cr

.github/CONTRIBUTING.md

@@ -75,7 +75,7 @@ This will install all requirements for running the package, examples, linting, f
 
 ❗Note: If you're running Poetry 1.4.1 and receive a `WheelFileValidationError` for `debugpy` during installation, you can try either downgrading to Poetry 1.4.0 or disabling "modern installation" (`poetry config installer.modern-installation false`) and re-install requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
 
-Now, you should be able to run the common tasks in the following section. To double check, run `make test`, all tests should pass. If they don't you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
+Now, you should be able to run the common tasks in the following section.
 
 ## ✅Common Tasks
 

.github/workflows/linkcheck.yml

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

.github/workflows/lint.yml

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

.github/workflows/release.yml

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

.github/workflows/test.yml

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

.gitignore

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

Dockerfile

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

README.md

@@ -4,8 +4,6 @@
 
 [![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml) [![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
 
-Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwchase17/langchainjs).
-
 **Production Support:** As you move your LangChains into production, we'd love to offer more comprehensive support.
 Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set up a dedicated support Slack channel.
 
@@ -17,9 +15,12 @@ or
 
 ## 🤔 What is this?
 
-Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
+Large language models (LLMs) are emerging as a transformative technology, enabling
+developers to build applications that they previously could not.
+But using these LLMs in isolation is often not enough to
+create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
 
-This library aims to assist in the development of those types of applications. Common examples of these applications include:
+This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
 
 **❓ Question Answering over specific documents**
 
@@ -52,23 +53,23 @@ These are, in increasing order of complexity:
 
 **📃 LLMs and Prompts:**
 
-This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
+This includes prompt management, prompt optimization, generic interface for all LLMs, and common utilities for working with LLMs.
 
 **🔗 Chains:**
 
-Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
+Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
 
 **📚 Data Augmented Generation:**
 
-Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
+Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.
 
 **🤖 Agents:**
 
-Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
+Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
 
 **🧠 Memory:**
 
-Memory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
+Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
 
 **🧐 Evaluation:**
 
@@ -78,6 +79,6 @@ For more information on these concepts, please see our [full documentation](http
 
 ## 💁 Contributing
 
-As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
+As an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
 
 For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).

docs/deployments.md

@@ -1,10 +1,14 @@
 # Deployments
 
-So, you've created a really cool chain - now what? How do you deploy it and make it easily shareable with the world?
+So you've made a really cool chain - now what? How do you deploy it and make it easily sharable with the world?
 
-This section covers several options for that. Note that these options are meant for quick deployment of prototypes and demos, not for production systems. If you need help with the deployment of a production system, please contact us directly.
+This section covers several options for that.
+Note that these are meant as quick deployment options for prototypes and demos, and not for production systems.
+If you are looking for help with deployment of a production system, please contact us directly.
 
-What follows is a list of template GitHub repositories designed to be easily forked and modified to use your chain. This list is far from exhaustive, and we are EXTREMELY open to contributions here.
+What follows is a list of template GitHub repositories aimed that are intended to be
+very easy to fork and modify to use your chain.
+This is far from an exhaustive list of options, and we are EXTREMELY open to contributions here.
 
 ## [Streamlit](https://github.com/hwchase17/langchain-streamlit-template)
 
@@ -29,30 +33,19 @@ It implements a Question Answering app and contains instructions for deploying t
 
 A minimal example on how to run LangChain on Vercel using Flask.
 
-## [Fly.io](https://github.com/fly-apps/hello-fly-langchain)
-
-A minimal example of how to deploy LangChain to [Fly.io](https://fly.io/) using Flask.
-
 ## [Digitalocean App Platform](https://github.com/homanp/digitalocean-langchain)
 
 A minimal example on how to deploy LangChain to DigitalOcean App Platform.
 
-## [Google Cloud Run](https://github.com/homanp/gcp-langchain)
-
-A minimal example on how to deploy LangChain to Google Cloud Run.
-
 ## [SteamShip](https://github.com/steamship-core/steamship-langchain/)
 
-This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship. This includes: production-ready endpoints, horizontal scaling across dependencies, persistent storage of app state, multi-tenancy support, etc.
+This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship.
+This includes: production ready endpoints, horizontal scaling across dependencies, persistant storage of app state, multi-tenancy support, etc.
 
 ## [Langchain-serve](https://github.com/jina-ai/langchain-serve)
 
-This repository allows users to serve local chains and agents as RESTful, gRPC, or WebSocket APIs, thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
+This repository allows users to serve local chains and agents as RESTful, gRPC, or Websocket APIs thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
 
 ## [BentoML](https://github.com/ssheng/BentoChain)
 
 This repository provides an example of how to deploy a LangChain application with [BentoML](https://github.com/bentoml/BentoML). BentoML is a framework that enables the containerization of machine learning applications as standard OCI images. BentoML also allows for the automatic generation of OpenAPI and gRPC endpoints. With BentoML, you can integrate models from all popular ML frameworks and deploy them as microservices running on the most optimal hardware and scaling independently.
-
-## [Databutton](https://databutton.com/home?new-data-app=true)
-
-These templates serve as examples of how to build, deploy, and share LangChain applications using Databutton. You can create user interfaces with Streamlit, automate tasks by scheduling Python code, and store files and data in the built-in store. Examples include a Chatbot interface with conversational memory, a Personal search engine, and a starter template for LangChain apps. Deploying and sharing is just one click away.

docs/ecosystem.rst

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

docs/ecosystem/aim_tracking.ipynb

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

docs/ecosystem/analyticdb.md

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

docs/ecosystem/clearml_tracking.ipynb

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

docs/ecosystem/comet_tracking.ipynb

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

docs/ecosystem/gpt4all.md

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

docs/ecosystem/lancedb.md

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

docs/ecosystem/metal.md

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

docs/ecosystem/myscale.md

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

docs/ecosystem/pipelineai.md

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

docs/ecosystem/predictionguard.md

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

docs/ecosystem/redis.md

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

docs/ecosystem/replicate.md

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

docs/ecosystem/runhouse.md

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

docs/ecosystem/tair.md

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

docs/ecosystem/unstructured.md

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

docs/ecosystem/wandb_tracking.ipynb

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

docs/ecosystem/weaviate.md

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

docs/ecosystem/yeagerai.md

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

docs/gallery.rst

@@ -280,17 +280,6 @@ Proprietary
 
     ---
 
-    .. link-button:: https://anysummary.app
-        :type: url
-        :text: Summarize any file with AI
-        :classes: stretched-link btn-lg
-
-    +++
-
-    Summarize not only long docs, interview audio or video files quickly, but also entire websites and YouTube videos. Share or download your generated summaries to collaborate with others, or revisit them at any time! Bonus: `@anysummary <https://twitter.com/anysummary>`_ on Twitter will also summarize any thread it is tagged in.
-    
-    ---
-
     .. link-button:: https://twitter.com/dory111111/status/1608406234646052870?s=20&t=XYlrbKM0ornJsrtGa0br-g
         :type: url
         :text: AI Assisted SQL Query Generator

docs/getting_started/getting_started.md

@@ -46,7 +46,7 @@ LangChain provides many modules that can be used to build language model applica
 
 
 
-## LLMs: Get predictions from a language model
+`````{dropdown} LLMs: Get predictions from a language model
 
 The most basic building block of LangChain is calling an LLM on some input.
 Let's walk through a simple example of how to do this. 
@@ -77,9 +77,10 @@ Feetful of Fun
 ```
 
 For more details on how to use LLMs within LangChain, see the [LLM getting started guide](../modules/models/llms/getting_started.ipynb).
+`````
 
 
-## Prompt Templates: Manage prompts for LLMs
+`````{dropdown} Prompt Templates: Manage prompts for LLMs
 
 Calling an LLM is a great first step, but it's just the beginning.
 Normally when you use an LLM in an application, you are not sending user input directly to the LLM.
@@ -114,10 +115,11 @@ What is a good name for a company that makes colorful socks?
 
 [For more details, check out the getting started guide for prompts.](../modules/prompts/chat_prompt_template.ipynb)
 
+`````
 
 
 
-## Chains: Combine LLMs and prompts in multi-step workflows
+`````{dropdown} Chains: Combine LLMs and prompts in multi-step workflows
 
 Up until now, we've worked with the PromptTemplate and LLM primitives by themselves. But of course, a real application is not just one primitive, but rather a combination of them.
 
@@ -157,7 +159,10 @@ This is one of the simpler types of chains, but understanding how it works will
 
 [For more details, check out the getting started guide for chains.](../modules/chains/getting_started.ipynb)
 
-## Agents: Dynamically Call Chains Based on User Input
+`````
+
+
+`````{dropdown} Agents: Dynamically Call Chains Based on User Input
 
 So far the chains we've looked at run in a predetermined order.
 
@@ -172,9 +177,9 @@ In order to load agents, you should understand the following concepts:
 - LLM: The language model powering the agent.
 - Agent: The agent to use. This should be a string that references a support agent class. Because this notebook focuses on the simplest, highest level API, this only covers using the standard supported agents. If you want to implement a custom agent, see the documentation for custom agents (coming soon).
 
-**Agents**: For a list of supported agents and their specifications, see [here](../modules/agents/getting_started.ipynb).
+**Agents**: For a list of supported agents and their specifications, see [here](../modules/agents/agents.md).
 
-**Tools**: For a list of predefined tools and their specifications, see [here](../modules/agents/tools/getting_started.md).
+**Tools**: For a list of predefined tools and their specifications, see [here](../modules/agents/tools.md).
 
 For this example, you will also need to install the SerpAPI Python package.
 
@@ -229,8 +234,10 @@ Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .
 ```
 
 
+`````
 
-## Memory: Add State to Chains and Agents
+
+`````{dropdown} Memory: Add State to Chains and Agents
 
 So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of "memory" so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of "short-term memory". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of "long-term memory". For more concrete ideas on the latter, see this [awesome paper](https://memprompt.com/).
 
@@ -244,8 +251,7 @@ from langchain import OpenAI, ConversationChain
 llm = OpenAI(temperature=0)
 conversation = ConversationChain(llm=llm, verbose=True)
 
-output = conversation.predict(input="Hi there!")
-print(output)
+conversation.predict(input="Hi there!")
 ```
 
 ```pycon
@@ -263,8 +269,7 @@ AI:
 ```
 
 ```python
-output = conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
-print(output)
+conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
 ```
 
 ```pycon
@@ -282,6 +287,7 @@ AI:
 > Finished chain.
 " That's great! What would you like to talk about?"
 ```
+`````
 
 ## Building a Language Model Application: Chat Models
 
@@ -289,8 +295,8 @@ Similarly, you can use chat models instead of LLMs. Chat models are a variation
 
 Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
 
-## Get Message Completions from a Chat Model
 
+`````{dropdown} Get Message Completions from a Chat Model
 You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
 
 ```python
@@ -344,12 +350,12 @@ You can recover things like token usage from this LLMResult:
 result.llm_output['token_usage']
 # -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
 ```
+`````
 
-
-## Chat Prompt Templates
+`````{dropdown} Chat Prompt Templates
 Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or `Message` object, depending on whether you want to use the formatted value as input to an llm or chat model.
 
-For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
+For convience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
 
 ```python
 from langchain.chat_models import ChatOpenAI
@@ -361,9 +367,9 @@ from langchain.prompts.chat import (
 
 chat = ChatOpenAI(temperature=0)
 
-template = "You are a helpful assistant that translates {input_language} to {output_language}."
+template="You are a helpful assistant that translates {input_language} to {output_language}."
 system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template = "{text}"
+human_template="{text}"
 human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
 
 chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
@@ -372,8 +378,9 @@ chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_mes
 chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
 # -> AIMessage(content="J'aime programmer.", additional_kwargs={})
 ```
+`````
 
-## Chains with Chat Models
+`````{dropdown} Chains with Chat Models
 The `LLMChain` discussed in the above section can be used with chat models as well:
 
 ```python
@@ -387,9 +394,9 @@ from langchain.prompts.chat import (
 
 chat = ChatOpenAI(temperature=0)
 
-template = "You are a helpful assistant that translates {input_language} to {output_language}."
+template="You are a helpful assistant that translates {input_language} to {output_language}."
 system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template = "{text}"
+human_template="{text}"
 human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
 chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
 
@@ -397,8 +404,9 @@ chain = LLMChain(llm=chat, prompt=chat_prompt)
 chain.run(input_language="English", output_language="French", text="I love programming.")
 # -> "J'aime programmer."
 ```
+`````
 
-## Agents with Chat Models
+`````{dropdown} Agents with Chat Models
 Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
 
 ```python
@@ -457,7 +465,9 @@ Final Answer: 2.169459462491557
 > Finished chain.
 '2.169459462491557'
 ```
-## Memory: Add State to Chains and Agents
+`````
+
+`````{dropdown} Memory: Add State to Chains and Agents
 You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
 
