG, the only active output format by default is the HTML format.
+You need to activate the `json` format to use the API. This can be done by adding the following line to the `settings.yml` file:
+```yaml
+search:
+    formats:
+        - html
+        - json
+```
+You can make sure that the API is working by issuing a curl request to the API endpoint:
+
+`curl -kLX GET --data-urlencode q='langchain' -d format=json http://localhost:8888`
+
+This should return a JSON object with the results.
+
 
 ## Wrappers
 
 ### Utility
 
+To use the wrapper we need to pass the host of the SearxNG instance to the wrapper with:
+    1. the named parameter `searx_host` when creating the instance.
+    2. exporting the environment variable `SEARXNG_HOST`.
+
 You can use the wrapper to get results from a SearxNG instance. 
 
 ```python
 from langchain.utilities import SearxSearchWrapper
+s = SearxSearchWrapper(searx_host="http://localhost:8888")
+s.run("what is a large language model?")
 ```
 
 ### Tool
 
-You can also easily load this wrapper as a Tool (to use with an Agent).
+You can also load this wrapper as a Tool (to use with an Agent).
+
 You can do this with:
 
 ```python
 from langchain.agents import load_tools
-tools = load_tools(["searx-search"], searx_host="https://searx.example.com")
+tools = load_tools(["searx-search"],
+                    searx_host="http://localhost:8888",
+                    engines=["github"])
 ```
 
-For more information on this, see [this page](../modules/agents/tools.md)
+Note that we could _optionally_ pass custom engines to use.
+
+If you want to obtain results with metadata as *json* you can use:
+```python
+tools = load_tools(["searx-search-results-json"],
+                    searx_host="http://localhost:8888",
+                    num_results=5)
+```
+
+For more information on tools, see [this page](../modules/agents/tools/getting_started.md)

docs/ecosystem/serpapi.md

@@ -17,7 +17,7 @@ There exists a SerpAPI utility which wraps this API. To import this utility:
 from langchain.utilities import SerpAPIWrapper
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/serpapi.ipynb).
+For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/serpapi.ipynb).
 
 ### Tool
 
@@ -28,4 +28,4 @@ from langchain.agents import load_tools
 tools = load_tools(["serpapi"])
 ```
 
-For more information on this, see [this page](../modules/agents/tools.md)
+For more information on this, see [this page](../modules/agents/tools/getting_started.md)

docs/ecosystem/stochasticai.md

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

docs/ecosystem/unstructured.md

@@ -13,17 +13,17 @@ This page is broken into two parts: installation and setup, and then references
 - Install the Python SDK with `pip install "unstructured[local-inference]"`
 - Install the following system dependencies if they are not already available on your system.
   Depending on what document types you're parsing, you may not need all of these.
-    - `libmagic-dev`
-    - `poppler-utils`
-    - `tesseract-ocr`
-    - `libreoffice`
-- Run the following to install NLTK dependencies. `unstructured` will handle this automatically
-  soon.
-  - `python -c "import nltk; nltk.download('punkt')"`
-  - `python -c "import nltk; nltk.download('averaged_perceptron_tagger')"`
-- If you are parsing PDFs, run the following to install the `detectron2` model, which
+    - `libmagic-dev` (filetype detection)
+    - `poppler-utils` (images and PDFs)
+    - `tesseract-ocr`(images and PDFs)
+    - `libreoffice` (MS Office docs)
+    - `pandoc` (EPUBs)
+- If you are parsing PDFs using the `"hi_res"` strategy, run the following to install the `detectron2` model, which
   `unstructured` uses for layout detection:
     - `pip install "detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2"`
+    - If `detectron2` is not installed, `unstructured` will fallback to processing PDFs
+      using the `"fast"` strategy, which uses `pdfminer` directly and doesn't require
+      `detectron2`.
 
 ## Wrappers
 

docs/ecosystem/wandb_tracking.ipynb

@@ -0,0 +1,625 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Weights & Biases\n",
+    "\n",
+    "This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see.\n",
+    "\n",
+    "Run in Colab: https://colab.research.google.com/drive/1DXH4beT4HFaRKy_Vm4PoxhXVDRf7Ym8L?usp=sharing\n",
+    "\n",
+    "View Report: https://wandb.ai/a-sh0ts/langchain_callback_demo/reports/Prompt-Engineering-LLMs-with-LangChain-and-W-B--VmlldzozNjk1NTUw#👋-how-to-build-a-callback-in-langchain-for-better-prompt-engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install wandb\n",
+    "!pip install pandas\n",
+    "!pip install textstat\n",
+    "!pip install spacy\n",
+    "!python -m spacy download en_core_web_sm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "T1bSmKd6V2If"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"WANDB_API_KEY\"] = \"\"\n",
+    "# os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
+    "# os.environ[\"SERPAPI_API_KEY\"] = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "8WAGnTWpUUnD"
+   },
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "from langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandler\n",
+    "from langchain.callbacks.base import CallbackManager\n",
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "Callback Handler that logs to Weights and Biases.\n",
+    "\n",
+    "Parameters:\n",
+    "    job_type (str): The type of job.\n",
+    "    project (str): The project to log to.\n",
+    "    entity (str): The entity to log to.\n",
+    "    tags (list): The tags to log.\n",
+    "    group (str): The group to log to.\n",
+    "    name (str): The name of the run.\n",
+    "    notes (str): The notes to log.\n",
+    "    visualize (bool): Whether to visualize the run.\n",
+    "    complexity_metrics (bool): Whether to log complexity metrics.\n",
+    "    stream_logs (bool): Whether to stream callback actions to W&B\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cxBFfZR8d9FC"
+   },
+   "source": [
+    "```\n",
+    "Default values for WandbCallbackHandler(...)\n",
+    "\n",
+    "visualize: bool = False,\n",
+    "complexity_metrics: bool = False,\n",
+    "stream_logs: bool = False,\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NOTE: For beta workflows we have made the default analysis based on textstat and the visualizations based on spacy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "KAz8weWuUeXF"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mharrison-chase\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.14.0"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150408-e47j1914</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">llm</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The wandb callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/wandb/wandb/issues with the tag `langchain`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"Main function.\n",
+    "\n",
+    "This function is used to try the callback handler.\n",
+    "Scenarios:\n",
+    "1. OpenAI LLM\n",
+    "2. Chain with multiple SubChains on multiple generations\n",
+    "3. Agent with Tools\n",
+    "\"\"\"\n",
+    "session_group = datetime.now().strftime(\"%m.%d.%Y_%H.%M.%S\")\n",
+    "wandb_callback = WandbCallbackHandler(\n",
+    "    job_type=\"inference\",\n",
+    "    project=\"langchain_callback_demo\",\n",
+    "    group=f\"minimal_{session_group}\",\n",
+    "    name=\"llm\",\n",
+    "    tags=[\"test\"],\n",
+    ")\n",
+    "manager = CallbackManager([StdOutCallbackHandler(), wandb_callback])\n",
+    "llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Q-65jwrDeK6w"
+   },
+   "source": [
+    "\n",
+    "\n",
+    "```\n",
+    "# Defaults for WandbCallbackHandler.flush_tracker(...)\n",
+    "\n",
+    "reset: bool = True,\n",
+    "finish: bool = False,\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `flush_tracker` function is used to log LangChain sessions to Weights & Biases. It takes in the LangChain module or agent, and logs at minimum the prompts and generations alongside the serialized form of the LangChain module to the specified Weights & Biases project. By default we reset the session as opposed to concluding the session outright."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "o_VmneyIUyx8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">llm</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a><br/>Synced 5 W&B file(s), 2 media file(s), 5 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230318_150408-e47j1914/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d7b4307ccdb450ea631497174fca2d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016745895149999985, max=1.0…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.14.0"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150534-jyxma7hu</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">simple_sequential</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SCENARIO 1 - LLM\n",
+    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
+    "wandb_callback.flush_tracker(llm, name=\"simple_sequential\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "trxslyb1U28Y"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "uauQk10SUzF6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">simple_sequential</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a><br/>Synced 4 W&B file(s), 2 media file(s), 6 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230318_150534-jyxma7hu/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbdbf28fb8ed40a3a60218d2e6d1a987",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016736786816666675, max=1.0…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.14.0"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150550-wzy59zjq</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">agent</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SCENARIO 2 - Chain\n",
+    "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
+    "Title: {title}\n",
+    "Playwright: This is a synopsis for the above play:\"\"\"\n",
+    "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
+    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
+    "\n",
+    "test_prompts = [\n",
+    "    {\n",
+    "        \"title\": \"documentary about good video games that push the boundary of game design\"\n",
+    "    },\n",
+    "    {\"title\": \"cocaine bear vs heroin wolf\"},\n",
+    "    {\"title\": \"the best in class mlops tooling\"},\n",
+    "]\n",
+    "synopsis_chain.apply(test_prompts)\n",
+    "wandb_callback.flush_tracker(synopsis_chain, name=\"agent\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "_jN73xcPVEpI"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.agents import initialize_agent, load_tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "Gpq4rk6VT9cu"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mDiCaprio had a steady girlfriend in Camila Morrone. He had been with the model turned actress for nearly five years, as they were first said to be dating at the end of 2017. And the now 26-year-old Morrone is no stranger to Hollywood.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate her age raised to the 0.43 power.\n",
+      "Action: Calculator\n",
+      "Action Input: 26^0.43\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.059182145592686\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Leo DiCaprio's girlfriend is Camila Morrone and her current age raised to the 0.43 power is 4.059182145592686.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">agent</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a><br/>Synced 5 W&B file(s), 2 media file(s), 7 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>./wandb/run-20230318_150550-wzy59zjq/logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# SCENARIO 3 - Agent with Tools\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
+    "agent = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=\"zero-shot-react-description\",\n",
+    "    callback_manager=manager,\n",
+    "    verbose=True,\n",
+    ")\n",
+    "agent.run(\n",
+    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
+    ")\n",
+    "wandb_callback.flush_tracker(agent, reset=False, finish=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/ecosystem/weaviate.md

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

docs/ecosystem/wolfram_alpha.md

@@ -20,7 +20,7 @@ There exists a WolframAlphaAPIWrapper utility which wraps this API. To import th
 from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/wolfram_alpha.ipynb).
+For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/wolfram_alpha.ipynb).
 
 ### Tool
 
@@ -31,4 +31,4 @@ from langchain.agents import load_tools
 tools = load_tools(["wolfram-alpha"])
 ```
 
-For more information on this, see [this page](../modules/agents/tools.md)
+For more information on this, see [this page](../modules/agents/tools/getting_started.md)

docs/ecosystem/writer.md

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

docs/gallery.rst

@@ -158,14 +158,14 @@ Open Source
 
     ---
 
-    .. link-button:: https://github.com/jerryjliu/gpt_index
+    .. link-button:: https://github.com/jerryjliu/llama_index
         :type: url
-        :text: GPT Index
+        :text: LlamaIndex
         :classes: stretched-link btn-lg
     
     +++
 
-    GPT Index is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries.
+    LlamaIndex (formerly GPT Index) is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries.
 
     ---
 
@@ -322,5 +322,14 @@ Proprietary
 
     By Zahid Khawaja, this demo utilizes question answering to answer questions about a given website. A followup added this for `YouTube videos <https://twitter.com/chillzaza_/status/1593739682013220865?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ>`_, and then another followup added it for `Wikipedia <https://twitter.com/chillzaza_/status/1594847151238037505?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ>`_.
 
+    ---
 
+    .. link-button:: https://mynd.so
+        :type: url
+        :text: Mynd
+        :classes: stretched-link btn-lg
 
+    +++
+
+    A journaling app for self-care that uses AI to uncover insights and patterns over time.
+    

docs/getting_started/getting_started.md

@@ -36,7 +36,7 @@ os.environ["OPENAI_API_KEY"] = "..."
 ```
 
 
-## Building a Language Model Application
+## Building a Language Model Application: LLMs
 
 Now that we have installed LangChain and set up our environment, we can start building our language model application.
 
@@ -66,7 +66,7 @@ llm = OpenAI(temperature=0.9)
 We can now call it on some input!
 
 ```python
-text = "What would be a good company name a company that makes colorful socks?"
+text = "What would be a good company name for a company that makes colorful socks?"
 print(llm(text))
 ```
 
@@ -74,7 +74,7 @@ print(llm(text))
 Feetful of Fun
 ```
 
-For more details on how to use LLMs within LangChain, see the [LLM getting started guide](../modules/llms/getting_started.ipynb).
+For more details on how to use LLMs within LangChain, see the [LLM getting started guide](../modules/models/llms/getting_started.ipynb).
 `````
 
 
@@ -111,7 +111,7 @@ What is a good name for a company that makes colorful socks?
 ```
 
 
-[For more details, check out the getting started guide for prompts.](../modules/prompts/getting_started.ipynb)
+[For more details, check out the getting started guide for prompts.](../modules/prompts/chat_prompt_template.ipynb)
 
 `````
 
@@ -160,7 +160,7 @@ This is one of the simpler types of chains, but understanding how it works will
 `````
 
 
-`````{dropdown} Agents: Dynamically call chains based on user input
+`````{dropdown} Agents: Dynamically Call Chains Based on User Input
 
 So far the chains we've looked at run in a predetermined order.
 
@@ -210,35 +210,31 @@ tools = load_tools(["serpapi", "llm-math"], llm=llm)
 agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
 
 # Now let's test it out!
-agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
+agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")
 ```
 
 ```pycon
-Entering new AgentExecutor chain...
- I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.
+> Entering new AgentExecutor chain...
+ I need to find the temperature first, then use the calculator to raise it to the .023 power.
 Action: Search
-Action Input: "Olivia Wilde boyfriend"
-Observation: Jason Sudeikis
-Thought: I need to find out Jason Sudeikis' age
-Action: Search
-Action Input: "Jason Sudeikis age"
-Observation: 47 years
-Thought: I need to calculate 47 raised to the 0.23 power
+Action Input: "High temperature in SF yesterday"
+Observation: San Francisco Temperature Yesterday. Maximum temperature yesterday: 57 °F (at 1:56 pm) Minimum temperature yesterday: 49 °F (at 1:56 am) Average temperature ...
+Thought: I now have the temperature, so I can use the calculator to raise it to the .023 power.
 Action: Calculator
-Action Input: 47^0.23
-Observation: Answer: 2.4242784855673896
+Action Input: 57^.023
+Observation: Answer: 1.0974509573251117
 
 Thought: I now know the final answer
-Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.
-> Finished AgentExecutor chain.
-"Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896."
+Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .023 power is 1.0974509573251117.
+
+> Finished chain.
 ```
 
 
 `````
 
 
-`````{dropdown} Memory: Add state to chains and agents
+`````{dropdown} Memory: Add State to Chains and Agents
 
 So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of "memory" so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of "short-term memory". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of "long-term memory". For more concrete ideas on the latter, see this [awesome paper](https://memprompt.com/).
 
