Add decorator

.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

@@ -142,10 +142,3 @@ wandb/
 # asdf tool versions
 .tool-versions
 /.ruff_cache/
-
-*.pkl
-*.bin
-
-# integration test artifacts
-data_map*
-\[('_type', 'fake'), ('stop', None)]

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/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/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"
    ]
   },
   {

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/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/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.
 
@@ -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
@@ -344,9 +350,9 @@ 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 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:
@@ -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
@@ -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

@@ -63,10 +63,6 @@ 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.
@@ -93,8 +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
@@ -159,8 +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.
 
 
@@ -177,5 +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/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_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/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

@@ -9,30 +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 or validation for expected parameters.\n",
     "\n",
-    "The function that should be called when the tool is selected should return a single string.\n",
+    "The function that should be called when the tool is selected should take as input a single string and return a single string.\n",
     "\n",
     "There are two ways to define a tool, we will cover both in the example below."
    ]
   },
   {
    "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, Tool, initialize_agent, tool\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.tools import BaseTool"
+    "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"
    ]
   },
   {
@@ -45,14 +43,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "36ed392e",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "llm = ChatOpenAI(temperature=0)"
+    "llm = OpenAI(temperature=0)"
    ]
   },
   {
@@ -78,9 +74,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "56ff7670",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Load the tool configs that are needed.\n",
@@ -92,31 +86,19 @@
     "        func=search.run,\n",
     "        description=\"useful for when you need to answer questions about current events\"\n",
     "    ),\n",
-    "]\n",
-    "# You can also define an args_schema to provide more information about inputs\n",
-    "from pydantic import BaseModel, Field\n",
-    "\n",
-    "class CalculatorInput(BaseModel):\n",
-    "    question: str = Field()\n",
-    "        \n",
-    "\n",
-    "tools.append(\n",
     "    Tool(\n",
     "        name=\"Calculator\",\n",
     "        func=llm_math_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about math\",\n",
-    "        args_schema=CalculatorInput\n",
+    "        description=\"useful for when you need to answer questions about math\"\n",
     "    )\n",
-    ")"
+    "]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "id": "5b93047d",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
@@ -128,9 +110,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "6f96a891",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -139,22 +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\u001b[36;1m\u001b[1;3mDiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years.\u001b[0m\u001b[32;1m\u001b[1;3mI need to find out Camila Morrone's current age\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age raised to the 0.43 power\n",
       "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",
-      "\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
-      "Final Answer: 3.991298452658078\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -162,7 +149,7 @@
     {
      "data": {
       "text/plain": [
-       "'3.991298452658078'"
+       "\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
       ]
      },
      "execution_count": 5,
@@ -184,15 +171,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "c58a7c40",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Type\n",
-    "\n",
     "class CustomSearchTool(BaseTool):\n",
     "    name = \"Search\"\n",
     "    description = \"useful for when you need to answer questions about current events\"\n",
@@ -208,7 +191,6 @@
     "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) -> str:\n",
     "        \"\"\"Use the tool.\"\"\"\n",
@@ -221,11 +203,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "3318a46f",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "tools = [CustomSearchTool(), CustomCalculatorTool()]"
@@ -233,11 +213,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "ee2d0f3a",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
@@ -245,11 +223,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "6a2cebbf",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -258,22 +234,29 @@
       "\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\u001b[36;1m\u001b[1;3mDiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years.\u001b[0m\u001b[32;1m\u001b[1;3mI need to find out Camila Morrone's current age\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now need to calculate her age raised to the 0.43 power\n",
       "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",
-      "\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
-      "Final Answer: 3.991298452658078\u001b[0m\n",
+      "\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -281,10 +264,10 @@
     {
      "data": {
       "text/plain": [
-       "'3.991298452658078'"
+       "\"Camila Morrone's age raised to the 0.43 power is 3.777824273683966.\""
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -305,11 +288,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "8f15307d",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.agents import tool\n",
@@ -317,24 +298,22 @@
     "@tool\n",
     "def search_api(query: str) -> str:\n",
     "    \"\"\"Searches the API for the query.\"\"\"\n",
-    "    return f\"Results for query {query}\""
+    "    return \"Results\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "id": "0a23b91b",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd664c0>, coroutine=None)"
+       "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": 11,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -353,11 +332,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "28cdf04d",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "@tool(\"search\", return_direct=True)\n",
@@ -368,62 +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,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "search_api"
-   ]
-  },
-  {
-   "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,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -444,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "id": "79213f40",
    "metadata": {},
    "outputs": [],
@@ -454,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "id": "e1067dcb",
    "metadata": {},
    "outputs": [],
@@ -464,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "id": "6c66ffe8",
    "metadata": {},
    "outputs": [],
@@ -474,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "id": "f45b5bc3",
    "metadata": {},
    "outputs": [],
@@ -484,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "id": "565e2b9b",
    "metadata": {},
    "outputs": [
@@ -495,12 +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\u001b[36;1m\u001b[1;3mI draw the lime at going to get a Mohawk, though.\" DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid.\u001b[0m\u001b[32;1m\u001b[1;3mNow I need to find out Camila Morrone's current age.\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
+      "Action: Google Search\n",
+      "Action Input: \"Camila Morrone age\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
       "Action: Calculator\n",
-      "Action Input: 25^0.43\u001b[0m\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\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",
+      "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"
      ]
@@ -508,10 +449,10 @@
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
+       "\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\""
       ]
      },
-     "execution_count": 18,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -537,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "id": "3450512e",
    "metadata": {},
    "outputs": [],
@@ -566,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "id": "4b9a7849",
    "metadata": {},
    "outputs": [
@@ -579,7 +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"
@@ -591,7 +534,7 @@
        "\"'All I Want For Christmas Is You' by Mariah Carey.\""
       ]
      },
-     "execution_count": 20,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -611,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "id": "3bb6185f",
    "metadata": {},
    "outputs": [],
@@ -629,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "id": "113ddb84",
    "metadata": {},
    "outputs": [],
@@ -640,11 +583,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "id": "582439a6",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -655,7 +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"
      ]
@@ -663,10 +606,10 @@
     {
      "data": {
       "text/plain": [
-       "'Answer: 1.086734862526058'"
+       "'Answer: 1.2599210498948732'"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -675,149 +618,10 @@
     "agent.run(\"whats 2**.12\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "8aa3c353-bd89-467c-9c27-b83a90cd4daa",
-   "metadata": {},
-   "source": [
-    "## Multi-argument tools\n",
-    "\n",
-    "Many functions expect structured inputs. These can also be supported using the Tool decorator or by directly subclassing `BaseTool`! We have to modify the LLM's OutputParser to map its string output to a dictionary to pass to the action, however."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "537bc628",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from typing import Optional, Union\n",
-    "\n",
-    "@tool\n",
-    "def custom_search(k: int, query: str, other_arg: Optional[str] = None):\n",
-    "    \"\"\"The custom search function.\"\"\"\n",
-    "    return f\"Here are the results for the custom search: k={k}, query={query}, other_arg={other_arg}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "d5c992cf-776a-40cd-a6c4-e7cf65ea709e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "from langchain.schema import (\n",
-    "    AgentAction,\n",
-    "    AgentFinish,\n",
-    ")\n",
-    "from langchain.agents import AgentOutputParser\n",
-    "\n",
-    "# We will add a custom parser to map the arguments to a dictionary\n",
-    "class CustomOutputParser(AgentOutputParser):\n",
-    "    \n",
-    "    def parse_tool_input(self, action_input: str) -> dict:\n",
-    "        # Regex pattern to match arguments and their values\n",
-    "        pattern = r\"(\\w+)\\s*=\\s*(None|\\\"[^\\\"]*\\\"|\\d+)\"\n",
-    "        matches = re.findall(pattern, action_input)\n",
-    "        \n",
-    "        if not matches:\n",
-    "            raise ValueError(f\"Could not parse action input: `{action_input}`\")\n",
-    "\n",
-    "        # Create a dictionary with the parsed arguments and their values\n",
-    "        parsed_input = {}\n",
-    "        for arg, value in matches:\n",
-    "            if value == \"None\":\n",
-    "                parsed_value = None\n",
-    "            elif value.isdigit():\n",
-    "                parsed_value = int(value)\n",
-    "            else:\n",
-    "                parsed_value = value.strip('\"')\n",
-    "            parsed_input[arg] = parsed_value\n",
-    "\n",
-    "        return parsed_input\n",
-    "    \n",
-    "    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
-    "        # Check if agent should finish\n",
-    "        if \"Final Answer:\" in llm_output:\n",
-    "            return AgentFinish(\n",
-    "                # Return values is generally always a dictionary with a single `output` key\n",
-    "                # It is not recommended to try anything else at the moment :)\n",
-    "                return_values={\"output\": llm_output.split(\"Final Answer:\")[-1].strip()},\n",
-    "                log=llm_output,\n",
-    "            )\n",
-    "        # Parse out the action and action input\n",
-    "        regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
-    "        match = re.search(regex, llm_output, re.DOTALL)\n",
-    "        if not match:\n",
-    "            raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
-    "        action = match.group(1).strip()\n",
-    "        action_input = match.group(2)\n",
-    "        tool_input = self.parse_tool_input(action_input)\n",
-    "        # Return the action and action \n",
-    "        return AgentAction(tool=action, tool_input=tool_input, log=llm_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "68269547-1482-4138-a6ea-58f00b4a9548",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "agent = initialize_agent([custom_search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, agent_kwargs={\"output_parser\": CustomOutputParser()})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "0947835a-691c-4f51-b8f4-6744e0e48ab1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to use a search function to find the answer\n",
-      "Action: custom_search\n",
-      "Action Input: k=1, query=\"me\"\u001b[0m\u001b[36;1m\u001b[1;3mHere are the results for the custom search: k=1, query=me, other_arg=None\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: The results of the custom search for k=1, query=me, other_arg=None.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The results of the custom search for k=1, query=me, other_arg=None.'"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"Search for me and tell me whatever it says\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "caf39c66-102b-42c1-baf2-777a49886ce4",
+   "id": "537bc628",
    "metadata": {},
    "outputs": [],
    "source": []

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

@@ -1,170 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "245a954a",
-   "metadata": {},
-   "source": [
-    "# Arxiv API\n",
-    "\n",
-    "This notebook goes over how to use the `arxiv` component. \n",
-    "\n",
-    "First, you need to install `arxiv` python package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5a7209e",
-   "metadata": {
-    "tags": [],
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install arxiv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "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": 4,
-   "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": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "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": 5,
-   "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": 5,
-     "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": 6,
-   "id": "3580aeeb-086f-45ba-bcdc-b46f5134b3dd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'No good Arxiv Result was found'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = arxiv.run(\"1605.08386WWW\")\n",
-    "docs"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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

@@ -39,27 +39,11 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "apify.ipynb\n",
-            "arxiv.ipynb\n",
             "bash.ipynb\n",
-            "bing_search.ipynb\n",
-            "chatgpt_plugins.ipynb\n",
-            "ddg.ipynb\n",
-            "google_places.ipynb\n",
             "google_search.ipynb\n",
-            "google_serper.ipynb\n",
-            "gradio_tools.ipynb\n",
-            "human_tools.ipynb\n",
-            "ifttt.ipynb\n",
-            "openweathermap.ipynb\n",
             "python.ipynb\n",
             "requests.ipynb\n",
-            "search_tools.ipynb\n",
-            "searx_search.ipynb\n",
             "serpapi.ipynb\n",
-            "wikipedia.ipynb\n",
-            "wolfram_alpha.ipynb\n",
-            "zapier.ipynb\n",
             "\n"
           ]
         }
@@ -68,95 +52,10 @@
         "print(bash.run(\"ls\"))"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "e7896f8e",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "apify.ipynb\n",
-            "arxiv.ipynb\n",
-            "bash.ipynb\n",
-            "bing_search.ipynb\n",
-            "chatgpt_plugins.ipynb\n",
-            "ddg.ipynb\n",
-            "google_places.ipynb\n",
-            "google_search.ipynb\n",
-            "google_serper.ipynb\n",
-            "gradio_tools.ipynb\n",
-            "human_tools.ipynb\n",
-            "ifttt.ipynb\n",
-            "openweathermap.ipynb\n",
-            "python.ipynb\n",
-            "requests.ipynb\n",
-            "search_tools.ipynb\n",
-            "searx_search.ipynb\n",
-            "serpapi.ipynb\n",
-            "wikipedia.ipynb\n",
-            "wolfram_alpha.ipynb\n",
-            "zapier.ipynb\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "bash.run(\"cd ..\")\n",
-        "# The commands are executed in a new subprocess each time, meaning that\n",
-        "# this call will return the same results as the last.\n",
-        "print(bash.run(\"ls\"))"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "id": "851fee9f",
-      "metadata": {},
-      "source": [
-        "## Terminal Persistance\n",
-        "\n",
-        "By default, the bash command will be executed in a new subprocess each time. To retain a persistent bash session, we can use the `persistent=True` arg."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "4a93ea2c",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "bash = BashProcess(persistent=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "a1e98b78",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "custom_tools.ipynb\t\tmulti_input_tool.ipynb\n",
-            "examples\t\t\ttool_input_validation.ipynb\n",
-            "getting_started.md\n"
-          ]
-        }
-      ],
-      "source": [
-        "bash.run(\"cd ..\")\n",
-        "# Note the list of files is different\n",
-        "print(bash.run(\"ls\"))"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
-      "id": "e13c1c9c",
+      "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/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/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/llm_bash.ipynb