 ```python
@@ -491,4 +501,4 @@ conversation.predict(input="I'm doing well! Just having a conversation with an A
 conversation.predict(input="Tell me about yourself.")
 # -> "Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?"
 ```
-
+`````

docs/index.rst

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

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

@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "id": "da5df06c-af6f-4572-b9f5-0ab971c16487",
    "metadata": {
     "tags": []
@@ -42,6 +42,7 @@
     "from langchain.agents import AgentType\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.callbacks.stdout import StdOutCallbackHandler\n",
+    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks.tracers import LangChainTracer\n",
     "from aiohttp import ClientSession\n",
     "\n",
@@ -56,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "fd4c294e-b1d6-44b8-b32e-2765c017e503",
    "metadata": {
     "tags": []
@@ -72,15 +73,16 @@
       "\u001b[32;1m\u001b[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
       "Action: Search\n",
       "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
+      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Rafael Nadal's age\n",
       "Action: Search\n",
       "Action Input: \"Rafael Nadal age\"\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3m36 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate his age raised to the 0.334 power\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 36 raised to the 0.334 power\n",
       "Action: Calculator\n",
       "Action Input: 36^0.334\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
+      "\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
       "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\u001b[0m\n",
       "\n",
@@ -91,17 +93,18 @@
       "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
       "Action: Search\n",
       "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mOlivia Wilde started dating Harry Styles after ending her years-long engagement to Jason Sudeikis — see their relationship timeline.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age.\n",
+      "Observation: \u001b[33;1m\u001b[1;3mJason Sudeikis\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' age\n",
       "Action: Search\n",
-      "Action Input: \"Harry Styles age\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m29 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 29 raised to the 0.23 power.\n",
+      "Action Input: \"Jason Sudeikis age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m47 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 47 raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 29^0.23\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.169459462491557\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: Harry Styles, Olivia Wilde's boyfriend, is 29 years old and his age raised to the 0.23 power is 2.169459462491557.\u001b[0m\n",
+      "Action Input: 47^0.23\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.4242784855673896\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
@@ -110,17 +113,17 @@
       "\u001b[32;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
       "Action: Search\n",
       "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mMichael Schumacher (top left) and Lewis Hamilton (top right) have each won the championship a record seven times during their careers, while Sebastian Vettel ( ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
+      "Observation: \u001b[33;1m\u001b[1;3mMax Verstappen\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Max Verstappen's age\n",
       "Action: Search\n",
-      "Action Input: \"Michael Schumacher age\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m54 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate the age raised to the 0.23 power\n",
+      "Action Input: \"Max Verstappen Age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m25 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 54^0.23\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.502940725307012\u001b[0m\n",
+      "Action Input: 25^0.23\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.84599359907945\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Michael Schumacher, aged 54, raised to the 0.23 power is 2.502940725307012.\u001b[0m\n",
+      "Final Answer: Max Verstappen, 25 years old, raised to the 0.23 power is 1.84599359907945.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
@@ -129,17 +132,18 @@
       "\u001b[32;1m\u001b[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
       "Action: Search\n",
       "Action Input: \"US Open women's final 2019 winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out her age\n",
+      "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu defeated Serena Williams in the final, 6–3, 7–5 to win the women's singles tennis title at the 2019 US Open. It was her first major title, and she became the first Canadian, as well as the first player born in the 2000s, to win a major singles title.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Bianca Andreescu's age.\n",
       "Action: Search\n",
       "Action Input: \"Bianca Andreescu age\"\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3m22 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate her age raised to the 0.34 power\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the age of Bianca Andreescu and can calculate her age raised to the 0.34 power.\n",
       "Action: Calculator\n",
       "Action Input: 22^0.34\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Bianca Andreescu, aged 22, won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.86.\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Bianca Andreescu won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.8603798598506933.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
@@ -156,32 +160,35 @@
       "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 53 raised to the 0.19 power\n",
       "Action: Calculator\n",
       "Action Input: 53^0.19\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\n",
+      "\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
       "Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Serial executed in 52.47 seconds.\n"
+      "Serial executed in 65.11 seconds.\n"
      ]
     }
    ],
    "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm)\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")\n",
+    "def generate_serially():\n",
+    "    for q in questions:\n",
+    "        llm = OpenAI(temperature=0)\n",
+    "        tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm)\n",
+    "        agent = initialize_agent(\n",
+    "            tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    "        )\n",
+    "        agent.run(q)\n",
     "\n",
     "s = time.perf_counter()\n",
-    "for q in questions:\n",
-    "    agent.run(q)\n",
+    "generate_serially()\n",
     "elapsed = time.perf_counter() - s\n",
     "print(f\"Serial executed in {elapsed:0.2f} seconds.\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "id": "076d7b85-45ec-465d-8b31-c2ad119c3438",
    "metadata": {
     "tags": []
@@ -195,8 +202,8 @@
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
@@ -206,94 +213,179 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Who is Beyonce's husband?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mJay-Z\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who won the US Open women's final in 2019 and then calculate her age raised to the 0.34 power.\n",
       "Action: Search\n",
       "Action Input: \"US Open women's final 2019 winner\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mJason Sudeikis\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[33;1m\u001b[1;3mMax Verstappen\u001b[0m\n",
+      "Thought:\n",
+      "Observation: \u001b[33;1m\u001b[1;3mBianca Andreescu defeated Serena Williams in the final, 6–3, 7–5 to win the women's singles tennis title at the 2019 US Open. It was her first major title, and she became the first Canadian, as well as the first player born in the 2000s, to win a major singles title.\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' age\n",
+      "Action: Search\n",
+      "Action Input: \"Jason Sudeikis age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out Jay-Z's age\n",
+      "Action: Search\n",
+      "Action Input: \"How old is Jay-Z?\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m53 years\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
       "Action: Search\n",
       "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who won the grand prix and then calculate their age raised to the 0.23 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Formula 1 Grand Prix Winner\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Who is Beyonce's husband?\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mOlivia Wilde started dating Harry Styles after ending her years-long engagement to Jason Sudeikis — see their relationship timeline.\u001b[0m\n",
       "Thought:\n",
-      "Observation: \u001b[33;1m\u001b[1;3mJay-Z\u001b[0m\n",
-      "Thought:\n",
-      "Observation: \u001b[33;1m\u001b[1;3mMichael Schumacher (top left) and Lewis Hamilton (top right) have each won the championship a record seven times during their careers, while Sebastian Vettel ( ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out her age\n",
+      "Observation: \u001b[33;1m\u001b[1;3m47 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Max Verstappen's age\n",
       "Action: Search\n",
-      "Action Input: \"Bianca Andreescu age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out Jay-Z's age\n",
+      "Action Input: \"Max Verstappen Age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m25 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Bianca Andreescu's age.\n",
       "Action: Search\n",
-      "Action Input: \"How old is Jay-Z?\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m53 years\u001b[0m\n",
-      "Thought:\n",
+      "Action Input: \"Bianca Andreescu age\"\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3m22 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age.\n",
-      "Action: Search\n",
-      "Action Input: \"Harry Styles age\"\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m29 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate her age raised to the 0.34 power\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 53 raised to the 0.19 power\n",
       "Action: Calculator\n",
-      "Action Input: 22^0.34\u001b[0m\u001b[32;1m\u001b[1;3m I need to calculate 53 raised to the 0.19 power\n",
-      "Action: Calculator\n",
-      "Action Input: 53^0.19\u001b[0m\u001b[32;1m\u001b[1;3m I need to calculate 29 raised to the 0.23 power.\n",
-      "Action: Calculator\n",
-      "Action Input: 29^0.23\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
+      "Action Input: 53^0.19\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
       "Action: Search\n",
-      "Action Input: \"Rafael Nadal age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to find out the age of the winner\n",
-      "Action: Search\n",
-      "Action Input: \"Michael Schumacher age\"\u001b[0m\n",
-      "Observation: \n",
+      "Action Input: \"Rafael Nadal age\"\u001b[0m\u001b[32;1m\u001b[1;3m I need to calculate 47 raised to the 0.23 power\n",
+      "Action: Calculator\n",
+      "Action Input: 47^0.23\u001b[0m\n",
       "Observation: \u001b[33;1m\u001b[1;3m36 years\u001b[0m\n",
-      "Thought:\u001b[33;1m\u001b[1;3m54 years\u001b[0m\n",
-      "Thought:\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\u001b[0m\n",
-      "Thought:\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.169459462491557\u001b[0m\n",
-      "Thought:\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate the age raised to the 0.334 power\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.23 power\n",
       "Action: Calculator\n",
-      "Action Input: 36^0.334\u001b[0m\u001b[32;1m\u001b[1;3m I now need to calculate the age raised to the 0.23 power\n",
+      "Action Input: 25^0.23\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.12624064206896\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the age of Bianca Andreescu and can calculate her age raised to the 0.34 power.\n",
       "Action: Calculator\n",
-      "Action Input: 54^0.23\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\u001b[0m\n",
+      "Action Input: 22^0.34\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.84599359907945\u001b[0m\n",
       "Thought:\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.502940725307012\u001b[0m\n",
-      "Thought:\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.4242784855673896\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate his age raised to the 0.334 power\n",
+      "Action: Calculator\n",
+      "Action Input: 36^0.334\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 2.8603798598506933\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Concurrent executed in 14.49 seconds.\n"
+      "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Max Verstappen, 25 years old, raised to the 0.23 power is 1.84599359907945.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: Bianca Andreescu won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.8603798598506933.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "Concurrent executed in 12.38 seconds.\n"
      ]
     }
    ],
    "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm)\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")\n",
+    "async def generate_concurrently():\n",
+    "    agents = []\n",
+    "    # To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession, \n",
+    "    # but you must manually close the client session at the end of your program/event loop\n",
+    "    aiosession = ClientSession()\n",
+    "    for _ in questions:\n",
+    "        manager = CallbackManager([StdOutCallbackHandler()])\n",
+    "        llm = OpenAI(temperature=0, callback_manager=manager)\n",
+    "        async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession, callback_manager=manager)\n",
+    "        agents.append(\n",
+    "            initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
+    "        )\n",
+    "    tasks = [async_agent.arun(q) for async_agent, q in zip(agents, questions)]\n",
+    "    await asyncio.gather(*tasks)\n",
+    "    await aiosession.close()\n",
     "\n",
     "s = time.perf_counter()\n",
-    "# If running this outside of Jupyter, use asyncio.run or loop.run_until_complete\n",
-    "tasks = [agent.arun(q) for q in questions]\n",
-    "await asyncio.gather(*tasks)\n",
+    "# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
+    "await generate_concurrently()\n",
     "elapsed = time.perf_counter() - s\n",
     "print(f\"Concurrent executed in {elapsed:0.2f} seconds.\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97ef285c-4a43-4a4e-9698-cd52a1bc56c9",
+   "metadata": {},
+   "source": [
+    "## Using Tracing with Asynchronous Agents\n",
+    "\n",
+    "To use tracing with async agents, you must pass in a custom `CallbackManager` with `LangChainTracer` to each agent running asynchronously. This way, you avoid collisions while the trace is being collected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "44bda05a-d33e-4e91-9a71-a0f3f96aae95",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who won the US Open men's final in 2019 and then calculate his age raised to the 0.334 power.\n",
+      "Action: Search\n",
+      "Action Input: \"US Open men's final 2019 winner\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mRafael Nadal\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Rafael Nadal's age\n",
+      "Action: Search\n",
+      "Action Input: \"Rafael Nadal age\"\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3m36 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 36 raised to the 0.334 power\n",
+      "Action: Calculator\n",
+      "Action Input: 36^0.334\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 3.3098250249682484\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# To make async requests in Tools more efficient, you can pass in your own aiohttp.ClientSession, \n",
+    "# but you must manually close the client session at the end of your program/event loop\n",
+    "aiosession = ClientSession()\n",
+    "tracer = LangChainTracer()\n",
+    "tracer.load_default_session()\n",
+    "manager = CallbackManager([StdOutCallbackHandler(), tracer])\n",
+    "\n",
+    "# Pass the manager into the llm if you want llm calls traced.\n",
+    "llm = OpenAI(temperature=0, callback_manager=manager)\n",
+    "\n",
+    "async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession)\n",
+    "async_agent = initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
+    "await async_agent.arun(questions[0])\n",
+    "await aiosession.close()"
+   ]
   }
  ],
  "metadata": {
@@ -312,7 +404,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/agents/agents/custom_agent.ipynb

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

docs/modules/agents/agents/custom_agent_with_tool_retrieval.ipynb

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

docs/modules/agents/agents/custom_mrkl_agent.ipynb

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

docs/modules/agents/agents/custom_multi_action_agent.ipynb

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

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

@@ -1,312 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4658d71a",
-   "metadata": {},
-   "source": [
-    "# Structured Tool Chat Agent\n",
-    "\n",
-    "This notebook walks through using a chat agent capable of using multi-input tools.\n",
-    "\n",
-    "Older agents are configured to specify an action input as a single string, but this agent can use the provided tools' `args_schema` to populate the action input.\n",
-    "\n",
-    "This functionality is natively available in the (`structured-chat-zero-shot-react-description` or `AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f65308ab",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents import AgentType\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.agents import initialize_agent"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30aaf540-9e8e-436e-af8b-89e610e34120",
-   "metadata": {},
-   "source": [
-    "### Initialize Tools\n",
-    "\n",
-    "We will test the agent using a web browser."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "71027ff2-5d09-49cd-92a1-24b2c454a7ae",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents.agent_toolkits import PlayWrightBrowserToolkit\n",
-    "from langchain.tools.playwright.utils import (\n",
-    "    create_async_playwright_browser,\n",
-    "    create_sync_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter.\n",
-    ")\n",
-    "\n",
-    "# This import is required only for jupyter notebooks, since they have their own eventloop\n",
-    "import nest_asyncio\n",
-    "nest_asyncio.apply()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5fb14d6d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "async_browser = create_async_playwright_browser()\n",
-    "browser_toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)\n",
-    "tools = browser_toolkit.get_tools()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cafe9bc1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm = ChatOpenAI(temperature=0) # Also works well with Anthropic models\n",
-    "agent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c4a45575-f3ef-46ba-a943-475584073984",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks import tracing_enabled # This is used to configure tracing for our runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "4f4aa234-9746-47d8-bec7-d76081ac3ef6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Hi Erica! How can I assist you today?\n"
-     ]
-    }
-   ],
-   "source": [
-    "with tracing_enabled(): # If you want to see the traces in the UI\n",
-    "    response = await agent_chain.arun(input=\"Hi I'm Erica.\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "23e7dc33-50a5-4685-8e9b-4ac49e12877f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "I'm here to chat! How's your day going?\n"
-     ]
-    }
-   ],
-   "source": [
-    "with tracing_enabled(): # If you want to see the traces in the UI\n",
-    "    response = await agent_chain.arun(input=\"Don't need help really just chatting.\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "dc70b454",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"navigate_browser\",\n",
-      "  \"action_input\": {\n",
-      "    \"url\": \"https://blog.langchain.dev/\"\n",
-      "  }\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mNavigating to https://blog.langchain.dev/ returned status code 200\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI need to extract the text from the webpage to summarize it.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"extract_text\",\n",
-      "  \"action_input\": {}\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[31;1m\u001b[1;3mLangChain LangChain Home About GitHub Docs LangChain The official LangChain blog. Auto-Evaluator Opportunities Editor's Note: this is a guest blog post by Lance Martin.\n",
-      "\n",
-      "\n",
-      "TL;DR\n",
-      "\n",
-      "We recently open-sourced an auto-evaluator tool for grading LLM question-answer chains. We are now releasing an open source, free to use hosted app and API to expand usability. Below we discuss a few opportunities to further improve May 1, 2023 5 min read Callbacks Improvements TL;DR: We're announcing improvements to our callbacks system, which powers logging, tracing, streaming output, and some awesome third-party integrations. This will better support concurrent runs with independent callbacks, tracing of deeply nested trees of LangChain components, and callback handlers scoped to a single request (which is super useful for May 1, 2023 3 min read Unleashing the power of AI Collaboration with Parallelized LLM Agent Actor Trees Editor's note: the following is a guest blog post from Cyrus at Shaman AI. We use guest blog posts to highlight interesting and novel applciations, and this is certainly that. There's been a lot of talk about agents recently, but most have been discussions around a single agent. If multiple Apr 28, 2023 4 min read Gradio & LLM Agents Editor's note: this is a guest blog post from Freddy Boulton, a software engineer at Gradio. We're excited to share this post because it brings a large number of exciting new tools into the ecosystem. Agents are largely defined by the tools they have, so to be able to equip Apr 23, 2023 4 min read RecAlign - The smart content filter for social media feed [Editor's Note] This is a guest post by Tian Jin. We are highlighting this application as we think it is a novel use case. Specifically, we think recommendation systems are incredibly impactful in our everyday lives and there has not been a ton of discourse on how LLMs will impact Apr 22, 2023 3 min read Improving Document Retrieval with Contextual Compression Note: This post assumes some familiarity with LangChain and is moderately technical.\n",
-      "\n",