@@ -287,4 +283,218 @@ AI:
 
 > Finished chain.
 " That's great! What would you like to talk about?"
-```
+```
+`````
+
+## Building a Language Model Application: Chat Models
+
+Similarly, you can use chat models instead of LLMs. Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
+
+Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
+
+
+`````{dropdown} Get Message Completions from a Chat Model
+You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
+
+```python
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage
+)
+
+chat = ChatOpenAI(temperature=0)
+```
+
+You can get completions by passing in a single message.
+
+```python
+chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
+# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
+```
+
+You can also pass in multiple messages for OpenAI's gpt-3.5-turbo and gpt-4 models.
+
+```python
+messages = [
+    SystemMessage(content="You are a helpful assistant that translates English to French."),
+    HumanMessage(content="Translate this sentence from English to French. I love programming.")
+]
+chat(messages)
+# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
+```
+
+You can go one step further and generate completions for multiple sets of messages using `generate`. This returns an `LLMResult` with an additional `message` parameter:
+```python
+batch_messages = [
+    [
+        SystemMessage(content="You are a helpful assistant that translates English to French."),
+        HumanMessage(content="Translate this sentence from English to French. I love programming.")
+    ],
+    [
+        SystemMessage(content="You are a helpful assistant that translates English to French."),
+        HumanMessage(content="Translate this sentence from English to French. I love artificial intelligence.")
+    ],
+]
+result = chat.generate(batch_messages)
+result
+# -> LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}})
+```
+
+You can recover things like token usage from this LLMResult:
+```
+result.llm_output['token_usage']
+# -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
+```
+`````
+
+`````{dropdown} Chat Prompt Templates
+Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or `Message` object, depending on whether you want to use the formatted value as input to an llm or chat model.
+
+For convience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
+
+```python
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+chat = ChatOpenAI(temperature=0)
+
+template="You are a helpful assistant that translates {input_language} to {output_language}."
+system_message_prompt = SystemMessagePromptTemplate.from_template(template)
+human_template="{text}"
+human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+
+chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
+
+# get a chat completion from the formatted messages
+chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
+# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
+```
+`````
+
+`````{dropdown} Chains with Chat Models
+The `LLMChain` discussed in the above section can be used with chat models as well:
+
+```python
+from langchain.chat_models import ChatOpenAI
+from langchain import LLMChain
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+chat = ChatOpenAI(temperature=0)
+
+template="You are a helpful assistant that translates {input_language} to {output_language}."
+system_message_prompt = SystemMessagePromptTemplate.from_template(template)
+human_template="{text}"
+human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
+
+chain = LLMChain(llm=chat, prompt=chat_prompt)
+chain.run(input_language="English", output_language="French", text="I love programming.")
+# -> "J'aime programmer."
+```
+`````
+
+`````{dropdown} Agents with Chat Models
+Agents can also be used with chat models, you can initialize one using `"chat-zero-shot-react-description"` as the agent type.
+
+```python
+from langchain.agents import load_tools
+from langchain.agents import initialize_agent
+from langchain.chat_models import ChatOpenAI
+from langchain.llms import OpenAI
+
+# First, let's load the language model we're going to use to control the agent.
+chat = ChatOpenAI(temperature=0)
+
+# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
+llm = OpenAI(temperature=0)
+tools = load_tools(["serpapi", "llm-math"], llm=llm)
+
+
+# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
+agent = initialize_agent(tools, chat, agent="chat-zero-shot-react-description", verbose=True)
+
+# Now let's test it out!
+agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
+```
+
+```pycon
+
+> Entering new AgentExecutor chain...
+Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
+Action:
+{
+  "action": "Search",
+  "action_input": "Olivia Wilde boyfriend"
+}
+
+Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.
+Thought:I need to use a search engine to find Harry Styles' current age.
+Action:
+{
+  "action": "Search",
+  "action_input": "Harry Styles age"
+}
+
+Observation: 29 years
+Thought:Now I need to calculate 29 raised to the 0.23 power.
+Action:
+{
+  "action": "Calculator",
+  "action_input": "29^0.23"
+}
+
+Observation: Answer: 2.169459462491557
+
+Thought:I now know the final answer.
+Final Answer: 2.169459462491557
+
+> Finished chain.
+'2.169459462491557'
+```
+`````
+
+`````{dropdown} Memory: Add State to Chains and Agents
+You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
+
+```python
+from langchain.prompts import (
+    ChatPromptTemplate, 
+    MessagesPlaceholder, 
+    SystemMessagePromptTemplate, 
+    HumanMessagePromptTemplate
+)
+from langchain.chains import ConversationChain
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+
+prompt = ChatPromptTemplate.from_messages([
+    SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."),
+    MessagesPlaceholder(variable_name="history"),
+    HumanMessagePromptTemplate.from_template("{input}")
+])
+
+llm = ChatOpenAI(temperature=0)
+memory = ConversationBufferMemory(return_messages=True)
+conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)
+
+conversation.predict(input="Hi there!")
+# -> 'Hello! How can I assist you today?'
+
+
+conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
+# -> "That sounds like fun! I'm happy to chat with you. Is there anything specific you'd like to talk about?"
+
+conversation.predict(input="Tell me about yourself.")
+# -> "Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?"
+```
+`````

docs/glossary.md

@@ -32,7 +32,7 @@ This induces the to model to think about what action to take, then take it.
 Resources:
 
 - [Paper](https://arxiv.org/pdf/2210.03629.pdf)
-- [LangChain Example](./modules/agents/implementations/react.ipynb)
+- [LangChain Example](modules/agents/agents/examples/react.ipynb)
 
 ## Self-ask
 
@@ -42,7 +42,7 @@ In this method, the model explicitly asks itself follow-up questions, which are
 Resources:
 
 - [Paper](https://ofir.io/self-ask.pdf)
-- [LangChain Example](./modules/agents/implementations/self_ask_with_search.ipynb)
+- [LangChain Example](modules/agents/agents/examples/self_ask_with_search.ipynb)
 
 ## Prompt Chaining
 

docs/index.rst

@@ -1,28 +1,14 @@
 Welcome to LangChain
 ==========================
 
-Large language models (LLMs) are emerging as a transformative technology, enabling
-developers to build applications that they previously could not.
-But using these LLMs in isolation is often not enough to
-create a truly powerful app - the real power comes when you are able to
-combine them with other sources of computation or knowledge.
+LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:
 
-This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
+- *Be data-aware*: connect a language model to other sources of data
+- *Be agentic*: allow a language model to interact with its environment
 
-**❓ Question Answering over specific documents**
+The LangChain framework is designed with the above principles in mind.
 
-- `Documentation <./use_cases/question_answering.html>`_
-- End-to-end Example: `Question Answering over Notion Database <https://github.com/hwchase17/notion-qa>`_
-
-**💬 Chatbots**
-
-- `Documentation <./use_cases/chatbots.html>`_
-- End-to-end Example: `Chat-LangChain <https://github.com/hwchase17/chat-langchain>`_
-
-**🤖 Agents**
-
-- `Documentation <./use_cases/agents.html>`_
-- End-to-end Example: `GPT+WolframAlpha <https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain>`_
+This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see `here <https://docs.langchain.com/docs/>`_. For the JavaScript documentation, see `here <https://js.langchain.com/docs/>`_.
 
 Getting Started
 ----------------
@@ -46,22 +32,17 @@ There are several main modules that LangChain provides support for.
 For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.
 These modules are, in increasing order of complexity:
 
+- `Models <./modules/models.html>`_: The various model types and model integrations LangChain supports.
 
 - `Prompts <./modules/prompts.html>`_: This includes prompt management, prompt optimization, and prompt serialization.
 
-- `LLMs <./modules/llms.html>`_: This includes a generic interface for all LLMs, and common utilities for working with LLMs.
-
-- `Document Loaders <./modules/document_loaders.html>`_: This includes a standard interface for loading documents, as well as specific integrations to all types of text data sources.
-
-- `Utils <./modules/utils.html>`_: Language models are often more powerful when interacting with other sources of knowledge or computation. This can include Python REPLs, embeddings, search engines, and more. LangChain provides a large collection of common utils to use in your application.
-
-- `Chains <./modules/chains.html>`_: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
+- `Memory <./modules/memory.html>`_: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
 
 - `Indexes <./modules/indexes.html>`_: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.
 
-- `Agents <./modules/agents.html>`_: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
+- `Chains <./modules/chains.html>`_: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
 
-- `Memory <./modules/memory.html>`_: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
+- `Agents <./modules/agents.html>`_: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
 
 
 .. toctree::
@@ -70,37 +51,34 @@ These modules are, in increasing order of complexity:
    :name: modules
    :hidden:
 
-   ./modules/prompts.md
-   ./modules/llms.md
-   ./modules/document_loaders.md
-   ./modules/utils.md
+   ./modules/models.rst
+   ./modules/prompts.rst
    ./modules/indexes.md
+   ./modules/memory.md
    ./modules/chains.md
    ./modules/agents.md
-   ./modules/memory.md
 
 Use Cases
 ----------
 
 The above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.
 
-- `Agents <./use_cases/agents.html>`_: Agents are systems that use a language model to interact with other tools. These can be used to do more grounded question/answering, interact with APIs, or even take actions.
+- `Personal Assistants <./use_cases/personal_assistants.html>`_: The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data.
+
+- `Question Answering <./use_cases/question_answering.html>`_: The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.
 
 - `Chatbots <./use_cases/chatbots.html>`_: Since language models are good at producing text, that makes them ideal for creating chatbots.
 
-- `Data Augmented Generation <./use_cases/combine_docs.html>`_: Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.
+- `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.
 
-- `Question Answering <./use_cases/question_answering.html>`_: Answering questions over specific documents, only utilizing the information in those documents to construct an answer. A type of Data Augmented Generation.
+- `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.
 
 - `Summarization <./use_cases/summarization.html>`_: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.
 
 - `Evaluation <./use_cases/evaluation.html>`_: 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.
 
-- `Generate similar examples <./use_cases/generate_examples.html>`_: Generating similar examples to a given input. This is a common use case for many applications, and LangChain provides some prompts/chains for assisting in this.
-
-- `Compare models <./use_cases/model_laboratory.html>`_: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
-
-
 
 .. toctree::
    :maxdepth: 1
@@ -108,14 +86,14 @@ The above modules can be used in a variety of ways. LangChain also provides guid
    :name: use_cases
    :hidden:
 
-   ./use_cases/agents.md
-   ./use_cases/chatbots.md
-   ./use_cases/generate_examples.ipynb
-   ./use_cases/combine_docs.md
+   ./use_cases/personal_assistants.md
    ./use_cases/question_answering.md
+   ./use_cases/chatbots.md
+   ./use_cases/tabular.rst
+   ./use_cases/apis.md
    ./use_cases/summarization.md
+   ./use_cases/extraction.md
    ./use_cases/evaluation.rst
-   ./use_cases/model_laboratory.ipynb
 
 
 Reference Docs
@@ -166,10 +144,12 @@ Additional collection of resources we think may be useful as you develop your ap
 
 - `Deployments <./deployments.html>`_: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.
 
-- `Discord <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain!
-
 - `Tracing <./tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
 
+- `Model Laboratory <./model_laboratory.html>`_: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
+
+- `Discord <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain!
+
 - `Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>`_: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.
 
 
@@ -184,5 +164,6 @@ Additional collection of resources we think may be useful as you develop your ap
    ./gallery.rst
    ./deployments.md
    ./tracing.md
+   ./use_cases/model_laboratory.ipynb
    Discord <https://discord.gg/6adMQxSpJS>
    Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>

docs/modules/agents.rst

@@ -1,30 +1,52 @@
 Agents
 ==========================
 
+.. note::
+   `Conceptual Guide <https://docs.langchain.com/docs/components/agents>`_
+
+
 Some applications will require not just a predetermined chain of calls to LLMs/other tools,
-but potentially an unknown chain that depends on the user input.
+but potentially an unknown chain that depends on the user's input.
 In these types of chains, there is a “agent” which has access to a suite of tools.
 Depending on the user input, the agent can then decide which, if any, of these tools to call.
 
-The following sections of documentation are provided:
-
-- `Getting Started <./agents/getting_started.html>`_: A notebook to help you get started working with agents as quickly as possible.
-
-- `Key Concepts <./agents/key_concepts.html>`_: A conceptual guide going over the various concepts related to agents.
-
-- `How-To Guides <./agents/how_to_guides.html>`_: A collection of how-to guides. These highlight how to integrate various types of tools, how to work with different types of agent, and how to customize agents.
-
-- `Reference <../reference/modules/agents.html>`_: API reference documentation for all Agent classes.
-
-
+In this section of documentation, we first start with a Getting Started notebook to over over how to use all things related to agents in an end-to-end manner.
 
 .. toctree::
    :maxdepth: 1
-   :caption: Agents
-   :name: Agents
    :hidden:
 
    ./agents/getting_started.ipynb
-   ./agents/key_concepts.md
-   ./agents/how_to_guides.rst
-   Reference<../reference/modules/agents.rst>
+
+
+We then split the documentation into the following sections:
+
+**Tools**
+
+An overview of the various tools LangChain supports.
+
+
+**Agents**
+
+An overview of the different agent types.
+
+
+**Toolkits**
+
+An overview of toolkits, and examples of the different ones LangChain supports.
+
+
+**Agent Executor**
+
+An overview of the Agent Executor class and examples of how to use it.
+
+Go Deeper
+---------
+
+.. toctree::
+   :maxdepth: 1
+
+   ./agents/tools.rst
+   ./agents/agents.rst
+   ./agents/toolkits.rst
+   ./agents/agent_executors.rst

docs/modules/agents/agent_executors.rst

@@ -0,0 +1,17 @@
+Agent Executors
+===============
+
+.. note::
+   `Conceptual Guide <https://docs.langchain.com/docs/components/agents/agent-executor>`_
+
+Agent executors take an agent and tools and use the agent to decide which tools to call and in what order.
+
+In this part of the documentation we cover other related functionality to agent executors
+
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   ./agent_executors/examples/*
+

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

@@ -0,0 +1,511 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "68b24990",
+   "metadata": {},
+   "source": [
+    "# How to combine agents and vectorstores\n",
+    "\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 VectorDBQAChain 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."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b22020a",
+   "metadata": {},
+   "source": [
+    "## Create the Vectorstore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2e87c10a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.chains import RetrievalQA\n",
+    "llm = OpenAI(temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0b7b772b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "relevant_parts = []\n",
+    "for p in Path(\".\").absolute().parts:\n",
+    "    relevant_parts.append(p)\n",
+    "    if relevant_parts[-3:] == [\"langchain\", \"docs\", \"modules\"]:\n",
+    "        break\n",
+    "doc_path = str(Path(*relevant_parts) / \"state_of_the_union.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f2675861",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "Using DuckDB in-memory for database. Data will be transient.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader(doc_path)\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",
+    "docsearch = Chroma.from_documents(texts, embeddings, collection_name=\"state-of-union\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bc5403d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_of_union = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docsearch.as_retriever())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1431cded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import WebBaseLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "915d3ff3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = WebBaseLoader(\"https://beta.ruff.rs/docs/faq/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "96a2edf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "Using DuckDB in-memory for database. Data will be transient.\n"
+     ]
+    }
+   ],
+   "source": [
+    "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())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71ecef90",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a6c031",
+   "metadata": {},
+   "source": [
+    "## Create the Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "eb142786",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import things that are needed generically\n",
+    "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.tools import BaseTool\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain import LLMMathChain, SerpAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "850bc4e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [\n",
+    "    Tool(\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",
+    "    ),\n",
+    "    Tool(\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",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "fc47f230",
+   "metadata": {},
+   "outputs": [],
+   "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=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "10ca2db8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out what Biden said about Ketanji Brown Jackson in the State of the Union address.\n",
+      "Action: State of Union QA System\n",
+      "Action Input: What did Biden say about Ketanji Brown Jackson in the State of the Union address?\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "4e91b811",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out the advantages of using ruff over flake8\n",
+      "Action: Ruff QA System\n",
+      "Action Input: What are the advantages of using ruff over flake8?\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"Why use ruff over flake8?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "787a9b5e",
+   "metadata": {},
+   "source": [
+    "## Use the Agent solely as a router"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9161ba91",
+   "metadata": {},
+   "source": [
+    "You can also set `return_direct=True` if you intend to use the agent as a router and just want to directly return the result of the RetrievalQAChain.\n",
+    "\n",
+    "Notice that in the above examples the agent did some extra work after querying the RetrievalQAChain. You can avoid that and just return the result directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "f59b377e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [\n",
+    "    Tool(\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",
+    "    ),\n",
+    "    Tool(\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",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "8615707a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "36e718a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out what Biden said about Ketanji Brown Jackson in the State of the Union address.\n",
+      "Action: State of Union QA System\n",
+      "Action Input: What did Biden say about Ketanji Brown Jackson in the State of the Union address?\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\" Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"What did biden say about ketanji brown jackson in the state of the union address?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "edfd0a1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out the advantages of using ruff over flake8\n",
+      "Action: Ruff QA System\n",
+      "Action Input: What are the advantages of using ruff over flake8?\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.'"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"Why use ruff over flake8?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49a0cbbe",
+   "metadata": {},
+   "source": [
+    "## Multi-Hop vectorstore reasoning\n",
+    "\n",
+    "Because vectorstores are easily usable as tools in agents, it is easy to use answer multi-hop questions that depend on vectorstores using the existing agent framework"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "d397a233",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [\n",
+    "    Tool(\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",
+    "    ),\n",
+    "    Tool(\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",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "06157240",
+   "metadata": {},
+   "outputs": [],
+   "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=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "b492b520",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out what tool ruff uses to run over Jupyter Notebooks, and if the president mentioned it in the state of the union.\n",
+      "Action: Ruff QA System\n",
+      "Action Input: What tool does ruff use to run over Jupyter Notebooks?\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to find out if the president mentioned this tool in the state of the union.\n",
+      "Action: State of Union QA System\n",
+      "Action Input: Did the president mention nbQA in the state of the union?\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'No, the president did not mention nbQA in the state of the union.'"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3b857d6",
+   "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/agent_executors/examples/async_agent.ipynb