@@ -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"
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -107,8 +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"
@@ -120,7 +120,7 @@
        "'Hello World\\n'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -132,114 +132,6 @@
     "\n",
     "bash_chain.run(text)"
    ]
-  },
-  {
-   "attachments": {},
-   "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": 4,
-   "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": 4,
-     "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_bash_process(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": 5,
-   "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": 5,
-     "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": "3.8.16"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

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,
@@ -133,201 +114,46 @@
     }
    ],
    "source": [
-    "llm_chain.generate(input_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a178173b-b183-432a-a517-250fe3191173",
-   "metadata": {},
-   "source": [
-    "- `predict` is similar to `run` method except in 2 ways:\n",
-    "    - Input key is specified as keyword argument instead of a Python dict\n",
-    "    - It supports multiple input keys."
-   ]
-  },
-  {
-   "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"
     }
@@ -335,6 +161,14 @@
    "source": [
     "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f0adbc7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -353,7 +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"
      ]
     }
    ],
@@ -124,253 +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)"
-   ]
-  },
-  {
-   "attachments": {},
-   "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."
    ]
   },
   {
@@ -388,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -408,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -418,15 +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"
      ]
     }
    ],
@@ -457,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -491,13 +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": [
     {
@@ -507,9 +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"
      ]
     }
    ],
@@ -555,7 +311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "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"
     }
@@ -738,7 +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

@@ -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/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 fromat."
-   ]
-  },
-  {
-   "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 defaul 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": null,
-   "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": 2,
-   "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": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "doc[0].metadata  # meta-information of the Document"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "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": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "doc[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/blockchain.ipynb

@@ -1,444 +0,0 @@
-{
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vm8vn9t8DvC_"
-      },
-      "source": [
-        "# Blockchain Document Loader"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5WjXERXzFEhg"
-      },
-      "source": [
-        "## Overview"
-      ]
-    },
-    {
-      "attachments": {},
-      "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",
-        "\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",
-        "To run this notebook, the user will need:\n",
-        "\n",
-        "\n",
-        "*   An OpenAI key (for OpenAI models)\n",
-        "*   A free [Alchemy API Key](https://www.alchemy.com/)\n",
-        "\n",
-        "\n",
-        "\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 48,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        }
-      ],
-      "source": [
-        "%pip install langchain -q"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 49,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain.document_loaders import BlockchainDocumentLoader\n",
-        "from langchain.document_loaders.blockchain import BlockchainType\n",
-        "import os"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 50,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "alchemyApiKey = \"get your own key from https://www.alchemy.com/\" \n",
-        "os.environ[\"ALCHEMY_API_KEY\"] = alchemyApiKey"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nzuPWRaBNCMx"
-      },
-      "source": [
-        "## Create a Blockchain Document Loader"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Option 1: Ethereum Mainnet (default BlockchainType)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "J3LWHARC-Kn0"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[Document(page_content=\"{'contract': {'address': '0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d'}, 'id': {'tokenId': '0x0000000000000000000000000000000000000000000000000000000000000000', 'tokenMetadata': {'tokenType': 'ERC721'}}, 'title': '', 'description': '', 'tokenUri': {'gateway': 'https://alchemy.mypinata.cloud/ipfs/QmeSjSinHpPnmXmspMjwiXyN6zS4E9zccariGR3jxcaWtq/0', 'raw': 'ipfs://QmeSjSinHpPnmXmspMjwiXyN6zS4E9zccariGR3jxcaWtq/0'}, 'media': [{'gateway': 'https://nft-cdn.alchemy.com/eth-mainnet/415d618f5fef7bfe683e02d4653c4289', 'thumbnail': 'https://res.cloudinary.com/alchemyapi/image/upload/thumbnailv2/eth-mainnet/415d618f5fef7bfe683e02d4653c4289', 'raw': 'ipfs://QmRRPWG96cmgTn2qSzjwr2qvfNEuhunv6FNeMFGa9bx6mQ', 'format': 'png', 'bytes': 133270}], 'metadata': {'image': 'ipfs://QmRRPWG96cmgTn2qSzjwr2qvfNEuhunv6FNeMFGa9bx6mQ', 'attributes': [{'value': 'Silver Hoop', 'trait_type': 'Earring'}, {'value': 'Orange', 'trait_type': 'Background'}, {'value': 'Robot', 'trait_type': 'Fur'}, {'value': 'Striped Tee', 'trait_type': 'Clothes'}, {'value': 'Discomfort', 'trait_type': 'Mouth'}, {'value': 'X Eyes', 'trait_type': 'Eyes'}]}, 'timeLastUpdated': '2023-04-18T04:05:27.817Z', 'contractMetadata': {'name': 'BoredApeYachtClub', 'symbol': 'BAYC', 'totalSupply': '10000', 'tokenType': 'ERC721', 'contractDeployer': '0xaba7161a7fb69c88e16ed9f455ce62b791ee4d03', 'deployedBlockNumber': 12287507, 'openSea': {'floorPrice': 68.16, 'collectionName': 'Bored Ape Yacht Club', 'safelistRequestStatus': 'verified', 'imageUrl': 'https://i.seadn.io/gae/Ju9CkWtV-1Okvf45wo8UctR-M9He2PjILP0oOvxE89AyiPPGtrR3gysu1Zgy0hjd2xKIgjJJtWIc0ybj4Vd7wv8t3pxDGHoJBzDB?w=500&auto=format', 'description': 'The Bored Ape Yacht Club is a collection of 10,000 unique Bored Ape NFTs— unique digital collectibles living on the Ethereum blockchain. Your Bored Ape doubles as your Yacht Club membership card, and grants access to members-only benefits, the first of which is access to THE BATHROOM, a collaborative graffiti board. Future areas and perks can be unlocked by the community through roadmap activation. Visit www.BoredApeYachtClub.com for more details.', 'externalUrl': 'http://www.boredapeyachtclub.com/', 'twitterUsername': 'BoredApeYC', 'discordUrl': 'https://discord.gg/3P5K3dzgdB', 'lastIngestedAt': '2023-03-21T03:54:33.000Z'}}}\", metadata={'tokenId': '0x0000000000000000000000000000000000000000000000000000000000000000'}),\n",
-              " Document(page_content=\"{'contract': {'address': '0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d'}, 'id': {'tokenId': '0x0000000000000000000000000000000000000000000000000000000000000001', 'tokenMetadata': {'tokenType': 'ERC721'}}, 'title': '', 'description': '', 'tokenUri': {'gateway': 'https://alchemy.mypinata.cloud/ipfs/QmeSjSinHpPnmXmspMjwiXyN6zS4E9zccariGR3jxcaWtq/1', 'raw': 'ipfs://QmeSjSinHpPnmXmspMjwiXyN6zS4E9zccariGR3jxcaWtq/1'}, 'media': [{'gateway': 'https://nft-cdn.alchemy.com/eth-mainnet/65558a4d0c5b0c56fbc50bf03f55e3fa', 'thumbnail': 'https://res.cloudinary.com/alchemyapi/image/upload/thumbnailv2/eth-mainnet/65558a4d0c5b0c56fbc50bf03f55e3fa', 'raw': 'ipfs://QmPbxeGcXhYQQNgsC6a36dDyYUcHgMLnGKnF8pVFmGsvqi', 'format': 'png', 'bytes': 171425}], 'metadata': {'image': 'ipfs://QmPbxeGcXhYQQNgsC6a36dDyYUcHgMLnGKnF8pVFmGsvqi', 'attributes': [{'value': 'Grin', 'trait_type': 'Mouth'}, {'value': 'Vietnam Jacket', 'trait_type': 'Clothes'}, {'value': 'Orange', 'trait_type': 'Background'}, {'value': 'Blue Beams', 'trait_type': 'Eyes'}, {'value': 'Robot', 'trait_type': 'Fur'}]}, 'timeLastUpdated': '2023-04-24T04:37:37.738Z', 'contractMetadata': {'name': 'BoredApeYachtClub', 'symbol': 'BAYC', 'totalSupply': '10000', 'tokenType': 'ERC721', 'contractDeployer': '0xaba7161a7fb69c88e16ed9f455ce62b791ee4d03', 'deployedBlockNumber': 12287507, 'openSea': {'floorPrice': 68.16, 'collectionName': 'Bored Ape Yacht Club', 'safelistRequestStatus': 'verified', 'imageUrl': 'https://i.seadn.io/gae/Ju9CkWtV-1Okvf45wo8UctR-M9He2PjILP0oOvxE89AyiPPGtrR3gysu1Zgy0hjd2xKIgjJJtWIc0ybj4Vd7wv8t3pxDGHoJBzDB?w=500&auto=format', 'description': 'The Bored Ape Yacht Club is a collection of 10,000 unique Bored Ape NFTs— unique digital collectibles living on the Ethereum blockchain. Your Bored Ape doubles as your Yacht Club membership card, and grants access to members-only benefits, the first of which is access to THE BATHROOM, a collaborative graffiti board. Future areas and perks can be unlocked by the community through roadmap activation. Visit www.BoredApeYachtClub.com for more details.', 'externalUrl': 'http://www.boredapeyachtclub.com/', 'twitterUsername': 'BoredApeYC', 'discordUrl': 'https://discord.gg/3P5K3dzgdB', 'lastIngestedAt': '2023-03-21T03:54:33.000Z'}}}\", metadata={'tokenId': '0x0000000000000000000000000000000000000000000000000000000000000001'})]"
-            ]
-          },
-          "execution_count": 24,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "contractAddress = \"0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d\" # Bored Ape Yacht Club contract address\n",
-        "\n",
-        "blockchainType = BlockchainType.ETH_MAINNET  #default value, optional parameter\n",
-        "\n",
-        "blockchainLoader = BlockchainDocumentLoader(contractAddress)\n",
-        "\n",
-        "nfts = blockchainLoader.load()\n",
-        "\n",
-        "nfts[:2]"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Option 2: Polygon Mainnet"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[Document(page_content=\"{'contract': {'address': '0x448676ffcd0adf2d85c1f0565e8dde6924a9a7d9'}, 'id': {'tokenId': '0x01', 'tokenMetadata': {'tokenType': 'ERC1155'}}, 'title': 'Wyatt Horton #0001', 'description': 'A sleepy capybara', 'tokenUri': {'gateway': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/metadata/1.json', 'raw': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/metadata/1.json'}, 'media': [{'gateway': 'https://nft-cdn.alchemy.com/matic-mainnet/9085e06ff9f6c9074de91801d1c72d26', 'thumbnail': 'https://res.cloudinary.com/alchemyapi/image/upload/thumbnailv2/matic-mainnet/9085e06ff9f6c9074de91801d1c72d26', 'raw': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/images/1.png', 'format': 'png', 'bytes': 769622}], 'metadata': {'name': 'Wyatt Horton #0001', 'description': 'A sleepy capybara', 'image': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/images/1.png', 'attributes': [{'value': 'Avatar', 'trait_type': 'Type'}, {'value': 'Animal', 'trait_type': 'Category'}, {'value': 'Capybara', 'trait_type': 'Class'}, {'value': 'Fall 2022', 'trait_type': 'Collection'}, {'value': 'Furry', 'trait_type': 'Feature'}]}, 'timeLastUpdated': '2023-04-20T14:38:24.947Z', 'contractMetadata': {'name': 'Smoothstack - Avatars', 'symbol': 'SMTH', 'tokenType': 'ERC1155', 'contractDeployer': '0x23075b2523c6563b06920a302a8be4f90ef6e974', 'deployedBlockNumber': 34752389, 'openSea': {'lastIngestedAt': '2023-04-17T20:59:42.000Z'}}}\", metadata={'tokenId': '0x01'}),\n",
-              " Document(page_content=\"{'contract': {'address': '0x448676ffcd0adf2d85c1f0565e8dde6924a9a7d9'}, 'id': {'tokenId': '0x02', 'tokenMetadata': {'tokenType': 'ERC1155'}}, 'title': 'Dylan Leisler #0002', 'description': 'A chipper cat with a big, red bowtie', 'tokenUri': {'gateway': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/metadata/2.json', 'raw': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/metadata/2.json'}, 'media': [{'gateway': 'https://nft-cdn.alchemy.com/matic-mainnet/67c3c7ccef44b32bf2ce758e8e73dbcd', 'thumbnail': 'https://res.cloudinary.com/alchemyapi/image/upload/thumbnailv2/matic-mainnet/67c3c7ccef44b32bf2ce758e8e73dbcd', 'raw': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/images/2.png', 'format': 'png', 'bytes': 1187749}], 'metadata': {'name': 'Dylan Leisler #0002', 'description': 'A chipper cat with a big, red bowtie', 'image': 'https://storage.googleapis.com/minted-nfts/smoothstack/avatars/images/2.png', 'attributes': [{'value': 'Avatar', 'trait_type': 'Type'}, {'value': 'Animal', 'trait_type': 'Category'}, {'value': 'Cat', 'trait_type': 'Class'}, {'value': 'Fall 2022', 'trait_type': 'Collection'}, {'value': 'Red Bowtie', 'trait_type': 'Feature'}]}, 'timeLastUpdated': '2023-04-23T13:38:29.316Z', 'contractMetadata': {'name': 'Smoothstack - Avatars', 'symbol': 'SMTH', 'tokenType': 'ERC1155', 'contractDeployer': '0x23075b2523c6563b06920a302a8be4f90ef6e974', 'deployedBlockNumber': 34752389, 'openSea': {'lastIngestedAt': '2023-04-17T20:59:42.000Z'}}}\", metadata={'tokenId': '0x02'})]"
-            ]
-          },
-          "execution_count": 36,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "contractAddress = \"0x448676ffCd0aDf2D85C1f0565e8dde6924A9A7D9\" # Polygon Mainnet contract address\n",
-        "\n",
-        "blockchainType = BlockchainType.POLYGON_MAINNET \n",
-        "\n",
-        "blockchainLoader = BlockchainDocumentLoader(contractAddress, blockchainType)\n",
-        "\n",
-        "nfts = blockchainLoader.load()\n",
-        "\n",
-        "nfts[:2]"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## (Optional) Using the Blockchain Document Loader"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_PGkFfMCB8J3"
-      },
-      "source": [
-        "### Setup Splitter and Index"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 37,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        }
-      ],
-      "source": [
-        "%pip install sentence_transformers chromadb openai tiktoken -q"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 38,
-      "metadata": {
-        "id": "BwxxopOCCABh"
-      },
-      "outputs": [],
-      "source": [
-        "from langchain.indexes import VectorstoreIndexCreator\n",
-        "from langchain.embeddings import HuggingFaceEmbeddings\n",
-        "from langchain.text_splitter import RecursiveCharacterTextSplitter"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 39,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "JE_myAulCDSZ",
-        "outputId": "99e16b6a-03b4-4e67-d4b4-9dd611a866ef"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "NUMBER OF DOCUMENTS:  424\n"
-          ]
-        }
-      ],
-      "source": [
-        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=400, chunk_overlap=0)\n",
-        "\n",
-        "docs = text_splitter.split_documents(nfts)\n",
-        "print(\"NUMBER OF DOCUMENTS: \", len(docs))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {
-        "id": "d83yFuAuCKQS"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Using embedded DuckDB without persistence: data will be transient\n"
-          ]
-        }
-      ],
-      "source": [
-        "index = VectorstoreIndexCreator(\n",
-        "    embedding=HuggingFaceEmbeddings(),\n",
-        "    text_splitter=text_splitter).from_loaders([blockchainLoader])"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "y0VfObeXDEXB"
-      },
-      "source": [
-        "## Setup Models and Chains"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 42,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "openAiKey = \"put OpenAI key here\"\n",
-        "os.environ[\"OPENAI_API_KEY\"] = openAiKey"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "id": "hiNjDzP9C4pA"
-      },
-      "outputs": [],
-      "source": [
-        "from langchain.chains import RetrievalQA\n",
-        "from langchain.llms import OpenAI"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "u-xDlKPaC_xg"
-      },
-      "source": [
-        "### Retrieval Chain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 43,
-      "metadata": {
-        "id": "BqP00JovC9R4"
-      },
-      "outputs": [],
-      "source": [
-        "llmOpenAI = OpenAI()\n",
-        "\n",
-        "chainQA = RetrievalQA.from_chain_type(llm=llmOpenAI, \n",
-        "                                    chain_type=\"map_reduce\",\n",
-        "                                    retriever=index.vectorstore.as_retriever(), \n",
-        "                                    verbose=True,\n",
-        "                                    input_key=\"question\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 44,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 122
-        },
-        "id": "2Y3cVVKZDVNq",
-        "outputId": "dfeea416-5193-47cf-e9dc-c17a5c1cd780"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "\n",
-            "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
-            "\n",
-            "\u001b[1m> Finished chain.\u001b[0m\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "' Popular attributes include \"Avatar\" (Type), \"Character\" (Category), and \"Human\" or \"Wizard\" (Class).'"
-            ]
-          },
-          "execution_count": 44,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "chainQA.run(\"What are some of the popular attributes?\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 122
-        },
-        "id": "7o6ArPo9DXbz",
-        "outputId": "b1f8ad43-27c7-4cdb-95a7-8c8bd6381c5a"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "\n",
-            "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
-            "\n",
-            "\u001b[1m> Finished chain.\u001b[0m\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            },
-            "text/plain": [
-              "' There are 10,000 unique Bored Ape NFTs.'"
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "chainQA.run(\"How many NFTs are there?\")"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [
-        "5WjXERXzFEhg"
-      ],
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