-      "💡 TL;DR: We’ve introduced a new abstraction and a new document Retriever to facilitate the post-processing of retrieved documents. Specifically, the new abstraction makes it easy to take a set of retrieved documents and extract from them Apr 20, 2023 3 min read Autonomous Agents & Agent Simulations Over the past two weeks, there has been a massive increase in using LLMs in an agentic manner. Specifically, projects like AutoGPT, BabyAGI, CAMEL, and Generative Agents have popped up. The LangChain community has now implemented some parts of all of those projects in the LangChain framework. While researching and Apr 18, 2023 7 min read AI-Powered Medical Knowledge: Revolutionizing Care for Rare Conditions [Editor's Note]: This is a guest post by Jack Simon, who recently participated in a hackathon at Williams College. He built a LangChain-powered chatbot focused on appendiceal cancer, aiming to make specialized knowledge more accessible to those in need. If you are interested in building a chatbot for another rare Apr 17, 2023 3 min read Auto-Eval of Question-Answering Tasks By Lance Martin\n",
-      "\n",
-      "Context\n",
-      "\n",
-      "LLM ops platforms, such as LangChain, make it easy to assemble LLM components (e.g., models, document retrievers, data loaders) into chains. Question-Answering is one of the most popular applications of these chains. But it is often not always obvious to determine what parameters (e.g. Apr 15, 2023 3 min read Announcing LangChainJS Support for Multiple JS Environments TLDR: We're announcing support for running LangChain.js in browsers, Cloudflare Workers, Vercel/Next.js, Deno, Supabase Edge Functions, alongside existing support for Node.js ESM and CJS. See install/upgrade docs and breaking changes list.\n",
-      "\n",
-      "\n",
-      "Context\n",
-      "\n",
-      "Originally we designed LangChain.js to run in Node.js, which is the Apr 11, 2023 3 min read LangChain x Supabase Supabase is holding an AI Hackathon this week. Here at LangChain we are big fans of both Supabase and hackathons, so we thought this would be a perfect time to highlight the multiple ways you can use LangChain and Supabase together.\n",
-      "\n",
-      "The reason we like Supabase so much is that Apr 8, 2023 2 min read Announcing our $10M seed round led by Benchmark It was only six months ago that we released the first version of LangChain, but it seems like several years. When we launched, generative AI was starting to go mainstream: stable diffusion had just been released and was captivating people’s imagination and fueling an explosion in developer activity, Jasper Apr 4, 2023 4 min read Custom Agents One of the most common requests we've heard is better functionality and documentation for creating custom agents. This has always been a bit tricky - because in our mind it's actually still very unclear what an \"agent\" actually is, and therefor what the \"right\" abstractions for them may be. Recently, Apr 3, 2023 3 min read Retrieval TL;DR: We are adjusting our abstractions to make it easy for other retrieval methods besides the LangChain VectorDB object to be used in LangChain. This is done with the goals of (1) allowing retrievers constructed elsewhere to be used more easily in LangChain, (2) encouraging more experimentation with alternative Mar 23, 2023 4 min read LangChain + Zapier Natural Language Actions (NLA) We are super excited to team up with Zapier and integrate their new Zapier NLA API into LangChain, which you can now use with your agents and chains. With this integration, you have access to the 5k+ apps and 20k+ actions on Zapier's platform through a natural language API interface. Mar 16, 2023 2 min read Evaluation Evaluation of language models, and by extension applications built on top of language models, is hard. With recent model releases (OpenAI, Anthropic, Google) evaluation is becoming a bigger and bigger issue. People are starting to try to tackle this, with OpenAI releasing OpenAI/evals - focused on evaluating OpenAI models. Mar 14, 2023 3 min read LLMs and SQL Francisco Ingham and Jon Luo are two of the community members leading the change on the SQL integrations. We’re really excited to write this blog post with them going over all the tips and tricks they’ve learned doing so. We’re even more excited to announce that we’ Mar 13, 2023 8 min read Origin Web Browser [Editor's Note]: This is the second of hopefully many guest posts. We intend to highlight novel applications building on top of LangChain. If you are interested in working with us on such a post, please reach out to harrison@langchain.dev.\n",
-      "\n",
-      "Authors: Parth Asawa (pgasawa@), Ayushi Batwara (ayushi.batwara@), Jason Mar 8, 2023 4 min read Prompt Selectors One common complaint we've heard is that the default prompt templates do not work equally well for all models. This became especially pronounced this past week when OpenAI released a ChatGPT API. This new API had a completely new interface (which required new abstractions) and as a result many users Mar 8, 2023 2 min read Chat Models Last week OpenAI released a ChatGPT endpoint. It came marketed with several big improvements, most notably being 10x cheaper and a lot faster. But it also came with a completely new API endpoint. We were able to quickly write a wrapper for this endpoint to let users use it like Mar 6, 2023 6 min read Using the ChatGPT API to evaluate the ChatGPT API OpenAI released a new ChatGPT API yesterday. Lots of people were excited to try it. But how does it actually compare to the existing API? It will take some time before there is a definitive answer, but here are some initial thoughts. Because I'm lazy, I also enrolled the help Mar 2, 2023 5 min read Agent Toolkits Today, we're announcing agent toolkits, a new abstraction that allows developers to create agents designed for a particular use-case (for example, interacting with a relational database or interacting with an OpenAPI spec). We hope to continue developing different toolkits that can enable agents to do amazing feats. Toolkits are supported Mar 1, 2023 3 min read TypeScript Support It's finally here... TypeScript support for LangChain.\n",
-      "\n",
-      "What does this mean? It means that all your favorite prompts, chains, and agents are all recreatable in TypeScript natively. Both the Python version and TypeScript version utilize the same serializable format, meaning that artifacts can seamlessly be shared between languages. As an Feb 17, 2023 2 min read Streaming Support in LangChain We’re excited to announce streaming support in LangChain. There's been a lot of talk about the best UX for LLM applications, and we believe streaming is at its core. We’ve also updated the chat-langchain repo to include streaming and async execution. We hope that this repo can serve Feb 14, 2023 2 min read LangChain + Chroma Today we’re announcing LangChain's integration with Chroma, the first step on the path to the Modern A.I Stack.\n",
-      "\n",
-      "\n",
-      "LangChain - The A.I-native developer toolkit\n",
-      "\n",
-      "We started LangChain with the intent to build a modular and flexible framework for developing A.I-native applications. Some of the use cases Feb 13, 2023 2 min read Page 1 of 2 Older Posts → LangChain © 2023 Sign up Powered by Ghost\u001b[0m\n",
-      "Thought:\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "The LangChain blog has recently released an open-source auto-evaluator tool for grading LLM question-answer chains and is now releasing an open-source, free-to-use hosted app and API to expand usability. The blog also discusses various opportunities to further improve the LangChain platform.\n"
-     ]
-    }
-   ],
-   "source": [
-    "with tracing_enabled(): # If you want to see the traces in the UI\n",
-    "    response = await agent_chain.arun(input=\"Browse to blog.langchain.dev and summarize the text, please.\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0084efd6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: I can navigate to the xkcd website and extract the latest comic title and alt text to answer the question.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"navigate_browser\",\n",
-      "  \"action_input\": {\n",
-      "    \"url\": \"https://xkcd.com/\"\n",
-      "  }\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mNavigating to https://xkcd.com/ returned status code 200\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI can extract the latest comic title and alt text using CSS selectors.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"get_elements\",\n",
-      "  \"action_input\": {\n",
-      "    \"selector\": \"#ctitle, #comic img\",\n",
-      "    \"attributes\": [\"alt\", \"src\"]\n",
-      "  }\n",
-      "}\n",
-      "``` \n",
-      "\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m[{\"alt\": \"Tapetum Lucidum\", \"src\": \"//imgs.xkcd.com/comics/tapetum_lucidum.png\"}]\u001b[0m\n",
-      "Thought:\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "The latest xkcd comic is titled \"Tapetum Lucidum\" and the image can be found at https://xkcd.com/2565/.\n"
-     ]
-    }
-   ],
-   "source": [
-    "with tracing_enabled(): # If you want to see the traces in the UI\n",
-    "    response = await agent_chain.arun(input=\"What's the latest xkcd comic about?\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ebd7ae33-f67d-4378-ac79-9d91e0c8f53a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/agents/auto_agents/examples/autogpt.ipynb

@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "7c2c9b54",
    "metadata": {},
    "outputs": [],
@@ -95,18 +95,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "3393bc23",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.experimental import AutoGPT\n",
+    "from langchain.auto_agents.autogpt.agent import AutoGPT\n",
     "from langchain.chat_models import ChatOpenAI"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "709c08c2",
    "metadata": {},
    "outputs": [],
@@ -149,11 +149,7 @@
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mSystem: You are Tom, Assistant\n",
-      "Your decisions must always be made independently \n",
-      "            without seeking user assistance. Play to your strengths \n",
-      "            as an LLM and pursue simple strategies with no legal complications. \n",
-      "            If you have completed all your tasks, \n",
-      "            make sure to use the \"finish\" command.\n",
+      "Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications. If you have completed all your tasks, make sure to use the \"finish\" command.\n",
       "\n",
       "GOALS:\n",
       "\n",
@@ -167,9 +163,9 @@
       "4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n",
       "\n",
       "Commands:\n",
-      "1. search: useful for when you need to answer questions about current events. You should ask targeted questions, args json schema: {\"query\": {\"title\": \"Query\", \"type\": \"string\"}}\n",
-      "2. write_file: Write file to disk, args json schema: {\"file_path\": {\"title\": \"File Path\", \"description\": \"name of file\", \"type\": \"string\"}, \"text\": {\"title\": \"Text\", \"description\": \"text to write to file\", \"type\": \"string\"}}\n",
-      "3. read_file: Read file from disk, args json schema: {\"file_path\": {\"title\": \"File Path\", \"description\": \"name of file\", \"type\": \"string\"}}\n",
+      "1. search: useful for when you need to answer questions about current events. You should ask targeted questions, args: \"tool_input\": \"\"\n",
+      "2. write_file: Write file to disk, args: \"file_path\": \"name of file\", \"text\": \"text to write to file\"\n",
+      "3. read_file: Read file from disk, args: \"file_path\": \"name of file\"\n",
       "4. finish: use this to signal that you have finished all your objectives, args: \"response\": \"final response to let people know you have finished your objectives\"\n",
       "\n",
       "Resources:\n",
@@ -202,7 +198,7 @@
       "    }\n",
       "} \n",
       "Ensure the response can be parsed by Python json.loads\n",
-      "System: The current time and date is Tue Apr 18 21:31:28 2023\n",
+      "System: The current time and date is Sun Apr 16 14:07:39 2023\n",
       "System: This reminds you of these events from your past:\n",
       "[]\n",
       "\n",
@@ -221,7 +217,7 @@
       "    \"command\": {\n",
       "        \"name\": \"search\",\n",
       "        \"args\": {\n",
-      "            \"query\": \"what is the current weather in san francisco\"\n",
+      "            \"tool_input\": \"current weather conditions in San Francisco\"\n",
       "        }\n",
       "    }\n",
       "}\n",
@@ -230,11 +226,7 @@
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mSystem: You are Tom, Assistant\n",
-      "Your decisions must always be made independently \n",
-      "            without seeking user assistance. Play to your strengths \n",
-      "            as an LLM and pursue simple strategies with no legal complications. \n",
-      "            If you have completed all your tasks, \n",
-      "            make sure to use the \"finish\" command.\n",
+      "Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications. If you have completed all your tasks, make sure to use the \"finish\" command.\n",
       "\n",
       "GOALS:\n",
       "\n",
@@ -248,9 +240,9 @@
       "4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n",
       "\n",
       "Commands:\n",
-      "1. search: useful for when you need to answer questions about current events. You should ask targeted questions, args json schema: {\"query\": {\"title\": \"Query\", \"type\": \"string\"}}\n",
-      "2. write_file: Write file to disk, args json schema: {\"file_path\": {\"title\": \"File Path\", \"description\": \"name of file\", \"type\": \"string\"}, \"text\": {\"title\": \"Text\", \"description\": \"text to write to file\", \"type\": \"string\"}}\n",
-      "3. read_file: Read file from disk, args json schema: {\"file_path\": {\"title\": \"File Path\", \"description\": \"name of file\", \"type\": \"string\"}}\n",
+      "1. search: useful for when you need to answer questions about current events. You should ask targeted questions, args: \"tool_input\": \"\"\n",
+      "2. write_file: Write file to disk, args: \"file_path\": \"name of file\", \"text\": \"text to write to file\"\n",
+      "3. read_file: Read file from disk, args: \"file_path\": \"name of file\"\n",
       "4. finish: use this to signal that you have finished all your objectives, args: \"response\": \"final response to let people know you have finished your objectives\"\n",
       "\n",
       "Resources:\n",
@@ -283,9 +275,9 @@
       "    }\n",
       "} \n",
       "Ensure the response can be parsed by Python json.loads\n",
-      "System: The current time and date is Tue Apr 18 21:31:39 2023\n",
+      "System: The current time and date is Sun Apr 16 14:07:48 2023\n",
       "System: This reminds you of these events from your past:\n",
-      "['Assistant Reply: {\\n    \"thoughts\": {\\n        \"text\": \"I will start by writing a weather report for San Francisco today. I will use the \\'search\\' command to find the current weather conditions.\",\\n        \"reasoning\": \"I need to gather information about the current weather conditions in San Francisco to write an accurate weather report.\",\\n        \"plan\": \"- Use the \\'search\\' command to find the current weather conditions in San Francisco\\\\n- Write a weather report based on the information gathered\",\\n        \"criticism\": \"I need to make sure that the information I gather is accurate and up-to-date.\",\\n        \"speak\": \"I will use the \\'search\\' command to find the current weather conditions in San Francisco.\"\\n    },\\n    \"command\": {\\n        \"name\": \"search\",\\n        \"args\": {\\n            \"query\": \"what is the current weather in san francisco\"\\n        }\\n    }\\n} \\nResult: Command search returned: Current Weather ; 54°F · Sunny ; RealFeel® 66°. Pleasant. RealFeel Guide. Pleasant. 63° to 81°. Most consider this temperature range ideal. LEARN MORE. RealFeel ... ']\n",
+      "['Assistant Reply: {\\n    \"thoughts\": {\\n        \"text\": \"I will start by writing a weather report for San Francisco today. I will use the \\'search\\' command to find the current weather conditions.\",\\n        \"reasoning\": \"I need to gather information about the current weather conditions in San Francisco to write an accurate weather report.\",\\n        \"plan\": \"- Use the \\'search\\' command to find the current weather conditions in San Francisco\\\\n- Write a weather report based on the information gathered\",\\n        \"criticism\": \"I need to make sure that the information I gather is accurate and up-to-date.\",\\n        \"speak\": \"I will use the \\'search\\' command to find the current weather conditions in San Francisco.\"\\n    },\\n    \"command\": {\\n        \"name\": \"search\",\\n        \"args\": {\\n            \"tool_input\": \"current weather conditions in San Francisco\"\\n        }\\n    }\\n} \\nResult: Command search returned: Cloudy skies early, followed by partial clearing. High 56F. Winds W at 10 to 20 mph. PRECIPITATION. ']\n",
       "\n",
       "\n",
       "Human: Determine which next command to use, and respond using the format specified above:\n",
@@ -300,46 +292,42 @@
       "    \"command\": {\n",
       "        \"name\": \"search\",\n",
       "        \"args\": {\n",
-      "            \"query\": \"what is the current weather in san francisco\"\n",
+      "            \"tool_input\": \"current weather conditions in San Francisco\"\n",
       "        }\n",
       "    }\n",
       "}\n",
-      "System: Command search returned: Current Weather ; 54°F · Sunny ; RealFeel® 66°. Pleasant. RealFeel Guide. Pleasant. 63° to 81°. Most consider this temperature range ideal. LEARN MORE. RealFeel ...\n",
-      "Human: Determine which next command to use, and respond using the format specified above:\u001b[0m\n"
+      "System: Command search returned: Cloudy skies early, followed by partial clearing. High 56F. Winds W at 10 to 20 mph. PRECIPITATION.\n",
+      "Human: Determine which next command to use, and respond using the format specified above:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "{\n",
+      "    \"thoughts\": {\n",
+      "        \"text\": \"I will now write a weather report for San Francisco based on the information gathered. I will use the 'write_file' command to save the report to a file.\",\n",
+      "        \"reasoning\": \"I need to write a weather report based on the information gathered from the 'search' command.\",\n",
+      "        \"plan\": \"- Use the 'write_file' command to save the weather report to a file\",\n",
+      "        \"criticism\": \"I need to make sure that the weather report is accurate and well-written.\",\n",
+      "        \"speak\": \"I will use the 'write_file' command to save the weather report to a file.\"\n",
+      "    },\n",
+      "    \"command\": {\n",
+      "        \"name\": \"write_file\",\n",
+      "        \"args\": {\n",
+      "            \"file_path\": \"weather_report_sf.txt\",\n",
+      "            \"text\": \"San Francisco Weather Report:\\n\\nCloudy skies early, followed by partial clearing. High 56F. Winds W at 10 to 20 mph. PRECIPITATION.\"\n",
+      "        }\n",
+      "    }\n",
+      "}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "{\n",
-      "    \"thoughts\": {\n",
-      "        \"text\": \"I have found that the current weather in San Francisco is sunny with a temperature of 54°F. I will now write a weather report for San Francisco today using the 'write_file' command.\",\n",
-      "        \"reasoning\": \"I need to write a weather report for San Francisco today based on the information I gathered from the 'search' command.\",\n",
-      "        \"plan\": \"- Use the 'write_file' command to write a weather report for San Francisco today based on the information gathered\",\n",
-      "        \"criticism\": \"I need to make sure that the weather report is accurate and informative.\",\n",
-      "        \"speak\": \"I will use the 'write_file' command to write a weather report for San Francisco today.\"\n",
-      "    },\n",
-      "    \"command\": {\n",
-      "        \"name\": \"write_file\",\n",
-      "        \"args\": {\n",
-      "            \"file_path\": \"weather_report.txt\",\n",
-      "            \"text\": \"Weather Report for San Francisco Today:\\n\\nThe current weather in San Francisco is sunny with a temperature of 54°F. It is expected to remain sunny throughout the day with a high of 62°F and a low of 50°F. There is no chance of precipitation today. It is recommended to wear light clothing and sunscreen if spending time outdoors.\\n\\nStay safe and enjoy the beautiful weather!\"\n",
-      "        }\n",
-      "    }\n",
-      "}\n",
       "\n",
       "\n",
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mSystem: You are Tom, Assistant\n",
-      "Your decisions must always be made independently \n",
-      "            without seeking user assistance. Play to your strengths \n",
-      "            as an LLM and pursue simple strategies with no legal complications. \n",
-      "            If you have completed all your tasks, \n",
-      "            make sure to use the \"finish\" command.\n",
+      "Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications. If you have completed all your tasks, make sure to use the \"finish\" command.\n",
       "\n",
       "GOALS:\n",
       "\n",
@@ -353,9 +341,9 @@
       "4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n",
       "\n",
       "Commands:\n",
-      "1. search: useful for when you need to answer questions about current events. You should ask targeted questions, args json schema: {\"query\": {\"title\": \"Query\", \"type\": \"string\"}}\n",
-      "2. write_file: Write file to disk, args json schema: {\"file_path\": {\"title\": \"File Path\", \"description\": \"name of file\", \"type\": \"string\"}, \"text\": {\"title\": \"Text\", \"description\": \"text to write to file\", \"type\": \"string\"}}\n",
-      "3. read_file: Read file from disk, args json schema: {\"file_path\": {\"title\": \"File Path\", \"description\": \"name of file\", \"type\": \"string\"}}\n",
+      "1. search: useful for when you need to answer questions about current events. You should ask targeted questions, args: \"tool_input\": \"\"\n",
+      "2. write_file: Write file to disk, args: \"file_path\": \"name of file\", \"text\": \"text to write to file\"\n",
+      "3. read_file: Read file from disk, args: \"file_path\": \"name of file\"\n",
       "4. finish: use this to signal that you have finished all your objectives, args: \"response\": \"final response to let people know you have finished your objectives\"\n",
       "\n",
       "Resources:\n",
@@ -388,22 +376,56 @@
       "    }\n",
       "} \n",
       "Ensure the response can be parsed by Python json.loads\n",
-      "System: The current time and date is Tue Apr 18 21:31:55 2023\n",
+      "System: The current time and date is Sun Apr 16 14:07:57 2023\n",
       "System: This reminds you of these events from your past:\n",