@@ -5,7 +5,7 @@
    "id": "6fb92deb-d89e-439b-855d-c7f2607d794b",
    "metadata": {},
    "source": [
-    "# Async API for Agent\n",
+    "# How to use the async API for Agents\n",
     "\n",
     "LangChain provides async support for Agents by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
     "\n",

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

@@ -5,7 +5,7 @@
    "id": "b253f4d5",
    "metadata": {},
    "source": [
-    "# ChatGPT Clone\n",
+    "# How to create ChatGPT Clone\n",
     "\n",
     "This chain replicates ChatGPT by combining (1) a specific prompt, and (2) the concept of memory.\n",
     "\n",
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "a99acd89",
    "metadata": {},
    "outputs": [
@@ -38,18 +38,17 @@
       "Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\n",
       "Assistant:\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "```\n",
-      "$ pwd\n",
-      "/\n",
+      "/home/user\n",
       "```\n"
      ]
     }
    ],
    "source": [
     "from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate\n",
-    "from langchain.chains.conversation.memory import ConversationalBufferWindowMemory\n",
+    "from langchain.memory import ConversationBufferWindowMemory\n",
     "\n",
     "\n",
     "template = \"\"\"Assistant is a large language model trained by OpenAI.\n",
@@ -74,7 +73,7 @@
     "    llm=OpenAI(temperature=0), \n",
     "    prompt=prompt, \n",
     "    verbose=True, \n",
-    "    memory=ConversationalBufferWindowMemory(k=2),\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",
@@ -961,7 +960,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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

@@ -5,7 +5,7 @@
    "id": "5436020b",
    "metadata": {},
    "source": [
-    "# Intermediate Steps\n",
+    "# How to access intermediate steps\n",
     "\n",
     "In order to get more visibility into what an agent is doing, we can also return intermediate steps. This comes in the form of an extra key in the return value, which is a list of (action, observation) tuples."
    ]

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

@@ -5,7 +5,7 @@
    "id": "75c041b7",
    "metadata": {},
    "source": [
-    "# Max Iterations\n",
+    "# How to cap the max number of iterations\n",
     "\n",
     "This notebook walks through how to cap an agent at taking a certain number of steps. This can be useful to ensure that they do not go haywire and take too many steps."
    ]

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

@@ -0,0 +1,548 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fa6802ac",
+   "metadata": {},
+   "source": [
+    "# How to add SharedMemory to an Agent and its Tools\n",
+    "\n",
+    "This notebook goes over adding memory to **both** of an Agent and its tools. Before going through this notebook, please walk through the following notebooks, as this will build on top of both of them:\n",
+    "\n",
+    "- [Adding memory to an LLM Chain](../../memory/examples/adding_memory.ipynb)\n",
+    "- [Custom Agents](custom_agent.ipynb)\n",
+    "\n",
+    "We are going to create a custom Agent. The agent has access to a conversation memory, search tool, and a summarization tool. And, the summarization tool also needs access to the conversation memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8db95912",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
+    "from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory\n",
+    "from langchain import OpenAI, LLMChain, PromptTemplate\n",
+    "from langchain.utilities import GoogleSearchAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "06b7187b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"This is a conversation between a human and a bot:\n",
+    "\n",
+    "{chat_history}\n",
+    "\n",
+    "Write a summary of the conversation for {input}:\n",
+    "\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    input_variables=[\"input\", \"chat_history\"], \n",
+    "    template=template\n",
+    ")\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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "97ad8467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = GoogleSearchAPIWrapper()\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name = \"Search\",\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "    ),\n",
+    "    Tool(\n",
+    "        name = \"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",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e3439cd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
+    "suffix = \"\"\"Begin!\"\n",
+    "\n",
+    "{chat_history}\n",
+    "Question: {input}\n",
+    "{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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0021675b",
+   "metadata": {},
+   "source": [
+    "We can now construct the LLMChain, with the Memory object, and then create the agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c56a0e73",
+   "metadata": {},
+   "outputs": [],
+   "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ca4bc1fb",
+   "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 should research ChatGPT to answer this question.\n",
+      "Action: Search\n",
+      "Action Input: \"ChatGPT\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mNov 30, 2022 ... We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... ChatGPT. We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... Feb 2, 2023 ... ChatGPT, the popular chatbot from OpenAI, is estimated to have reached 100 million monthly active users in January, just two months after ... 2 days ago ... ChatGPT recently launched a new version of its own plagiarism detection tool, with hopes that it will squelch some of the criticism around how ... An API for accessing new AI models developed by OpenAI. Feb 19, 2023 ... ChatGPT is an AI chatbot system that OpenAI released in November to show off and test what a very large, powerful AI system can accomplish. You ... ChatGPT is fine-tuned from GPT-3.5, a language model trained to produce text. ChatGPT was optimized for dialogue by using Reinforcement Learning with Human ... 3 days ago ... Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. Dec 1, 2022 ... ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with a ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(input=\"What is ChatGPT?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45627664",
+   "metadata": {},
+   "source": [
+    "To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "eecc0462",
+   "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 who developed ChatGPT\n",
+      "Action: Search\n",
+      "Action Input: Who developed ChatGPT\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... Feb 15, 2023 ... Who owns Chat GPT? Chat GPT is owned and developed by AI research and deployment company, OpenAI. The organization is headquartered in San ... Feb 8, 2023 ... ChatGPT is an AI chatbot developed by San Francisco-based startup OpenAI. OpenAI was co-founded in 2015 by Elon Musk and Sam Altman and is ... Dec 7, 2022 ... ChatGPT is an AI chatbot designed and developed by OpenAI. The bot works by generating text responses based on human-user input, like questions ... Jan 12, 2023 ... In 2019, Microsoft invested $1 billion in OpenAI, the tiny San Francisco company that designed ChatGPT. And in the years since, it has quietly ... Jan 25, 2023 ... The inside story of ChatGPT: How OpenAI founder Sam Altman built the world's hottest technology with billions from Microsoft. Dec 3, 2022 ... ChatGPT went viral on social media for its ability to do anything from code to write essays. · The company that created the AI chatbot has a ... Jan 17, 2023 ... While many Americans were nursing hangovers on New Year's Day, 22-year-old Edward Tian was working feverishly on a new app to combat misuse ... ChatGPT is a language model created by OpenAI, an artificial intelligence research laboratory consisting of a team of researchers and engineers focused on ... 1 day ago ... Everyone is talking about ChatGPT, developed by OpenAI. This is such a great tool that has helped to make AI more accessible to a wider ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: ChatGPT was developed by OpenAI.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'ChatGPT was developed by OpenAI.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(input=\"Who developed it?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c34424cf",
+   "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 simplify the conversation for a 5 year old.\n",
+      "Action: Summary\n",
+      "Action Input: My daughter 5 years old\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mThis is a conversation between a human and a bot:\n",
+      "\n",
+      "Human: What is ChatGPT?\n",
+      "AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
+      "Human: Who developed it?\n",
+      "AI: ChatGPT was developed by OpenAI.\n",
+      "\n",
+      "Write a summary of the conversation for My daughter 5 years old:\n",
+      "\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "The conversation was about ChatGPT, an artificial intelligence chatbot. It was created by OpenAI and can send and receive images while chatting.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: ChatGPT is an artificial intelligence chatbot created by OpenAI that can send and receive images while chatting.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'ChatGPT is an artificial intelligence chatbot created by OpenAI that can send and receive images while chatting.'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ebd8326",
+   "metadata": {},
+   "source": [
+    "Confirm that the memory was correctly updated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b91f8c85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Human: What is ChatGPT?\n",
+      "AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
+      "Human: Who developed it?\n",
+      "AI: ChatGPT was developed by OpenAI.\n",
+      "Human: Thanks. Summarize the conversation, for my daughter 5 years old.\n",
+      "AI: ChatGPT is an artificial intelligence chatbot created by OpenAI that can send and receive images while chatting.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(agent_chain.memory.buffer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc3d0aa4",
+   "metadata": {},
+   "source": [
+    "For comparison, below is a bad example that uses the same memory for both the Agent and the tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3359d043",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## This is a bad practice for using the memory.\n",
+    "## Use the ReadOnlySharedMemory class, as shown above.\n",
+    "\n",
+    "template = \"\"\"This is a conversation between a human and a bot:\n",
+    "\n",
+    "{chat_history}\n",
+    "\n",
+    "Write a summary of the conversation for {input}:\n",
+    "\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    input_variables=[\"input\", \"chat_history\"], \n",
+    "    template=template\n",
+    ")\n",
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
+    "summry_chain = LLMChain(\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",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to answer questions about current events\"\n",
+    "    ),\n",
+    "    Tool(\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",
+    "]\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",
+    "suffix = \"\"\"Begin!\"\n",
+    "\n",
+    "{chat_history}\n",
+    "Question: {input}\n",
+    "{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",
+    ")\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "970d23df",
+   "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 should research ChatGPT to answer this question.\n",
+      "Action: Search\n",
+      "Action Input: \"ChatGPT\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mNov 30, 2022 ... We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... ChatGPT. We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... Feb 2, 2023 ... ChatGPT, the popular chatbot from OpenAI, is estimated to have reached 100 million monthly active users in January, just two months after ... 2 days ago ... ChatGPT recently launched a new version of its own plagiarism detection tool, with hopes that it will squelch some of the criticism around how ... An API for accessing new AI models developed by OpenAI. Feb 19, 2023 ... ChatGPT is an AI chatbot system that OpenAI released in November to show off and test what a very large, powerful AI system can accomplish. You ... ChatGPT is fine-tuned from GPT-3.5, a language model trained to produce text. ChatGPT was optimized for dialogue by using Reinforcement Learning with Human ... 3 days ago ... Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. Dec 1, 2022 ... ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with a ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\""
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(input=\"What is ChatGPT?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d9ea82f0",
+   "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 who developed ChatGPT\n",
+      "Action: Search\n",
+      "Action Input: Who developed ChatGPT\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... Feb 15, 2023 ... Who owns Chat GPT? Chat GPT is owned and developed by AI research and deployment company, OpenAI. The organization is headquartered in San ... Feb 8, 2023 ... ChatGPT is an AI chatbot developed by San Francisco-based startup OpenAI. OpenAI was co-founded in 2015 by Elon Musk and Sam Altman and is ... Dec 7, 2022 ... ChatGPT is an AI chatbot designed and developed by OpenAI. The bot works by generating text responses based on human-user input, like questions ... Jan 12, 2023 ... In 2019, Microsoft invested $1 billion in OpenAI, the tiny San Francisco company that designed ChatGPT. And in the years since, it has quietly ... Jan 25, 2023 ... The inside story of ChatGPT: How OpenAI founder Sam Altman built the world's hottest technology with billions from Microsoft. Dec 3, 2022 ... ChatGPT went viral on social media for its ability to do anything from code to write essays. · The company that created the AI chatbot has a ... Jan 17, 2023 ... While many Americans were nursing hangovers on New Year's Day, 22-year-old Edward Tian was working feverishly on a new app to combat misuse ... ChatGPT is a language model created by OpenAI, an artificial intelligence research laboratory consisting of a team of researchers and engineers focused on ... 1 day ago ... Everyone is talking about ChatGPT, developed by OpenAI. This is such a great tool that has helped to make AI more accessible to a wider ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: ChatGPT was developed by OpenAI.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'ChatGPT was developed by OpenAI.'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(input=\"Who developed it?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5b1f9223",
+   "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 simplify the conversation for a 5 year old.\n",
+      "Action: Summary\n",
+      "Action Input: My daughter 5 years old\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mThis is a conversation between a human and a bot:\n",
+      "\n",
+      "Human: What is ChatGPT?\n",
+      "AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
+      "Human: Who developed it?\n",
+      "AI: ChatGPT was developed by OpenAI.\n",
+      "\n",
+      "Write a summary of the conversation for My daughter 5 years old:\n",
+      "\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "The conversation was about ChatGPT, an artificial intelligence chatbot developed by OpenAI. It is designed to have conversations with humans and can also send and receive images.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI that can have conversations with humans and send and receive images.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'ChatGPT is an artificial intelligence chatbot developed by OpenAI that can have conversations with humans and send and receive images.'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d07415da",
+   "metadata": {},
+   "source": [
+    "The final answer is not wrong, but we see the 3rd Human input is actually from the agent in the memory because the memory was modified by the summary tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "32f97b21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Human: What is ChatGPT?\n",
+      "AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
+      "Human: Who developed it?\n",
+      "AI: ChatGPT was developed by OpenAI.\n",
+      "Human: My daughter 5 years old\n",
+      "AI: \n",
+      "The conversation was about ChatGPT, an artificial intelligence chatbot developed by OpenAI. It is designed to have conversations with humans and can also send and receive images.\n",
+      "Human: Thanks. Summarize the conversation, for my daughter 5 years old.\n",
+      "AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI that can have conversations with humans and send and receive images.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(agent_chain.memory.buffer)"
+   ]
+  }
+ ],
+ "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/agents.rst

@@ -0,0 +1,35 @@
+Agents
+=============
+
+.. note::
+   `Conceptual Guide <https://docs.langchain.com/docs/components/agents/agent>`_
+
+
+In this part of the documentation we cover the different types of agents, disregarding which specific tools they are used with.
+
+For a high level overview of the different types of agents, see the below documentation.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   ./agents/agent_types.md
+
+For documentation on how to create a custom agent, see the below.
+
+We also have documentation for an in-depth dive into each agent type.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   ./agents/custom_agent.ipynb
+
+We also have documentation for an in-depth dive into each agent type.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   ./agents/examples/*
+

docs/modules/agents/agents/agent_types.md

@@ -1,12 +1,9 @@
-# Agents
+# Agent Types
 
 Agents use an LLM to determine which actions to take and in what order.
-An action can either be using a tool and observing its output, or returning to the user.
-For a list of easily loadable tools, see [here](tools.md).
+An action can either be using a tool and observing its output, or returning a response to the user.
 Here are the agents available in LangChain.
 
-For a tutorial on how to load agents, see [here](getting_started.ipynb).
-
 ## `zero-shot-react-description`
 
 This agent uses the ReAct framework to determine which tool to use

docs/modules/agents/agents/custom_agent.ipynb

@@ -13,7 +13,7 @@
     "    \n",
     "    - Tools: The tools the agent has available to use.\n",
     "    - LLMChain: The LLMChain that produces the text that is parsed in a certain way to determine which action to take.\n",
-    "    - The agent class itself: this parses the output of the LLMChain to determin which action to take.\n",
+    "    - The agent class itself: this parses the output of the LLMChain to determine which action to take.\n",
     "        \n",
     "        \n",
     "In this notebook we walk through two types of custom agents. The first type shows how to create a custom LLMChain, but still use an existing agent class to parse the output. The second shows how to create a custom agent class."