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

@@ -1,76 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "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": 1,
-   "metadata": {},
-   "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",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

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

@@ -1,66 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Confluence\n",
-    "\n",
-    "A loader for 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": {},
-   "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)\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": "3.9.1"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

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": [],
@@ -68,51 +68,6 @@
     "len(docs)"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e633d62f",
-   "metadata": {},
-   "source": [
-    "## Show a progress bar"
-   ]
-  },
-  {
-   "attachments": {},
-   "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",
@@ -173,69 +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": []
@@ -257,7 +153,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.3"
+   "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",
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-1.94921E+18,2023-04-19T15:14:45.904819+00:00,px ymxfdxqgxjtbqqqegakvrrjxcvvakctfysdhklmwyewlwbb,
-2.36906E+18,2023-04-19T15:14:45.904819+00:00,yqidtvcw  gdkfynaapjuicujgsbjptzytbnbjeyqcjx jyedb,

docs/modules/indexes/document_loaders/examples/example_data/fake_discord_data/package/messages/c279692806442844161/messages.csv

@@ -1,48 +0,0 @@
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docs/modules/indexes/document_loaders/examples/example_data/fake_discord_data/package/messages/c280973436971515906/messages.csv

@@ -1,6 +0,0 @@
-ID,Timestamp,Contents,Attachments
-2.79079E+18,2023-04-19T15:14:45.904819+00:00,cl iqaczcrrlprzvbdtvpmduzrdlmtquejjhjfjnt zdsqyksh,
-1.51164E+18,2023-04-19T15:14:45.904819+00:00,ywvnjmtybk f ghdagriyswf exupccijgl calztfvujxhujt,
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-3.04157E+18,2023-04-19T15:14:45.904819+00:00,nn vitqoscgsiauiezyyficcbgnjyhaujvthdydmoeistkyskl,

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

@@ -16,7 +16,7 @@
     "1. `pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib`\n",
     "\n",
     "## 🧑 Instructions for ingesting your Google Docs data\n",
-    "By default, the `GoogleDriveLoader` expects the `credentials.json` file to be `~/.credentials/credentials.json`, but this is configurable using the `credentials_path` keyword argument. Same thing with `token.json` - `token_path`. Note that `token.json` will be created automatically the first time you use the loader.\n",
+    "By default, the `GoogleDriveLoader` expects the `credentials.json` file to be `~/.credentials/credentials.json`, but this is configurable using the `credentials_file` keyword argument. Same thing with `token.json`. Note that `token.json` will be created automatically the first time you use the loader.\n",
     "\n",
     "`GoogleDriveLoader` can load from a list of Google Docs document ids or a folder id. You can obtain your folder and document id from the URL:\n",
     "* Folder: https://drive.google.com/drive/u/0/folders/1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5 -> folder id is `\"1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5\"`\n",
@@ -44,11 +44,7 @@
    },
    "outputs": [],
    "source": [
-    "loader = GoogleDriveLoader(\n",
-    "    folder_id=\"1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5\",\n",
-    "    # Optional: configure whether to recursively fetch files from subfolders. Defaults to False.\n",
-    "    recursive=False\n",
-    ")"
+    "loader = GoogleDriveLoader(folder_id=\"1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5\")"
    ]
   },
   {