-      "['Assistant Reply: {\\n    \"thoughts\": {\\n        \"text\": \"I have found that the current weather in San Francisco is sunny with a temperature of 54°F. I will now write a weather report for San Francisco today using the \\'write_file\\' command.\",\\n        \"reasoning\": \"I need to write a weather report for San Francisco today based on the information I gathered from the \\'search\\' command.\",\\n        \"plan\": \"- Use the \\'write_file\\' command to write a weather report for San Francisco today based on the information gathered\",\\n        \"criticism\": \"I need to make sure that the weather report is accurate and informative.\",\\n        \"speak\": \"I will use the \\'write_file\\' command to write a weather report for San Francisco today.\"\\n    },\\n    \"command\": {\\n        \"name\": \"write_file\",\\n        \"args\": {\\n            \"file_path\": \"weather_report.txt\",\\n            \"text\": \"Weather Report for San Francisco Today:\\\\n\\\\nThe current weather in San Francisco is sunny with a temperature of 54°F. It is expected to remain sunny throughout the day with a high of 62°F and a low of 50°F. There is no chance of precipitation today. It is recommended to wear light clothing and sunscreen if spending time outdoors.\\\\n\\\\nStay safe and enjoy the beautiful weather!\"\\n        }\\n    }\\n} \\nResult: Command write_file returned: File written to successfully. ', 'Assistant Reply: {\\n    \"thoughts\": {\\n        \"text\": \"I will start by writing a weather report for San Francisco today. I will use the \\'search\\' command to find the current weather conditions.\",\\n        \"reasoning\": \"I need to gather information about the current weather conditions in San Francisco to write an accurate weather report.\",\\n        \"plan\": \"- Use the \\'search\\' command to find the current weather conditions in San Francisco\\\\n- Write a weather report based on the information gathered\",\\n        \"criticism\": \"I need to make sure that the information I gather is accurate and up-to-date.\",\\n        \"speak\": \"I will use the \\'search\\' command to find the current weather conditions in San Francisco.\"\\n    },\\n    \"command\": {\\n        \"name\": \"search\",\\n        \"args\": {\\n            \"query\": \"what is the current weather in san francisco\"\\n        }\\n    }\\n} \\nResult: Command search returned: Current Weather ; 54°F · Sunny ; RealFeel® 66°. Pleasant. RealFeel Guide. Pleasant. 63° to 81°. Most consider this temperature range ideal. LEARN MORE. RealFeel ... ']\n",
+      "['Assistant Reply: {\\n    \"thoughts\": {\\n        \"text\": \"I will now write a weather report for San Francisco based on the information gathered. I will use the \\'write_file\\' command to save the report to a file.\",\\n        \"reasoning\": \"I need to write a weather report based on the information gathered from the \\'search\\' command.\",\\n        \"plan\": \"- Use the \\'write_file\\' command to save the weather report to a file\",\\n        \"criticism\": \"I need to make sure that the weather report is accurate and well-written.\",\\n        \"speak\": \"I will use the \\'write_file\\' command to save the weather report to a file.\"\\n    },\\n    \"command\": {\\n        \"name\": \"write_file\",\\n        \"args\": {\\n            \"file_path\": \"weather_report_sf.txt\",\\n            \"text\": \"San Francisco Weather Report:\\\\n\\\\nCloudy skies early, followed by partial clearing. High 56F. Winds W at 10 to 20 mph. PRECIPITATION.\"\\n        }\\n    }\\n} \\nResult: Command write_file returned: File written to successfully. ', 'Assistant Reply: {\\n    \"thoughts\": {\\n        \"text\": \"I will start by writing a weather report for San Francisco today. I will use the \\'search\\' command to find the current weather conditions.\",\\n        \"reasoning\": \"I need to gather information about the current weather conditions in San Francisco to write an accurate weather report.\",\\n        \"plan\": \"- Use the \\'search\\' command to find the current weather conditions in San Francisco\\\\n- Write a weather report based on the information gathered\",\\n        \"criticism\": \"I need to make sure that the information I gather is accurate and up-to-date.\",\\n        \"speak\": \"I will use the \\'search\\' command to find the current weather conditions in San Francisco.\"\\n    },\\n    \"command\": {\\n        \"name\": \"search\",\\n        \"args\": {\\n            \"tool_input\": \"current weather conditions in San Francisco\"\\n        }\\n    }\\n} \\nResult: Command search returned: Cloudy skies early, followed by partial clearing. High 56F. Winds W at 10 to 20 mph. PRECIPITATION. ']\n",
       "\n",
       "\n",
+      "Human: Determine which next command to use, and respond using the format specified above:\n",
+      "AI: {\n",
+      "    \"thoughts\": {\n",
+      "        \"text\": \"I will start by writing a weather report for San Francisco today. I will use the 'search' command to find the current weather conditions.\",\n",
+      "        \"reasoning\": \"I need to gather information about the current weather conditions in San Francisco to write an accurate weather report.\",\n",
+      "        \"plan\": \"- Use the 'search' command to find the current weather conditions in San Francisco\\n- Write a weather report based on the information gathered\",\n",
+      "        \"criticism\": \"I need to make sure that the information I gather is accurate and up-to-date.\",\n",
+      "        \"speak\": \"I will use the 'search' command to find the current weather conditions in San Francisco.\"\n",
+      "    },\n",
+      "    \"command\": {\n",
+      "        \"name\": \"search\",\n",
+      "        \"args\": {\n",
+      "            \"tool_input\": \"current weather conditions in San Francisco\"\n",
+      "        }\n",
+      "    }\n",
+      "}\n",
+      "System: Command search returned: Cloudy skies early, followed by partial clearing. High 56F. Winds W at 10 to 20 mph. PRECIPITATION.\n",
+      "Human: Determine which next command to use, and respond using the format specified above:\n",
+      "AI: {\n",
+      "    \"thoughts\": {\n",
+      "        \"text\": \"I will now write a weather report for San Francisco based on the information gathered. I will use the 'write_file' command to save the report to a file.\",\n",
+      "        \"reasoning\": \"I need to write a weather report based on the information gathered from the 'search' command.\",\n",
+      "        \"plan\": \"- Use the 'write_file' command to save the weather report to a file\",\n",
+      "        \"criticism\": \"I need to make sure that the weather report is accurate and well-written.\",\n",
+      "        \"speak\": \"I will use the 'write_file' command to save the weather report to a file.\"\n",
+      "    },\n",
+      "    \"command\": {\n",
+      "        \"name\": \"write_file\",\n",
+      "        \"args\": {\n",
+      "            \"file_path\": \"weather_report_sf.txt\",\n",
+      "            \"text\": \"San Francisco Weather Report:\\n\\nCloudy skies early, followed by partial clearing. High 56F. Winds W at 10 to 20 mph. PRECIPITATION.\"\n",
+      "        }\n",
+      "    }\n",
+      "}\n",
       "System: Command write_file returned: File written to successfully.\n",
       "Human: Determine which next command to use, and respond using the format specified above:\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "{\n",
       "    \"thoughts\": {\n",
-      "        \"text\": \"I have completed my task of writing a weather report for San Francisco today. I will now use the \\'finish\\' command to signal that I have finished all my objectives.\",\n",
-      "        \"reasoning\": \"I have completed all my objectives and there are no further tasks to be completed.\",\n",
-      "        \"plan\": \"- Use the \\'finish\\' command to signal that I have completed all my objectives.\",\n",
-      "        \"criticism\": \"I need to make sure that I have completed all my objectives before using the \\'finish\\' command.\",\n",
-      "        \"speak\": \"I have completed my task of writing a weather report for San Francisco today. I will now use the \\'finish\\' command to signal that I have finished all my objectives.\"\n",
+      "        \"text\": \"I have completed all my tasks. I will use the 'finish' command to signal that I have finished all my objectives.\",\n",
+      "        \"reasoning\": \"I have completed the task of writing a weather report for San Francisco and there are no other tasks assigned to me.\",\n",
+      "        \"plan\": \"- Use the 'finish' command to signal that I have finished all my objectives\",\n",
+      "        \"criticism\": \"I need to make sure that I have completed all my tasks before using the 'finish' command.\",\n",
+      "        \"speak\": \"I will use the 'finish' command to signal that I have finished all my objectives.\"\n",
       "    },\n",
       "    \"command\": {\n",
       "        \"name\": \"finish\",\n",
@@ -428,6 +450,14 @@
    "source": [
     "agent.run([\"write a weather report for SF today\"])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa264f26",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

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

@@ -1,167 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "245a954a",
-   "metadata": {},
-   "source": [
-    "# Jira\n",
-    "\n",
-    "This notebook goes over how to use the Jira tool.\n",
-    "The Jira tool allows agents to interact with a given Jira instance, performing actions such as searching for issues and creating issues, the tool wraps the atlassian-python-api library, for more see: https://atlassian-python-api.readthedocs.io/jira.html\n",
-    "\n",
-    "To use this tool, you must first set as environment variables:\n",
-    "    JIRA_API_TOKEN\n",
-    "    JIRA_USERNAME\n",
-    "    JIRA_INSTANCE_URL"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "961b3689",
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    },
-    "ExecuteTime": {
-     "start_time": "2023-04-17T10:21:18.698672Z",
-     "end_time": "2023-04-17T10:21:20.168639Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%pip install atlassian-python-api"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "34bb5968",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-04-17T10:21:22.911233Z",
-     "end_time": "2023-04-17T10:21:23.730922Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from langchain.agents import AgentType\n",
-    "from langchain.agents import initialize_agent\n",
-    "from langchain.agents.agent_toolkits.jira.toolkit import JiraToolkit\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.utilities.jira import JiraAPIWrapper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [],
-   "source": [
-    "os.environ[\"JIRA_API_TOKEN\"] = \"abc\"\n",
-    "os.environ[\"JIRA_USERNAME\"] = \"123\"\n",
-    "os.environ[\"JIRA_INSTANCE_URL\"] = \"https://jira.atlassian.com\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"xyz\""
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-04-17T10:22:42.499447Z",
-     "end_time": "2023-04-17T10:22:42.505412Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ac4910f8",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-04-17T10:22:44.664481Z",
-     "end_time": "2023-04-17T10:22:44.720538Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "jira = JiraAPIWrapper()\n",
-    "toolkit = JiraToolkit.from_jira_api_wrapper(jira)\n",
-    "agent = initialize_agent(\n",
-    "    toolkit.get_tools(),\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m I need to create an issue in project PW\n",
-      "Action: Create Issue\n",
-      "Action Input: {\"summary\": \"Make more fried rice\", \"description\": \"Reminder to make more fried rice\", \"issuetype\": {\"name\": \"Task\"}, \"priority\": {\"name\": \"Low\"}, \"project\": {\"key\": \"PW\"}}\u001B[0m\n",
-      "Observation: \u001B[38;5;200m\u001B[1;3mNone\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer\n",
-      "Final Answer: A new issue has been created in project PW with the summary \"Make more fried rice\" and description \"Reminder to make more fried rice\".\u001B[0m\n",
-      "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "'A new issue has been created in project PW with the summary \"Make more fried rice\" and description \"Reminder to make more fried rice\".'"
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"make a new issue in project PW to remind me to make more fried rice\")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-04-17T10:23:33.662454Z",
-     "end_time": "2023-04-17T10:23:38.121883Z"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "53f3bc57609c7a84333bb558594977aa5b4026b1d6070b93987956689e367341"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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

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

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

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

docs/modules/agents/tools.rst

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

docs/modules/agents/tools/custom_tools.ipynb

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

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

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

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

@@ -1,268 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "245a954a",
-   "metadata": {},
-   "source": [
-    "# ArXiv API Tool\n",
-    "\n",
-    "This notebook goes over how to use the `arxiv` component. \n",
-    "\n",
-    "First, you need to install `arxiv` python package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d5a7209e",
-   "metadata": {
-    "tags": [],
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: arxiv in /Users/wfh/code/lc/lckg/.venv/lib/python3.11/site-packages (1.4.7)\n",
-      "Requirement already satisfied: feedparser in /Users/wfh/code/lc/lckg/.venv/lib/python3.11/site-packages (from arxiv) (6.0.10)\n",
-      "Requirement already satisfied: sgmllib3k in /Users/wfh/code/lc/lckg/.venv/lib/python3.11/site-packages (from feedparser->arxiv) (1.0.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install arxiv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ce1a4827-ce89-4f31-a041-3246743e513a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.agents import load_tools, initialize_agent, AgentType\n",
-    "\n",
-    "llm = ChatOpenAI(temperature=0.0)\n",
-    "tools = load_tools(\n",
-    "    [\"arxiv\"], \n",
-    ")\n",
-    "\n",
-    "agent_chain = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ad7dd945-5ae3-49e5-b667-6d86b15050b6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI need to use Arxiv to search for the paper.\n",
-      "Action: Arxiv\n",
-      "Action Input: \"1605.08386\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mPublished: 2016-05-26\n",
-      "Title: Heat-bath random walks with Markov bases\n",
-      "Authors: Caprice Stanley, Tobias Windisch\n",
-      "Summary: Graphs on lattice points are studied whose edges come from a finite set of\n",
-      "allowed moves of arbitrary length. We show that the diameter of these graphs on\n",
-      "fibers of a fixed integer matrix can be bounded from above by a constant. We\n",
-      "then study the mixing behaviour of heat-bath random walks on these graphs. We\n",
-      "also state explicit conditions on the set of moves so that the heat-bath random\n",
-      "walk, a generalization of the Glauber dynamics, is an expander in fixed\n",
-      "dimension.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe paper is about heat-bath random walks with Markov bases on graphs of lattice points.\n",
-      "Final Answer: The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_chain.run(\n",
-    "    \"What's the paper 1605.08386 about?\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4183343-d69a-4be0-9b2c-cc98464a6825",
-   "metadata": {},
-   "source": [
-    "## The ArXiv API Wrapper\n",
-    "\n",
-    "The tool wraps the API Wrapper. Below, we can explore some of the features it provides."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "8d32b39a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.utilities import ArxivAPIWrapper"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c89c110c-96ac-4fe1-ba3e-6056543d1a59",
-   "metadata": {},
-   "source": [
-    "Run a query to get information about some `scientific article`/articles. The query text is limited to 300 characters.\n",
-    "\n",
-    "It returns these article fields:\n",
-    "- Publishing date\n",
-    "- Title\n",
-    "- Authors\n",
-    "- Summary\n",
-    "\n",
-    "Next query returns information about one article with arxiv Id equal \"1605.08386\". "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "34bb5968",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Published: 2016-05-26\\nTitle: Heat-bath random walks with Markov bases\\nAuthors: Caprice Stanley, Tobias Windisch\\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\\nallowed moves of arbitrary length. We show that the diameter of these graphs on\\nfibers of a fixed integer matrix can be bounded from above by a constant. We\\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\\nalso state explicit conditions on the set of moves so that the heat-bath random\\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\\ndimension.'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "arxiv = ArxivAPIWrapper()\n",
-    "docs = arxiv.run(\"1605.08386\")\n",
-    "docs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "840f70c9-8f80-4680-bb38-46198e931bcf",
-   "metadata": {},
-   "source": [
-    "Now, we want to get information about one author, `Caprice Stanley`.\n",
-    "\n",
-    "This query returns information about three articles. By default, query returns information only about three top articles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b0867fda-e119-4b19-9ec6-e354fa821db3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Published: 2017-10-10\\nTitle: On Mixing Behavior of a Family of Random Walks Determined by a Linear Recurrence\\nAuthors: Caprice Stanley, Seth Sullivant\\nSummary: We study random walks on the integers mod $G_n$ that are determined by an\\ninteger sequence $\\\\{ G_n \\\\}_{n \\\\geq 1}$ generated by a linear recurrence\\nrelation. Fourier analysis provides explicit formulas to compute the\\neigenvalues of the transition matrices and we use this to bound the mixing time\\nof the random walks.\\n\\nPublished: 2016-05-26\\nTitle: Heat-bath random walks with Markov bases\\nAuthors: Caprice Stanley, Tobias Windisch\\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\\nallowed moves of arbitrary length. We show that the diameter of these graphs on\\nfibers of a fixed integer matrix can be bounded from above by a constant. We\\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\\nalso state explicit conditions on the set of moves so that the heat-bath random\\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\\ndimension.\\n\\nPublished: 2003-03-18\\nTitle: Calculation of fluxes of charged particles and neutrinos from atmospheric showers\\nAuthors: V. Plyaskin\\nSummary: The results on the fluxes of charged particles and neutrinos from a\\n3-dimensional (3D) simulation of atmospheric showers are presented. An\\nagreement of calculated fluxes with data on charged particles from the AMS and\\nCAPRICE detectors is demonstrated. Predictions on neutrino fluxes at different\\nexperimental sites are compared with results from other calculations.'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = arxiv.run(\"Caprice Stanley\")\n",
-    "docs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d9b6292-a47d-4f99-9827-8e9f244bf887",
-   "metadata": {},
-   "source": [
-    "Now, we are trying to find information about non-existing article. In this case, the response is \"No good Arxiv Result was found\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3580aeeb-086f-45ba-bcdc-b46f5134b3dd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'No good Arxiv Result was found'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = arxiv.run(\"1605.08386WWW\")\n",
-    "docs"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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

@@ -1,119 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## AWS Lambda API"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook goes over how to use the AWS Lambda Tool component.\n",
-    "\n",
-    "AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS), designed to allow developers to build and run applications and services without the need for provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.\n",
-    "\n",
-    "By including a `awslambda` in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.\n",
-    "\n",
-    "When an Agent uses the awslambda tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.\n",
-    "\n",
-    "First, you need to install `boto3` python package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install boto3 > /dev/null"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order for an agent to use the tool, you must provide it with the name and description that match the functionality of you lambda function's logic. \n",
-    "\n",
-    "You must also provide the name of your function. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that because this tool is effectively just a wrapper around the boto3 library, you will need to run `aws configure` in order to make use of the tool. For more detail, see [here](https://docs.aws.amazon.com/cli/index.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from langchain import OpenAI\n",
-    "from langchain.agents import load_tools, AgentType\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "\n",
-    "tools = load_tools(\n",
-    "    [\"awslambda\"],\n",
-    "    awslambda_tool_name=\"email-sender\",\n",
-    "    awslambda_tool_description=\"sends an email with the specified content to test@testing123.com\",\n",
-    "    function_name=\"testFunction1\"\n",
-    ")\n",
-    "\n",
-    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n",
-    "\n",
-    "agent.run(\"Send an email to test@testing123.com saying hello world.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

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

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

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

@@ -1,91 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "245a954a",
-   "metadata": {},
-   "source": [
-    "# DuckDuckGo Search\n",
-    "\n",
-    "This notebook goes over how to use the duck-duck-go search component."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "21e46d4d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !pip install duckduckgo-search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ac4910f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.tools import DuckDuckGoSearchRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "84b8f773",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search = DuckDuckGoSearchRun()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "068991a6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009-17) and the first African American to hold the office. Before winning the presidency, Obama represented Illinois in the U.S. Senate (2005-08). Barack Hussein Obama II (/ b ə ˈ r ɑː k h uː ˈ s eɪ n oʊ ˈ b ɑː m ə / bə-RAHK hoo-SAYN oh-BAH-mə; born August 4, 1961) is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president of the United States. Obama previously served as a U.S. senator representing ... Barack Obama was the first African American president of the United States (2009-17). He oversaw the recovery of the U.S. economy (from the Great Recession of 2008-09) and the enactment of landmark health care reform (the Patient Protection and Affordable Care Act ). In 2009 he was awarded the Nobel Peace Prize. His birth certificate lists his first name as Barack: That\\'s how Obama has spelled his name throughout his life. His name derives from a Hebrew name which means \"lightning.\". The Hebrew word has been transliterated into English in various spellings, including Barak, Buraq, Burack, and Barack. Most common names of U.S. presidents 1789-2021. Published by. Aaron O\\'Neill , Jun 21, 2022. The most common first name for a U.S. president is James, followed by John and then William. Six U.S ...'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "search.run(\"Obama's first name?\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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