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

@@ -0,0 +1,309 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4658d71a",
+   "metadata": {},
+   "source": [
+    "# Conversation Agent (for Chat Models)\n",
+    "\n",
+    "This notebook walks through using an agent optimized for conversation, using ChatModels. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.\n",
+    "\n",
+    "This is accomplished with a specific type of agent (`chat-conversational-react-description`) which expects to be used with a memory component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f4f5d1a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"LANGCHAIN_HANDLER\"] = \"langchain\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f65308ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import Tool\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.utilities import SerpAPIWrapper\n",
+    "from langchain.agents import initialize_agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5fb14d6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = SerpAPIWrapper()\n",
+    "tools = [\n",
+    "    Tool(\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",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dddc34c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cafe9bc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm=ChatOpenAI(temperature=0)\n",
+    "agent_chain = initialize_agent(tools, llm, agent=\"chat-conversational-react-description\", verbose=True, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dc70b454",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m{\n",
+      "    \"action\": \"Final Answer\",\n",
+      "    \"action_input\": \"Hello Bob! How can I assist you today?\"\n",
+      "}\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Hello Bob! How can I assist you today?'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(input=\"hi, i am bob\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3dcf7953",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m{\n",
+      "    \"action\": \"Final Answer\",\n",
+      "    \"action_input\": \"Your name is Bob.\"\n",
+      "}\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Your name is Bob.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(input=\"what's my name?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "aa05f566",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m{\n",
+      "    \"action\": \"Current Search\",\n",
+      "    \"action_input\": \"Thai food dinner recipes\"\n",
+      "}\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's ...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m{\n",
+      "    \"action\": \"Final Answer\",\n",
+      "    \"action_input\": \"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website.\"\n",
+      "}\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website.\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(\"what are some good dinners to make this week, if i like thai food?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c5d8b7ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m```json\n",
+      "{\n",
+      "    \"action\": \"Current Search\",\n",
+      "    \"action_input\": \"who won the world cup in 1978\"\n",
+      "}\n",
+      "```\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mThe Argentina national football team represents Argentina in men's international football and is administered by the Argentine Football Association, the governing body for football in Argentina. Nicknamed La Albiceleste, they are the reigning world champions, having won the most recent World Cup in 2022.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m```json\n",
+      "{\n",
+      "    \"action\": \"Final Answer\",\n",
+      "    \"action_input\": \"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978.\"\n",
+      "}\n",
+      "```\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978.\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f608889b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m{\n",
+      "    \"action\": \"Current Search\",\n",
+      "    \"action_input\": \"weather in pomfret\"\n",
+      "}\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mMostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers possible. High near 40F. Winds NNW at 20 to 30 mph.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m{\n",
+      "    \"action\": \"Final Answer\",\n",
+      "    \"action_input\": \"The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.\"\n",
+      "}\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_chain.run(input=\"whats the weather like in pomfret?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0084efd6",
+   "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/agents/examples/conversational_agent.ipynb

@@ -20,7 +20,7 @@
    "outputs": [],
    "source": [
     "from langchain.agents import Tool\n",
-    "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
+    "from langchain.memory import ConversationBufferMemory\n",
     "from langchain import OpenAI\n",
     "from langchain.utilities import GoogleSearchAPIWrapper\n",
     "from langchain.agents import initialize_agent"
@@ -272,7 +272,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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

@@ -0,0 +1,253 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f1390152",
+   "metadata": {},
+   "source": [
+    "# MRKL Chat\n",
+    "\n",
+    "This notebook showcases using an agent to replicate the MRKL chain using an agent optimized for chat models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39ea3638",
+   "metadata": {},
+   "source": [
+    "This uses the example Chinook database.\n",
+    "To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ac561cc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, LLMMathChain, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
+    "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.chat_models import ChatOpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "07e96d99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = ChatOpenAI(temperature=0)\n",
+    "llm1 = OpenAI(temperature=0)\n",
+    "search = SerpAPIWrapper()\n",
+    "llm_math_chain = LLMMathChain(llm=llm1, verbose=True)\n",
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
+    "db_chain = SQLDatabaseChain(llm=llm1, database=db, verbose=True)\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name = \"Search\",\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to answer questions about current events. 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",
+    "    ),\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",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a069c4b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mrkl = initialize_agent(tools, llm, agent=\"chat-zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e603cd7d",
+   "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: The first question requires a search, while the second question requires a calculator.\n",
+      "Action:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"Search\",\n",
+      "  \"action_input\": \"Who is Leo DiCaprio's girlfriend?\"\n",
+      "}\n",
+      "```\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mFor the second question, I need to use the calculator tool to raise her current age to the 0.43 power.\n",
+      "Action:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"Calculator\",\n",
+      "  \"action_input\": \"22.0^(0.43)\"\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
+      "22.0^(0.43)\u001b[32;1m\u001b[1;3m\n",
+      "```python\n",
+      "import math\n",
+      "print(math.pow(22.0, 0.43))\n",
+      "```\n",
+      "\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
+      "\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
+      "Final Answer: Camila Morrone, 3.777824273683966.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Camila Morrone, 3.777824273683966.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrkl.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a5c07010",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mQuestion: What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\n",
+      "Thought: I should use the Search tool to find the answer to the first part of the question and then use the FooBar DB tool to find the answer to the second part of the question.\n",
+      "Action:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"Search\",\n",
+      "  \"action_input\": \"Who recently released an album called 'The Storm Before the Calm'\"\n",
+      "}\n",
+      "```\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAlanis Morissette\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mNow that I have the name of the artist, I can use the FooBar DB tool to find their albums in the database.\n",
+      "Action:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"FooBar DB\",\n",
+      "  \"action_input\": \"What albums does Alanis Morissette have in the database?\"\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
+      "What albums does Alanis Morissette have in the database? \n",
+      "SQLQuery:"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:141: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
+      "  sample_rows = connection.execute(command)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m SELECT Title FROM Album WHERE ArtistId IN (SELECT ArtistId FROM Artist WHERE Name = 'Alanis Morissette') LIMIT 5;\u001b[0m\n",
+      "SQLResult: \u001b[33;1m\u001b[1;3m[('Jagged Little Pill',)]\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI have found the answer to both parts of the question.\n",
+      "Final Answer: The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af016a70",
+   "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/agents/examples/react.ipynb

@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "82140df0",
+   "metadata": {},
+   "source": [
+    "# ReAct\n",
+    "\n",
+    "This notebook showcases using an agent to implement the ReAct logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4e272b47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, Wikipedia\n",
+    "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.react.base import DocstoreExplorer\n",
+    "docstore=DocstoreExplorer(Wikipedia())\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name=\"Search\",\n",
+    "        func=docstore.search,\n",
+    "        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",
+    "]\n",
+    "\n",
+    "llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")\n",
+    "react = initialize_agent(tools, llm, agent=\"react-docstore\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8078c8f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "Thought: I need to search David Chanoff and find the U.S. Navy admiral he collaborated with. Then I need to find which President the admiral served under.\n",
+      "\n",
+      "Action: Search[David Chanoff]\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mDavid Chanoff is a noted author of non-fiction work. His work has typically involved collaborations with the principal protagonist of the work concerned. His collaborators have included; Augustus A. White, Joycelyn Elders, Đoàn Văn Toại, William J. Crowe, Ariel Sharon, Kenneth Good and Felix Zandman. He has also written about a wide range of subjects including literary history, education and foreign for The Washington Post, The New Republic and The New York Times Magazine. He has published more than twelve books.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m The U.S. Navy admiral David Chanoff collaborated with is William J. Crowe. I need to find which President he served under.\n",
+      "\n",
+      "Action: Search[William J. Crowe]\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mWilliam James Crowe Jr. (January 2, 1925 – October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents Ronald Reagan and George H. W. Bush, and as the ambassador to the United Kingdom and Chair of the Intelligence Oversight Board under President Bill Clinton.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m William J. Crowe served as the ambassador to the United Kingdom under President Bill Clinton, so the answer is Bill Clinton.\n",
+      "\n",
+      "Action: Finish[Bill Clinton]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Bill Clinton'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?\"\n",
+    "react.run(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09604a7f",
+   "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"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

@@ -52,7 +52,8 @@
     "tools = [\n",
     "    Tool(\n",
     "        name=\"Intermediate Answer\",\n",
-    "        func=search.run\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to ask with search\"\n",
     "    )\n",
     "]\n",
     "\n",
@@ -63,7 +64,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.0 64-bit ('llm-env')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -77,7 +78,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/examples/load_from_hub.ipynb

@@ -1,130 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "991b1cc1",
-   "metadata": {},
-   "source": [
-    "# Loading from LangChainHub\n",
-    "\n",
-    "This notebook covers how to load agents from [LangChainHub](https://github.com/hwchase17/langchain-hub)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "bd4450a2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No `_type` key found, defaulting to `prompt`.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "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;3m2016 · SUI · Stan Wawrinka ; 2017 · ESP · Rafael Nadal ; 2018 · SRB · Novak Djokovic ; 2019 · ESP · Rafael Nadal.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mSo the reigning men's U.S. Open champion is Rafael Nadal.\n",
-      "Follow up: What is Rafael Nadal's hometown?\u001b[0m\n",
-      "Intermediate answer: \u001b[36;1m\u001b[1;3mIn 2016, he once again showed his deep ties to Mallorca and opened the Rafa Nadal Academy in his hometown of Manacor.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mSo the final answer is: Manacor, Mallorca, Spain.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Manacor, Mallorca, Spain.'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain import OpenAI, SerpAPIWrapper\n",
-    "from langchain.agents import initialize_agent, Tool\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "search = SerpAPIWrapper()\n",
-    "tools = [\n",
-    "    Tool(\n",
-    "        name=\"Intermediate Answer\",\n",
-    "        func=search.run\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc://agents/self-ask-with-search/agent.json\", verbose=True)\n",
-    "self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3aede965",
-   "metadata": {},
-   "source": [
-    "# Pinning Dependencies\n",
-    "\n",
-    "Specific versions of LangChainHub agents can be pinned with the `lc@<ref>://` syntax."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e679f7b6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No `_type` key found, defaulting to `prompt`.\n"
-     ]
-    }
-   ],
-   "source": [
-    "self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc@2826ef9e8acdf88465e1e5fc8a7bf59e0f9d0a85://agents/self-ask-with-search/agent.json\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9d3d6697",
-   "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/examples/serialization.ipynb

@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bfe18e28",
-   "metadata": {},
-   "source": [
-    "# Serialization\n",
-    "\n",
-    "This notebook goes over how to serialize agents. For this notebook, it is important to understand the distinction we draw between `agents` and `tools`. An agent is the LLM powered decision maker that decides which actions to take and in which order. Tools are various instruments (functions) an agent has access to, through which an agent can interact with the outside world. When people generally use agents, they primarily talk about using an agent WITH tools. However, when we talk about serialization of agents, we are talking about the agent by itself. We plan to add support for serializing an agent WITH tools sometime in the future.\n",
-    "\n",
-    "Let's start by creating an agent with tools as we normally do:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "eb729f16",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import load_tools\n",
-    "from langchain.agents import initialize_agent\n",
-    "from langchain.llms import OpenAI\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0578f566",
-   "metadata": {},
-   "source": [
-    "Let's now serialize the agent. To be explicit that we are serializing ONLY the agent, we will call the `save_agent` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "dc544de6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent.save_agent('agent.json')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "62dd45bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\r\n",
-      "    \"llm_chain\": {\r\n",
-      "        \"memory\": null,\r\n",
-      "        \"verbose\": false,\r\n",
-      "        \"prompt\": {\r\n",
-      "            \"input_variables\": [\r\n",
-      "                \"input\",\r\n",
-      "                \"agent_scratchpad\"\r\n",
-      "            ],\r\n",
-      "            \"output_parser\": null,\r\n",
-      "            \"template\": \"Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\r\n",
-      "            \"template_format\": \"f-string\",\r\n",
-      "            \"validate_template\": true,\r\n",
-      "            \"_type\": \"prompt\"\r\n",
-      "        },\r\n",
-      "        \"llm\": {\r\n",
-      "            \"model_name\": \"text-davinci-003\",\r\n",
-      "            \"temperature\": 0.0,\r\n",
-      "            \"max_tokens\": 256,\r\n",
-      "            \"top_p\": 1,\r\n",
-      "            \"frequency_penalty\": 0,\r\n",
-      "            \"presence_penalty\": 0,\r\n",
-      "            \"n\": 1,\r\n",
-      "            \"best_of\": 1,\r\n",
-      "            \"request_timeout\": null,\r\n",
-      "            \"logit_bias\": {},\r\n",
-      "            \"_type\": \"openai\"\r\n",
-      "        },\r\n",
-      "        \"output_key\": \"text\",\r\n",
-      "        \"_type\": \"llm_chain\"\r\n",
-      "    },\r\n",
-      "    \"allowed_tools\": [\r\n",
-      "        \"Search\",\r\n",
-      "        \"Calculator\"\r\n",
-      "    ],\r\n",
-      "    \"return_values\": [\r\n",
-      "        \"output\"\r\n",
-      "    ],\r\n",
-      "    \"_type\": \"zero-shot-react-description\"\r\n",
-      "}"
-     ]
-    }
-   ],
-   "source": [
-    "!cat agent.json"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0eb72510",
-   "metadata": {},
-   "source": [
-    "We can now load the agent back in"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "eb660b76",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent = initialize_agent(tools, llm, agent_path=\"agent.json\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aa624ea5",
-   "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/how_to_guides.rst

@@ -1,12 +1,23 @@
 How-To Guides
 =============
 
+There are three types of examples in this section:
+
+1. Agent Overview: how-to-guides for generic agent functionality
+2. Agent Toolkits: how-to-guides for specific agent toolkits (agents optimized for interacting with a certain resource)
+3. Agent Types: how-to-guides for working with the different agent types
+
+Agent Overview
+---------------
+
 The first category of how-to guides here cover specific parts of working with agents.
 
 `Load From Hub <./examples/load_from_hub.html>`_: This notebook covers how to load agents from `LangChainHub <https://github.com/hwchase17/langchain-hub>`_.
 
 `Custom Tools <./examples/custom_tools.html>`_: How to create custom tools that an agent can use.
 
+`Agents With Vectorstores <./examples/agent_vectorstore.html>`_: How to use vectorstores with agents.
+
 `Intermediate Steps <./examples/intermediate_steps.html>`_: How to access and use intermediate steps to get more visibility into the internals of an agent.
 
 `Custom Agent <./examples/custom_agent.html>`_: How to create a custom agent (specifically, a custom LLM + prompt to drive that agent).
@@ -19,7 +30,48 @@ The first category of how-to guides here cover specific parts of working with ag
 
 `Asynchronous <./examples/async_agent.html>`_: Covering asynchronous functionality.
 
-The next set of examples are all end-to-end agents for specific applications.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :hidden:
+
+   ./examples/*
+
+
+Agent Toolkits
+---------------
+
+The next set of examples covers agents with toolkits.
+As opposed to the examples above, these examples are not intended to show off an agent `type`,
+but rather to show off an agent applied to particular use case.
+
+`SQLDatabase Agent <./agent_toolkits/sql_database.html>`_: This notebook covers how to interact with an arbitrary SQL database using an agent.
+
+`JSON Agent <./agent_toolkits/json.html>`_: This notebook covers how to interact with a JSON dictionary using an agent.
+
+`OpenAPI Agent <./agent_toolkits/openapi.html>`_: This notebook covers how to interact with an arbitrary OpenAPI endpoint using an agent.
+
+`VectorStore Agent <./agent_toolkits/vectorstore.html>`_: This notebook covers how to interact with VectorStores using an agent.
+
+`Python Agent <./agent_toolkits/python.html>`_: This notebook covers how to produce and execute python code using an agent.
+
+`Pandas DataFrame Agent <./agent_toolkits/pandas.html>`_: This notebook covers how to do question answering over a pandas dataframe using an agent. Under the hood this calls the Python agent..
+
+`CSV Agent <./agent_toolkits/csv.html>`_: This notebook covers how to do question answering over a csv file. Under the hood this calls the Pandas DataFrame agent.
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+   :hidden:
+
+   ./agent_toolkits/*
+
+
+Agent Types
+---------------
+
+The final set of examples are all end-to-end example of different agent types.
 In all examples there is an Agent with a particular set of tools.
 
 - Tools: A tool can be anything that takes in a string and returns a string. This means that you can use both the primitives AND the chains found in `this <../chains.html>`_ documentation. LangChain also provides a list of easily loadable tools. For detailed information on those, please see `this documentation <./tools.html>`_
@@ -49,16 +101,13 @@ In all examples there is an Agent with a particular set of tools.
 