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

@@ -1,220 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "04c9fdc5",
-   "metadata": {},
-   "source": [
-    "# HuggingFace dataset loader \n",
-    "\n",
-    "This notebook shows how to load Hugging Face Hub datasets to LangChain.\n",
-    "\n",
-    "The Hugging Face Hub hosts a large number of community-curated datasets for a diverse range of tasks such as translation, automatic speech recognition, and image classification.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1815c866",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import HuggingFaceDatasetLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "3611e092",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset_name=\"imdb\"\n",
-    "page_content_column=\"text\"\n",
-    "\n",
-    "\n",
-    "loader=HuggingFaceDatasetLoader(dataset_name,page_content_column)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5e903ebc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e8559946",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered \"controversial\" I really had to see this for myself.<br /><br />The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.<br /><br />What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\\'s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.<br /><br />I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\\'t have much of a plot.', metadata={'label': 0}),\n",
-       " Document(page_content='\"I Am Curious: Yellow\" is a risible and pretentious steaming pile. It doesn\\'t matter what one\\'s political views are because this film can hardly be taken seriously on any level. As for the claim that frontal male nudity is an automatic NC-17, that isn\\'t true. I\\'ve seen R-rated films with male nudity. Granted, they only offer some fleeting views, but where are the R-rated films with gaping vulvas and flapping labia? Nowhere, because they don\\'t exist. The same goes for those crappy cable shows: schlongs swinging in the breeze but not a clitoris in sight. And those pretentious indie movies like The Brown Bunny, in which we\\'re treated to the site of Vincent Gallo\\'s throbbing johnson, but not a trace of pink visible on Chloe Sevigny. Before crying (or implying) \"double-standard\" in matters of nudity, the mentally obtuse should take into account one unavoidably obvious anatomical difference between men and women: there are no genitals on display when actresses appears nude, and the same cannot be said for a man. In fact, you generally won\\'t see female genitals in an American film in anything short of porn or explicit erotica. This alleged double-standard is less a double standard than an admittedly depressing ability to come to terms culturally with the insides of women\\'s bodies.', metadata={'label': 0}),\n",
-       " Document(page_content=\"If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.<br /><br />One might feel virtuous for sitting thru it because it touches on so many IMPORTANT issues but it does so without any discernable motive. The viewer comes away with no new perspectives (unless one comes up with one while one's mind wanders, as it will invariably do during this pointless film).<br /><br />One might better spend one's time staring out a window at a tree growing.<br /><br />\", metadata={'label': 0}),\n",
-       " Document(page_content=\"This film was probably inspired by Godard's Masculin, féminin and I urge you to see that film instead.<br /><br />The film has two strong elements and those are, (1) the realistic acting (2) the impressive, undeservedly good, photo. Apart from that, what strikes me most is the endless stream of silliness. Lena Nyman has to be most annoying actress in the world. She acts so stupid and with all the nudity in this film,...it's unattractive. Comparing to Godard's film, intellectuality has been replaced with stupidity. Without going too far on this subject, I would say that follows from the difference in ideals between the French and the Swedish society.<br /><br />A movie of its time, and place. 2/10.\", metadata={'label': 0}),\n",
-       " Document(page_content='Oh, brother...after hearing about this ridiculous film for umpteen years all I can think of is that old Peggy Lee song..<br /><br />\"Is that all there is??\" ...I was just an early teen when this smoked fish hit the U.S. I was too young to get in the theater (although I did manage to sneak into \"Goodbye Columbus\"). Then a screening at a local film museum beckoned - Finally I could see this film, except now I was as old as my parents were when they schlepped to see it!!<br /><br />The ONLY reason this film was not condemned to the anonymous sands of time was because of the obscenity case sparked by its U.S. release. MILLIONS of people flocked to this stinker, thinking they were going to see a sex film...Instead, they got lots of closeups of gnarly, repulsive Swedes, on-street interviews in bland shopping malls, asinie political pretension...and feeble who-cares simulated sex scenes with saggy, pale actors.<br /><br />Cultural icon, holy grail, historic artifact..whatever this thing was, shred it, burn it, then stuff the ashes in a lead box!<br /><br />Elite esthetes still scrape to find value in its boring pseudo revolutionary political spewings..But if it weren\\'t for the censorship scandal, it would have been ignored, then forgotten.<br /><br />Instead, the \"I Am Blank, Blank\" rhythymed title was repeated endlessly for years as a titilation for porno films (I am Curious, Lavender - for gay films, I Am Curious, Black - for blaxploitation films, etc..) and every ten years or so the thing rises from the dead, to be viewed by a new generation of suckers who want to see that \"naughty sex film\" that \"revolutionized the film industry\"...<br /><br />Yeesh, avoid like the plague..Or if you MUST see it - rent the video and fast forward to the \"dirty\" parts, just to get it over with.<br /><br />', metadata={'label': 0}),\n",
-       " Document(page_content=\"I would put this at the top of my list of films in the category of unwatchable trash! There are films that are bad, but the worst kind are the ones that are unwatchable but you are suppose to like them because they are supposed to be good for you! The sex sequences, so shocking in its day, couldn't even arouse a rabbit. The so called controversial politics is strictly high school sophomore amateur night Marxism. The film is self-consciously arty in the worst sense of the term. The photography is in a harsh grainy black and white. Some scenes are out of focus or taken from the wrong angle. Even the sound is bad! And some people call this art?<br /><br />\", metadata={'label': 0}),\n",
-       " Document(page_content=\"Whoever wrote the screenplay for this movie obviously never consulted any books about Lucille Ball, especially her autobiography. I've never seen so many mistakes in a biopic, ranging from her early years in Celoron and Jamestown to her later years with Desi. I could write a whole list of factual errors, but it would go on for pages. In all, I believe that Lucille Ball is one of those inimitable people who simply cannot be portrayed by anyone other than themselves. If I were Lucie Arnaz and Desi, Jr., I would be irate at how many mistakes were made in this film. The filmmakers tried hard, but the movie seems awfully sloppy to me.\", metadata={'label': 0}),\n",
-       " Document(page_content='When I first saw a glimpse of this movie, I quickly noticed the actress who was playing the role of Lucille Ball. Rachel York\\'s portrayal of Lucy is absolutely awful. Lucille Ball was an astounding comedian with incredible talent. To think about a legend like Lucille Ball being portrayed the way she was in the movie is horrendous. I cannot believe out of all the actresses in the world who could play a much better Lucy, the producers decided to get Rachel York. She might be a good actress in other roles but to play the role of Lucille Ball is tough. It is pretty hard to find someone who could resemble Lucille Ball, but they could at least find someone a bit similar in looks and talent. If you noticed York\\'s portrayal of Lucy in episodes of I Love Lucy like the chocolate factory or vitavetavegamin, nothing is similar in any way-her expression, voice, or movement.<br /><br />To top it all off, Danny Pino playing Desi Arnaz is horrible. Pino does not qualify to play as Ricky. He\\'s small and skinny, his accent is unreal, and once again, his acting is unbelievable. Although Fred and Ethel were not similar either, they were not as bad as the characters of Lucy and Ricky.<br /><br />Overall, extremely horrible casting and the story is badly told. If people want to understand the real life situation of Lucille Ball, I suggest watching A&E Biography of Lucy and Desi, read the book from Lucille Ball herself, or PBS\\' American Masters: Finding Lucy. If you want to see a docudrama, \"Before the Laughter\" would be a better choice. The casting of Lucille Ball and Desi Arnaz in \"Before the Laughter\" is much better compared to this. At least, a similar aspect is shown rather than nothing.', metadata={'label': 0}),\n",
-       " Document(page_content='Who are these \"They\"- the actors? the filmmakers? Certainly couldn\\'t be the audience- this is among the most air-puffed productions in existence. It\\'s the kind of movie that looks like it was a lot of fun to shoot\\x97 TOO much fun, nobody is getting any actual work done, and that almost always makes for a movie that\\'s no fun to watch.<br /><br />Ritter dons glasses so as to hammer home his character\\'s status as a sort of doppleganger of the bespectacled Bogdanovich; the scenes with the breezy Ms. Stratten are sweet, but have an embarrassing, look-guys-I\\'m-dating-the-prom-queen feel to them. Ben Gazzara sports his usual cat\\'s-got-canary grin in a futile attempt to elevate the meager plot, which requires him to pursue Audrey Hepburn with all the interest of a narcoleptic at an insomnia clinic. In the meantime, the budding couple\\'s respective children (nepotism alert: Bogdanovich\\'s daughters) spew cute and pick up some fairly disturbing pointers on \\'love\\' while observing their parents. (Ms. Hepburn, drawing on her dignity, manages to rise above the proceedings- but she has the monumental challenge of playing herself, ostensibly.) Everybody looks great, but so what? It\\'s a movie and we can expect that much, if that\\'s what you\\'re looking for you\\'d be better off picking up a copy of Vogue.<br /><br />Oh- and it has to be mentioned that Colleen Camp thoroughly annoys, even apart from her singing, which, while competent, is wholly unconvincing... the country and western numbers are woefully mismatched with the standards on the soundtrack. Surely this is NOT what Gershwin (who wrote the song from which the movie\\'s title is derived) had in mind; his stage musicals of the 20\\'s may have been slight, but at least they were long on charm. \"They All Laughed\" tries to coast on its good intentions, but nobody- least of all Peter Bogdanovich - has the good sense to put on the brakes.<br /><br />Due in no small part to the tragic death of Dorothy Stratten, this movie has a special place in the heart of Mr. Bogdanovich- he even bought it back from its producers, then distributed it on his own and went bankrupt when it didn\\'t prove popular. His rise and fall is among the more sympathetic and tragic of Hollywood stories, so there\\'s no joy in criticizing the film... there _is_ real emotional investment in Ms. Stratten\\'s scenes. But \"Laughed\" is a faint echo of \"The Last Picture Show\", \"Paper Moon\" or \"What\\'s Up, Doc\"- following \"Daisy Miller\" and \"At Long Last Love\", it was a thundering confirmation of the phase from which P.B. has never emerged.<br /><br />All in all, though, the movie is harmless, only a waste of rental. I want to watch people having a good time, I\\'ll go to the park on a sunny day. For filmic expressions of joy and love, I\\'ll stick to Ernest Lubitsch and Jaques Demy...', metadata={'label': 0}),\n",
-       " Document(page_content=\"This is said to be a personal film for Peter Bogdonavitch. He based it on his life but changed things around to fit the characters, who are detectives. These detectives date beautiful models and have no problem getting them. Sounds more like a millionaire playboy filmmaker than a detective, doesn't it? This entire movie was written by Peter, and it shows how out of touch with real people he was. You're supposed to write what you know, and he did that, indeed. And leaves the audience bored and confused, and jealous, for that matter. This is a curio for people who want to see Dorothy Stratten, who was murdered right after filming. But Patti Hanson, who would, in real life, marry Keith Richards, was also a model, like Stratten, but is a lot better and has a more ample part. In fact, Stratten's part seemed forced; added. She doesn't have a lot to do with the story, which is pretty convoluted to begin with. All in all, every character in this film is somebody that very few people can relate with, unless you're millionaire from Manhattan with beautiful supermodels at your beckon call. For the rest of us, it's an irritating snore fest. That's what happens when you're out of touch. You entertain your few friends with inside jokes, and bore all the rest.\", metadata={'label': 0}),\n",
-       " Document(page_content='It was great to see some of my favorite stars of 30 years ago including John Ritter, Ben Gazarra and Audrey Hepburn. They looked quite wonderful. But that was it. They were not given any characters or good lines to work with. I neither understood or cared what the characters were doing.<br /><br />Some of the smaller female roles were fine, Patty Henson and Colleen Camp were quite competent and confident in their small sidekick parts. They showed some talent and it is sad they didn\\'t go on to star in more and better films. Sadly, I didn\\'t think Dorothy Stratten got a chance to act in this her only important film role.<br /><br />The film appears to have some fans, and I was very open-minded when I started watching it. I am a big Peter Bogdanovich fan and I enjoyed his last movie, \"Cat\\'s Meow\" and all his early ones from \"Targets\" to \"Nickleodeon\". So, it really surprised me that I was barely able to keep awake watching this one.<br /><br />It is ironic that this movie is about a detective agency where the detectives and clients get romantically involved with each other. Five years later, Bogdanovich\\'s ex-girlfriend, Cybil Shepherd had a hit television series called \"Moonlighting\" stealing the story idea from Bogdanovich. Of course, there was a great difference in that the series relied on tons of witty dialogue, while this tries to make do with slapstick and a few screwball lines.<br /><br />Bottom line: It ain\\'t no \"Paper Moon\" and only a very pale version of \"What\\'s Up, Doc\".', metadata={'label': 0}),\n",
-       " Document(page_content=\"I can't believe that those praising this movie herein aren't thinking of some other film. I was prepared for the possibility that this would be awful, but the script (or lack thereof) makes for a film that's also pointless. On the plus side, the general level of craft on the part of the actors and technical crew is quite competent, but when you've got a sow's ear to work with you can't make a silk purse. Ben G fans should stick with just about any other movie he's been in. Dorothy S fans should stick to Galaxina. Peter B fans should stick to Last Picture Show and Target. Fans of cheap laughs at the expense of those who seem to be asking for it should stick to Peter B's amazingly awful book, Killing of the Unicorn.\", metadata={'label': 0}),\n",
-       " Document(page_content='Never cast models and Playboy bunnies in your films! Bob Fosse\\'s \"Star 80\" about Dorothy Stratten, of whom Bogdanovich was obsessed enough to have married her SISTER after her murder at the hands of her low-life husband, is a zillion times more interesting than Dorothy herself on the silver screen. Patty Hansen is no actress either..I expected to see some sort of lost masterpiece a la Orson Welles but instead got Audrey Hepburn cavorting in jeans and a god-awful \"poodlesque\" hair-do....Very disappointing....\"Paper Moon\" and \"The Last Picture Show\" I could watch again and again. This clunker I could barely sit through once. This movie was reputedly not released because of the brouhaha surrounding Ms. Stratten\\'s tawdry death; I think the real reason was because it was so bad!', metadata={'label': 0}),\n",
-       " Document(page_content=\"Its not the cast. A finer group of actors, you could not find. Its not the setting. The director is in love with New York City, and by the end of the film, so are we all! Woody Allen could not improve upon what Bogdonovich has done here. If you are going to fall in love, or find love, Manhattan is the place to go. No, the problem with the movie is the script. There is none. The actors fall in love at first sight, words are unnecessary. In the director's own experience in Hollywood that is what happens when they go to work on the set. It is reality to him, and his peers, but it is a fantasy to most of us in the real world. So, in the end, the movie is hollow, and shallow, and message-less.\", metadata={'label': 0}),\n",
-       " Document(page_content='Today I found \"They All Laughed\" on VHS on sale in a rental. It was a really old and very used VHS, I had no information about this movie, but I liked the references listed on its cover: the names of Peter Bogdanovich, Audrey Hepburn, John Ritter and specially Dorothy Stratten attracted me, the price was very low and I decided to risk and buy it. I searched IMDb, and the User Rating of 6.0 was an excellent reference. I looked in \"Mick Martin & Marsha Porter Video & DVD Guide 2003\" and \\x96 wow \\x96 four stars! So, I decided that I could not waste more time and immediately see it. Indeed, I have just finished watching \"They All Laughed\" and I found it a very boring overrated movie. The characters are badly developed, and I spent lots of minutes to understand their roles in the story. The plot is supposed to be funny (private eyes who fall in love for the women they are chasing), but I have not laughed along the whole story. The coincidences, in a huge city like New York, are ridiculous. Ben Gazarra as an attractive and very seductive man, with the women falling for him as if her were a Brad Pitt, Antonio Banderas or George Clooney, is quite ridiculous. In the end, the greater attractions certainly are the presence of the Playboy centerfold and playmate of the year Dorothy Stratten, murdered by her husband pretty after the release of this movie, and whose life was showed in \"Star 80\" and \"Death of a Centerfold: The Dorothy Stratten Story\"; the amazing beauty of the sexy Patti Hansen, the future Mrs. Keith Richards; the always wonderful, even being fifty-two years old, Audrey Hepburn; and the song \"Amigo\", from Roberto Carlos. Although I do not like him, Roberto Carlos has been the most popular Brazilian singer since the end of the 60\\'s and is called by his fans as \"The King\". I will keep this movie in my collection only because of these attractions (manly Dorothy Stratten). My vote is four.<br /><br />Title (Brazil): \"Muito Riso e Muita Alegria\" (\"Many Laughs and Lots of Happiness\")', metadata={'label': 0})]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[:15]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "021bc377",
-   "metadata": {},
-   "source": [
-    "### Example \n",
-    "In this example, we use data from a dataset to answer a question"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d924885c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.indexes import VectorstoreIndexCreator\n",
-    "from langchain.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "f94ce6a3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset_name=\"tweet_eval\"\n",
-    "page_content_column=\"text\"\n",
-    "name=\"stance_climate\"\n",
-    "\n",
-    "\n",
-    "loader=HuggingFaceDatasetLoader(dataset_name,page_content_column,name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "abb51899",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Found cached dataset tweet_eval\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b10969d08df4e6792eaafc6d41fe366",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using embedded DuckDB without persistence: data will be transient\n"
-     ]
-    }
-   ],
-   "source": [
-    "index = VectorstoreIndexCreator().from_loaders([loader])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "c0108277",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"What are the most used hashtag?\"\n",
-    "result = index.query(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "548b6e56",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' The most used hashtags in this context are #UKClimate2015, #Sustainability, #TakeDownTheFlag, #LoveWins, #CSOTA, #ClimateSummitoftheAmericas, #SM, and #SocialMedia.'"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "89c30c2d",
-   "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/indexes/document_loaders/examples/obsidian.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "1dc7df1d",
    "metadata": {},
@@ -9,9 +8,7 @@
     "# Obsidian\n",
     "This notebook covers how to load documents from an Obsidian database.\n",
     "\n",
-    "Since Obsidian is just stored on disk as a folder of Markdown files, the loader just takes a path to this directory.\n",
-    "\n",
-    "Obsidian files also sometimes contain [metadata](https://help.obsidian.md/Editing+and+formatting/Metadata) which is a YAML block at the top of the file. These values will be added to the document's metadata. (`ObsidianLoader` can also be passed a `collect_metadata=False` argument to disable this behavior.)"
+    "Since Obsidian is just stored on disk as a folder of Markdown files, the loader just takes a path to this directory."
    ]
   },
   {

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

@@ -40,7 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = ReadTheDocsLoader(\"rtdocs\", features='html.parser')"
+    "loader = ReadTheDocsLoader(\"rtdocs\")"
    ]
   },
   {