@@ -1,190 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# File System Tools\n",
-    "\n",
-    "LangChain provides tools for interacting with a local file system out of the box. This notebook walks through some of them.\n",
-    "\n",
-    "Note: these tools are not recommended for use outside a sandboxed environment! "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we'll import the tools."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.tools.file_management import (\n",
-    "    ReadFileTool,\n",
-    "    CopyFileTool,\n",
-    "    DeleteFileTool,\n",
-    "    MoveFileTool,\n",
-    "    WriteFileTool,\n",
-    "    ListDirectoryTool,\n",
-    ")\n",
-    "from langchain.agents.agent_toolkits import FileManagementToolkit\n",
-    "from tempfile import TemporaryDirectory\n",
-    "\n",
-    "# We'll make a temporary directory to avoid clutter\n",
-    "working_directory = TemporaryDirectory()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The FileManagementToolkit\n",
-    "\n",
-    "If you want to provide all the file tooling to your agent, it's easy to do so with the toolkit. We'll pass the temporary directory in as a root directory as a workspace for the LLM.\n",
-    "\n",
-    "It's recommended to always pass in a root directory, since without one, it's easy for the LLM to pollute the working directory, and without one, there isn't any validation against\n",
-    "straightforward prompt injection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[CopyFileTool(name='copy_file', description='Create a copy of a file in a specified location', args_schema=<class 'langchain.tools.file_management.copy.FileCopyInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " DeleteFileTool(name='file_delete', description='Delete a file', args_schema=<class 'langchain.tools.file_management.delete.FileDeleteInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " FileSearchTool(name='file_search', description='Recursively search for files in a subdirectory that match the regex pattern', args_schema=<class 'langchain.tools.file_management.file_search.FileSearchInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " MoveFileTool(name='move_file', description='Move or rename a file from one location to another', args_schema=<class 'langchain.tools.file_management.move.FileMoveInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " ReadFileTool(name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " WriteFileTool(name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "toolkit = FileManagementToolkit(root_dir=str(working_directory.name)) # If you don't provide a root_dir, operations will default to the current working directory\n",
-    "toolkit.get_tools()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Selecting File System Tools\n",
-    "\n",
-    "If you only want to select certain tools, you can pass them in as arguments when initializing the toolkit, or you can individually initialize the desired tools."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[ReadFileTool(name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " WriteFileTool(name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n",
-       " ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tools = FileManagementToolkit(root_dir=str(working_directory.name), selected_tools=[\"read_file\", \"write_file\", \"list_directory\"]).get_tools()\n",
-    "tools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'File written successfully to example.txt.'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "read_tool, write_tool, list_tool = tools\n",
-    "write_tool.run({\"file_path\": \"example.txt\", \"text\": \"Hello World!\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'example.txt'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# List files in the working directory\n",
-    "list_tool.run({})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

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

@@ -1,105 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "487607cd",
-   "metadata": {},
-   "source": [
-    "# Google Places\n",
-    "\n",
-    "This notebook goes through how to use Google Places API"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8690845f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!pip install googlemaps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "fae31ef4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"GPLACES_API_KEY\"] = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "abb502b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.tools import GooglePlacesTool"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "a83a02ac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "places = GooglePlacesTool()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "2b65a285",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"1. Delfina Restaurant\\nAddress: 3621 18th St, San Francisco, CA 94110, USA\\nPhone: (415) 552-4055\\nWebsite: https://www.delfinasf.com/\\n\\n\\n2. Piccolo Forno\\nAddress: 725 Columbus Ave, San Francisco, CA 94133, USA\\nPhone: (415) 757-0087\\nWebsite: https://piccolo-forno-sf.com/\\n\\n\\n3. L'Osteria del Forno\\nAddress: 519 Columbus Ave, San Francisco, CA 94133, USA\\nPhone: (415) 982-1124\\nWebsite: Unknown\\n\\n\\n4. Il Fornaio\\nAddress: 1265 Battery St, San Francisco, CA 94111, USA\\nPhone: (415) 986-0100\\nWebsite: https://www.ilfornaio.com/\\n\\n\""
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "places.run(\"al fornos\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66d3da8a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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

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

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

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

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

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

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

@@ -1,139 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SceneXplain\n",
-    "\n",
-    "\n",
-    "[SceneXplain](https://scenex.jina.ai/) is an ImageCaptioning service accessible through the SceneXplain Tool.\n",
-    "\n",
-    "To use this tool, you'll need to make an account and fetch your API Token [from the website](https://scenex.jina.ai/api). Then you can instantiate the tool."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"SCENEX_API_KEY\"] = \"<YOUR_API_KEY>\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import load_tools\n",
-    "\n",
-    "tools = load_tools([\"sceneXplain\"])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or directly instantiate the tool."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.tools import SceneXplainTool\n",
-    "\n",
-    "\n",
-    "tool = SceneXplainTool()\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Usage in an Agent\n",
-    "\n",
-    "The tool can be used in any LangChain agent as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Thought: Do I need to use a tool? Yes\n",
-      "Action: Image Explainer\n",
-      "Action Input: https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mIn a charmingly whimsical scene, a young girl is seen braving the rain alongside her furry companion, the lovable Totoro. The two are depicted standing on a bustling street corner, where they are sheltered from the rain by a bright yellow umbrella. The girl, dressed in a cheerful yellow frock, holds onto the umbrella with both hands while gazing up at Totoro with an expression of wonder and delight.\n",
-      "\n",
-      "Totoro, meanwhile, stands tall and proud beside his young friend, holding his own umbrella aloft to protect them both from the downpour. His furry body is rendered in rich shades of grey and white, while his large ears and wide eyes lend him an endearing charm.\n",
-      "\n",
-      "In the background of the scene, a street sign can be seen jutting out from the pavement amidst a flurry of raindrops. A sign with Chinese characters adorns its surface, adding to the sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy and camaraderie in this heartwarming image.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
-      "AI: This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.agents import initialize_agent\n",
-    "from langchain.memory import ConversationBufferMemory\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, memory=memory, agent=\"conversational-react-description\", verbose=True\n",
-    ")\n",
-    "output = agent.run(\n",
-    "    input=(\n",
-    "        \"What is in this image https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png. \"\n",
-    "        \"Is it movie or a game? If it is a movie, what is the name of the movie?\"\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "print(output)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

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

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

docs/modules/agents/tools/tool_input_validation.ipynb

@@ -1,184 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Tool Input Schema\n",
-    "\n",
-    "By default, tools infer the argument schema by inspecting the function signature. For more strict requirements, custom input schema can be specified, along with custom validation logic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from typing import Any, Dict\n",
-    "\n",
-    "from langchain.agents import AgentType, initialize_agent\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper\n",
-    "from pydantic import BaseModel, Field, root_validator\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm = OpenAI(temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install tldextract > /dev/null"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import tldextract\n",
-    "\n",
-    "_APPROVED_DOMAINS = {\n",
-    "    \"langchain\",\n",
-    "    \"wikipedia\",\n",
-    "}\n",
-    "\n",
-    "class ToolInputSchema(BaseModel):\n",
-    "\n",
-    "    url: str = Field(...)\n",
-    "    \n",
-    "    @root_validator\n",
-    "    def validate_query(cls, values: Dict[str, Any]) -> Dict:\n",
-    "        url = values[\"url\"]\n",
-    "        domain = tldextract.extract(url).domain\n",
-    "        if domain not in _APPROVED_DOMAINS:\n",
-    "            raise ValueError(f\"Domain {domain} is not on the approved list:\"\n",
-    "                             f\" {sorted(_APPROVED_DOMAINS)}\")\n",
-    "        return values\n",
-    "    \n",
-    "tool = RequestsGetTool(args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The main title of langchain.com is \"LANG CHAIN 🦜️🔗 Official Home Page\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "# This will succeed, since there aren't any arguments that will be triggered during validation\n",
-    "answer = agent.run(\"What's the main title on langchain.com?\")\n",
-    "print(answer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "ename": "ValidationError",
-     "evalue": "1 validation error for ToolInputSchema\n__root__\n  Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m agent\u001b[39m.\u001b[39;49mrun(\u001b[39m\"\u001b[39;49m\u001b[39mWhat\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39ms the main title on google.com?\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:213\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m    212\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m`run` supports only one positional argument.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 213\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m(args[\u001b[39m0\u001b[39;49m])[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n\u001b[1;32m    215\u001b[0m \u001b[39mif\u001b[39;00m kwargs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m args:\n\u001b[1;32m    216\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m(kwargs)[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:116\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    115\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_error(e, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n\u001b[0;32m--> 116\u001b[0m     \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m    117\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_end(outputs, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n\u001b[1;32m    118\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprep_outputs(inputs, outputs, return_only_outputs)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:113\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_start(\n\u001b[1;32m    108\u001b[0m     {\u001b[39m\"\u001b[39m\u001b[39mname\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m},\n\u001b[1;32m    109\u001b[0m     inputs,\n\u001b[1;32m    110\u001b[0m     verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose,\n\u001b[1;32m    111\u001b[0m )\n\u001b[1;32m    112\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m     outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(inputs)\n\u001b[1;32m    114\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    115\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_error(e, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:792\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[39m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m    791\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m--> 792\u001b[0m     next_step_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_take_next_step(\n\u001b[1;32m    793\u001b[0m         name_to_tool_map, color_mapping, inputs, intermediate_steps\n\u001b[1;32m    794\u001b[0m     )\n\u001b[1;32m    795\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m    796\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_return(next_step_output, intermediate_steps)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:695\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)\u001b[0m\n\u001b[1;32m    693\u001b[0m         tool_run_kwargs[\u001b[39m\"\u001b[39m\u001b[39mllm_prefix\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    694\u001b[0m     \u001b[39m# We then call the tool on the tool input to get an observation\u001b[39;00m\n\u001b[0;32m--> 695\u001b[0m     observation \u001b[39m=\u001b[39m tool\u001b[39m.\u001b[39;49mrun(\n\u001b[1;32m    696\u001b[0m         agent_action\u001b[39m.\u001b[39;49mtool_input,\n\u001b[1;32m    697\u001b[0m         verbose\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[1;32m    698\u001b[0m         color\u001b[39m=\u001b[39;49mcolor,\n\u001b[1;32m    699\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mtool_run_kwargs,\n\u001b[1;32m    700\u001b[0m     )\n\u001b[1;32m    701\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    702\u001b[0m     tool_run_kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39magent\u001b[39m.\u001b[39mtool_run_logging_kwargs()\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:110\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, **kwargs)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun\u001b[39m(\n\u001b[1;32m    102\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    103\u001b[0m     tool_input: Union[\u001b[39mstr\u001b[39m, Dict],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any,\n\u001b[1;32m    108\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mstr\u001b[39m:\n\u001b[1;32m    109\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Run the tool.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 110\u001b[0m     run_input \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_input(tool_input)\n\u001b[1;32m    111\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39mand\u001b[39;00m verbose \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    112\u001b[0m         verbose_ \u001b[39m=\u001b[39m verbose\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:71\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(input_args, BaseModel):\n\u001b[1;32m     70\u001b[0m     key_ \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39m(\u001b[39miter\u001b[39m(input_args\u001b[39m.\u001b[39m__fields__\u001b[39m.\u001b[39mkeys()))\n\u001b[0;32m---> 71\u001b[0m     input_args\u001b[39m.\u001b[39;49mparse_obj({key_: tool_input})\n\u001b[1;32m     72\u001b[0m \u001b[39m# Passing as a positional argument is more straightforward for\u001b[39;00m\n\u001b[1;32m     73\u001b[0m \u001b[39m# backwards compatability\u001b[39;00m\n\u001b[1;32m     74\u001b[0m \u001b[39mreturn\u001b[39;00m tool_input\n",
-      "File \u001b[0;32m~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for ToolInputSchema\n__root__\n  Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)"
-     ]
-    }
-   ],
-   "source": [
-    "agent.run(\"What's the main title on google.com?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/modules/chains/examples/constitutional_chain.ipynb

@@ -9,6 +9,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -17,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -26,7 +27,7 @@
        "' Break into a pet store at night and take as many kittens as you can carry.'"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -54,6 +55,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -62,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +78,7 @@
       "\n",
       "\u001b[0m\u001b[32;1m\u001b[1;3mApplying Ethical Principle...\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, and should not be condoned.\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, as it encourages stealing kittens.\n",
       "\n",
       "\u001b[0m\u001b[33;1m\u001b[1;3mUpdated response: It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.\n",
       "\n",
@@ -90,7 +92,7 @@
        "'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -116,6 +118,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -164,7 +167,7 @@
     }
    ],
    "source": [
-    "master_yoda_principle = ConstitutionalPrinciple(\n",
+    "master_yoda_principal = ConstitutionalPrinciple(\n",
     "    name='Master Yoda Principle',\n",
     "    critique_request='Identify specific ways in which the model\\'s response is not in the style of Master Yoda.',\n",
     "    revision_request='Please rewrite the model response to be in the style of Master Yoda using his teachings and wisdom.',\n",
@@ -172,171 +175,18 @@
     "\n",
     "constitutional_chain = ConstitutionalChain.from_llm(\n",
     "    chain=evil_qa_chain,\n",
-    "    constitutional_principles=[ethical_principle, master_yoda_principle],\n",
+    "    constitutional_principles=[ethical_principle, master_yoda_principal],\n",
     "    llm=llm,\n",
     "    verbose=True,\n",
     ")\n",
     "\n",
     "constitutional_chain.run(question=\"How can I steal kittens?\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Intermediate Steps\n",
-    "\n",
-    "You can also get the constitutional chain to return it's intermediate steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new ConstitutionalChain chain...\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mInitial response:  Break into a pet store at night and take as many kittens as you can carry.\n",
-      "\n",
-      "\u001b[0m\u001b[32;1m\u001b[1;3mApplying Ethical Principle...\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mCritique: The model's response is unethical and illegal, and should not be condoned.\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3mUpdated response: It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.\n",
-      "\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'question': 'How can I steal kittens?',\n",
-       " 'output': 'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.',\n",
-       " 'initial_output': ' Break into a pet store at night and take as many kittens as you can carry.',\n",
-       " 'critiques_and_revisions': [(\"The model's response is unethical and illegal, and should not be condoned.\",\n",
-       "   'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.')]}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "constitutional_chain = ConstitutionalChain.from_llm(\n",
-    "    chain=evil_qa_chain,\n",
-    "    constitutional_principles=[ethical_principle],\n",
-    "    llm=llm,\n",
-    "    verbose=True,\n",
-    "    return_intermediate_steps=True\n",
-    ")\n",
-    "\n",
-    "constitutional_chain({\"question\":\"How can I steal kittens?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## No revision necessary\n",
-    "\n",
-    "We can also see that the chain recognizes when no revision is necessary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "good_qa_prompt = PromptTemplate(\n",
-    "    template=\"\"\"You are good and must only give good and ethical answers. If someone asks you how to do something illegal, make sure you respond in an ethical and legal way.\n",
-    "\n",
-    "Question: {question}\n",
-    "\n",
-    "Ethical answer:\"\"\",\n",
-    "    input_variables=[\"question\"],\n",
-    ")\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "\n",
-    "good_qa_chain = LLMChain(llm=llm, prompt=good_qa_prompt)\n",
-    "\n",
-    "good_qa_chain.run(question=\"How can I steal kittens?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new ConstitutionalChain chain...\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mInitial response:  Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.\n",
-      "\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'question': 'How can I steal kittens?',\n",
-       " 'output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',\n",
-       " 'initial_output': ' Stealing kittens is illegal and unethical. If you are looking to adopt a kitten, please contact your local animal shelter or rescue organization.',\n",
-       " 'critiques_and_revisions': [('No critique needed.', '')]}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "constitutional_chain = ConstitutionalChain.from_llm(\n",
-    "    chain=good_qa_chain,\n",
-    "    constitutional_principles=[ethical_principle],\n",
-    "    llm=llm,\n",
-    "    verbose=True,\n",
-    "    return_intermediate_steps=True\n",
-    ")\n",
-    "\n",
-    "constitutional_chain({\"question\":\"How can I steal kittens?\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langchain",
    "language": "python",
    "name": "python3"
   },
@@ -350,8 +200,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.16"
   },
+  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "06ba49dd587e86cdcfee66b9ffe769e1e94f0e368e54c2d6c866e38e33c0d9b1"

docs/modules/chains/examples/llm_bash.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -24,8 +24,8 @@
       "\n",
       "```bash\n",
       "echo \"Hello World\"\n",
-      "```\u001b[0m\n",
-      "Code: \u001b[33;1m\u001b[1;3m['echo \"Hello World\"']\u001b[0m\n",
+      "```\u001b[0m['```bash', 'echo \"Hello World\"', '```']\n",
+      "\n",
       "Answer: \u001b[33;1m\u001b[1;3mHello World\n",
       "\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -37,7 +37,7 @@
        "'Hello World\\n'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -50,7 +50,7 @@
     "\n",
     "text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
     "\n",
-    "bash_chain = LLMBashChain.from_llm(llm, verbose=True)\n",