 
 
-.. toctree::
-   :maxdepth: 1
-   :glob:
-   :hidden:
 
-   ./examples/*
 
 .. toctree::
    :maxdepth: 1
    :glob:
    :hidden:
 
-   ./implementations/*
+   ./implementations/*
+
+

docs/modules/agents/implementations/natbot.py

@@ -1,87 +0,0 @@
-"""Run NatBot."""
-import time
-
-from langchain.chains.natbot.base import NatBotChain
-from langchain.chains.natbot.crawler import Crawler
-
-
-def run_cmd(cmd: str, _crawler: Crawler) -> None:
-    """Run command."""
-    cmd = cmd.split("\n")[0]
-
-    if cmd.startswith("SCROLL UP"):
-        _crawler.scroll("up")
-    elif cmd.startswith("SCROLL DOWN"):
-        _crawler.scroll("down")
-    elif cmd.startswith("CLICK"):
-        commasplit = cmd.split(",")
-        id = commasplit[0].split(" ")[1]
-        _crawler.click(id)
-    elif cmd.startswith("TYPE"):
-        spacesplit = cmd.split(" ")
-        id = spacesplit[1]
-        text_pieces = spacesplit[2:]
-        text = " ".join(text_pieces)
-        # Strip leading and trailing double quotes
-        text = text[1:-1]
-
-        if cmd.startswith("TYPESUBMIT"):
-            text += "\n"
-        _crawler.type(id, text)
-
-    time.sleep(2)
-
-
-if __name__ == "__main__":
-    objective = "Make a reservation for 2 at 7pm at bistro vida in menlo park"
-    print("\nWelcome to natbot! What is your objective?")
-    i = input()
-    if len(i) > 0:
-        objective = i
-    quiet = False
-    nat_bot_chain = NatBotChain.from_default(objective)
-    _crawler = Crawler()
-    _crawler.go_to_page("google.com")
-    try:
-        while True:
-            browser_content = "\n".join(_crawler.crawl())
-            llm_command = nat_bot_chain.execute(_crawler.page.url, browser_content)
-            if not quiet:
-                print("URL: " + _crawler.page.url)
-                print("Objective: " + objective)
-                print("----------------\n" + browser_content + "\n----------------\n")
-            if len(llm_command) > 0:
-                print("Suggested command: " + llm_command)
-
-            command = input()
-            if command == "r" or command == "":
-                run_cmd(llm_command, _crawler)
-            elif command == "g":
-                url = input("URL:")
-                _crawler.go_to_page(url)
-            elif command == "u":
-                _crawler.scroll("up")
-                time.sleep(1)
-            elif command == "d":
-                _crawler.scroll("down")
-                time.sleep(1)
-            elif command == "c":
-                id = input("id:")
-                _crawler.click(id)
-                time.sleep(1)
-            elif command == "t":
-                id = input("id:")
-                text = input("text:")
-                _crawler.type(id, text)
-                time.sleep(1)
-            elif command == "o":
-                objective = input("Objective:")
-            else:
-                print(
-                    "(g) to visit url\n(u) scroll up\n(d) scroll down\n(c) to click"
-                    "\n(t) to type\n(h) to view commands again"
-                    "\n(r/enter) to run suggested command\n(o) change objective"
-                )
-    except KeyboardInterrupt:
-        print("\n[!] Ctrl+C detected, exiting gracefully.")
-        exit(0)

docs/modules/agents/implementations/react.ipynb

@@ -1,108 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "82140df0",
-   "metadata": {},
-   "source": [
-    "# ReAct\n",
-    "\n",
-    "This notebook showcases using an agent to implement the ReAct logic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4e272b47",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import OpenAI, Wikipedia\n",
-    "from langchain.agents import initialize_agent, Tool\n",
-    "from langchain.agents.react.base import DocstoreExplorer\n",
-    "docstore=DocstoreExplorer(Wikipedia())\n",
-    "tools = [\n",
-    "    Tool(\n",
-    "        name=\"Search\",\n",
-    "        func=docstore.search\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name=\"Lookup\",\n",
-    "        func=docstore.lookup\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")\n",
-    "react = initialize_agent(tools, llm, agent=\"react-docstore\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8078c8f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Thought 1: I need to search David Chanoff and find the U.S. Navy admiral he collaborated\n",
-      "with.\n",
-      "Action 1: Search[David Chanoff]\u001b[0m\n",
-      "Observation 1: \u001b[36;1m\u001b[1;3mDavid Chanoff is a noted author of non-fiction work. His work has typically involved collaborations with the principal protagonist of the work concerned. His collaborators have included; Augustus A. White, Joycelyn Elders, Đoàn Văn Toại, William J. Crowe, Ariel Sharon, Kenneth Good and Felix Zandman. He has also written about a wide range of subjects including literary history, education and foreign for The Washington Post, The New Republic and The New York Times Magazine. He has published more than twelve books.\u001b[0m\n",
-      "Thought 2:\u001b[32;1m\u001b[1;3m The U.S. Navy admiral David Chanoff collaborated with is William J. Crowe.\n",
-      "Action 2: Search[William J. Crowe]\u001b[0m\n",
-      "Observation 2: \u001b[36;1m\u001b[1;3mWilliam James Crowe Jr. (January 2, 1925 – October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents Ronald Reagan and George H. W. Bush, and as the ambassador to the United Kingdom and Chair of the Intelligence Oversight Board under President Bill Clinton.\u001b[0m\n",
-      "Thought 3:\u001b[32;1m\u001b[1;3m The President William J. Crowe served as the ambassador to the United Kingdom under is Bill Clinton.\n",
-      "Action 3: Finish[Bill Clinton]\u001b[0m\n",
-      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Bill Clinton'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?\"\n",
-    "react.run(question)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.0 64-bit ('llm-env')",
-   "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": "3.9.0"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/agents/key_concepts.md

@@ -1,10 +0,0 @@
-# Key Concepts
-
-## Agents
-Agents use an LLM to determine which actions to take and in what order.
-For more detailed information on agents, and different types of agents in LangChain, see [this documentation](agents.md).
-
-## Tools
-Tools are functions that agents can use to interact with the world.
-These tools can be generic utilities (e.g. search), other chains, or even other agents.
-For more detailed information on tools, and different types of tools in LangChain, see [this documentation](tools.md).

docs/modules/agents/toolkits.rst

@@ -0,0 +1,18 @@
+Toolkits
+==============
+
+.. note::
+   `Conceptual Guide <https://docs.langchain.com/docs/components/agents/toolkit>`_
+
+
+This section of documentation covers agents with toolkits - eg an agent applied to a particular use case.
+
+See below for a full list of agent toolkits
+
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   ./toolkits/examples/*
+

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

@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7094e328",
+   "metadata": {},
+   "source": [
+    "# CSV Agent\n",
+    "\n",
+    "This notebook shows how to use agents to interact with a csv. It is mostly optimized for question answering.\n",
+    "\n",
+    "**NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "827982c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import create_csv_agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "caae0bec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "16c4dc59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = create_csv_agent(OpenAI(temperature=0), 'titanic.csv', verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "46b9489d",
+   "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 count the number of rows\n",
+      "Action: python_repl_ast\n",
+      "Action Input: len(df)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'There are 891 rows in the dataframe.'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many rows are there?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a96309be",
+   "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 count the number of people with more than 3 siblings\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df[df['SibSp'] > 3].shape[0]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'30 people have more than 3 siblings.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many people have more than 3 sibligngs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "964a09f7",
+   "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 calculate the average age first\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['Age'].mean()\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
+      "Action: python_repl_ast\n",
+      "Action Input: import math\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 5.449689683556195\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'5.449689683556195'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"whats the square root of the average age?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "551de2be",
+   "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/json.ipynb

@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "85fb2c03-ab88-4c8c-97e3-a7f2954555ab",
+   "metadata": {},
+   "source": [
+    "# JSON Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to interact with large JSON/dict objects. This is useful when you want to answer questions about a JSON blob that's too large to fit in the context window of an LLM. The agent is able to iteratively explore the blob to find what it needs to answer the user's question.\n",
+    "\n",
+    "In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find [here](https://github.com/openai/openai-openapi/blob/master/openapi.yaml).\n",
+    "\n",
+    "We will use the JSON agent to answer some questions about the API spec."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "893f90fd-f8f6-470a-a76d-1f200ba02e2f",
+   "metadata": {},
+   "source": [
+    "## Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ff988466-c389-4ec6-b6ac-14364a537fd5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "\n",
+    "from langchain.agents import (\n",
+    "    create_json_agent,\n",
+    "    AgentExecutor\n",
+    ")\n",
+    "from langchain.agents.agent_toolkits import JsonToolkit\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms.openai import OpenAI\n",
+    "from langchain.requests import RequestsWrapper\n",
+    "from langchain.tools.json.tool import JsonSpec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ecd1ba0-3937-4359-a41e-68605f0596a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"openai_openapi.yml\") as f:\n",
+    "    data = yaml.load(f, Loader=yaml.FullLoader)\n",
+    "json_spec = JsonSpec(dict_=data, max_value_length=4000)\n",
+    "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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05cfcb24-4389-4b8f-ad9e-466e3fca8db0",
+   "metadata": {},
+   "source": [
+    "## Example: getting the required POST parameters for a request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "faf13702-50f0-4d1b-b91f-48c750ccfd98",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
+      "Action Input: data\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the paths key to see what endpoints exist\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the /completions endpoint to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['post']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the post key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['operationId', 'tags', 'summary', 'requestBody', 'responses', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the requestBody key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['required', 'content']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the required key to see what parameters are required\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"required\"]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mTrue\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the content key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['application/json']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the application/json key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['schema']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['$ref']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the $ref key to see what parameters are required\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m#/components/schemas/CreateCompletionRequest\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the CreateCompletionRequest schema to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['type', 'properties', 'required']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the required key to see what parameters are required\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"][\"required\"]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m['model']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The required parameters in the request body to the /completions endpoint are 'model'.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"The required parameters in the request body to the /completions endpoint are 'model'.\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json_agent_executor.run(\"What are the required parameters in the request body to the /completions endpoint?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba9c9d30",
+   "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/agents/toolkits/examples/openapi.ipynb

@@ -0,0 +1,242 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "85fb2c03-ab88-4c8c-97e3-a7f2954555ab",
+   "metadata": {},
+   "source": [
+    "# OpenAPI Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to interact with an OpenAPI spec and make a correct API request based on the information it has gathered from the spec.\n",
+    "\n",
+    "In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find [here](https://github.com/openai/openai-openapi/blob/master/openapi.yaml)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "893f90fd-f8f6-470a-a76d-1f200ba02e2f",
+   "metadata": {},
+   "source": [
+    "## Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ff988466-c389-4ec6-b6ac-14364a537fd5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "\n",
+    "from langchain.agents import create_openapi_agent\n",
+    "from langchain.agents.agent_toolkits import OpenAPIToolkit\n",
+    "from langchain.llms.openai import OpenAI\n",
+    "from langchain.requests import RequestsWrapper\n",
+    "from langchain.tools.json.tool import JsonSpec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ecd1ba0-3937-4359-a41e-68605f0596a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"openai_openapi.yml\") as f:\n",
+    "    data = yaml.load(f, Loader=yaml.FullLoader)\n",
+    "json_spec=JsonSpec(dict_=data, max_value_length=4000)\n",
+    "headers = {\n",
+    "    \"Authorization\": f\"Bearer {os.getenv('OPENAI_API_KEY')}\"\n",
+    "}\n",
+    "requests_wrapper=RequestsWrapper(headers=headers)\n",
+    "openapi_toolkit = OpenAPIToolkit.from_llm(OpenAI(temperature=0), json_spec, requests_wrapper, verbose=True)\n",
+    "openapi_agent_executor = create_openapi_agent(\n",
+    "    llm=OpenAI(temperature=0),\n",
+    "    toolkit=openapi_toolkit,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f111879d-ae84-41f9-ad82-d3e6b72c41ba",
+   "metadata": {},
+   "source": [
+    "## Example: agent capable of analyzing OpenAPI spec and making requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "548db7f7-337b-4ba8-905c-e7fd58c01799",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: json_explorer\n",
+      "Action Input: What is the base url for the API?\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
+      "Action Input: data\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the servers key to see what the base url is\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"servers\"][0]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mValueError('Value at path `data[\"servers\"][0]` is not a dict, get the value directly.')\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should get the value of the servers key\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"servers\"][0]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m{'url': 'https://api.openai.com/v1'}\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the base url for the API\n",
+      "Final Answer: The base url for the API is https://api.openai.com/v1\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3mThe base url for the API is https://api.openai.com/v1\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should find the path for the /completions endpoint.\n",
+      "Action: json_explorer\n",
+      "Action Input: What is the path for the /completions endpoint?\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
+      "Action Input: data\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the paths key to see what endpoints exist\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the path for the /completions endpoint\n",
+      "Final Answer: data[\"paths\"][2]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3mdata[\"paths\"][2]\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should find the required parameters for the POST request.\n",
+      "Action: json_explorer\n",
+      "Action Input: What are the required parameters for a POST request to the /completions endpoint?\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
+      "Action Input: data\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the paths key to see what endpoints exist\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the /completions endpoint to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['post']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the post key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['operationId', 'tags', 'summary', 'requestBody', 'responses', 'x-oaiMeta']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the requestBody key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['required', 'content']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the content key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['application/json']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the application/json key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['schema']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['$ref']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the $ref key to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mValueError('Value at path `data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]` is not a dict, get the value directly.')\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the $ref key to get the value directly\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m#/components/schemas/CreateCompletionRequest\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the CreateCompletionRequest schema to see what parameters are required\n",
+      "Action: json_spec_list_keys\n",
+      "Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m['type', 'properties', 'required']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the required key to see what parameters are required\n",
+      "Action: json_spec_get_value\n",
+      "Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"][\"required\"]\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m['model']\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The required parameters for a POST request to the /completions endpoint are 'model'.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3mThe required parameters for a POST request to the /completions endpoint are 'model'.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the parameters needed to make the request.\n",
+      "Action: requests_post\n",
+      "Action Input: { \"url\": \"https://api.openai.com/v1/completions\", \"data\": { \"model\": \"davinci\", \"prompt\": \"tell me a joke\" } }\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m{\"id\":\"cmpl-6oeEcNETfq8TOuIUQvAct6NrBXihs\",\"object\":\"text_completion\",\"created\":1677529082,\"model\":\"davinci\",\"choices\":[{\"text\":\"\\n\\n\\n\\nLove is a battlefield\\n\\n\\n\\nIt's me...And some\",\"index\":0,\"logprobs\":null,\"finish_reason\":\"length\"}],\"usage\":{\"prompt_tokens\":4,\"completion_tokens\":16,\"total_tokens\":20}}\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Love is a battlefield. It's me...And some.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Love is a battlefield. It's me...And some.\""
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openapi_agent_executor.run(\"Make a post request to openai /completions. The prompt should be 'tell me a joke.'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ec9582b",
+   "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/agents/toolkits/examples/pandas.ipynb

@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c81da886",
+   "metadata": {},
+   "source": [
+    "# Pandas Dataframe Agent\n",
+    "\n",
+    "This notebook shows how to use agents to interact with a pandas dataframe. It is mostly optimized for question answering.\n",
+    "\n",
+    "**NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0cdd9bf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import create_pandas_dataframe_agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "051ebe84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('titanic.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4185ff46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a9207a2e",
+   "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 count the number of rows\n",
+      "Action: python_repl_ast\n",
+      "Action Input: len(df)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'There are 891 rows in the dataframe.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many rows are there?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bd43617c",
+   "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 count the number of people with more than 3 siblings\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df[df['SibSp'] > 3].shape[0]\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'30 people have more than 3 siblings.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"how many people have more than 3 sibligngs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "94e64b58",
+   "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 calculate the average age first\n",
+      "Action: python_repl_ast\n",
+      "Action Input: df['Age'].mean()\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
+      "Action: python_repl_ast\n",
+      "Action Input: import math\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
+      "Action: python_repl_ast\n",
+      "Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 5.449689683556195\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'5.449689683556195'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"whats the square root of the average age?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eba13b4d",
+   "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/python.ipynb