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

@@ -1,114 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "66a7777e",
-   "metadata": {},
-   "source": [
-    "# Twitter\n",
-    "\n",
-    "This loader fetches the text from the Tweets of a list of Twitter users, using the `tweepy` Python package.\n",
-    "You must initialize the loader with your Twitter API token, and you need to pass in the Twitter username you want to extract."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "9ec8a3b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import TwitterTweetLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "43128d8d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!pip install tweepy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "35d6809a",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "loader = TwitterTweetLoader.from_bearer_token(\n",
-    "    oauth2_bearer_token=\"YOUR BEARER TOKEN\",\n",
-    "    twitter_users=['elonmusk'],\n",
-    "    number_tweets=50,  # Default value is 100\n",
-    ")\n",
-    "\n",
-    "# Or load from access token and consumer keys\n",
-    "# loader = TwitterTweetLoader.from_secrets(\n",
-    "#     access_token='YOUR ACCESS TOKEN',\n",
-    "#     access_token_secret='YOUR ACCESS TOKEN SECRET',\n",
-    "#     consumer_key='YOUR CONSUMER KEY',\n",
-    "#     consumer_secret='YOUR CONSUMER SECRET',\n",
-    "#     twitter_users=['elonmusk'],\n",
-    "#     number_tweets=50,\n",
-    "# )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "05fe33b9",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='@MrAndyNgo @REI One store after another shutting down', metadata={'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ngô 🏳️\\u200d🌈', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, 'lang': 'en'}, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/44196397/1576183471', 'profile_link_color': '0084B4', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': None, 'follow_request_sent': None, 'notifications': None, 'translator_type': 'none', 'withheld_in_countries': []}}),\n",
-       " Document(page_content='@KanekoaTheGreat @joshrogin @glennbeck Large ships are fundamentally vulnerable to ballistic (hypersonic) missiles', metadata={'created_at': 'Tue Apr 18 03:43:25 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ngô 🏳️\\u200d🌈', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, 'lang': 'en'}, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/44196397/1576183471', 'profile_link_color': '0084B4', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': None, 'follow_request_sent': None, 'notifications': None, 'translator_type': 'none', 'withheld_in_countries': []}}),\n",
-       " Document(page_content='@KanekoaTheGreat The Golden Rule', metadata={'created_at': 'Tue Apr 18 03:37:17 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ngô 🏳️\\u200d🌈', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, 'lang': 'en'}, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/44196397/1576183471', 'profile_link_color': '0084B4', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': None, 'follow_request_sent': None, 'notifications': None, 'translator_type': 'none', 'withheld_in_countries': []}}),\n",
-       " Document(page_content='@KanekoaTheGreat 🧐', metadata={'created_at': 'Tue Apr 18 03:35:48 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ngô 🏳️\\u200d🌈', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, 'lang': 'en'}, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/44196397/1576183471', 'profile_link_color': '0084B4', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': None, 'follow_request_sent': None, 'notifications': None, 'translator_type': 'none', 'withheld_in_countries': []}}),\n",
-       " Document(page_content='@TRHLofficial What’s he talking about and why is it sponsored by Erik’s son?', metadata={'created_at': 'Tue Apr 18 03:32:17 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ngô 🏳️\\u200d🌈', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, 'lang': 'en'}, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/44196397/1576183471', 'profile_link_color': '0084B4', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': None, 'follow_request_sent': None, 'notifications': None, 'translator_type': 'none', 'withheld_in_countries': []}})]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "documents = loader.load()\n",
-    "documents[:5]"
-   ]
-  }
- ],
- "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/indexes/document_loaders/examples/unstructured_file.ipynb

@@ -163,7 +163,7 @@
    "source": [
     "## Define a Partitioning Strategy\n",
     "\n",
-    "Unstructured document loader allow users to pass in a `strategy` parameter that lets `unstructured` know how to partition the document. Currently supported strategies are `\"hi_res\"` (the default) and `\"fast\"`. Hi res partitioning strategies are more accurate, but take longer to process. Fast strategies partition the document more quickly, but trade-off accuracy. Not all document types have separate hi res and fast partitioning strategies. For those document types, the `strategy` kwarg is ignored. In some cases, the high res strategy will fallback to fast if there is a dependency missing (i.e. a model for document partitioning). You can see how to apply a strategy to an `UnstructuredFileLoader` below."
+    "Unstructured document loader allow users to pass in a `strategy` parameter that lets `unstructured` know how to partitioning the document. Currently supported strategies are `\"hi_res\"` (the default) and `\"fast\"`. Hi res partitioning strategies are more accurate, but take longer to process. Fast strategies partition the document more quickly, but trade-off accuracy. Not all document types have separate hi res and fast partitioning strategies. For those document types, the `strategy` kwarg is ignored. In some cases, the high res strategy will fallback to fast if there is a dependency missing (i.e. a model for document partitioning). You can see how to apply a strategy to an `UnstructuredFileLoader` below."
    ]
   },
   {

docs/modules/indexes/retrievers/examples/contextual-compression.ipynb

@@ -1,371 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fc0db1bc",
-   "metadata": {},
-   "source": [
-    "# Contextual Compression Retriever\n",
-    "\n",
-    "This notebook introduces the concept of DocumentCompressors and the ContextualCompressionRetriever. The core idea is simple: given a specific query, we should be able to return only the documents relevant to that query, and only the parts of those documents that are relevant. The ContextualCompressionsRetriever is a wrapper for another retriever that iterates over the initial output of the base retriever and filters and compresses those initial documents, so that only the most relevant information is returned."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "28e8dc12",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Helper function for printing docs\n",
-    "\n",
-    "def pretty_print_docs(docs):\n",
-    "    print(f\"\\n{'-' * 100}\\n\".join([f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6fa3d916",
-   "metadata": {},
-   "source": [
-    "## Using a vanilla vector store retriever\n",
-    "Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can see that given an example question our retriever returns one or two relevant docs and a few irrelevant docs. And even the relevant docs have a lot of irrelevant information in them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9fbcc58f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Document 1:\n",
-      "\n",
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 2:\n",
-      "\n",
-      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
-      "\n",
-      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
-      "\n",
-      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
-      "\n",
-      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
-      "\n",
-      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
-      "\n",
-      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 3:\n",
-      "\n",
-      "And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n",
-      "\n",
-      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
-      "\n",
-      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n",
-      "\n",
-      "And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n",
-      "\n",
-      "So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \n",
-      "\n",
-      "First, beat the opioid epidemic.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 4:\n",
-      "\n",
-      "Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n",
-      "\n",
-      "And as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \n",
-      "\n",
-      "That ends on my watch. \n",
-      "\n",
-      "Medicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n",
-      "\n",
-      "We’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n",
-      "\n",
-      "Let’s pass the Paycheck Fairness Act and paid leave.  \n",
-      "\n",
-      "Raise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n",
-      "\n",
-      "Let’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.document_loaders import TextLoader\n",
-    "from langchain.vectorstores import FAISS\n",
-    "\n",
-    "documents = TextLoader('../../../state_of_the_union.txt').load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "texts = text_splitter.split_documents(documents)\n",
-    "retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever()\n",
-    "\n",
-    "docs = retriever.get_relevant_documents(\"What did the president say about Ketanji Brown Jackson\")\n",
-    "pretty_print_docs(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7648612",
-   "metadata": {},
-   "source": [
-    "## Adding contextual compression with an `LLMChainExtractor`\n",
-    "Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll add an `LLMChainExtractor`, which will iterate over the initially returned documents and extract from each only the content that is relevant to the query."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9a658023",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Document 1:\n",
-      "\n",
-      "\"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\"\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 2:\n",
-      "\n",
-      "\"A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.retrievers import ContextualCompressionRetriever\n",
-    "from langchain.retrievers.document_compressors import LLMChainExtractor\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "compressor = LLMChainExtractor.from_llm(llm)\n",
-    "compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)\n",
-    "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
-    "pretty_print_docs(compressed_docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2cd38f3a",
-   "metadata": {},
-   "source": [
-    "## More built-in compressors: filters\n",
-    "### `LLMChainFilter`\n",
-    "The `LLMChainFilter` is slightly simpler but more robust compressor that uses an LLM chain to decide which of the initially retrieved documents to filter out and which ones to return, without manipulating the document contents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b216a767",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Document 1:\n",
-      "\n",
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.retrievers.document_compressors import LLMChainFilter\n",
-    "\n",
-    "_filter = LLMChainFilter.from_llm(llm)\n",
-    "compression_retriever = ContextualCompressionRetriever(base_compressor=_filter, base_retriever=retriever)\n",
-    "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
-    "pretty_print_docs(compressed_docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8c709598",
-   "metadata": {},
-   "source": [
-    "### `EmbeddingsFilter`\n",
-    "\n",
-    "Making an extra LLM call over each retrieved document is expensive and slow. The `EmbeddingsFilter` provides a cheaper and faster option by embedding the documents and query and only returning those documents which have sufficiently similar embeddings to the query."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "6fbc801f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Document 1:\n",
-      "\n",
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 2:\n",
-      "\n",
-      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
-      "\n",
-      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
-      "\n",
-      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
-      "\n",
-      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
-      "\n",
-      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
-      "\n",
-      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 3:\n",
-      "\n",
-      "And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n",
-      "\n",
-      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
-      "\n",
-      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n",
-      "\n",
-      "And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n",
-      "\n",
-      "So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \n",
-      "\n",
-      "First, beat the opioid epidemic.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.retrievers.document_compressors import EmbeddingsFilter\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "embeddings_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)\n",
-    "compression_retriever = ContextualCompressionRetriever(base_compressor=embeddings_filter, base_retriever=retriever)\n",
-    "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
-    "pretty_print_docs(compressed_docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07365d36",
-   "metadata": {},
-   "source": [
-    "# Stringing compressors and document transformers together\n",
-    "Using the `DocumentCompressorPipeline` we can also easily combine multiple compressors in sequence. Along with compressors we can add `BaseDocumentTransformer`s to our pipeline, which don't perform any contextual compression but simply perform some transformation on a set of documents. For example `TextSplitter`s can be used as document transformers to split documents into smaller pieces, and the `EmbeddingsRedundantFilter` can be used to filter out redundant documents based on embedding similarity between documents.\n",
-    "\n",
-    "Below we create a compressor pipeline by first splitting our docs into smaller chunks, then removing redundant documents, and then filtering based on relevance to the query."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2a150a63",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_transformers import EmbeddingsRedundantFilter\n",
-    "from langchain.retrievers.document_compressors import DocumentCompressorPipeline\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "\n",
-    "splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0, separator=\". \")\n",
-    "redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)\n",
-    "relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)\n",
-    "pipeline_compressor = DocumentCompressorPipeline(\n",
-    "    transformers=[splitter, redundant_filter, relevant_filter]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3ceab64a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Document 1:\n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 2:\n",
-      "\n",
-      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
-      "\n",
-      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 3:\n",
-      "\n",
-      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder\n"
-     ]
-    }
-   ],
-   "source": [
-    "compression_retriever = ContextualCompressionRetriever(base_compressor=pipeline_compressor, base_retriever=retriever)\n",
-    "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\"What did the president say about Ketanji Jackson Brown\")\n",
-    "pretty_print_docs(compressed_docs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8cfd9fc5",
-   "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/indexes/retrievers/examples/metal.ipynb

@@ -32,9 +32,9 @@
     "from metal_sdk.metal import Metal\n",
     "API_KEY = \"\"\n",
     "CLIENT_ID = \"\"\n",
-    "INDEX_ID = \"\"\n",
+    "APP_ID = \"\"\n",
     "\n",
-    "metal = Metal(API_KEY, CLIENT_ID, INDEX_ID);\n"
+    "metal = Metal(API_KEY, CLIENT_ID, APP_ID);\n"
    ]
   },
   {

docs/modules/indexes/retrievers/examples/self_query_retriever.ipynb

@@ -1,330 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "13afcae7",
-   "metadata": {},
-   "source": [
-    "# Self-querying retriever\n",
-    "In the notebook we'll demo the `SelfQueryRetriever`, which, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to it's underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documented, but to also extract filters from the user query on the metadata of stored documents and to execute those filter."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "68e75fb9",
-   "metadata": {},
-   "source": [
-    "## Creating a Pinecone index\n",
-    "First we'll want to create a Pinecone VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
-    "\n",
-    "NOTE: The self-query retriever currently only has built-in support for Pinecone VectorStore.\n",
-    "\n",
-    "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "63a8af5b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !pip install lark"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3eb9c9a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
-      "  from tqdm.autonotebook import tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "import pinecone\n",
-    "\n",
-    "\n",
-    "pinecone.init(api_key=os.environ[\"PINECONE_API_KEY\"], environment=os.environ[\"PINECONE_ENV\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cb4a5787",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.schema import Document\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import Pinecone\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "# create new index\n",
-    "pinecone.create_index(\"langchain-self-retriever-demo\", dimension=1536)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "bcbe04d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs = [\n",
-    "    Document(page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\", metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": [\"action\", \"science fiction\"]}),\n",
-    "    Document(page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\", metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2}),\n",
-    "    Document(page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\", metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6}),\n",
-    "    Document(page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\", metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3}),\n",
-    "    Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"year\": 1995, \"genre\": \"animated\"}),\n",
-    "    Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"year\": 1979, \"rating\": 9.9, \"director\": \"Andrei Tarkovsky\", \"genre\": [\"science fiction\", \"thriller\"], \"rating\": 9.9})\n",
-    "]\n",
-    "vectorstore = Pinecone.from_documents(\n",
-    "    docs, embeddings, index_name=\"langchain-self-retriever-demo\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ecaab6d",
-   "metadata": {},
-   "source": [
-    "# Creating our self-querying retriever\n",
-    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "86e34dbf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
-    "from langchain.chains.query_constructor.base import AttributeInfo\n",
-    "\n",
-    "metadata_field_info=[\n",
-    "    AttributeInfo(\n",
-    "        name=\"genre\",\n",
-    "        description=\"The genre of the movie\", \n",
-    "        type=\"string or list[string]\", \n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"year\",\n",
-    "        description=\"The year the movie was released\", \n",
-    "        type=\"integer\", \n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"director\",\n",
-    "        description=\"The name of the movie director\", \n",
-    "        type=\"string\", \n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"rating\",\n",
-    "        description=\"A 1-10 rating for the movie\",\n",
-    "        type=\"float\"\n",
-    "    ),\n",
-    "]\n",
-    "document_content_description = \"Brief summary of a movie\"\n",
-    "llm = OpenAI(temperature=0)\n",
-    "retriever = SelfQueryRetriever.from_llm(llm, vectorstore, document_content_description, metadata_field_info, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea9df8d4",
-   "metadata": {},
-   "source": [
-    "# Testing it out\n",
-    "And now we can try actually using our retriever!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "38a126e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query='dinosaur' filter=None\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': ['action', 'science fiction'], 'rating': 7.7, 'year': 1993.0}),\n",
-       " Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0}),\n",
-       " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),\n",
-       " Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'director': 'Christopher Nolan', 'rating': 8.2, 'year': 2010.0})]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "fc3f1e6e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),\n",
-       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': ['science fiction', 'thriller'], 'rating': 9.9, 'year': 1979.0})]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "b19d4da0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig')\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'director': 'Greta Gerwig', 'rating': 8.3, 'year': 2019.0})]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "f900e40e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)])\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': ['science fiction', 'thriller'], 'rating': 9.9, 'year': 1979.0})]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\"What's a highly rated (above 8.5) science fiction film?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "12a51522",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990.0), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005.0), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')])\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0})]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example specifies a query and composite filter\n",
-    "retriever.get_relevant_documents(\"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "69bbd809",
-   "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/indexes/retrievers/examples/time_weighted_vectorstore.ipynb