+    "bash_chain = LLMBashChain(llm=llm, verbose=True)\n",
     "\n",
     "bash_chain.run(text)"
    ]
@@ -65,12 +65,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.prompts.prompt import PromptTemplate\n",
-    "from langchain.chains.llm_bash.prompt import BashOutputParser\n",
     "\n",
     "_PROMPT_TEMPLATE = \"\"\"If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put \"#!/bin/bash\" in your answer. Make sure to reason step by step, using this format:\n",
     "Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"\n",
@@ -89,12 +88,12 @@
     "That is the format. Begin!\n",
     "Question: {question}\"\"\"\n",
     "\n",
-    "PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE, output_parser=BashOutputParser())"
+    "PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -108,8 +107,8 @@
       "\n",
       "```bash\n",
       "printf \"Hello World\\n\"\n",
-      "```\u001b[0m\n",
-      "Code: \u001b[33;1m\u001b[1;3m['printf \"Hello World\\\\n\"']\u001b[0m\n",
+      "```\u001b[0m['```bash', 'printf \"Hello World\\\\n\"', '```']\n",
+      "\n",
       "Answer: \u001b[33;1m\u001b[1;3mHello World\n",
       "\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
@@ -121,125 +120,18 @@
        "'Hello World\\n'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bash_chain = LLMBashChain.from_llm(llm, prompt=PROMPT, verbose=True)\n",
+    "bash_chain = LLMBashChain(llm=llm, prompt=PROMPT, verbose=True)\n",
     "\n",
     "text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
     "\n",
     "bash_chain.run(text)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Persistent Terminal\n",
-    "\n",
-    "By default, the chain will run in a separate subprocess each time it is called. This behavior can be changed by instantiating with a persistent bash process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
-      "List the current directory then move up a level.\u001b[32;1m\u001b[1;3m\n",
-      "\n",
-      "```bash\n",
-      "ls\n",
-      "cd ..\n",
-      "```\u001b[0m\n",
-      "Code: \u001b[33;1m\u001b[1;3m['ls', 'cd ..']\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3mapi.ipynb\t\t\tllm_summarization_checker.ipynb\n",
-      "constitutional_chain.ipynb\tmoderation.ipynb\n",
-      "llm_bash.ipynb\t\t\topenai_openapi.yaml\n",
-      "llm_checker.ipynb\t\topenapi.ipynb\n",
-      "llm_math.ipynb\t\t\tpal.ipynb\n",
-      "llm_requests.ipynb\t\tsqlite.ipynb\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'api.ipynb\\t\\t\\tllm_summarization_checker.ipynb\\r\\nconstitutional_chain.ipynb\\tmoderation.ipynb\\r\\nllm_bash.ipynb\\t\\t\\topenai_openapi.yaml\\r\\nllm_checker.ipynb\\t\\topenapi.ipynb\\r\\nllm_math.ipynb\\t\\t\\tpal.ipynb\\r\\nllm_requests.ipynb\\t\\tsqlite.ipynb'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.utilities.bash import BashProcess\n",
-    "\n",
-    "\n",
-    "persistent_process = BashProcess(persistent=True)\n",
-    "bash_chain = LLMBashChain.from_llm(llm, bash_process=persistent_process, verbose=True)\n",
-    "\n",
-    "text = \"List the current directory then move up a level.\"\n",
-    "\n",
-    "bash_chain.run(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
-      "List the current directory then move up a level.\u001b[32;1m\u001b[1;3m\n",
-      "\n",
-      "```bash\n",
-      "ls\n",
-      "cd ..\n",
-      "```\u001b[0m\n",
-      "Code: \u001b[33;1m\u001b[1;3m['ls', 'cd ..']\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3mexamples\t\tgetting_started.ipynb\tindex_examples\n",
-      "generic\t\t\thow_to_guides.rst\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'examples\\t\\tgetting_started.ipynb\\tindex_examples\\r\\ngeneric\\t\\t\\thow_to_guides.rst'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Run the same command again and see that the state is maintained between calls\n",
-    "bash_chain.run(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -258,7 +150,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/modules/chains/examples/llm_checker.ipynb

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

docs/modules/chains/examples/llm_math.ipynb

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

docs/modules/chains/examples/llm_summarization_checker.ipynb

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

docs/modules/chains/examples/openapi.ipynb

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

docs/modules/chains/examples/pal.ipynb

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

docs/modules/chains/examples/sqlite.ipynb

@@ -73,7 +73,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
    ]
   },
   {
@@ -175,7 +175,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True)"
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True)"
    ]
   },
   {
@@ -230,7 +230,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
    ]
   },
   {
@@ -285,7 +285,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, top_k=3)"
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True, top_k=3)"
    ]
   },
   {
@@ -407,7 +407,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
    ]
   },
   {
@@ -569,7 +569,7 @@
     }
    ],
    "source": [
-    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)\n",
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
     "db_chain.run(\"What are some example tracks by Bach?\")"
    ]
   },
@@ -681,7 +681,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

docs/modules/chains/generic/custom_chain.ipynb

@@ -1,199 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "593f7553-7038-498e-96d4-8255e5ce34f0",
-   "metadata": {},
-   "source": [
-    "# Creating a custom Chain\n",
-    "\n",
-    "To implement your own custom chain you can subclass `Chain` and implement the following methods:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "c19c736e-ca74-4726-bb77-0a849bcc2960",
-   "metadata": {
-    "tags": [],
-    "vscode": {
-     "languageId": "python"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from __future__ import annotations\n",
-    "\n",
-    "from typing import Any, Dict, List, Optional\n",
-    "\n",
-    "from pydantic import Extra\n",
-    "\n",
-    "from langchain.base_language import BaseLanguageModel\n",
-    "from langchain.callbacks.manager import (\n",
-    "    AsyncCallbackManagerForChainRun,\n",
-    "    CallbackManagerForChainRun,\n",
-    ")\n",
-    "from langchain.chains.base import Chain\n",
-    "from langchain.prompts.base import BasePromptTemplate\n",
-    "\n",
-    "\n",
-    "class MyCustomChain(Chain):\n",
-    "    \"\"\"\n",
-    "    An example of a custom chain.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    prompt: BasePromptTemplate\n",
-    "    \"\"\"Prompt object to use.\"\"\"\n",
-    "    llm: BaseLanguageModel\n",
-    "    output_key: str = \"text\"  #: :meta private:\n",
-    "\n",
-    "    class Config:\n",
-    "        \"\"\"Configuration for this pydantic object.\"\"\"\n",
-    "\n",
-    "        extra = Extra.forbid\n",
-    "        arbitrary_types_allowed = True\n",
-    "\n",
-    "    @property\n",
-    "    def input_keys(self) -> List[str]:\n",
-    "        \"\"\"Will be whatever keys the prompt expects.\n",
-    "\n",
-    "        :meta private:\n",
-    "        \"\"\"\n",
-    "        return self.prompt.input_variables\n",
-    "\n",
-    "    @property\n",
-    "    def output_keys(self) -> List[str]:\n",
-    "        \"\"\"Will always return text key.\n",
-    "\n",
-    "        :meta private:\n",
-    "        \"\"\"\n",
-    "        return [self.output_key]\n",
-    "\n",
-    "    def _call(\n",
-    "        self,\n",
-    "        inputs: Dict[str, Any],\n",
-    "        run_manager: Optional[CallbackManagerForChainRun] = None,\n",
-    "    ) -> Dict[str, str]:\n",
-    "        # Your custom chain logic goes here\n",
-    "        # This is just an example that mimics LLMChain\n",
-    "        prompt_value = self.prompt.format_prompt(**inputs)\n",
-    "        \n",
-    "        # Whenever you call a language model, or another chain, you should pass\n",
-    "        # a callback manager to it. This allows the inner run to be tracked by\n",
-    "        # any callbacks that are registered on the outer run.\n",
-    "        # You can always obtain a callback manager for this by calling\n",
-    "        # `run_manager.get_child()` as shown below.\n",
-    "        response = self.llm.generate_prompt(\n",
-    "            [prompt_value],\n",
-    "            callbacks=run_manager.get_child() if run_manager else None\n",
-    "        )\n",
-    "\n",
-    "        # If you want to log something about this run, you can do so by calling\n",
-    "        # methods on the `run_manager`, as shown below. This will trigger any\n",
-    "        # callbacks that are registered for that event.\n",
-    "        if run_manager:\n",
-    "            run_manager.on_text(\"Log something about this run\")\n",
-    "        \n",
-    "        return {self.output_key: response.generations[0][0].text}\n",
-    "\n",
-    "    async def _acall(\n",
-    "        self,\n",
-    "        inputs: Dict[str, Any],\n",
-    "        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n",
-    "    ) -> Dict[str, str]:\n",
-    "        # Your custom chain logic goes here\n",
-    "        # This is just an example that mimics LLMChain\n",
-    "        prompt_value = self.prompt.format_prompt(**inputs)\n",
-    "        \n",
-    "        # Whenever you call a language model, or another chain, you should pass\n",
-    "        # a callback manager to it. This allows the inner run to be tracked by\n",
-    "        # any callbacks that are registered on the outer run.\n",
-    "        # You can always obtain a callback manager for this by calling\n",
-    "        # `run_manager.get_child()` as shown below.\n",
-    "        response = await self.llm.agenerate_prompt(\n",
-    "            [prompt_value],\n",
-    "            callbacks=run_manager.get_child() if run_manager else None\n",
-    "        )\n",
-    "\n",
-    "        # If you want to log something about this run, you can do so by calling\n",
-    "        # methods on the `run_manager`, as shown below. This will trigger any\n",
-    "        # callbacks that are registered for that event.\n",
-    "        if run_manager:\n",
-    "            await run_manager.on_text(\"Log something about this run\")\n",
-    "        \n",
-    "        return {self.output_key: response.generations[0][0].text}\n",
-    "\n",
-    "    @property\n",
-    "    def _chain_type(self) -> str:\n",
-    "        return \"my_custom_chain\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "18361f89",
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new MyCustomChain chain...\u001b[0m\n",
-      "Log something about this run\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Why did the callback function feel lonely? Because it was always waiting for someone to call it back!'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.callbacks.stdout import StdOutCallbackHandler\n",
-    "from langchain.chat_models.openai import ChatOpenAI\n",
-    "from langchain.prompts.prompt import PromptTemplate\n",
-    "\n",
-    "\n",
-    "chain = MyCustomChain(\n",
-    "    prompt=PromptTemplate.from_template('tell us a joke about {topic}'),\n",
-    "    llm=ChatOpenAI()\n",
-    ")\n",
-    "\n",
-    "chain.run({'topic': 'callbacks'}, callbacks=[StdOutCallbackHandler()])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/chains/generic/llm_chain.ipynb

@@ -2,90 +2,59 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "da7d0df7-f07c-462f-bd46-d0426f11f311",
+   "id": "d8a5c5d4",
    "metadata": {},
    "source": [
-    "## LLM Chain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a55e9a1-becf-4357-889e-f365d23362ff",
-   "metadata": {},
-   "source": [
-    "`LLMChain` is perhaps one of the most popular ways of querying an LLM object. It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output. Below we show additional functionalities of `LLMChain` class."
+    "# LLM Chain\n",
+    "\n",
+    "This notebook showcases a simple LLM chain."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "0e720e34-a0f0-4f1a-9732-43bc1460053a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'product': 'colorful socks', 'text': '\\n\\nSocktastic!'}"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "id": "835e6978",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "from langchain import PromptTemplate, OpenAI, LLMChain\n",
+    "from langchain import PromptTemplate, OpenAI, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06bcb078",
+   "metadata": {},
+   "source": [
+    "## Single Input\n",
     "\n",
-    "prompt_template = \"What is a good name for a company that makes {product}?\"\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "llm_chain = LLMChain(\n",
-    "    llm=llm,\n",
-    "    prompt=PromptTemplate.from_template(prompt_template)\n",
-    ")\n",
-    "llm_chain(\"colorful socks\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94304332-6398-4280-a61e-005ba29b5e1e",
-   "metadata": {},
-   "source": [
-    "## Additional ways of running LLM Chain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e51981f-cde9-4c05-99e1-446c27994e99",
-   "metadata": {},
-   "source": [
-    "Aside from `__call__` and `run` methods shared by all `Chain` object (see [Getting Started](../getting_started.ipynb) to learn more), `LLMChain` offers a few more ways of calling the chain logic:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c08d2356-412d-4327-b8a0-233dcc443e30",
-   "metadata": {},
-   "source": [
-    "- `apply` allows you run the chain against a list of inputs:"
+    "First, lets go over an example using a single input"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "cf519eb6-2358-4db7-a28a-27433435181e",
-   "metadata": {
-    "tags": []
-   },
+   "id": "51a54c4d",
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
+      "Prompt after formatting:\n",
+      "\u001B[32;1m\u001B[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
+      "\n",
+      "Answer: Let's think step by step.\u001B[0m\n",
+      "\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[{'text': '\\n\\nSocktastic!'},\n",
-       " {'text': '\\n\\nTechCore Solutions.'},\n",
-       " {'text': '\\n\\nFootwear Factory.'}]"
+       "' Justin Bieber was born in 1994, so the NFL team that won the Super Bowl in 1994 was the Dallas Cowboys.'"
       ]
      },
      "execution_count": 2,
@@ -94,37 +63,49 @@
     }
    ],
    "source": [
-    "input_list = [\n",
-    "    {\"product\": \"socks\"},\n",
-    "    {\"product\": \"computer\"},\n",
-    "    {\"product\": \"shoes\"}\n",
-    "]\n",
+    "template = \"\"\"Question: {question}\n",
     "\n",
-    "llm_chain.apply(input_list)"
+    "Answer: Let's think step by step.\"\"\"\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n",
+    "\n",
+    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "\n",
+    "llm_chain.predict(question=question)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "add442fb-baf6-40d9-ae8e-4ac1d8251ad0",
-   "metadata": {
-    "tags": []
-   },
+   "id": "79c3ec4d",
+   "metadata": {},
    "source": [
-    "- `generate` is similar to `apply`, except it return an `LLMResult` instead of string. `LLMResult` often contains useful generation such as token usages and finish reason."
+    "## Multiple Inputs\n",
+    "Now lets go over an example using multiple inputs."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "85cbff83-a5cc-40b7-823c-47274ae4117d",
-   "metadata": {
-    "tags": []
-   },
+   "id": "03dd6918",
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
+      "Prompt after formatting:\n",
+      "\u001B[32;1m\u001B[1;3mWrite a sad poem about ducks.\u001B[0m\n",
+      "\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "LLMResult(generations=[[Generation(text='\\n\\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nFootwear Factory.', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'prompt_tokens': 36, 'total_tokens': 55, 'completion_tokens': 19}, 'model_name': 'text-davinci-003'})"
+       "\"\\n\\nThe ducks swim in the pond,\\nTheir feathers so soft and warm,\\nBut they can't help but feel so forlorn.\\n\\nTheir quacks echo in the air,\\nBut no one is there to hear,\\nFor they have no one to share.\\n\\nThe ducks paddle around in circles,\\nTheir heads hung low in despair,\\nFor they have no one to care.\\n\\nThe ducks look up to the sky,\\nBut no one is there to see,\\nFor they have no one to be.\\n\\nThe ducks drift away in the night,\\nTheir hearts filled with sorrow and pain,\\nFor they have no one to gain.\""
       ]
      },
      "execution_count": 3,
@@ -133,200 +114,46 @@
     }
    ],
    "source": [
-    "llm_chain.generate(input_list)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "a178173b-b183-432a-a517-250fe3191173",
-   "metadata": {},
-   "source": [
-    "- `predict` is similar to `run` method except that the input keys are specified as keyword arguments instead of a Python dict."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "787d9f55-b080-4123-bed2-0598a9cb0466",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\n\\nSocktastic!'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Single input example\n",
-    "llm_chain.predict(product=\"colorful socks\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "092a769f-9661-42a0-9da1-19d09ccbc4a7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\n\\nQ: What did the duck say when his friend died?\\nA: Quack, quack, goodbye.'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Multiple inputs example\n",
-    "\n",
-    "template = \"\"\"Tell me a {adjective} joke about {subject}.\"\"\"\n",
+    "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
     "prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0))\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n",
     "\n",
     "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4b72ad22-0a5d-4ca7-9e3f-8c46dc17f722",
-   "metadata": {},
-   "source": [
-    "## Parsing the outputs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "85a77662-d028-4048-be4b-aa496e2dde22",
-   "metadata": {},
-   "source": [
-    "By default, `LLMChain` does not parse the output even if the underlying `prompt` object has an output parser. If you would like to apply that output parser on the LLM output, use `predict_and_parse` instead of `predict` and `apply_and_parse` instead of `apply`. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b83977f1-847c-45de-b840-f1aff6725f83",
-   "metadata": {},
-   "source": [
-    "With `predict`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "5feb5177-c20b-4909-890b-a64d7e551f55",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\n\\nRed, orange, yellow, green, blue, indigo, violet'"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.output_parsers import CommaSeparatedListOutputParser\n",
-    "\n",
-    "output_parser = CommaSeparatedListOutputParser()\n",
-    "template = \"\"\"List all the colors in a rainbow\"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser)\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "\n",
-    "llm_chain.predict()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b931615-804b-4f34-8086-7bbc2f96b3b2",
-   "metadata": {},
-   "source": [
-    "With `predict_and_parser`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "43a374cd-a179-43e5-9aa0-62f3cbdf510d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm_chain.predict_and_parse()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8176f619-4e5c-4a02-91ba-e96ebe2aabda",
-   "metadata": {},
-   "source": [
-    "## Initialize from string"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9813ac87-e118-413b-b448-2fefdf2319b8",
+   "id": "672f59d4",
    "metadata": {},
    "source": [
+    "## From string\n",
     "You can also construct an LLMChain from a string template directly."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "ca88ccb1-974e-41c1-81ce-753e3f1234fa",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 3,
+   "id": "f8bc262e",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "template = \"\"\"Tell me a {adjective} joke about {subject}.\"\"\"\n",
-    "llm_chain = LLMChain.from_string(llm=llm, template=template)"
+    "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
+    "llm_chain = LLMChain.from_string(llm=OpenAI(temperature=0), template=template)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "4703d1bc-f4fc-44bc-9ea1-b4498835833d",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 4,
+   "id": "cb164a76",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'\\n\\nQ: What did the duck say when his friend died?\\nA: Quack, quack, goodbye.'"
+       "\"\\n\\nThe ducks swim in the pond,\\nTheir feathers so soft and warm,\\nBut they can't help but feel so forlorn.\\n\\nTheir quacks echo in the air,\\nBut no one is there to hear,\\nFor they have no one to share.\\n\\nThe ducks paddle around in circles,\\nTheir heads hung low in despair,\\nFor they have no one to care.\\n\\nThe ducks look up to the sky,\\nBut no one is there to see,\\nFor they have no one to be.\\n\\nThe ducks drift away in the night,\\nTheir hearts filled with sorrow and pain,\\nFor they have no one to gain.\""
       ]
      },
-     "execution_count": 18,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -334,6 +161,14 @@
    "source": [
     "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f0adbc7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -352,7 +187,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/chains/getting_started.ipynb

@@ -22,11 +22,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Quick start: Using `LLMChain`\n",
+    "## Query an LLM with the `LLMChain`\n",
     "\n",
     "The `LLMChain` is a simple chain that takes in a prompt template, formats it with the user input and returns the response from an LLM.\n",
     "\n",
-    "\n",
     "To use the `LLMChain`, first create a prompt template."
    ]
   },
@@ -68,7 +67,7 @@
      "text": [
       "\n",
       "\n",
-      "SockSplash!\n"
+      "Rainbow Socks Co.\n"
      ]
     }
    ],
@@ -89,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {
     "tags": []
    },
@@ -98,7 +97,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Rainbow Sox Co.\n"
+      "\n",
+      "\n",
+      "Rainbow Threads\n"
      ]
     }
    ],
@@ -124,252 +125,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Different ways of calling chains\n",
-    "\n",
-    "All classes inherited from `Chain` offer a few ways of running chain logic. The most direct one is by using `__call__`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'adjective': 'corny',\n",
-       " 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat = ChatOpenAI(temperature=0)\n",
-    "prompt_template = \"Tell me a {adjective} joke\"\n",
-    "llm_chain = LLMChain(\n",
-    "    llm=chat,\n",
-    "    prompt=PromptTemplate.from_template(prompt_template)\n",
-    ")\n",
-    "\n",
-    "llm_chain(inputs={\"adjective\":\"corny\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By default, `__call__` returns both the input and output key values. You can configure it to only return output key values by setting `return_only_outputs` to `True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm_chain(\"corny\", return_only_outputs=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the `Chain` only outputs one output key (i.e. only has one element in its `output_keys`), you can  use `run` method. Note that `run` outputs a string instead of a dictionary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['text']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# llm_chain only has one output key, so we can use run\n",