@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "82a4c2cc-20ea-4b20-a565-63e905dee8ff",
+   "metadata": {},
+   "source": [
+    "# Python Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to write and execute python code to answer a question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f98e9c90-5c37-4fb9-af3e-d09693af8543",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.agents.agent_toolkits import create_python_agent\n",
+    "from langchain.tools.python.tool import PythonREPLTool\n",
+    "from langchain.python import PythonREPL\n",
+    "from langchain.llms.openai import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cc422f53-c51c-4694-a834-72ecd1e68363",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "agent_executor = create_python_agent(\n",
+    "    llm=OpenAI(temperature=0, max_tokens=1000),\n",
+    "    tool=PythonREPLTool(),\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c16161de",
+   "metadata": {},
+   "source": [
+    "## Fibonacci Example\n",
+    "This example was created by [John Wiseman](https://twitter.com/lemonodor/status/1628270074074398720?s=20)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "25cd4f92-ea9b-4fe6-9838-a4f85f81eebe",
+   "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 calculate the 10th fibonacci number\n",
+      "Action: Python REPL\n",
+      "Action Input: def fibonacci(n):\n",
+      "    if n == 0:\n",
+      "        return 0\n",
+      "    elif n == 1:\n",
+      "        return 1\n",
+      "    else:\n",
+      "        return fibonacci(n-1) + fibonacci(n-2)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to call the function with 10 as the argument\n",
+      "Action: Python REPL\n",
+      "Action Input: fibonacci(10)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 55\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'55'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What is the 10th fibonacci number?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7caa30de",
+   "metadata": {},
+   "source": [
+    "## Training neural net\n",
+    "This example was created by [Samee Ur Rehman](https://twitter.com/sameeurehman/status/1630130518133207046?s=20)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4b9f60e7-eb6a-4f14-8604-498d863d4482",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to write a neural network in PyTorch and train it on the given data.\n",
+      "Action: Python REPL\n",
+      "Action Input: \n",
+      "import torch\n",
+      "\n",
+      "# Define the model\n",
+      "model = torch.nn.Sequential(\n",
+      "    torch.nn.Linear(1, 1)\n",
+      ")\n",
+      "\n",
+      "# Define the loss\n",
+      "loss_fn = torch.nn.MSELoss()\n",
+      "\n",
+      "# Define the optimizer\n",
+      "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
+      "\n",
+      "# Define the data\n",
+      "x_data = torch.tensor([[1.0], [2.0], [3.0], [4.0]])\n",
+      "y_data = torch.tensor([[2.0], [4.0], [6.0], [8.0]])\n",
+      "\n",
+      "# Train the model\n",
+      "for epoch in range(1000):\n",
+      "    # Forward pass\n",
+      "    y_pred = model(x_data)\n",
+      "\n",
+      "    # Compute and print loss\n",
+      "    loss = loss_fn(y_pred, y_data)\n",
+      "    if (epoch+1) % 100 == 0:\n",
+      "        print(f'Epoch {epoch+1}: loss = {loss.item():.4f}')\n",
+      "\n",
+      "    # Zero the gradients\n",
+      "    optimizer.zero_grad()\n",
+      "\n",
+      "    # Backward pass\n",
+      "    loss.backward()\n",
+      "\n",
+      "    # Update the weights\n",
+      "    optimizer.step()\n",
+      "\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mEpoch 100: loss = 0.0013\n",
+      "Epoch 200: loss = 0.0007\n",
+      "Epoch 300: loss = 0.0004\n",
+      "Epoch 400: loss = 0.0002\n",
+      "Epoch 500: loss = 0.0001\n",
+      "Epoch 600: loss = 0.0001\n",
+      "Epoch 700: loss = 0.0000\n",
+      "Epoch 800: loss = 0.0000\n",
+      "Epoch 900: loss = 0.0000\n",
+      "Epoch 1000: loss = 0.0000\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The prediction for x = 5 is 10.0.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The prediction for x = 5 is 10.0.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"\"\"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\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb654671",
+   "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/sql_database.ipynb

@@ -0,0 +1,527 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0e499e90-7a6d-4fab-8aab-31a4df417601",
+   "metadata": {},
+   "source": [
+    "# SQL Database Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to interact with a sql databases. The agent builds off of [SQLDatabaseChain](https://langchain.readthedocs.io/en/latest/modules/chains/examples/sqlite.html) and is designed to answer more general questions about a database, as well as recover from errors.\n",
+    "\n",
+    "Note that, as this agent is in active development, all answers might not be correct. Additionally, it is not guaranteed that the agent won't perform DML statements on your database given certain questions. Be careful running it on sensitive data!\n",
+    "\n",
+    "This uses the example Chinook database. To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at the root of this repository."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec927ac6-9b2a-4e8a-9a6e-3e429191875c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "53422913-967b-4f2a-8022-00269c1be1b1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.agents import create_sql_agent\n",
+    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
+    "from langchain.sql_database import SQLDatabase\n",
+    "from langchain.llms.openai import OpenAI\n",
+    "from langchain.agents import AgentExecutor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "090f3699-79c6-4ce1-ab96-a94f0121fd64",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
+    "toolkit = SQLDatabaseToolkit(db=db)\n",
+    "\n",
+    "agent_executor = create_sql_agent(\n",
+    "    llm=OpenAI(temperature=0),\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ae48c7-cb08-4fef-977e-c7d4b96a464b",
+   "metadata": {},
+   "source": [
+    "## Example: describing a table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ff70e83d-5ad0-4fc7-bb96-27d82ac166d7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mArtist, Invoice, Playlist, Genre, Album, PlaylistTrack, Track, InvoiceLine, MediaType, Employee, Customer\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the playlisttrack table\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"PlaylistTrack\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "CREATE TABLE \"PlaylistTrack\" (\n",
+      "\t\"PlaylistId\" INTEGER NOT NULL, \n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \n",
+      "\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
+      "\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'PlaylistTrack' LIMIT 3;\n",
+      "PlaylistId TrackId\n",
+      "1 3402\n",
+      "1 3389\n",
+      "1 3390\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The PlaylistTrack table has two columns, PlaylistId and TrackId, and is linked to the Playlist and Track tables.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The PlaylistTrack table has two columns, PlaylistId and TrackId, and is linked to the Playlist and Track tables.'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"Describe the playlisttrack table\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9abcfe8e-1868-42a4-8345-ad2d9b44c681",
+   "metadata": {},
+   "source": [
+    "## Example: describing a table, recovering from an error\n",
+    "\n",
+    "In this example, the agent tries to search for a table that doesn't exist, but finds the next best result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bea76658-a65b-47e2-b294-6d52c5556246",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mGenre, PlaylistTrack, MediaType, Invoice, InvoiceLine, Track, Playlist, Customer, Album, Employee, Artist\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the PlaylistSong table\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"PlaylistSong\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mError: table_names {'PlaylistSong'} not found in database\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should check the spelling of the table\n",
+      "Action: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mGenre, PlaylistTrack, MediaType, Invoice, InvoiceLine, Track, Playlist, Customer, Album, Employee, Artist\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m The table is called PlaylistTrack\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"PlaylistTrack\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "CREATE TABLE \"PlaylistTrack\" (\n",
+      "\t\"PlaylistId\" INTEGER NOT NULL, \n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \n",
+      "\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
+      "\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'PlaylistTrack' LIMIT 3;\n",
+      "PlaylistId TrackId\n",
+      "1 3402\n",
+      "1 3389\n",
+      "1 3390\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables.'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"Describe the playlistsong table\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fbc26af-97e4-4a21-82aa-48bdc992da26",
+   "metadata": {},
+   "source": [
+    "## Example: running queries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "17bea710-4a23-4de0-b48e-21d57be48293",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mInvoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the relevant tables to see what columns I can use.\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"Invoice, Customer\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "CREATE TABLE \"Customer\" (\n",
+      "\t\"CustomerId\" INTEGER NOT NULL, \n",
+      "\t\"FirstName\" NVARCHAR(40) NOT NULL, \n",
+      "\t\"LastName\" NVARCHAR(20) NOT NULL, \n",
+      "\t\"Company\" NVARCHAR(80), \n",
+      "\t\"Address\" NVARCHAR(70), \n",
+      "\t\"City\" NVARCHAR(40), \n",
+      "\t\"State\" NVARCHAR(40), \n",
+      "\t\"Country\" NVARCHAR(40), \n",
+      "\t\"PostalCode\" NVARCHAR(10), \n",
+      "\t\"Phone\" NVARCHAR(24), \n",
+      "\t\"Fax\" NVARCHAR(24), \n",
+      "\t\"Email\" NVARCHAR(60) NOT NULL, \n",
+      "\t\"SupportRepId\" INTEGER, \n",
+      "\tPRIMARY KEY (\"CustomerId\"), \n",
+      "\tFOREIGN KEY(\"SupportRepId\") REFERENCES \"Employee\" (\"EmployeeId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Customer' LIMIT 3;\n",
+      "CustomerId FirstName LastName Company Address City State Country PostalCode Phone Fax Email SupportRepId\n",
+      "1 Luís Gonçalves Embraer - Empresa Brasileira de Aeronáutica S.A. Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP Brazil 12227-000 +55 (12) 3923-5555 +55 (12) 3923-5566 luisg@embraer.com.br 3\n",
+      "2 Leonie Köhler None Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 +49 0711 2842222 None leonekohler@surfeu.de 5\n",
+      "3 François Tremblay None 1498 rue Bélanger Montréal QC Canada H2G 1A7 +1 (514) 721-4711 None ftremblay@gmail.com 3\n",
+      "\n",
+      "\n",
+      "CREATE TABLE \"Invoice\" (\n",
+      "\t\"InvoiceId\" INTEGER NOT NULL, \n",
+      "\t\"CustomerId\" INTEGER NOT NULL, \n",
+      "\t\"InvoiceDate\" DATETIME NOT NULL, \n",
+      "\t\"BillingAddress\" NVARCHAR(70), \n",
+      "\t\"BillingCity\" NVARCHAR(40), \n",
+      "\t\"BillingState\" NVARCHAR(40), \n",
+      "\t\"BillingCountry\" NVARCHAR(40), \n",
+      "\t\"BillingPostalCode\" NVARCHAR(10), \n",
+      "\t\"Total\" NUMERIC(10, 2) NOT NULL, \n",
+      "\tPRIMARY KEY (\"InvoiceId\"), \n",
+      "\tFOREIGN KEY(\"CustomerId\") REFERENCES \"Customer\" (\"CustomerId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Invoice' LIMIT 3;\n",
+      "InvoiceId CustomerId InvoiceDate BillingAddress BillingCity BillingState BillingCountry BillingPostalCode Total\n",
+      "1 2 2009-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 1.98\n",
+      "2 4 2009-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96\n",
+      "3 8 2009-01-03 00:00:00 Grétrystraat 63 Brussels None Belgium 1000 5.94\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should query the Invoice and Customer tables to get the total sales per country.\n",
+      "Action: query_sql_db\n",
+      "Action Input: SELECT c.Country, SUM(i.Total) AS TotalSales FROM Invoice i INNER JOIN Customer c ON i.CustomerId = c.CustomerId GROUP BY c.Country ORDER BY TotalSales DESC LIMIT 10\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m[('USA', 523.0600000000003), ('Canada', 303.9599999999999), ('France', 195.09999999999994), ('Brazil', 190.09999999999997), ('Germany', 156.48), ('United Kingdom', 112.85999999999999), ('Czech Republic', 90.24000000000001), ('Portugal', 77.23999999999998), ('India', 75.25999999999999), ('Chile', 46.62)]\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The customers from the USA spent the most, with a total of $523.06.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The customers from the USA spent the most, with a total of $523.06.'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"List the total sales per country. Which country's customers spent the most?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "474dddda-c067-4eeb-98b1-e763ee78b18c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mInvoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the Playlist and PlaylistTrack tables to see what columns I can use.\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"Playlist, PlaylistTrack\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "CREATE TABLE \"Playlist\" (\n",
+      "\t\"PlaylistId\" INTEGER NOT NULL, \n",
+      "\t\"Name\" NVARCHAR(120), \n",
+      "\tPRIMARY KEY (\"PlaylistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Playlist' LIMIT 3;\n",
+      "PlaylistId Name\n",
+      "1 Music\n",
+      "2 Movies\n",
+      "3 TV Shows\n",
+      "\n",
+      "\n",
+      "CREATE TABLE \"PlaylistTrack\" (\n",
+      "\t\"PlaylistId\" INTEGER NOT NULL, \n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \n",
+      "\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
+      "\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'PlaylistTrack' LIMIT 3;\n",
+      "PlaylistId TrackId\n",
+      "1 3402\n",
+      "1 3389\n",
+      "1 3390\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I can use a SELECT statement to get the total number of tracks in each playlist.\n",
+      "Action: query_checker_sql_db\n",
+      "Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3m\n",
+      "\n",
+      "SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m The query looks correct, I can now execute it.\n",
+      "Action: query_sql_db\n",
+      "Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name LIMIT 10\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m[('90’s Music', 1477), ('Brazilian Music', 39), ('Classical', 75), ('Classical 101 - Deep Cuts', 25), ('Classical 101 - Next Steps', 25), ('Classical 101 - The Basics', 25), ('Grunge', 15), ('Heavy Metal Classic', 26), ('Music', 6580), ('Music Videos', 1)]\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: The total number of tracks in each playlist are: '90’s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' (26), 'Music' (6580), 'Music Videos' (1).\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"The total number of tracks in each playlist are: '90’s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' (26), 'Music' (6580), 'Music Videos' (1).\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"Show the total number of tracks in each playlist. The Playlist name should be included in the result.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c7503b5-d9d9-4faa-b064-29fcdb5ff213",
+   "metadata": {},
+   "source": [
+    "## Recovering from an error\n",
+    "\n",
+    "In this example, the agent is able to recover from an error after initially trying to access an attribute (`Track.ArtistId`) which doesn't exist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9fe4901e-f9e1-4022-b6bc-80e2b2d6a3a4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
+      "Action Input: \"\"\u001b[0m\n",
+      "Observation: \u001b[38;5;200m\u001b[1;3mMediaType, Track, Invoice, Album, Playlist, Customer, Employee, InvoiceLine, PlaylistTrack, Genre, Artist\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the Artist, InvoiceLine, and Track tables to see what columns I can use.\n",
+      "Action: schema_sql_db\n",
+      "Action Input: \"Artist, InvoiceLine, Track\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m\n",
+      "CREATE TABLE \"Artist\" (\n",
+      "\t\"ArtistId\" INTEGER NOT NULL, \n",
+      "\t\"Name\" NVARCHAR(120), \n",
+      "\tPRIMARY KEY (\"ArtistId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Artist' LIMIT 3;\n",
+      "ArtistId Name\n",
+      "1 AC/DC\n",
+      "2 Accept\n",
+      "3 Aerosmith\n",
+      "\n",
+      "\n",
+      "CREATE TABLE \"Track\" (\n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\t\"Name\" NVARCHAR(200) NOT NULL, \n",
+      "\t\"AlbumId\" INTEGER, \n",
+      "\t\"MediaTypeId\" INTEGER NOT NULL, \n",
+      "\t\"GenreId\" INTEGER, \n",
+      "\t\"Composer\" NVARCHAR(220), \n",
+      "\t\"Milliseconds\" INTEGER NOT NULL, \n",
+      "\t\"Bytes\" INTEGER, \n",
+      "\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
+      "\tPRIMARY KEY (\"TrackId\"), \n",
+      "\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n",
+      "\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n",
+      "\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'Track' LIMIT 3;\n",
+      "TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice\n",
+      "1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99\n",
+      "2 Balls to the Wall 2 2 1 None 342562 5510424 0.99\n",
+      "3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99\n",
+      "\n",
+      "\n",
+      "CREATE TABLE \"InvoiceLine\" (\n",
+      "\t\"InvoiceLineId\" INTEGER NOT NULL, \n",
+      "\t\"InvoiceId\" INTEGER NOT NULL, \n",
+      "\t\"TrackId\" INTEGER NOT NULL, \n",
+      "\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
+      "\t\"Quantity\" INTEGER NOT NULL, \n",
+      "\tPRIMARY KEY (\"InvoiceLineId\"), \n",
+      "\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
+      "\tFOREIGN KEY(\"InvoiceId\") REFERENCES \"Invoice\" (\"InvoiceId\")\n",
+      ")\n",
+      "\n",
+      "SELECT * FROM 'InvoiceLine' LIMIT 3;\n",
+      "InvoiceLineId InvoiceId TrackId UnitPrice Quantity\n",
+      "1 1 2 0.99 1\n",
+      "2 1 4 0.99 1\n",
+      "3 2 6 0.99 1\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should query the database to get the top 3 best selling artists.\n",
+      "Action: query_sql_db\n",
+      "Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mError: (sqlite3.OperationalError) no such column: Track.ArtistId\n",
+      "[SQL: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3]\n",
+      "(Background on this error at: https://sqlalche.me/e/14/e3q8)\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I should double check my query before executing it.\n",
+      "Action: query_checker_sql_db\n",
+      "Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3m\n",
+      "\n",
+      "SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity \n",
+      "FROM Artist \n",
+      "INNER JOIN Track ON Artist.ArtistId = Track.ArtistId \n",
+      "INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId \n",
+      "GROUP BY Artist.Name \n",
+      "ORDER BY TotalQuantity DESC \n",
+      "LIMIT 3;\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Action: query_sql_db\n",
+      "Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Album ON Artist.ArtistId = Album.ArtistId INNER JOIN Track ON Album.AlbumId = Track.AlbumId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m[('Iron Maiden', 140), ('U2', 107), ('Metallica', 91)]\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: The top 3 best selling artists are Iron Maiden, U2, and Metallica.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The top 3 best selling artists are Iron Maiden, U2, and Metallica.'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"Who are the top 3 best selling artists?\")"
+   ]
+  }
+ ],
+ "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/agents/toolkits/examples/titanic.csv