@@ -1,210 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a90b7557",
-   "metadata": {},
-   "source": [
-    "# Time Weighted VectorStore Retriever\n",
-    "\n",
-    "This retriever uses a combination of semantic similarity and recency.\n",
-    "\n",
-    "The algorithm for scoring them is:\n",
-    "\n",
-    "```\n",
-    "semantic_similarity + (1.0 - decay_rate) ** hours_passed\n",
-    "```\n",
-    "\n",
-    "Notably, hours_passed refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain \"fresh.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f22cc96b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import faiss\n",
-    "\n",
-    "from datetime import datetime, timedelta\n",
-    "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.retrievers import TimeWeightedVectorStoreRetriever\n",
-    "from langchain.schema import Document\n",
-    "from langchain.vectorstores import FAISS\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6af7ea6b",
-   "metadata": {},
-   "source": [
-    "## Low Decay Rate\n",
-    "\n",
-    "A low decay rate (in this, to be extreme, we will set close to 0) means memories will be \"remembered\" for longer. A decay rate of 0 means memories never be forgotten, making this retriever equivalent to the vector lookup."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c10e7696",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define your embedding model\n",
-    "embeddings_model = OpenAIEmbeddings()\n",
-    "# Initialize the vectorstore as empty\n",
-    "embedding_size = 1536\n",
-    "index = faiss.IndexFlatL2(embedding_size)\n",
-    "vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})\n",
-    "retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, decay_rate=.0000000000000000000000001, k=1) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "86dbadb9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['5c9f7c06-c9eb-45f2-aea5-efce5fb9f2bd']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "yesterday = datetime.now() - timedelta(days=1)\n",
-    "retriever.add_documents([Document(page_content=\"hello world\", metadata={\"last_accessed_at\": yesterday})])\n",
-    "retriever.add_documents([Document(page_content=\"hello foo\")])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a580be32",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='hello world', metadata={'last_accessed_at': datetime.datetime(2023, 4, 16, 22, 9, 1, 966261), 'created_at': datetime.datetime(2023, 4, 16, 22, 9, 0, 374683), 'buffer_idx': 0})]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# \"Hello World\" is returned first because it is most salient, and the decay rate is close to 0., meaning it's still recent enough\n",
-    "retriever.get_relevant_documents(\"hello world\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca056896",
-   "metadata": {},
-   "source": [
-    "## High Decay Rate\n",
-    "\n",
-    "With a high decay factor (e.g., several 9's), the recency score quickly goes to 0! If you set this all the way to 1, recency is 0 for all objects, once again making this equivalent to a vector lookup.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "dc37669b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define your embedding model\n",
-    "embeddings_model = OpenAIEmbeddings()\n",
-    "# Initialize the vectorstore as empty\n",
-    "embedding_size = 1536\n",
-    "index = faiss.IndexFlatL2(embedding_size)\n",
-    "vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})\n",
-    "retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, decay_rate=.999, k=1) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "fa284384",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['40011466-5bbe-4101-bfd1-e22e7f505de2']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "yesterday = datetime.now() - timedelta(days=1)\n",
-    "retriever.add_documents([Document(page_content=\"hello world\", metadata={\"last_accessed_at\": yesterday})])\n",
-    "retriever.add_documents([Document(page_content=\"hello foo\")])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7558f94d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='hello foo', metadata={'last_accessed_at': datetime.datetime(2023, 4, 16, 22, 9, 2, 494798), 'created_at': datetime.datetime(2023, 4, 16, 22, 9, 2, 178722), 'buffer_idx': 1})]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# \"Hello Foo\" is returned first because \"hello world\" is mostly forgotten\n",
-    "retriever.get_relevant_documents(\"hello world\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf6d8c90",
-   "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/indexes/vectorstores/examples/analyticdb.ipynb

@@ -1,162 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# AnalyticDB\n",
-    "\n",
-    "This notebook shows how to use functionality related to the AnalyticDB vector database.\n",
-    "To run, you should have an [AnalyticDB](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview) instance up and running:\n",
-    "- Using [AnalyticDB Cloud Vector Database](https://www.alibabacloud.com/product/hybriddb-postgresql). Click here to fast deploy it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import AnalyticDB"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Split documents and get embeddings by call OpenAI API"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../../../state_of_the_union.txt')\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "docs = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Connect to AnalyticDB by setting related ENVIRONMENTS.\n",
-    "```\n",
-    "export PG_HOST={your_analyticdb_hostname}\n",
-    "export PG_PORT={your_analyticdb_port} # Optional, default is 5432\n",
-    "export PG_DATABASE={your_database} # Optional, default is postgres\n",
-    "export PG_USER={database_username}\n",
-    "export PG_PASSWORD={database_password}\n",
-    "```\n",
-    "\n",
-    "Then store your embeddings and documents into AnalyticDB"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "connection_string = AnalyticDB.connection_string_from_db_params(\n",
-    "    driver=os.environ.get(\"PG_DRIVER\", \"psycopg2cffi\"),\n",
-    "    host=os.environ.get(\"PG_HOST\", \"localhost\"),\n",
-    "    port=int(os.environ.get(\"PG_PORT\", \"5432\")),\n",
-    "    database=os.environ.get(\"PG_DATABASE\", \"postgres\"),\n",
-    "    user=os.environ.get(\"PG_USER\", \"postgres\"),\n",
-    "    password=os.environ.get(\"PG_PASSWORD\", \"postgres\"),\n",
-    ")\n",
-    "\n",
-    "vector_db = AnalyticDB.from_documents(\n",
-    "    docs,\n",
-    "    embeddings,\n",
-    "    connection_string= connection_string,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Query and retrieve data"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = vector_db.similarity_search(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].page_content)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

docs/modules/indexes/vectorstores/examples/annoy.ipynb

@@ -1,572 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "683953b3",
-   "metadata": {},
-   "source": [
-    "# Annoy\n",
-    "\n",
-    "This notebook shows how to use functionality related to the Annoy vector database.\n",
-    "\n",
-    "> \"Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.\"\n",
-    "\n",
-    "via [Annoy](https://github.com/spotify/annoy) \n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "3b450bdc",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "Annoy is read-only - once the index is built you cannot add any more emebddings!\n",
-    "If you want to progressively add to your VectorStore then better choose an alternative!\n",
-    "```"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "6613d222",
-   "metadata": {},
-   "source": [
-    "## Create VectorStore from texts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "dc7351b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings import HuggingFaceEmbeddings\n",
-    "from langchain.vectorstores import Annoy\n",
-    "\n",
-    "embeddings_func = HuggingFaceEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d2cb5f7d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "texts = [\"pizza is great\", \"I love salad\", \"my car\", \"a dog\"]\n",
-    "\n",
-    "# default metric is angular\n",
-    "vector_store = Annoy.from_texts(texts, embeddings_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a856b2d1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# allows for custom annoy parameters, defaults are n_trees=100, n_jobs=-1, metric=\"angular\"\n",
-    "vector_store_v2 = Annoy.from_texts(\n",
-    "    texts, embeddings_func, metric=\"dot\", n_trees=100, n_jobs=1\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8ada534a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='pizza is great', metadata={}),\n",
-       " Document(page_content='I love salad', metadata={}),\n",
-       " Document(page_content='my car', metadata={})]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vector_store.similarity_search(\"food\", k=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0470c5c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(Document(page_content='pizza is great', metadata={}), 1.0944390296936035),\n",
-       " (Document(page_content='I love salad', metadata={}), 1.1273186206817627),\n",
-       " (Document(page_content='my car', metadata={}), 1.1580758094787598)]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# the score is a distance metric, so lower is better\n",
-    "vector_store.similarity_search_with_score(\"food\", k=3)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "4583b231",
-   "metadata": {},
-   "source": [
-    "## Create VectorStore from docs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fbe898a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "\n",
-    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "docs = text_splitter.split_documents(documents)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "51ea6b5c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \\n\\nLast year COVID-19 kept us apart. This year we are finally together again. \\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \\n\\nWith a duty to one another to the American people to the Constitution. \\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \\n\\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \\n\\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \\n\\nHe met the Ukrainian people. \\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
-       " Document(page_content='Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \\n\\nIn this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. \\n\\nLet each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. \\n\\nPlease rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. \\n\\nThroughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos.   \\n\\nThey keep moving.   \\n\\nAnd the costs and the threats to America and the world keep rising.   \\n\\nThat’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2. \\n\\nThe United States is a member along with 29 other nations. \\n\\nIt matters. American diplomacy matters. American resolve matters.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
-       " Document(page_content='Putin’s latest attack on Ukraine was premeditated and unprovoked. \\n\\nHe rejected repeated efforts at diplomacy. \\n\\nHe thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready.  Here is what we did.   \\n\\nWe prepared extensively and carefully. \\n\\nWe spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin. \\n\\nI spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression.  \\n\\nWe countered Russia’s lies with truth.   \\n\\nAnd now that he has acted the free world is holding him accountable. \\n\\nAlong with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
-       " Document(page_content='We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. \\n\\nTogether with our allies –we are right now enforcing powerful economic sanctions. \\n\\nWe are cutting off Russia’s largest banks from the international financial system.  \\n\\nPreventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless.   \\n\\nWe are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come.  \\n\\nTonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. \\n\\nThe U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.  \\n\\nWe are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
-       " Document(page_content='And tonight I am announcing that we will join our allies in closing off American air space to all Russian flights – further isolating Russia – and adding an additional squeeze –on their economy. The Ruble has lost 30% of its value. \\n\\nThe Russian stock market has lost 40% of its value and trading remains suspended. Russia’s economy is reeling and Putin alone is to blame. \\n\\nTogether with our allies we are providing support to the Ukrainians in their fight for freedom. Military assistance. Economic assistance. Humanitarian assistance. \\n\\nWe are giving more than $1 Billion in direct assistance to Ukraine. \\n\\nAnd we will continue to aid the Ukrainian people as they defend their country and to help ease their suffering.  \\n\\nLet me be clear, our forces are not engaged and will not engage in conflict with Russian forces in Ukraine.  \\n\\nOur forces are not going to Europe to fight in Ukraine, but to defend our NATO Allies – in the event that Putin decides to keep moving west.', metadata={'source': '../../../state_of_the_union.txt'})]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "d080985b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vector_store_from_docs = Annoy.from_documents(docs, embeddings_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "4931cb99",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = vector_store_from_docs.similarity_search(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "97969d5b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Ac\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].page_content[:100])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79628542",
-   "metadata": {},
-   "source": [
-    "## Create VectorStore via existing embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3432eddb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embs = embeddings_func.embed_documents(texts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "b69f8408",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = list(zip(texts, embs))\n",
-    "\n",
-    "vector_store_from_embeddings = Annoy.from_embeddings(data, embeddings_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e260758d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(Document(page_content='pizza is great', metadata={}), 1.0944390296936035),\n",
-       " (Document(page_content='I love salad', metadata={}), 1.1273186206817627),\n",
-       " (Document(page_content='my car', metadata={}), 1.1580758094787598)]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vector_store_from_embeddings.similarity_search_with_score(\"food\", k=3)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "341390c2",
-   "metadata": {},
-   "source": [
-    "## Search via embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "b9bce06d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "motorbike_emb = embeddings_func.embed_query(\"motorbike\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "af2552c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='my car', metadata={}),\n",
-       " Document(page_content='a dog', metadata={}),\n",
-       " Document(page_content='pizza is great', metadata={})]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vector_store.similarity_search_by_vector(motorbike_emb, k=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "c7a1a924",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(Document(page_content='my car', metadata={}), 1.0870471000671387),\n",
-       " (Document(page_content='a dog', metadata={}), 1.2095637321472168),\n",
-       " (Document(page_content='pizza is great', metadata={}), 1.3254905939102173)]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vector_store.similarity_search_with_score_by_vector(motorbike_emb, k=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b77be77",
-   "metadata": {},
-   "source": [
-    "## Search via docstore id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "bbd971f0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: '2d1498a8-a37c-4798-acb9-0016504ed798',\n",
-       " 1: '2d30aecc-88e0-4469-9d51-0ef7e9858e6d',\n",
-       " 2: '927f1120-985b-4691-b577-ad5cb42e011c',\n",
-       " 3: '3056ddcf-a62f-48c8-bd98-b9e57a3dfcae'}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vector_store.index_to_docstore_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "6dbf3365",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(page_content='pizza is great', metadata={})"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "some_docstore_id = 0  # texts[0]\n",
-    "\n",
-    "vector_store.docstore._dict[vector_store.index_to_docstore_id[some_docstore_id]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "98b27172",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(Document(page_content='pizza is great', metadata={}), 0.0),\n",
-       " (Document(page_content='I love salad', metadata={}), 1.0734446048736572),\n",
-       " (Document(page_content='my car', metadata={}), 1.2895267009735107)]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# same document has distance 0\n",
-    "vector_store.similarity_search_with_score_by_index(some_docstore_id, k=3)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "6f570f69",
-   "metadata": {},
-   "source": [
-    "## Save and load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "ef91cc69",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "saving config\n"
-     ]
-    }
-   ],
-   "source": [
-    "vector_store.save_local(\"my_annoy_index_and_docstore\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "7a9d2fce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loaded_vector_store = Annoy.load_local(\n",
-    "    \"my_annoy_index_and_docstore\", embeddings=embeddings_func\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "bba77cae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(Document(page_content='pizza is great', metadata={}), 0.0),\n",
-       " (Document(page_content='I love salad', metadata={}), 1.0734446048736572),\n",
-       " (Document(page_content='my car', metadata={}), 1.2895267009735107)]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# same document has distance 0\n",
-    "loaded_vector_store.similarity_search_with_score_by_index(some_docstore_id, k=3)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "df4beb83",
-   "metadata": {},
-   "source": [
-    "## Construct from scratch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "26fcf742",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import uuid\n",
-    "from annoy import AnnoyIndex\n",
-    "from langchain.docstore.document import Document\n",
-    "from langchain.docstore.in_memory import InMemoryDocstore\n",
-    "\n",
-    "metadatas = [{\"x\": \"food\"}, {\"x\": \"food\"}, {\"x\": \"stuff\"}, {\"x\": \"animal\"}]\n",
-    "\n",
-    "# embeddings\n",
-    "embeddings = embeddings_func.embed_documents(texts)\n",
-    "\n",
-    "# embedding dim\n",
-    "f = len(embeddings[0])\n",
-    "\n",
-    "# index\n",
-    "metric = \"angular\"\n",
-    "index = AnnoyIndex(f, metric=metric)\n",
-    "for i, emb in enumerate(embeddings):\n",
-    "    index.add_item(i, emb)\n",
-    "index.build(10)\n",
-    "\n",
-    "# docstore\n",
-    "documents = []\n",
-    "for i, text in enumerate(texts):\n",
-    "    metadata = metadatas[i] if metadatas else {}\n",
-    "    documents.append(Document(page_content=text, metadata=metadata))\n",
-    "index_to_docstore_id = {i: str(uuid.uuid4()) for i in range(len(documents))}\n",
-    "docstore = InMemoryDocstore(\n",
-    "    {index_to_docstore_id[i]: doc for i, doc in enumerate(documents)}\n",
-    ")\n",
-    "\n",
-    "db_manually = Annoy(\n",
-    "    embeddings_func.embed_query, index, metric, docstore, index_to_docstore_id\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "2b3f6f5c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(Document(page_content='pizza is great', metadata={'x': 'food'}),\n",
-       "  1.1314140558242798),\n",
-       " (Document(page_content='I love salad', metadata={'x': 'food'}),\n",
-       "  1.1668788194656372),\n",
-       " (Document(page_content='my car', metadata={'x': 'stuff'}), 1.226445198059082)]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db_manually.similarity_search_with_score(\"eating!\", k=3)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/indexes/vectorstores/examples/deeplake.ipynb