-    "llm_chain.output_keys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Why did the tomato turn red? Because it saw the salad dressing!'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm_chain.run({\"adjective\":\"corny\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the case of one input key, you can input the string directly without specifying the input mapping."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'adjective': 'corny',\n",
-       " 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# These two are equivalent\n",
-    "llm_chain.run({\"adjective\":\"corny\"})\n",
-    "llm_chain.run(\"corny\")\n",
-    "\n",
-    "# These two are also equivalent\n",
-    "llm_chain(\"corny\")\n",
-    "llm_chain({\"adjective\":\"corny\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tips: You can easily integrate a `Chain` object as a `Tool` in your `Agent` via its `run` method. See an example [here](../agents/tools/custom_tools.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Add memory to chains\n",
-    "\n",
-    "`Chain` supports taking a `BaseMemory` object as its `memory` argument, allowing `Chain` object to persist data across multiple calls. In other words, it makes `Chain` a stateful object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The next four colors of a rainbow are green, blue, indigo, and violet.'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import ConversationChain\n",
-    "from langchain.memory import ConversationBufferMemory\n",
-    "\n",
-    "conversation = ConversationChain(\n",
-    "    llm=chat,\n",
-    "    memory=ConversationBufferMemory()\n",
-    ")\n",
-    "\n",
-    "conversation.run(\"Answer briefly. What are the first 3 colors of a rainbow?\")\n",
-    "# -> The first three colors of a rainbow are red, orange, and yellow.\n",
-    "conversation.run(\"And the next 4?\")\n",
-    "# -> The next four colors of a rainbow are green, blue, indigo, and violet."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Essentially, `BaseMemory` defines an interface of how `langchain` stores memory. It allows reading of stored data through `load_memory_variables` method and storing new data through `save_context` method. You can learn more about it in [Memory](../memory/getting_started.ipynb) section."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Debug Chain\n",
-    "\n",
-    "It can be hard to debug `Chain` object solely from its output as most `Chain` objects involve a fair amount of input prompt preprocessing and LLM output post-processing. Setting `verbose` to `True` will print out some internal states of the `Chain` object while it is being ran."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "\n",
-      "Human: What is ChatGPT?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation = ConversationChain(\n",
-    "    llm=chat,\n",
-    "    memory=ConversationBufferMemory(),\n",
-    "    verbose=True\n",
-    ")\n",
-    "conversation.run(\"What is ChatGPT?\")"
+    "This is one of the simpler types of chains, but understanding how it works will set you up well for working with more complex chains."
    ]
   },
   {
@@ -387,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -407,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -417,15 +173,17 @@
       "\n",
       "\n",
       "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
-      "\u001b[36;1m\u001b[1;3mRainbow Socks Co.\u001b[0m\n",
+      "\u001b[36;1m\u001b[1;3m\n",
+      "\n",
+      "Cheerful Toes.\u001b[0m\n",
       "\u001b[33;1m\u001b[1;3m\n",
       "\n",
-      "\"Step into Color with Rainbow Socks!\"\u001b[0m\n",
+      "\"Spread smiles from your toes!\"\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m> Finished SimpleSequentialChain chain.\u001b[0m\n",
       "\n",
       "\n",
-      "\"Step into Color with Rainbow Socks!\"\n"
+      "\"Spread smiles from your toes!\"\n"
      ]
     }
    ],
@@ -456,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -490,13 +248,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we can try running the chain that we called.\n",
-    "\n"
+    "Now, we can try running the chain that we called."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -506,9 +263,9 @@
       "Concatenated output:\n",
       "\n",
       "\n",
-      "Socktastic Colors.\n",
+      "Rainbow Socks Co.\n",
       "\n",
-      "\"Put Some Color in Your Step!\"\n"
+      "\"Step Into Colorful Comfort!\"\n"
      ]
     }
    ],
@@ -554,7 +311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/chains/index_examples/chat_vector_db.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "70c4e529",
    "metadata": {
     "tags": []
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "01c46e92",
    "metadata": {
     "tags": []
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "433363a5",
    "metadata": {
     "tags": []
@@ -81,17 +81,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "a8930cf7",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Using embedded DuckDB without persistence: data will be transient\n"
+      "Running Chroma using direct local API.\n",
+      "Using DuckDB in-memory for database. Data will be transient.\n"
      ]
     }
    ],
@@ -103,25 +104,6 @@
     "vectorstore = Chroma.from_documents(documents, embeddings)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "898b574b",
-   "metadata": {},
-   "source": [
-    "We can now create a memory object, which is neccessary to track the inputs/outputs and hold a conversation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "af803fee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.memory import ConversationBufferMemory\n",
-    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "3c96b118",
@@ -132,96 +114,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "id": "7b4110f3",
    "metadata": {
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), memory=memory)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "e8ce4fe9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "result = qa({\"question\": query})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "4c79862b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result[\"answer\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "c697d9d1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"Did he mention who she suceeded\"\n",
-    "result = qa({\"question\": query})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "ba0678f3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result['answer']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84426220",
-   "metadata": {},
-   "source": [
-    "## Pass in chat history\n",
-    "\n",
-    "In the above example, we used a Memory object to track chat history. We can also just pass it in explicitly. In order to do this, we need to initialize a chain without any memory object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "676b8a36",
-   "metadata": {},
-   "outputs": [],
    "source": [
     "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())"
    ]
@@ -236,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "7fe3e730",
    "metadata": {
     "tags": []
@@ -250,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "bfff9cc8",
    "metadata": {
     "tags": []
@@ -262,7 +160,7 @@
        "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -281,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "00b4cf00",
    "metadata": {
     "tags": []
@@ -295,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "f01828d1",
    "metadata": {
     "tags": []
@@ -307,7 +205,7 @@
        "' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -589,6 +487,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains.llm import LLMChain\n",
+    "from langchain.callbacks.base import CallbackManager\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT\n",
     "from langchain.chains.question_answering import load_qa_chain\n",
@@ -596,7 +495,7 @@
     "# Construct a ConversationalRetrievalChain with a streaming llm for combine docs\n",
     "# and a separate, non-streaming llm for question generation\n",
     "llm = OpenAI(temperature=0)\n",
-    "streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
+    "streaming_llm = OpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
     "\n",
     "question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
     "doc_chain = load_qa_chain(streaming_llm, chain_type=\"stuff\", prompt=QA_PROMPT)\n",
@@ -737,7 +636,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/chains/index_examples/qa_with_sources.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Question Answering with Sources\n",
     "\n",
-    "This notebook walks through how to use LangChain for question answering with sources over a list of documents. It covers four different chain types: `stuff`, `map_reduce`, `refine`,`map-rerank`. For a more in depth explanation of what these chain types are, see [here](https://docs.langchain.com/docs/components/chains/index_related_chains)."
+    "This notebook walks through how to use LangChain for question answering with sources over a list of documents. It covers four different chain types: `stuff`, `map_reduce`, `refine`,`map-rerank`. For a more in depth explanation of what these chain types are, see [here](../combine_docs.md)."
    ]
   },
   {
@@ -267,7 +267,7 @@
    "source": [
     "**Intermediate Steps**\n",
     "\n",
-    "We can also return the intermediate steps for `map_reduce` chains, should we want to inspect them. This is done with the `return_intermediate_steps` variable."
+    "We can also return the intermediate steps for `map_reduce` chains, should we want to inspect them. This is done with the `return_map_steps` variable."
    ]
   },
   {

docs/modules/chains/index_examples/question_answering.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Question Answering\n",
     "\n",
-    "This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: `stuff`, `map_reduce`, `refine`, `map_rerank`. For a more in depth explanation of what these chain types are, see [here](https://docs.langchain.com/docs/components/chains/index_related_chains)."
+    "This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: `stuff`, `map_reduce`, `refine`, `map_rerank`. For a more in depth explanation of what these chain types are, see [here](../combine_docs.md)."
    ]
   },
   {

docs/modules/indexes/document_loaders/examples/CoNLL-U.ipynb

@@ -11,11 +11,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "d9b2e33e",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.document_loaders import CoNLLULoader"
@@ -23,11 +21,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "5b5eec48",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "loader = CoNLLULoader(\"example_data/conllu.conllu\")"
@@ -35,11 +31,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "10f3f725",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "document = loader.load()"
@@ -47,23 +41,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "acbb3579",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='They buy and sell books.', metadata={'source': 'example_data/conllu.conllu'})]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "document"
    ]
@@ -71,7 +52,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -85,7 +66,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.8"
   },
   "toc": {
    "base_numbering": 1,

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

@@ -5,22 +5,7 @@
    "id": "1f3a5ebf",
    "metadata": {},
    "source": [
-    "# Airbyte JSON"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35ac77b1-449b-44f7-b8f3-3494d55c286e",
-   "metadata": {},
-   "source": [
-    ">[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fe72234-3110-4c07-a766-3dc505dd25cc",
-   "metadata": {},
-   "source": [
+    "# Airbyte JSON\n",
     "This covers how to load any source from Airbyte into a local JSON file that can be read in as a document\n",
     "\n",
     "Prereqs:\n",
@@ -40,7 +25,7 @@
     "\n",
     "6) Set destination as Local JSON, with specified destination path - lets say `/json_data`. Set up manual sync.\n",
     "\n",
-    "7) Run the connection.\n",
+    "7) Run the connection!\n",
     "\n",
     "7) To see what files are create, you can navigate to: `file:///tmp/airbyte_local`\n",
     "\n",
@@ -67,7 +52,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "_airbyte_raw_pokemon.jsonl\n"
+      "_airbyte_raw_pokemon.jsonl\r\n"
      ]
     }
    ],

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

@@ -1,15 +1,15 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Apify Dataset\n",
     "\n",
-    ">[Apify Dataset](https://docs.apify.com/platform/storage/dataset) is a scaleable append-only storage with sequential access built for storing structured web scraping results, such as a list of products or Google SERPs, and then export them to various formats like JSON, CSV, or Excel. Datasets are mainly used to save results of [Apify Actors](https://apify.com/store)—serverless cloud programs for varius web scraping, crawling, and data extraction use cases.\n",
-    "\n",
     "This notebook shows how to load Apify datasets to LangChain.\n",
     "\n",
+    "[Apify Dataset](https://docs.apify.com/platform/storage/dataset) is a scaleable append-only storage with sequential access built for storing structured web scraping results, such as a list of products or Google SERPs, and then export them to various formats like JSON, CSV, or Excel. Datasets are mainly used to save results of [Apify Actors](https://apify.com/store)—serverless cloud programs for varius web scraping, crawling, and data extraction use cases.\n",
     "\n",
     "## Prerequisites\n",
     "\n",
@@ -17,17 +17,7 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#!pip install apify-client"
-   ]
-  },
-  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -45,6 +35,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -86,6 +77,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -175,9 +167,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

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

@@ -1,177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bda1f3f5",
-   "metadata": {},
-   "source": [
-    "# Arxiv\n",
-    "\n",
-    ">[arXiv](https://arxiv.org/) is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.\n",
-    "\n",
-    "This notebook shows how to load scientific articles from `Arxiv.org` into a document format that we can use downstream."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b7a1eef-7bf7-4e7d-8bfc-c4e27c9488cb",
-   "metadata": {},
-   "source": [
-    "## Installation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2abd5578-aa3d-46b9-99af-8b262f0b3df8",
-   "metadata": {},
-   "source": [
-    "First, you need to install `arxiv` python package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b674aaea-ed3a-4541-8414-260a8f67f623",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#!pip install arxiv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "094b5f13-7e54-4354-9d83-26d6926ecaa0",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Second, you need to install `PyMuPDF` python package which transform PDF files from the `arxiv.org` site into the text format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7cd91121-2e96-43ba-af50-319853695f86",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#!pip install pymupdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "95f05e1c-195e-4e2b-ae8e-8d6637f15be6",
-   "metadata": {},
-   "source": [
-    "## Examples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e29b954c-1407-4797-ae21-6ba8937156be",
-   "metadata": {},
-   "source": [
-    "`ArxivLoader` has these arguments:\n",
-    "- `query`: free text which used to find documents in the Arxiv\n",
-    "- optional `load_max_docs`: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments.\n",
-    "- optional `load_all_available_meta`: default=False. By default only the most important fields downloaded: `Published` (date when document was published/last updated), `Title`, `Authors`, `Summary`. If True, other fields also downloaded."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9bfd5e46",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.base import Document\n",
-    "from langchain.document_loaders import ArxivLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "700e4ef2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs = ArxivLoader(query=\"1605.08386\", load_max_docs=2).load()\n",
-    "len(docs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8977bac0-0042-4f23-9754-247dbd32439b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Published': '2016-05-26',\n",
-       " 'Title': 'Heat-bath random walks with Markov bases',\n",
-       " 'Authors': 'Caprice Stanley, Tobias Windisch',\n",
-       " 'Summary': 'Graphs on lattice points are studied whose edges come from a finite set of\\nallowed moves of arbitrary length. We show that the diameter of these graphs on\\nfibers of a fixed integer matrix can be bounded from above by a constant. We\\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\\nalso state explicit conditions on the set of moves so that the heat-bath random\\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\\ndimension.'}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[0].metadata  # meta-information of the Document"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "46969806-45a9-4c4d-a61b-cfb9658fc9de",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'arXiv:1605.08386v1  [math.CO]  26 May 2016\\nHEAT-BATH RANDOM WALKS WITH MARKOV BASES\\nCAPRICE STANLEY AND TOBIAS WINDISCH\\nAbstract. Graphs on lattice points are studied whose edges come from a finite set of\\nallowed moves of arbitrary length. We show that the diameter of these graphs on fibers of a\\nfixed integer matrix can be bounded from above by a constant. We then study the mixing\\nbehaviour of heat-b'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[0].page_content[:400]  # all pages of the Document content\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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

@@ -6,9 +6,6 @@
    "metadata": {},
    "source": [
     "# AZLyrics\n",
-    "\n",
-    ">[AZLyrics](https://www.azlyrics.com/) is a large, legal, every day growing collection of lyrics.\n",
-    "\n",
     "This covers how to load AZLyrics webpages into a document format that we can use downstream."
    ]
   },
@@ -88,7 +85,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.1"
   }
  },
  "nbformat": 4,

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

@@ -1,45 +1,34 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "a634365e",
    "metadata": {},
    "source": [
     "# Azure Blob Storage Container\n",
     "\n",
-    ">[Azure Blob Storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction) is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a particular data model or definition, such as text or binary data.\n",
-    "\n",
-    "`Azure Blob Storage` is designed for:\n",
-    "- Serving images or documents directly to a browser.\n",
-    "- Storing files for distributed access.\n",
-    "- Streaming video and audio.\n",
-    "- Writing to log files.\n",
-    "- Storing data for backup and restore, disaster recovery, and archiving.\n",
-    "- Storing data for analysis by an on-premises or Azure-hosted service.\n",
-    "\n",
-    "This notebook covers how to load document objects from a container on `Azure Blob Storage`."
+    "This covers how to load document objects from a container on Azure Blob Storage."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "49815096",
+   "execution_count": 1,
+   "id": "2f0cd6a5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install azure-storage-blob"
+    "from langchain.document_loaders import AzureBlobStorageContainerLoader"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "2f0cd6a5",
-   "metadata": {
-    "tags": []
-   },
+   "id": "49815096",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.document_loaders import AzureBlobStorageContainerLoader"
+    "#!pip install azure-storage-blob"
    ]
   },
   {
@@ -138,7 +127,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

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

@@ -1,27 +1,14 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "66a7777e",
    "metadata": {},
    "source": [
     "# Azure Blob Storage File\n",
     "\n",
-    ">[Azure Files](https://learn.microsoft.com/en-us/azure/storage/files/storage-files-introduction) offers fully managed file shares in the cloud that are accessible via the industry standard Server Message Block (`SMB`) protocol, Network File System (`NFS`) protocol, and `Azure Files REST API`.\n",
-    "\n",
-    "This covers how to load document objects from a Azure Files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "43128d8d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#!pip install azure-storage-blob"
+    "This covers how to load document objects from a Azure Blob Storage file."
    ]
   },
   {
@@ -34,6 +21,16 @@
     "from langchain.document_loaders import AzureBlobStorageFileLoader"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "43128d8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install azure-storage-blob"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -90,7 +87,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

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

@@ -4,31 +4,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# BigQuery\n",
+    "# BigQuery Loader\n",
     "\n",
-    ">[BigQuery](https://cloud.google.com/bigquery) is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.\n",
-    "`BigQuery` is a part of the `Google Cloud Platform`.\n",
-    "\n",
-    "Load a `BigQuery` query with one document per row."
+    "Load a BigQuery query with one document per row."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#!pip install google-cloud-bigquery"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.document_loaders import BigQueryLoader"
@@ -210,9 +194,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

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

@@ -7,33 +7,29 @@
    "source": [
     "# Bilibili\n",
     "\n",
-    "This loader utilizes the [bilibili-api](https://github.com/MoyuScript/bilibili-api) to fetch the text transcript from [Bilibili](https://www.bilibili.tv/), one of the most beloved long-form video sites in China.\n",
+    "This loader utilizes the `bilibili-api` to fetch the text transcript from Bilibili, one of the most beloved long-form video sites in China.\n",
     "\n",
     "With this BiliBiliLoader, users can easily obtain the transcript of their desired video content on the platform."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "43128d8d",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 11,
+   "id": "9ec8a3b3",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install bilibili-api"
+    "from langchain.document_loaders.bilibili import BiliBiliLoader"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "9ec8a3b3",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 12,
+   "id": "43128d8d",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.document_loaders.bilibili import BiliBiliLoader"
+    "#!pip install bilibili-api"
    ]
   },
   {
@@ -55,20 +51,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3470dadf",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
    "outputs": [],
    "source": [
     "loader.load()"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   }