@@ -0,0 +1,892 @@
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+878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
+879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
+882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q

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

@@ -0,0 +1,417 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "18ada398-dce6-4049-9b56-fc0ede63da9c",
+   "metadata": {},
+   "source": [
+    "# Vectorstore Agent\n",
+    "\n",
+    "This notebook showcases an agent designed to retrieve information from one or more vectorstores, either with or without sources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eecb683b-3a46-4b9d-81a3-7caefbfec1a1",
+   "metadata": {},
+   "source": [
+    "## Create the Vectorstores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9bfd0ed8-a5eb-443e-8e92-90be8cabb0a7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain import OpenAI, VectorDBQA\n",
+    "llm = OpenAI(temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "345bb078-4ec1-4e3a-827b-cd238c49054d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "Using DuckDB in-memory for database. Data will be transient.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.document_loaders import TextLoader\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\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5f50eb82-e1a5-4252-8306-8ec1b478d9b4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "Using DuckDB in-memory for database. Data will be transient.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.document_loaders import WebBaseLoader\n",
+    "loader = WebBaseLoader(\"https://beta.ruff.rs/docs/faq/\")\n",
+    "docs = loader.load()\n",
+    "ruff_texts = text_splitter.split_documents(docs)\n",
+    "ruff_store = Chroma.from_documents(ruff_texts, embeddings, collection_name=\"ruff\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4814175-964d-42f1-aa9d-22801ce1e912",
+   "metadata": {},
+   "source": [
+    "## Initialize Toolkit and Agent\n",
+    "\n",
+    "First, we'll create an agent with a single vectorstore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5b3b3206",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents.agent_toolkits import (\n",
+    "    create_vectorstore_agent,\n",
+    "    VectorStoreToolkit,\n",
+    "    VectorStoreInfo,\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",
+    ")\n",
+    "toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)\n",
+    "agent_executor = create_vectorstore_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a38ad10",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3f2f455c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find the answer in the state of the union address\n",
+      "Action: state_of_union_address\n",
+      "Action Input: What did biden say about ketanji brown jackson\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d61e1e63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to use the state_of_union_address_with_sources tool to answer this question.\n",
+      "Action: state_of_union_address_with_sources\n",
+      "Action Input: What did biden say about ketanji brown jackson\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m{\"answer\": \" Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence.\\n\", \"sources\": \"../../state_of_the_union.txt\"}\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. Sources: ../../state_of_the_union.txt\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. Sources: ../../state_of_the_union.txt\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address? List the source.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ca07707",
+   "metadata": {},
+   "source": [
+    "## Multiple Vectorstores\n",
+    "We can also easily use this initialize an agent with multiple vectorstores and use the agent to route between them. To do this. This agent is optimized for routing, so it is a different toolkit and initializer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c3209fd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents.agent_toolkits import (\n",
+    "    create_vectorstore_router_agent,\n",
+    "    VectorStoreRouterToolkit,\n",
+    "    VectorStoreInfo,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "815c4f39-308d-4949-b992-1361036e6e09",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ruff_vectorstore_info = VectorStoreInfo(\n",
+    "    name=\"ruff\",\n",
+    "    description=\"Information about the Ruff python linting library\",\n",
+    "    vectorstore=ruff_store\n",
+    ")\n",
+    "router_toolkit = VectorStoreRouterToolkit(\n",
+    "    vectorstores=[vectorstore_info, ruff_vectorstore_info],\n",
+    "    llm=llm\n",
+    ")\n",
+    "agent_executor = create_vectorstore_router_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=router_toolkit,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71680984-edaf-4a63-90f5-94edbd263550",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3cd1bf3e-e3df-4e69-bbe1-71c64b1af947",
+   "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 use the state_of_union_address tool to answer this question.\n",
+      "Action: state_of_union_address\n",
+      "Action Input: What did biden say about ketanji brown jackson\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c5998b8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out what tool ruff uses to run over Jupyter Notebooks\n",
+      "Action: ruff\n",
+      "Action Input: What tool does ruff use to run over Jupyter Notebooks?\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"What tool does ruff use to run over Jupyter Notebooks?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "744e9b51-fbd9-4778-b594-ea957d0f3467",
+   "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 tool ruff uses and if the president mentioned it in the state of the union.\n",
+      "Action: ruff\n",
+      "Action Input: What tool does ruff use to run over Jupyter Notebooks?\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out if the president mentioned nbQA in the state of the union.\n",
+      "Action: state_of_union_address\n",
+      "Action Input: Did the president mention nbQA in the state of the union?\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'No, the president did not mention nbQA in the state of the union.'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92203aa9-f63a-4ce1-b562-fadf4474ad9d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/agents/tools.rst

@@ -0,0 +1,38 @@
+Tools
+=============
+
+.. note::
+   `Conceptual Guide <https://docs.langchain.com/docs/components/agents/tool>`_
+
+
+
+Tools are ways that an agent can use to interact with the outside world.
+
+For an overview of what a tool is, how to use them, and a full list of examples, please see the getting started documentation
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   ./tools/getting_started.md
+
+Next, we have some examples of customizing and generically working with tools
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   ./tools/custom_tools.ipynb
+   ./tools/multi_input_tool.ipynb
+
+
+In this documentation we cover generic tooling functionality (eg how to create your own)
+as well as examples of tools and how to use them.
+
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   ./tools/examples/*
+

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

@@ -0,0 +1,164 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Apify\n",
+    "\n",
+    "This notebook shows how to use the [Apify integration](../../../../ecosystem/apify.md) for LangChain.\n",
+    "\n",
+    "[Apify](https://apify.com) is a cloud platform for web scraping and data extraction,\n",
+    "which provides an [ecosystem](https://apify.com/store) of more than a thousand\n",
+    "ready-made apps called *Actors* for various web scraping, crawling, and data extraction use cases.\n",
+    "For example, you can use it to extract Google Search results, Instagram and Facebook profiles, products from Amazon or Shopify, Google Maps reviews, etc. etc.\n",
+    "\n",
+    "In this example, we'll use the [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor,\n",
+    "which can deeply crawl websites such as documentation, knowledge bases, help centers, or blogs,\n",
+    "and extract text content from the web pages. Then we feed the documents into a vector index and answer questions from it.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, import `ApifyWrapper` into your source code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.base import Document\n",
+    "from langchain.indexes import VectorstoreIndexCreator\n",
+    "from langchain.utilities import ApifyWrapper"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize it using your [Apify API token](https://console.apify.com/account/integrations) and for the purpose of this example, also with your OpenAI API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"Your OpenAI API key\"\n",
+    "os.environ[\"APIFY_API_TOKEN\"] = \"Your Apify API token\"\n",
+    "\n",
+    "apify = ApifyWrapper()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then run the Actor, wait for it to finish, and fetch its results from the Apify dataset into a LangChain document loader.\n",
+    "\n",
+    "Note that if you already have some results in an Apify dataset, you can load them directly using `ApifyDatasetLoader`, as shown in [this notebook](../../../indexes/document_loaders/examples/apify_dataset.ipynb). In that notebook, you'll also find the explanation of the `dataset_mapping_function`, which is used to map fields from the Apify dataset records to LangChain `Document` fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = apify.call_actor(\n",
+    "    actor_id=\"apify/website-content-crawler\",\n",
+    "    run_input={\"startUrls\": [{\"url\": \"https://python.langchain.com/en/latest/\"}]},\n",
+    "    dataset_mapping_function=lambda item: Document(\n",
+    "        page_content=item[\"text\"] or \"\", metadata={\"source\": item[\"url\"]}\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize the vector index from the crawled documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = VectorstoreIndexCreator().from_loaders([loader])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And finally, query the vector index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What is LangChain?\"\n",
+    "result = index.query_with_sources(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules that can be used to build language model applications, and it also provides chains and agents with memory capabilities.\n",
+      "\n",
+      "https://python.langchain.com/en/latest/modules/models/llms.html, https://python.langchain.com/en/latest/getting_started/getting_started.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(result[\"answer\"])\n",
+    "print(result[\"sources\"])"
+   ]
+  }
+ ],
+ "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,85 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "8f210ec3",
+      "metadata": {},
+      "source": [
+        "# Bash\n",
+        "It can often be useful to have an LLM generate bash commands, and then run them. A common use case for this is letting the LLM interact with your local file system. We provide an easy util to execute bash commands."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "f7b3767b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain.utilities import BashProcess"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "cf1c92f0",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "bash = BashProcess()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "2fa952fc",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "bash.ipynb\n",
+            "google_search.ipynb\n",
+            "python.ipynb\n",
+            "requests.ipynb\n",
+            "serpapi.ipynb\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(bash.run(\"ls\"))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "851fee9f",
+      "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/agents/tools/examples/chatgpt_plugins.ipynb

@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3f34700b",
+   "metadata": {},
+   "source": [
+    "# ChatGPT Plugins\n",
+    "\n",
+    "This example shows how to use ChatGPT Plugins within LangChain abstractions.\n",
+    "\n",
+    "Note 1: This currently only works for plugins with no auth.\n",
+    "\n",
+    "Note 2: There are almost certainly other ways to do this, this is just a first pass. If you have better ideas, please open a PR!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d41405b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.agents import load_tools, initialize_agent\n",
+    "from langchain.tools import AIPluginTool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d9e61df5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tool = AIPluginTool.from_plugin_url(\"https://www.klarna.com/.well-known/ai-plugin.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "edc0ea0e",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI need to check the Klarna Shopping API to see if it has information on available t shirts.\n",
+      "Action: KlarnaProducts\n",
+      "Action Input: None\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mUsage Guide: Use the Klarna plugin to get relevant product suggestions for any shopping or researching purpose. The query to be sent should not include stopwords like articles, prepositions and determinants. The api works best when searching for words that are related to products, like their name, brand, model or category. Links will always be returned and should be shown to the user.\n",
+      "\n",
+      "OpenAPI Spec: {'openapi': '3.0.1', 'info': {'version': 'v0', 'title': 'Open AI Klarna product Api'}, 'servers': [{'url': 'https://www.klarna.com/us/shopping'}], 'tags': [{'name': 'open-ai-product-endpoint', 'description': 'Open AI Product Endpoint. Query for products.'}], 'paths': {'/public/openai/v0/products': {'get': {'tags': ['open-ai-product-endpoint'], 'summary': 'API for fetching Klarna product information', 'operationId': 'productsUsingGET', 'parameters': [{'name': 'q', 'in': 'query', 'description': 'query, must be between 2 and 100 characters', 'required': True, 'schema': {'type': 'string'}}, {'name': 'size', 'in': 'query', 'description': 'number of products returned', 'required': False, 'schema': {'type': 'integer'}}, {'name': 'budget', 'in': 'query', 'description': 'maximum price of the matching product in local currency, filters results', 'required': False, 'schema': {'type': 'integer'}}], 'responses': {'200': {'description': 'Products found', 'content': {'application/json': {'schema': {'$ref': '#/components/schemas/ProductResponse'}}}}, '503': {'description': 'one or more services are unavailable'}}, 'deprecated': False}}}, 'components': {'schemas': {'Product': {'type': 'object', 'properties': {'attributes': {'type': 'array', 'items': {'type': 'string'}}, 'name': {'type': 'string'}, 'price': {'type': 'string'}, 'url': {'type': 'string'}}, 'title': 'Product'}, 'ProductResponse': {'type': 'object', 'properties': {'products': {'type': 'array', 'items': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}}\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI need to use the Klarna Shopping API to search for t shirts.\n",
+      "Action: requests_get\n",
+      "Action Input: https://www.klarna.com/us/shopping/public/openai/v0/products?q=t%20shirts\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Lacoste Men's Pack of Plain T-Shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202043025/Clothing/Lacoste-Men-s-Pack-of-Plain-T-Shirts/?utm_source=openai\",\"price\":\"$26.60\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Black\"]},{\"name\":\"Hanes Men's Ultimate 6pk. Crewneck T-Shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201808270/Clothing/Hanes-Men-s-Ultimate-6pk.-Crewneck-T-Shirts/?utm_source=openai\",\"price\":\"$13.82\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White\"]},{\"name\":\"Nike Boy's Jordan Stretch T-shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3201863202/Children-s-Clothing/Nike-Boy-s-Jordan-Stretch-T-shirts/?utm_source=openai\",\"price\":\"$14.99\",\"attributes\":[\"Material:Cotton\",\"Color:White,Green\",\"Model:Boy\",\"Size (Small-Large):S,XL,L,M\"]},{\"name\":\"Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203028500/Clothing/Polo-Classic-Fit-Cotton-V-Neck-T-Shirts-3-Pack/?utm_source=openai\",\"price\":\"$29.95\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Blue,Black\"]},{\"name\":\"adidas Comfort T-shirts Men's 3-pack\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202640533/Clothing/adidas-Comfort-T-shirts-Men-s-3-pack/?utm_source=openai\",\"price\":\"$14.99\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Black\",\"Neckline:Round\"]}]}\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mThe available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.\n",
+      "Final Answer: The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.\""
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm = ChatOpenAI(temperature=0,)\n",
+    "tools = load_tools([\"requests\"] )\n",
+    "tools += [tool]\n",
+    "\n",
+    "agent_chain = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)\n",
+    "agent_chain.run(\"what t shirts are available in klarna?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e49318a4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

@@ -121,7 +121,8 @@
     "tools = [\n",
     "    Tool(\n",
     "        name=\"Intermediate Answer\",\n",
-    "        func=search.run\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to ask with search\"\n",
     "    )\n",
     "]\n",
     "\n",