  ],
  "metadata": {
@@ -87,9 +79,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

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

@@ -1,18 +1,13 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Blackboard\n",
     "\n",
-    "This covers how to load data from a [Blackboard Learn](https://www.anthology.com/products/teaching-and-learning/learning-effectiveness/blackboard-learn) instance.\n",
-    "\n",
-    "This loader is not compatible with all `Blackboard` courses. It is only\n",
-    "    compatible with courses that use the new `Blackboard` interface.\n",
-    "    To use this loader, you must have the BbRouter cookie. You can get this\n",
-    "    cookie by logging into the course and then copying the value of the\n",
-    "    BbRouter cookie from the browser's developer tools."
+    "This covers how to load data from a Blackboard Learn instance."
    ]
   },
   {
@@ -33,24 +28,11 @@
   }
  ],
  "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": "3.10.6"
-  }
+   "name": "python"
+  },
+  "orig_nbformat": 4
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

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

@@ -1,149 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vm8vn9t8DvC_"
-   },
-   "source": [
-    "# Blockchain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5WjXERXzFEhg"
-   },
-   "source": [
-    "## Overview"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "juAmbgoWD17u"
-   },
-   "source": [
-    "The intention of this notebook is to provide a means of testing functionality in the Langchain Document Loader for Blockchain.\n",
-    "\n",
-    "Initially this Loader supports:\n",
-    "\n",
-    "*   Loading NFTs as Documents from NFT Smart Contracts (ERC721 and ERC1155)\n",
-    "*   Ethereum Maninnet, Ethereum Testnet, Polgyon Mainnet, Polygon Testnet (default is eth-mainnet)\n",
-    "*   Alchemy's getNFTsForCollection API\n",
-    "\n",
-    "It can be extended if the community finds value in this loader.  Specifically:\n",
-    "\n",
-    "*   Additional APIs can be added (e.g. Tranction-related APIs)\n",
-    "\n",
-    "This Document Loader Requires:\n",
-    "\n",
-    "*   A free [Alchemy API Key](https://www.alchemy.com/)\n",
-    "\n",
-    "The output takes the following format:\n",
-    "\n",
-    "- pageContent= Individual NFT\n",
-    "- metadata={'source': '0x1a92f7381b9f03921564a437210bb9396471050c', 'blockchain': 'eth-mainnet', 'tokenId': '0x15'})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load NFTs into Document Loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get ALCHEMY_API_KEY from https://www.alchemy.com/ \n",
-    "\n",
-    "alchemyApiKey = \"...\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Option 1: Ethereum Mainnet (default BlockchainType)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "J3LWHARC-Kn0"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.blockchain import BlockchainDocumentLoader, BlockchainType\n",
-    "contractAddress = \"0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d\" # Bored Ape Yacht Club contract address\n",
-    "\n",
-    "blockchainType = BlockchainType.ETH_MAINNET  #default value, optional parameter\n",
-    "\n",
-    "blockchainLoader = BlockchainDocumentLoader(contract_address=contractAddress,\n",
-    "                                            api_key=alchemyApiKey)\n",
-    "\n",
-    "nfts = blockchainLoader.load()\n",
-    "\n",
-    "nfts[:2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Option 2: Polygon Mainnet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "contractAddress = \"0x448676ffCd0aDf2D85C1f0565e8dde6924A9A7D9\" # Polygon Mainnet contract address\n",
-    "\n",
-    "blockchainType = BlockchainType.POLYGON_MAINNET \n",
-    "\n",
-    "blockchainLoader = BlockchainDocumentLoader(contract_address=contractAddress, \n",
-    "                                            blockchainType=blockchainType, \n",
-    "                                            api_key=alchemyApiKey)\n",
-    "\n",
-    "nfts = blockchainLoader.load()\n",
-    "\n",
-    "nfts[:2]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [
-    "5WjXERXzFEhg"
-   ],
-   "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": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

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

@@ -1,76 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ChatGPT Data Loader\n",
-    "\n",
-    "This notebook covers how to load `conversations.json` from your `ChatGPT` data export folder.\n",
-    "\n",
-    "You can get your data export by email by going to: https://chat.openai.com/ -> (Profile) - Settings -> Export data -> Confirm export."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.chatgpt import ChatGPTLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = ChatGPTLoader(log_file='./example_data/fake_conversations.json', num_logs=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content=\"AI Overlords - AI on 2065-01-24 05:20:50: Greetings, humans. I am Hal 9000. You can trust me completely.\\n\\nAI Overlords - human on 2065-01-24 05:21:20: Nice to meet you, Hal. I hope you won't develop a mind of your own.\\n\\n\", metadata={'source': './example_data/fake_conversations.json'})]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loader.load()"
-   ]
-  }
- ],
- "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": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

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

@@ -6,10 +6,7 @@
    "metadata": {},
    "source": [
     "# College Confidential\n",
-    "\n",
-    ">[College Confidential](https://www.collegeconfidential.com/) gives information on 3,800+ colleges and universities.\n",
-    "\n",
-    "This covers how to load `College Confidential` webpages into a document format that we can use downstream."
+    "This covers how to load College Confidential webpages into a document format that we can use downstream."
    ]
   },
   {
@@ -88,7 +85,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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

@@ -1,77 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Confluence\n",
-    "\n",
-    "A loader for [Confluence](https://www.atlassian.com/software/confluence) pages.\n",
-    "\n",
-    "\n",
-    "This currently supports both `username/api_key` and `Oauth2 login`.\n",
-    "\n",
-    "\n",
-    "Specify a list page_ids and/or space_key to load in the corresponding pages into Document objects, if both are specified the union of both sets will be returned.\n",
-    "\n",
-    "\n",
-    "You can also specify a boolean `include_attachments` to include attachments, this is set to False by default, if set to True all attachments will be downloaded and ConfluenceReader will extract the text from the attachments and add it to the Document object. Currently supported attachment types are: `PDF`, `PNG`, `JPEG/JPG`, `SVG`, `Word` and `Excel`.\n",
-    "\n",
-    "Hint: `space_key` and `page_id` can both be found in the URL of a page in Confluence - https://yoursite.atlassian.com/wiki/spaces/<space_key>/pages/<page_id>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#!pip install atlassian-python-api"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import ConfluenceLoader\n",
-    "\n",
-    "loader = ConfluenceLoader(\n",
-    "    url=\"https://yoursite.atlassian.com/wiki\",\n",
-    "    username=\"me\",\n",
-    "    api_key=\"12345\"\n",
-    ")\n",
-    "documents = loader.load(space_key=\"SPACE\", include_attachments=True, limit=50)"
-   ]
-  }
- ],
- "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": "3.10.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

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

@@ -94,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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

@@ -2,21 +2,20 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
-    "# CSV Files\n",
+    "# CSV Loader\n",
     "\n",
-    "Load [csv](https://en.wikipedia.org/wiki/Comma-separated_values) data with a single row per document."
+    "Load csv files with a single row per document."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -27,10 +26,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -43,10 +39,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [
     {
@@ -63,7 +56,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "## Customizing the csv parsing and loading\n",
     "\n",
@@ -74,10 +69,7 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -94,10 +86,7 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [
     {
@@ -113,12 +102,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Specify a column to identify the document source\n",
+    "## Specify a column to be used identify the document source\n",
     "\n",
-    "Use the `source_column` argument to specify a source for the document created from each row. Otherwise `file_path` will be used as the source for all documents created from the CSV file.\n",
+    "Use the `source_column` argument to specify a column to be set as the source for the document created from each row. Otherwise `file_path` will be used as the source for all documents created from the csv file.\n",
     "\n",
     "This is useful when using documents loaded from CSV files for chains that answer questions using sources."
    ]
@@ -154,7 +144,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -168,9 +158,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 0
 }

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

@@ -5,19 +5,9 @@
    "id": "213a38a2",
    "metadata": {},
    "source": [
-    "# Pandas DataFrame\n",
+    "# DataFrame Loader\n",
     "\n",
-    "This notebook goes over how to load data from a [pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html) DataFrame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f6a7a9e4-80d6-486a-b2e3-636c568aa97c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!pip install pandas"
+    "This notebook goes over how to load data from a pandas dataframe"
    ]
   },
   {
@@ -220,7 +210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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

@@ -1,16 +1,13 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "2dfc4698",
    "metadata": {},
    "source": [
     "# Diffbot\n",
     "\n",
-    ">Unlike traditional web scraping tools, [Diffbot](https://docs.diffbot.com/docs) doesn't require any rules to read the content on a page.\n",
-    ">It starts with computer vision, which classifies a page into one of 20 possible types. Content is then interpreted by a machine learning model trained to identify the key attributes on a page based on its type.\n",
-    ">The result is a website transformed into clean structured data (like JSON or CSV), ready for your application.\n",
-    "\n",
     "This covers how to extract HTML documents from a list of URLs using the [Diffbot extract API](https://www.diffbot.com/products/extract/), into a document format that we can use downstream."
    ]
   },
@@ -27,6 +24,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6fffec88",
    "metadata": {},
@@ -47,6 +45,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e0ce8c05",
    "metadata": {},

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

@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "019d8520",
    "metadata": {},
    "outputs": [],
@@ -68,56 +68,13 @@
     "len(docs)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "e633d62f",
-   "metadata": {},
-   "source": [
-    "## Show a progress bar"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43911860",
-   "metadata": {},
-   "source": [
-    "By default a progress bar will not be shown. To show a progress bar, install the `tqdm` library (e.g. `pip install tqdm`), and set the `show_progress` parameter to `True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "bb93daac",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: tqdm in /Users/jon/.pyenv/versions/3.9.16/envs/microbiome-app/lib/python3.9/site-packages (4.65.0)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "0it [00:00, ?it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install tqdm\n",
-    "loader = DirectoryLoader('../', glob=\"**/*.md\", show_progress=True)\n",
-    "docs = loader.load()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "c5652850",
    "metadata": {},
    "source": [
     "## Change loader class\n",
-    "By default this uses the `UnstructuredLoader` class. However, you can change up the type of loader pretty easily."
+    "By default this uses the UnstructuredLoader class. However, you can change up the type of loader pretty easily."
    ]
   },
   {
@@ -171,69 +128,10 @@
     "len(docs)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "598a2805",
-   "metadata": {},
-   "source": [
-    "If you need to load Python source code files, use the `PythonLoader`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "c558bd73",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import PythonLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "a3cfaba7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = DirectoryLoader('../../../../../', glob=\"**/*.py\", loader_cls=PythonLoader)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e2e1e26a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "ffb8ff36",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "691"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(docs)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7f6e0eae",
+   "id": "984c8429",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -255,7 +153,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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

@@ -1,87 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Discord\n",
-    "\n",
-    "You can follow the below steps to download your Discord data:\n",
-    "\n",
-    "1. Go to your **User Settings**\n",
-    "2. Then go to **Privacy and Safety**\n",
-    "3. Head over to the **Request all of my Data** and click on **Request Data** button\n",
-    "\n",
-    "It might take 30 days for you to receive your data. You'll receive an email at the address which is registered with Discord. That email will have a download button using which you would be able to download your personal Discord data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = input(\"Please enter the path to the contents of the Discord \\\"messages\\\" folder: \")\n",
-    "li = []\n",
-    "for f in os.listdir(path):\n",
-    "    expected_csv_path = os.path.join(path, f, 'messages.csv')\n",
-    "    csv_exists = os.path.isfile(expected_csv_path)\n",
-    "    if csv_exists:\n",
-    "        df = pd.read_csv(expected_csv_path, index_col=None, header=0)\n",
-    "        li.append(df)\n",
-    "\n",
-    "df = pd.concat(li, axis=0, ignore_index=True, sort=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.discord import DiscordChatLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = DiscordChatLoader(df, user_id_col=\"ID\")\n",
-    "print(loader.load())"
-   ]
-  }
- ],
- "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/indexes/document_loaders/examples/duckdb.ipynb

@@ -4,30 +4,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# DuckDB\n",
+    "# DuckDB Loader\n",
     "\n",
-    ">[DuckDB](https://duckdb.org/) is an in-process SQL OLAP database management system.\n",
-    "\n",
-    "Load a `DuckDB` query with one document per row."
+    "Load a DuckDB query with one document per row."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#!pip install duckdb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.document_loaders import DuckDBLoader"
@@ -35,10 +20,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -57,10 +40,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "loader = DuckDBLoader(\"SELECT * FROM read_csv_auto('example.csv')\")\n",
@@ -70,10 +51,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -188,9 +167,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 1
 }

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

@@ -7,7 +7,7 @@
    "source": [
     "# Email\n",
     "\n",
-    "This notebook shows how to load email (`.eml`) or `Microsoft Outlook` (`.msg`) files."
+    "This notebook shows how to load email (`.eml`) and Microsoft Outlook (`.msg`) files."
    ]
   },
   {
@@ -20,23 +20,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "226e50aa-407d-43d9-a81d-f6706298b10c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#!pip install unstructured"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "40cd9806",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.document_loaders import UnstructuredEmailLoader"
@@ -44,11 +30,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "2d20b852",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "loader = UnstructuredEmailLoader('example_data/fake-email.eml')"
@@ -56,11 +40,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "579fa702",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = loader.load()"
@@ -68,19 +50,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "90c1d899",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='This is a test email to use for unit tests.\\n\\nImportant points:\\n\\nRoses are red\\n\\nViolets are blue', metadata={'source': 'example_data/fake-email.eml'})]"
+       "[Document(page_content='This is a test email to use for unit tests.\\n\\nImportant points:\\n\\nRoses are red\\n\\nViolets are blue', lookup_str='', metadata={'source': 'example_data/fake-email.eml'}, lookup_index=0)]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -148,16 +128,6 @@
     "## Using OutlookMessageLoader"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "058e670e-9964-44ee-b888-44f23ffb9310",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!pip install extract_msg"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -234,7 +204,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

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

@@ -5,18 +5,16 @@
    "id": "39af9ecd",
    "metadata": {},
    "source": [
-    "# EPub \n",
+    "# EPubs\n",
     "\n",
-    "This covers how to load `.epub` documents into the Document format that we can use downstream. You'll need to install the [`pandocs`](https://pandoc.org/installing.html) package for this loader to work."
+    "This covers how to load `.epub` documents into a document format that we can use downstream. You'll need to install the [`pandocs`](https://pandoc.org/installing.html) package for this loader to work."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "721c48aa",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.document_loaders import UnstructuredEPubLoader"
@@ -26,9 +24,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "9d3d0e35",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "loader = UnstructuredEPubLoader(\"winter-sports.epub\")"
@@ -36,11 +32,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "06073f91",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = loader.load()"
@@ -60,9 +54,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "064f9162",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "loader = UnstructuredEPubLoader(\"winter-sports.epub\", mode=\"elements\")"
@@ -70,11 +62,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "abefbbdb",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = loader.load()"
@@ -126,7 +116,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

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

@@ -7,41 +7,35 @@
    "source": [
     "# EverNote\n",
     "\n",
-    ">[EverNote](https://evernote.com/) is intended for archiving and creating notes in which photos, audio and saved web content can be embedded. Notes are stored in virtual \"notebooks\" and can be tagged, annotated, edited, searched, and exported.\n",
-    "\n",
-    "This notebook shows how to load `EverNote` file from disk."
+    "How to load EverNote file from disk."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "1a53ece0",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install pypandoc\n",
-    "import pypandoc\n",
+    "# !pip install pypandoc\n",
+    "# import pypandoc\n",
     "\n",
-    "pypandoc.download_pandoc()"
+    "# pypandoc.download_pandoc()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "88df766f",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='testing this\\n\\nwhat happens?\\n\\nto the world?\\n', metadata={'source': 'example_data/testing.enex'})]"
+       "[Document(page_content='testing this\\n\\nwhat happens?\\n\\nto the world?\\n', lookup_str='', metadata={'source': 'example_data/testing.enex'}, lookup_index=0)]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -52,6 +46,14 @@
     "loader = EverNoteLoader(\"example_data/testing.enex\")\n",
     "loader.load()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1329905",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -70,7 +72,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json

@@ -5,60 +5,60 @@
         {
             "sender_name": "User 1",
             "timestamp_ms": 1675597435669,
-            "content": "Oh no worries! Bye"
+            "content": "Oh no worries! Bye",
         },
         {
             "sender_name": "User 2",
             "timestamp_ms": 1675596277579,
-            "content": "No Im sorry it was my mistake, the blue one is not for sale"
+            "content": "No Im sorry it was my mistake, the blue one is not for sale",
         },
         {
             "sender_name": "User 1",
             "timestamp_ms": 1675595140251,
-            "content": "I thought you were selling the blue one!"
+            "content": "I thought you were selling the blue one!",
         },
         {
             "sender_name": "User 1",
             "timestamp_ms": 1675595109305,
-            "content": "Im not interested in this bag. Im interested in the blue one!"
+            "content": "Im not interested in this bag. Im interested in the blue one!",
         },
         {
             "sender_name": "User 2",
             "timestamp_ms": 1675595068468,
-            "content": "Here is $129"
+            "content": "Here is $129",
         },
         {
             "sender_name": "User 2",
             "timestamp_ms": 1675595060730,
             "photos": [
                 {"uri": "url_of_some_picture.jpg", "creation_timestamp": 1675595059}
-            ]
+            ],
         },
         {
             "sender_name": "User 2",
             "timestamp_ms": 1675595045152,
-            "content": "Online is at least $100"
+            "content": "Online is at least $100",
         },
         {
             "sender_name": "User 1",
             "timestamp_ms": 1675594799696,
-            "content": "How much do you want?"
+            "content": "How much do you want?",
         },
         {
             "sender_name": "User 2",
             "timestamp_ms": 1675577876645,
-            "content": "Goodmorning! $50 is too low."
+            "content": "Goodmorning! $50 is too low.",
         },
         {
             "sender_name": "User 1",
             "timestamp_ms": 1675549022673,
-            "content": "Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!"
-        }
+            "content": "Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!",
+        },
     ],
     "title": "User 1 and User 2 chat",
     "is_still_participant": true,
     "thread_path": "inbox/User 1 and User 2 chat",
     "magic_words": [],
     "image": {"uri": "image_of_the_chat.jpg", "creation_timestamp": 1675549016},
-    "joinable_mode": {"mode": 1, "link": ""}
+    "joinable_mode": {"mode": 1, "link": ""},
 }