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

@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Human as a tool\n",
+    "\n",
+    "Human are AGI so they can certainly be used as a tool to help out AI agent \n",
+    "when it is confused."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.agents import load_tools, initialize_agent\n",
+    "\n",
+    "llm = ChatOpenAI(temperature=0.0)\n",
+    "math_llm = OpenAI(temperature=0.0)\n",
+    "tools = load_tools(\n",
+    "    [\"human\", \"llm-math\"], \n",
+    "    llm=math_llm,\n",
+    ")\n",
+    "\n",
+    "agent_chain = initialize_agent(\n",
+    "    tools,\n",
+    "    llm,\n",
+    "    agent=\"zero-shot-react-description\",\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above code you can see the tool takes input directly from command line.\n",
+    "You can customize `prompt_func` and `input_func` according to your need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI don't know Eric Zhu, so I should ask a human for guidance.\n",
+      "Action: Human\n",
+      "Action Input: \"Do you know when Eric Zhu's birthday is?\"\u001b[0m\n",
+      "\n",
+      "Do you know when Eric Zhu's birthday is?\n",
+      "last week\n",
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3mlast week\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mThat's not very helpful. I should ask for more information.\n",
+      "Action: Human\n",
+      "Action Input: \"Do you know the specific date of Eric Zhu's birthday?\"\u001b[0m\n",
+      "\n",
+      "Do you know the specific date of Eric Zhu's birthday?\n",
+      "august 1st\n",
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3maugust 1st\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mNow that I have the date, I can check if it's a leap year or not.\n",
+      "Action: Calculator\n",
+      "Action Input: \"Is 2021 a leap year?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: False\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mI have all the information I need to answer the original question.\n",
+      "Final Answer: Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\""
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
+    "# Answer with \"last week\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "16763ed3",
+   "metadata": {},
+   "source": [
+    "# IFTTT WebHooks\n",
+    "\n",
+    "This notebook shows how to use IFTTT Webhooks.\n",
+    "\n",
+    "From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.\n",
+    "\n",
+    "## Creating a webhook\n",
+    "- Go to https://ifttt.com/create\n",
+    "\n",
+    "## Configuring the \"If This\"\n",
+    "- Click on the \"If This\" button in the IFTTT interface.\n",
+    "- Search for \"Webhooks\" in the search bar.\n",
+    "- Choose the first option for \"Receive a web request with a JSON payload.\"\n",
+    "- Choose an Event Name that is specific to the service you plan to connect to.\n",
+    "This will make it easier for you to manage the webhook URL.\n",
+    "For example, if you're connecting to Spotify, you could use \"Spotify\" as your\n",
+    "Event Name.\n",
+    "- Click the \"Create Trigger\" button to save your settings and create your webhook.\n",
+    "\n",
+    "## Configuring the \"Then That\"\n",
+    "- Tap on the \"Then That\" button in the IFTTT interface.\n",
+    "- Search for the service you want to connect, such as Spotify.\n",
+    "- Choose an action from the service, such as \"Add track to a playlist\".\n",
+    "- Configure the action by specifying the necessary details, such as the playlist name,\n",
+    "e.g., \"Songs from AI\".\n",
+    "- Reference the JSON Payload received by the Webhook in your action. For the Spotify\n",
+    "scenario, choose \"{{JsonPayload}}\" as your search query.\n",
+    "- Tap the \"Create Action\" button to save your action settings.\n",
+    "- Once you have finished configuring your action, click the \"Finish\" button to\n",
+    "complete the setup.\n",
+    "- Congratulations! You have successfully connected the Webhook to the desired\n",
+    "service, and you're ready to start receiving data and triggering actions 🎉\n",
+    "\n",
+    "## Finishing up\n",
+    "- To get your webhook URL go to https://ifttt.com/maker_webhooks/settings\n",
+    "- Copy the IFTTT key value from there. The URL is of the form\n",
+    "https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "10a46e7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools.ifttt import IFTTTWebhook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "12003d72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6e68f846",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Congratulations! You've fired the spotify JSON event\""
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tool.run(\"taylor swift\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7e599c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

@@ -0,0 +1,128 @@
+{
+  "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"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

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

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "e6860c2d",
    "metadata": {
     "pycharm": {
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "dadbcfcd",
    "metadata": {},
    "outputs": [],
@@ -238,6 +238,92 @@
    "source": [
     "agent.run(\"What is the weather in Pomfret?\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eabad3af",
+   "metadata": {},
+   "source": [
+    "## SearxNG Meta Search Engine\n",
+    "\n",
+    "Here we will be using a self hosted SearxNG meta search engine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b196c704",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = load_tools([\"searx-search\"], searx_host=\"http://localhost:8888\", llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9023eeaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3aad92c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I should look up the current weather\n",
+      "Action: SearX Search\n",
+      "Action Input: \"weather in Pomfret\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mMainly cloudy with snow showers around in the morning. High around 40F. Winds NNW at 5 to 10 mph. Chance of snow 40%. Snow accumulations less than one inch.\n",
+      "\n",
+      "10 Day Weather - Pomfret, MD As of 1:37 pm EST Today 49°/ 41° 52% Mon 27 | Day 49° 52% SE 14 mph Cloudy with occasional rain showers. High 49F. Winds SE at 10 to 20 mph. Chance of rain 50%....\n",
+      "\n",
+      "10 Day Weather - Pomfret, VT As of 3:51 am EST Special Weather Statement Today 39°/ 32° 37% Wed 01 | Day 39° 37% NE 4 mph Cloudy with snow showers developing for the afternoon. High 39F....\n",
+      "\n",
+      "Pomfret, CT ; Current Weather. 1:06 AM. 35°F · RealFeel® 32° ; TODAY'S WEATHER FORECAST. 3/3. 44°Hi. RealFeel® 50° ; TONIGHT'S WEATHER FORECAST. 3/3. 32°Lo.\n",
+      "\n",
+      "Pomfret, MD Forecast Today Hourly Daily Morning 41° 1% Afternoon 43° 0% Evening 35° 3% Overnight 34° 2% Don't Miss Finally, Here’s Why We Get More Colds and Flu When It’s Cold Coast-To-Coast...\n",
+      "\n",
+      "Pomfret, MD Weather Forecast | AccuWeather Current Weather 5:35 PM 35° F RealFeel® 36° RealFeel Shade™ 36° Air Quality Excellent Wind E 3 mph Wind Gusts 5 mph Cloudy More Details WinterCast...\n",
+      "\n",
+      "Pomfret, VT Weather Forecast | AccuWeather Current Weather 11:21 AM 23° F RealFeel® 27° RealFeel Shade™ 25° Air Quality Fair Wind ESE 3 mph Wind Gusts 7 mph Cloudy More Details WinterCast...\n",
+      "\n",
+      "Pomfret Center, CT Weather Forecast | AccuWeather Daily Current Weather 6:50 PM 39° F RealFeel® 36° Air Quality Fair Wind NW 6 mph Wind Gusts 16 mph Mostly clear More Details WinterCast...\n",
+      "\n",
+      "12:00 pm · Feels Like36° · WindN 5 mph · Humidity43% · UV Index3 of 10 · Cloud Cover65% · Rain Amount0 in ...\n",
+      "\n",
+      "Pomfret Center, CT Weather Conditions | Weather Underground star Popular Cities San Francisco, CA 49 °F Clear Manhattan, NY 37 °F Fair Schiller Park, IL (60176) warning39 °F Mostly Cloudy...\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"What is the weather in Pomfret\")"
+   ]
   }
  ],
  "metadata": {
@@ -256,7 +342,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.11"
   },
   "vscode": {
    "interpreter": {

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

@@ -73,7 +73,7 @@
     "jukit_cell_id": "OHyurqUPbS"
    },
    "source": [
-    "# Custom Parameters\n",
+    "## Custom Parameters\n",
     "\n",
     "SearxNG supports up to [139 search engines](https://docs.searxng.org/admin/engines/configured_engines.html#configured-engines). You can also customize the Searx wrapper with arbitrary named parameters that will be passed to the Searx search API . In the below example we will making a more interesting use of custom search parameters from searx search api."
    ]
@@ -104,7 +104,7 @@
    "metadata": {
     "jukit_cell_id": "3FyQ6yHI8K",
     "tags": [
-        "scroll-output"
+     "scroll-output"
     ]
    },
    "outputs": [
@@ -161,7 +161,7 @@
     "jukit_cell_id": "d0x164ssV1"
    },
    "source": [
-    "# Obtaining results with metadata"
+    "## Obtaining results with metadata"
    ]
   },
   {
@@ -192,7 +192,7 @@
    "metadata": {
     "jukit_cell_id": "r7qUtvKNOh",
     "tags": [
-        "scroll-output"
+     "scroll-output"
     ]
    },
    "outputs": [
@@ -263,7 +263,7 @@
    "metadata": {
     "jukit_cell_id": "JyNgoFm0vo",
     "tags": [
-        "scroll-output"
+     "scroll-output"
     ]
    },
    "outputs": [
@@ -444,7 +444,7 @@
    "metadata": {
     "jukit_cell_id": "5NrlredKxM",
     "tags": [
-        "scroll-output"
+     "scroll-output"
     ]
    },
    "outputs": [
@@ -600,7 +600,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.11"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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

@@ -0,0 +1,326 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "16763ed3",
+   "metadata": {},
+   "source": [
+    "# Zapier Natural Language Actions API\n",
+    "\\\n",
+    "Full docs here: https://nla.zapier.com/api/v1/docs\n",
+    "\n",
+    "**Zapier Natural Language Actions** gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface.\n",
+    "\n",
+    "NLA supports apps like Gmail, Salesforce, Trello, Slack, Asana, HubSpot, Google Sheets, Microsoft Teams, and thousands more apps: https://zapier.com/apps\n",
+    "\n",
+    "Zapier NLA handles ALL the underlying API auth and translation from natural language --> underlying API call --> return simplified output for LLMs. The key idea is you, or your users, expose a set of actions via an oauth-like setup window, which you can then query and execute via a REST API.\n",
+    "\n",
+    "NLA offers both API Key and OAuth for signing NLA API requests.\n",
+    "\n",
+    "1. Server-side (API Key): for quickly getting started, testing, and production scenarios where LangChain will only use actions exposed in the developer's Zapier account (and will use the developer's connected accounts on Zapier.com)\n",
+    "\n",
+    "2. User-facing (Oauth): for production scenarios where you are deploying an end-user facing application and LangChain needs access to end-user's exposed actions and connected accounts on Zapier.com\n",
+    "\n",
+    "This quick start will focus on the server-side use case for brevity. Review [full docs](https://nla.zapier.com/api/v1/docs) or reach out to nla@zapier.com for user-facing oauth developer support.\n",
+    "\n",
+    "This example goes over how to use the Zapier integration with a `SimpleSequentialChain`, then an `Agent`.\n",
+    "In code, below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a363309c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5cf33377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# 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",
+    "os.environ[\"ZAPIER_NLA_API_KEY\"] = os.environ.get(\"ZAPIER_NLA_API_KEY\", \"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4881b484-1b97-478f-b206-aec407ceff66",
+   "metadata": {},
+   "source": [
+    "## Example with Agent\n",
+    "Zapier tools can be used with an agent. See the example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b2044b17-c941-4ffb-8a03-027a35e2df81",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.agents.agent_toolkits import ZapierToolkit\n",
+    "from langchain.utilities.zapier import ZapierNLAWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7b505eeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## step 0. expose gmail 'find email' and slack 'send channel message' actions\n",
+    "\n",
+    "# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields \"Have AI guess\"\n",
+    "# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cab18227-c232-4214-9256-bb8dd352266c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "zapier = ZapierNLAWrapper()\n",
+    "toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)\n",
+    "agent = initialize_agent(toolkit.get_tools(), llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f94713de-b64d-465f-a087-00288b5f80ec",
+   "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 the email and summarize it.\n",
+      "Action: Gmail: Find Email\n",
+      "Action Input: Find the latest email from Silicon Valley Bank\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG\", \"reply_to__email\": \"sreply@svb.com\", \"subject\": \"Meet the new CEO Tim Mayopoulos\", \"date\": \"Tue, 14 Mar 2023 23:42:29 -0500 (CDT)\", \"message_url\": \"https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a\", \"attachment_count\": \"0\", \"to__emails\": \"ankush@langchain.dev\", \"message_id\": \"186e393b13cfdf0a\", \"labels\": \"IMPORTANT, CATEGORY_UPDATES, INBOX\"}\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to summarize the email and send it to the #test-zapier channel in Slack.\n",
+      "Action: Slack: Send Channel Message\n",
+      "Action Input: Send a slack message to the #test-zapier channel with the text \"Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m{\"message__text\": \"Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.\", \"message__permalink\": \"https://langchain.slack.com/archives/C04TSGU0RA7/p1678859932375259\", \"channel\": \"C04TSGU0RA7\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:58:52Z\", \"message__bot_profile__icons__image_36\": \"https://avatars.slack-edge.com/2022-08-02/3888649620612_f864dc1bb794cf7d82b0_36.png\", \"message__blocks[]block_id\": \"kdZZ\", \"message__blocks[]elements[]type\": \"['rich_text_section']\"}\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcdea831",
+   "metadata": {},
+   "source": [
+    "# Example with SimpleSequentialChain\n",
+    "If you need more explicit control, use a chain, like below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "10a46e7e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.chains import LLMChain, TransformChain, SimpleSequentialChain\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.tools.zapier.tool import ZapierNLARunAction\n",
+    "from langchain.utilities.zapier import ZapierNLAWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b9358048",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "## step 0. expose gmail 'find email' and slack 'send direct message' actions\n",
+    "\n",
+    "# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields \"Have AI guess\"\n",
+    "# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first\n",
+    "\n",
+    "actions = ZapierNLAWrapper().list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4e80f461",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## step 1. gmail find email\n",
+    "\n",
+    "GMAIL_SEARCH_INSTRUCTIONS = \"Grab the latest email from Silicon Valley Bank\"\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "46893233",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## step 2. generate draft reply\n",
+    "\n",
+    "template = \"\"\"You are an assisstant who drafts replies to an incoming email. Output draft reply in plain text (not JSON).\n",
+    "\n",
+    "Incoming email:\n",
+    "{email_data}\n",
+    "\n",
+    "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd85c4f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## step 3. send draft reply via a slack direct message\n",
+    "\n",
+    "SLACK_HANDLE = \"@Ankush Gola\"\n",
+    "\n",
+    "def nla_slack(inputs):\n",
+    "    action = next((a for a in actions if a[\"description\"].startswith(\"Slack: Send Direct Message\")), None)\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4829cab4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
+      "\u001b[36;1m\u001b[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG\", \"reply_to__email\": \"sreply@svb.com\", \"subject\": \"Meet the new CEO Tim Mayopoulos\", \"date\": \"Tue, 14 Mar 2023 23:42:29 -0500 (CDT)\", \"message_url\": \"https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a\", \"attachment_count\": \"0\", \"to__emails\": \"ankush@langchain.dev\", \"message_id\": \"186e393b13cfdf0a\", \"labels\": \"IMPORTANT, CATEGORY_UPDATES, INBOX\"}\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3m\n",
+      "Dear Silicon Valley Bridge Bank, \n",
+      "\n",
+      "Thank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \n",
+      "\n",
+      "Best regards, \n",
+      "[Your Name]\u001b[0m\n",
+      "\u001b[38;5;200m\u001b[1;3m{\"message__text\": \"Dear Silicon Valley Bridge Bank, \\n\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\n\\nBest regards, \\n[Your Name]\", \"message__permalink\": \"https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629\", \"channel\": \"D04TKF5BBHU\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:59:28Z\", \"message__blocks[]block_id\": \"p7i\", \"message__blocks[]elements[]elements[]type\": \"[['text']]\", \"message__blocks[]elements[]type\": \"['rich_text_section']\"}\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'{\"message__text\": \"Dear Silicon Valley Bridge Bank, \\\\n\\\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\\\n\\\\nBest regards, \\\\n[Your Name]\", \"message__permalink\": \"https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629\", \"channel\": \"D04TKF5BBHU\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:59:28Z\", \"message__blocks[]block_id\": \"p7i\", \"message__blocks[]elements[]elements[]type\": \"[[\\'text\\']]\", \"message__blocks[]elements[]type\": \"[\\'rich_text_section\\']\"}'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## finally, execute\n",
+    "\n",
+    "overall_chain = SimpleSequentialChain(chains=[gmail_chain, reply_chain, slack_chain], verbose=True)\n",
+    "overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09ff954e-45f2-4595-92ea-91627abde4a0",
+   "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
+}