@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,24 +60,16 @@
     "embeddings = OpenAIEmbeddings()"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Creates a dataset locally at `./deeplake/`, then runs similiarity search "
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "./my_deeplake/ loaded successfully.\n"
+      "mem://langchain loaded successfully.\n"
      ]
     },
     {
@@ -91,7 +83,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset(path='./my_deeplake/', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+      "Dataset(path='mem://langchain', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
       "\n",
       "  tensor     htype     shape     dtype  compression\n",
       "  -------   -------   -------   -------  ------- \n",
@@ -103,17 +95,15 @@
     }
    ],
    "source": [
-    "db = DeepLake(dataset_path=\"./my_deeplake/\", embedding_function=embeddings, overwrite=True)\n",
-    "db.add_documents(docs)\n",
-    "# or shorter\n",
-    "# db = DeepLake.from_documents(docs, dataset_path=\"./my_deeplake/\", embedding=embeddings, overwrite=True)\n",
+    "db = DeepLake.from_documents(docs, embeddings)\n",
+    "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "docs = db.similarity_search(query)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -134,62 +124,6 @@
     "print(docs[0].page_content)"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Later, you can reload the dataset without recomputing embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "./my_deeplake/ loaded successfully.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Deep Lake Dataset in ./my_deeplake/ already exists, loading from the storage\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='./my_deeplake/', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (4, 1536)  float32   None   \n",
-      "    ids      text     (4, 1)      str     None   \n",
-      " metadata    json     (4, 1)      str     None   \n",
-      "   text      text     (4, 1)      str     None   \n"
-     ]
-    }
-   ],
-   "source": [
-    "db = DeepLake(dataset_path=\"./my_deeplake/\", embedding_function=embeddings, read_only=True)\n",
-    "docs = db.similarity_search(query)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Deep Lake, for now, is single writer and multiple reader. Setting `read_only=True` helps to avoid acquring the writer lock."
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -200,14 +134,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/media/sdb/davit/Git/experiments/langchain/langchain/llms/openai.py:672: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
+      "/media/sdb/davit/.local/lib/python3.10/site-packages/langchain/llms/openai.py:624: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
       "  warnings.warn(\n"
      ]
     }
@@ -221,16 +155,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\"The president nominated Ketanji Brown Jackson to serve on the United States Supreme Court, describing her as one of the nation's top legal minds and a consensus builder with a background in private practice and public defense, and noting that she has received broad support from both Democrats and Republicans.\""
+       "'The president nominated Circuit Court of Appeals Judge Ketanji Brown Jackson for the United States Supreme Court and praised her qualifications and broad support from both Democrats and Republicans.'"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -250,14 +184,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "./my_deeplake/ loaded successfully.\n"
+      "mem://langchain loaded successfully.\n"
      ]
     },
     {
@@ -271,14 +205,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset(path='./my_deeplake/', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+      "Dataset(path='mem://langchain', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
       "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (4, 1536)  float32   None   \n",
-      "    ids      text     (4, 1)      str     None   \n",
-      " metadata    json     (4, 1)      str     None   \n",
-      "   text      text     (4, 1)      str     None   \n"
+      "  tensor     htype     shape      dtype  compression\n",
+      "  -------   -------   -------    -------  ------- \n",
+      " embedding  generic  (42, 1536)  float32   None   \n",
+      "    ids      text     (42, 1)      str     None   \n",
+      " metadata    json     (42, 1)      str     None   \n",
+      "   text      text     (42, 1)      str     None   \n"
      ]
     },
     {
@@ -293,29 +227,31 @@
     "for d in docs:\n",
     "    d.metadata['year'] = random.randint(2012, 2014)\n",
     "\n",
-    "db = DeepLake.from_documents(docs, embeddings, dataset_path=\"./my_deeplake/\", overwrite=True)"
+    "db = DeepLake.from_documents(docs, embeddings)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [00:00<00:00, 1080.24it/s]\n"
+      "100%|██████████| 42/42 [00:00<00:00, 3456.17it/s]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013})]"
+       "[Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
+       " Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
+       " Document(page_content='Vice President Harris and I ran for office with a new economic vision for America. \\n\\nInvest in America. Educate Americans. Grow the workforce. Build the economy from the bottom up  \\nand the middle out, not from the top down.  \\n\\nBecause we know that when the middle class grows, the poor have a ladder up and the wealthy do very well. \\n\\nAmerica used to have the best roads, bridges, and airports on Earth. \\n\\nNow our infrastructure is ranked 13th in the world. \\n\\nWe won’t be able to compete for the jobs of the 21st Century if we don’t fix that. \\n\\nThat’s why it was so important to pass the Bipartisan Infrastructure Law—the most sweeping investment to rebuild America in history. \\n\\nThis was a bipartisan effort, and I want to thank the members of both parties who worked to make it happen. \\n\\nWe’re done talking about infrastructure weeks. \\n\\nWe’re going to have an infrastructure decade.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
+       " Document(page_content='It is going to transform America and put us on a path to win the economic competition of the 21st Century that we face with the rest of the world—particularly with China.  \\n\\nAs I’ve told Xi Jinping, it is never a good bet to bet against the American people. \\n\\nWe’ll create good jobs for millions of Americans, modernizing roads, airports, ports, and waterways all across America. \\n\\nAnd we’ll do it all to withstand the devastating effects of the climate crisis and promote environmental justice. \\n\\nWe’ll build a national network of 500,000 electric vehicle charging stations, begin to replace poisonous lead pipes—so every child—and every American—has clean water to drink at home and at school, provide affordable high-speed internet for every American—urban, suburban, rural, and tribal communities. \\n\\n4,000 projects have already been announced. \\n\\nAnd tonight, I’m announcing that this year we will start fixing over 65,000 miles of highway and 1,500 bridges in disrepair.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013})]"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -335,19 +271,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),\n",
+       "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),\n",
+       " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
        " Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012})]"
+       " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2014})]"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -367,19 +303,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),\n",
-       " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),\n",
-       " Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2013})]"
+       "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),\n",
+       " Document(page_content='One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. \\n\\nWhen they came home, many of the world’s fittest and best trained warriors were never the same. \\n\\nHeadaches. Numbness. Dizziness. \\n\\nA cancer that would put them in a flag-draped coffin. \\n\\nI know. \\n\\nOne of those soldiers was my son Major Beau Biden. \\n\\nWe don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \\n\\nBut I’m committed to finding out everything we can. \\n\\nCommitted to military families like Danielle Robinson from Ohio. \\n\\nThe widow of Sergeant First Class Heath Robinson.  \\n\\nHe was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \\n\\nStationed near Baghdad, just yards from burn pits the size of football fields. \\n\\nHeath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2014}),\n",
+       " Document(page_content='As Ohio Senator Sherrod Brown says, “It’s time to bury the label “Rust Belt.” \\n\\nIt’s time. \\n\\nBut with all the bright spots in our economy, record job growth and higher wages, too many families are struggling to keep up with the bills.  \\n\\nInflation is robbing them of the gains they might otherwise feel. \\n\\nI get it. That’s why my top priority is getting prices under control. \\n\\nLook, our economy roared back faster than most predicted, but the pandemic meant that businesses had a hard time hiring enough workers to keep up production in their factories. \\n\\nThe pandemic also disrupted global supply chains. \\n\\nWhen factories close, it takes longer to make goods and get them from the warehouse to the store, and prices go up. \\n\\nLook at cars. \\n\\nLast year, there weren’t enough semiconductors to make all the cars that people wanted to buy. \\n\\nAnd guess what, prices of automobiles went up. \\n\\nSo—we have a choice. \\n\\nOne way to fight inflation is to drive down wages and make Americans poorer.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012}),\n",
+       " Document(page_content='We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \\n\\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \\n\\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \\n\\nOfficer Mora was 27 years old. \\n\\nOfficer Rivera was 22. \\n\\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \\n\\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \\n\\nI’ve worked on these issues a long time. \\n\\nI know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../state_of_the_union.txt', 'year': 2012})]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,46 +324,6 @@
     "db.max_marginal_relevance_search('What did the president say about Ketanji Brown Jackson?')"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Delete dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "db.delete_dataset()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "and if delete fails you can also force delete"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    }
-   ],
-   "source": [
-    "DeepLake.force_delete_by_path(\"./my_deeplake\")"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -439,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -448,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -456,16 +352,27 @@
      "output_type": "stream",
      "text": [
       "Your Deep Lake dataset has been successfully created!\n",
-      "The dataset is private so make sure you are logged in!\n",
-      "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test\n",
-      "hub://davitbun/langchain_test loaded successfully.\n"
+      "The dataset is private so make sure you are logged in!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\\"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/linkedin\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Evaluating ingest: 100%|██████████| 1/1 [00:14<00:00\n",
       " \r"
      ]
     },
@@ -473,43 +380,50 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset(path='hub://davitbun/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (4, 1536)  float32   None   \n",
-      "    ids      text     (4, 1)      str     None   \n",
-      " metadata    json     (4, 1)      str     None   \n",
-      "   text      text     (4, 1)      str     None   \n"
+      "hub://davitbun/linkedin loaded successfully.\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "['d6d6ccb4-e187-11ed-b66d-41c5f7b85421',\n",
-       " 'd6d6ccb5-e187-11ed-b66d-41c5f7b85421',\n",
-       " 'd6d6ccb6-e187-11ed-b66d-41c5f7b85421',\n",
-       " 'd6d6ccb7-e187-11ed-b66d-41c5f7b85421']"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating ingest: 100%|██████████| 1/1 [00:23<00:00\n",
+      "/"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset(path='hub://davitbun/linkedin', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+      "\n",
+      "  tensor     htype     shape      dtype  compression\n",
+      "  -------   -------   -------    -------  ------- \n",
+      " embedding  generic  (42, 1536)  float32   None   \n",
+      "    ids      text     (42, 1)      str     None   \n",
+      " metadata    json     (42, 1)      str     None   \n",
+      "   text      text     (42, 1)      str     None   \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " \r"
+     ]
     }
    ],
    "source": [
     "# Embed and store the texts\n",
-    "username = \"<username>\" # your username on app.activeloop.ai   \n",
-    "dataset_path = f\"hub://{username}/langchain_test\" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
+    "dataset_path = f\"hub://{USERNAME}/{DATASET_NAME}\" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
     "\n",
     "embedding = OpenAIEmbeddings()\n",
-    "db = DeepLake(dataset_path=dataset_path, embedding_function=embeddings, overwrite=True)\n",
-    "db.add_documents(docs)"
+    "vectordb = DeepLake.from_documents(documents=docs, embedding=embedding, dataset_path=dataset_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -532,263 +446,37 @@
     "print(docs[0].page_content)"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating dataset on AWS S3"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "s3://hub-2.0-datasets-n/langchain_test loaded successfully.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Evaluating ingest: 100%|██████████| 1/1 [00:10<00:00\n",
-      "\\"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+      "Dataset(path='hub://davitbun/linkedin', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
       "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (4, 1536)  float32   None   \n",
-      "    ids      text     (4, 1)      str     None   \n",
-      " metadata    json     (4, 1)      str     None   \n",
-      "   text      text     (4, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " \r"
+      "  tensor     htype     shape      dtype  compression\n",
+      "  -------   -------   -------    -------  ------- \n",
+      " embedding  generic  (42, 1536)  float32   None   \n",
+      "    ids      text     (42, 1)      str     None   \n",
+      " metadata    json     (42, 1)      str     None   \n",
+      "   text      text     (42, 1)      str     None   \n"
      ]
     }
    ],
    "source": [
-    "dataset_path = f\"s3://BUCKET/langchain_test\" # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
-    "\n",
-    "embedding = OpenAIEmbeddings()\n",
-    "db = DeepLake.from_documents(docs, dataset_path=dataset_path, embedding=embeddings, overwrite=True, creds = {\n",
-    "   'aws_access_key_id': os.environ['AWS_ACCESS_KEY_ID'], \n",
-    "   'aws_secret_access_key':  os.environ['AWS_SECRET_ACCESS_KEY'], \n",
-    "   'aws_session_token': os.environ['AWS_SESSION_TOKEN'], # Optional\n",
-    "})"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deep Lake API\n",
-    "you can access the Deep Lake  dataset at `db.ds`"
+    "vectordb.ds.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://davitbun/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (4, 1536)  float32   None   \n",
-      "    ids      text     (4, 1)      str     None   \n",
-      " metadata    json     (4, 1)      str     None   \n",
-      "   text      text     (4, 1)      str     None   \n"
-     ]
-    }
-   ],
-   "source": [
-    "# get structure of the dataset\n",
-    "db.ds.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# get embeddings numpy array\n",
-    "embeds = db.ds.embedding.numpy()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Transfer local dataset to cloud\n",
-    "Copy already created dataset to the cloud. You can also transfer from cloud to local."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Copying dataset: 100%|██████████| 56/56 [00:38<00:00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n",
-      "Your Deep Lake dataset has been successfully created!\n",
-      "The dataset is private so make sure you are logged in!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import deeplake\n",
-    "username = \"davitbun\" # your username on app.activeloop.ai   \n",
-    "source = f\"hub://{username}/langchain_test\" # could be local, s3, gcs, etc.\n",
-    "destination = f\"hub://{username}/langchain_test_copy\" # could be local, s3, gcs, etc.\n",
-    "\n",
-    "deeplake.deepcopy(src=source, dest=destination, overwrite=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hub://davitbun/langchain_test_copy loaded successfully.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (4, 1536)  float32   None   \n",
-      "    ids      text     (4, 1)      str     None   \n",
-      " metadata    json     (4, 1)      str     None   \n",
-      "   text      text     (4, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Evaluating ingest: 100%|██████████| 1/1 [00:31<00:00\n",
-      "-"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (8, 1536)  float32   None   \n",
-      "    ids      text     (8, 1)      str     None   \n",
-      " metadata    json     (8, 1)      str     None   \n",
-      "   text      text     (8, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " \r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['ad42f3fe-e188-11ed-b66d-41c5f7b85421',\n",
-       " 'ad42f3ff-e188-11ed-b66d-41c5f7b85421',\n",
-       " 'ad42f400-e188-11ed-b66d-41c5f7b85421',\n",
-       " 'ad42f401-e188-11ed-b66d-41c5f7b85421']"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db = DeepLake(dataset_path=destination, embedding_function=embeddings)\n",
-    "db.add_documents(docs)"
+    "embeddings = vectordb.ds.embedding.numpy()"
    ]
   },
   {

docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb

@@ -46,7 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db = ElasticVectorSearch.from_documents(docs, embeddings, elasticsearch_url=\"http://localhost:9200\")\n",
+    "db = ElasticVectorSearch.from_documents(docs, embeddings, elasticsearch_url=\"http://localhost:9200\"\n",
     "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "docs = db.similarity_search(query)"

docs/modules/indexes/vectorstores/examples/lanecdb.ipynb

@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "683953b3",
-   "metadata": {},
-   "source": [
-    "# LanceDB\n",
-    "\n",
-    "This notebook shows how to use functionality related to the LanceDB vector database based on the Lance data format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "bfcf346a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!pip install lancedb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "aac9563e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import LanceDB"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "a3c3999a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "loader = TextLoader('../../../state_of_the_union.txt')\n",
-    "documents = loader.load()\n",
-    "\n",
-    "documents = CharacterTextSplitter().split_documents(documents)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "6e104aee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import lancedb\n",
-    "\n",
-    "db = lancedb.connect('/tmp/lancedb')\n",
-    "table = db.create_table(\"my_table\", data=[\n",
-    "    {\"vector\": embeddings.embed_query(\"Hello World\"), \"text\": \"Hello World\", \"id\": \"1\"}\n",
-    "], mode=\"overwrite\")\n",
-    "\n",
-    "docsearch = LanceDB.from_documents(documents, embeddings, connection=table)\n",
-    "\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = docsearch.similarity_search(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "9c608226",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n",
-      "\n",
-      "Officer Mora was 27 years old. \n",
-      "\n",
-      "Officer Rivera was 22. \n",
-      "\n",
-      "Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n",
-      "\n",
-      "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n",
-      "\n",
-      "I’ve worked on these issues a long time. \n",
-      "\n",
-      "I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. \n",
-      "\n",
-      "So let’s not abandon our streets. Or choose between safety and equal justice. \n",
-      "\n",
-      "Let’s come together to protect our communities, restore trust, and hold law enforcement accountable. \n",
-      "\n",
-      "That’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers. \n",
-      "\n",
-      "That’s why the American Rescue Plan provided $350 Billion that cities, states, and counties can use to hire more police and invest in proven strategies like community violence interruption—trusted messengers breaking the cycle of violence and trauma and giving young people hope.  \n",
-      "\n",
-      "We should all agree: The answer is not to Defund the police. The answer is to FUND the police with the resources and training they need to protect our communities. \n",
-      "\n",
-      "I ask Democrats and Republicans alike: Pass my budget and keep our neighborhoods safe.  \n",
-      "\n",
-      "And I will keep doing everything in my power to crack down on gun trafficking and ghost guns you can buy online and make at home—they have no serial numbers and can’t be traced. \n",
-      "\n",
-      "And I ask Congress to pass proven measures to reduce gun violence. Pass universal background checks. Why should anyone on a terrorist list be able to purchase a weapon? \n",
-      "\n",
-      "Ban assault weapons and high-capacity magazines. \n",
-      "\n",
-      "Repeal the liability shield that makes gun manufacturers the only industry in America that can’t be sued. \n",
-      "\n",
-      "These laws don’t infringe on the Second Amendment. They save lives. \n",
-      "\n",
-      "The most fundamental right in America is the right to vote – and to have it counted. And it’s under assault. \n",
-      "\n",
-      "In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n",
-      "\n",
-      "We cannot let this happen. \n",
-      "\n",
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n",
-      "\n",
-      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
-      "\n",
-      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
-      "\n",
-      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
-      "\n",
-      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
-      "\n",
-      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].page_content)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a359ed74",
-   "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/indexes/vectorstores/examples/myscale.ipynb

@@ -1,267 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "683953b3",
-   "metadata": {},
-   "source": [
-    "# MyScale\n",
-    "\n",
-    "This notebook shows how to use functionality related to the MyScale vector database."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "aac9563e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import MyScale\n",
-    "from langchain.document_loaders import TextLoader"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "a9d16fa3",
-   "metadata": {},
-   "source": [
-    "## Setting up envrionments\n",
-    "\n",
-    "There are two ways to set up parameters for myscale index.\n",
-    "\n",
-    "1. Environment Variables\n",
-    "\n",
-    "    Before you run the app, please set the environment variable with `export`:\n",
-    "    `export MYSCALE_URL='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`\n",
-    "\n",
-    "    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/)\n",
-    "\n",
-    "    Every attributes under `MyScaleSettings` can be set with prefix `MYSCALE_` and is case insensitive.\n",
-    "\n",
-    "2. Create `MyScaleSettings` object with parameters\n",
-    "\n",
-    "\n",
-    "    ```python\n",
-    "    from langchain.vectorstores import MyScale, MyScaleSettings\n",
-    "    config = MyScaleSetting(host=\"<your-backend-url>\", port=8443, ...)\n",
-    "    index = MyScale(embedding_function, config)\n",
-    "    index.add_documents(...)\n",
-    "    ```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a3c3999a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../../../state_of_the_union.txt')\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "docs = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6e104aee",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Inserting data...: 100%|██████████| 42/42 [00:18<00:00,  2.21it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for d in docs:\n",
-    "    d.metadata = {'some': 'metadata'}\n",
-    "docsearch = MyScale.from_documents(docs, embeddings)\n",
-    "\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = docsearch.similarity_search(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9c608226",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "As Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. \n",
-      "\n",
-      "It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children. \n",
-      "\n",
-      "And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care. \n",
-      "\n",
-      "Third, support our veterans. \n",
-      "\n",
-      "Veterans are the best of us. \n",
-      "\n",
-      "I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. \n",
-      "\n",
-      "My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.  \n",
-      "\n",
-      "Our troops in Iraq and Afghanistan faced many dangers.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].page_content)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e3a8b105",
-   "metadata": {},
-   "source": [
-    "## Get connection info and data schema"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "69996818",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(str(docsearch))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f59360c0",
-   "metadata": {},
-   "source": [
-    "## Filtering\n",
-    "\n",
-    "You can have direct access to myscale SQL where statement. You can write `WHERE` clause following standard SQL.\n",
-    "\n",
-    "**NOTE**: Please be aware of SQL injection, this interface must not be directly called by end-user.\n",
-    "\n",
-    "If you custimized your `column_map` under your setting, you search with filter like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "232055f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Inserting data...: 100%|██████████| 42/42 [00:15<00:00,  2.69it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.vectorstores import MyScale, MyScaleSettings\n",
-    "from langchain.document_loaders import TextLoader\n",
-    "\n",
-    "loader = TextLoader('../../../state_of_the_union.txt')\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "docs = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "\n",
-    "for i, d in enumerate(docs):\n",
-    "    d.metadata = {'doc_id': i}\n",
-    "\n",
-    "docsearch = MyScale.from_documents(docs, embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "ddbcee77",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.252379834651947 {'doc_id': 6, 'some': ''} And I’m taking robus...\n",
-      "0.25022566318511963 {'doc_id': 1, 'some': ''} Groups of citizens b...\n",
-      "0.2469480037689209 {'doc_id': 8, 'some': ''} And so many families...\n",
-      "0.2428302764892578 {'doc_id': 0, 'some': 'metadata'} As Frances Haugen, w...\n"
-     ]
-    }
-   ],
-   "source": [
-    "meta = docsearch.metadata_column\n",
-    "output = docsearch.similarity_search_with_relevance_scores('What did the president say about Ketanji Brown Jackson?', \n",
-    "                                                           k=4, where_str=f\"{meta}.doc_id<10\")\n",
-    "for d, dist in output:\n",
-    "    print(dist, d.metadata, d.page_content[:20] + '...')"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "a359ed74",
-   "metadata": {},
-   "source": [
-    "## Deleting your data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fb6a9d36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docsearch.drop()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "48dbd8e0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/indexes/vectorstores/examples/pinecone.ipynb

@@ -58,9 +58,6 @@
     "\n",
     "docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)\n",
     "\n",
-    "# if you already have an index, you can load it like this\n",
-    "# docsearch = Pinecone.from_existing_index(index_name, embeddings)\n",
-    "\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "docs = docsearch.similarity_search(query)"
    ]

docs/modules/indexes/vectorstores/examples/supabase.ipynb

@@ -1,399 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "683953b3",
-   "metadata": {},
-   "source": [
-    "# SupabaseVectorStore\n",
-    "\n",
-    "This notebook shows how to use Supabase and `pgvector` as your VectorStore.\n",
-    "\n",
-    "To run this notebook, please ensure:\n",
-    "\n",
-    "- the `pgvector` extension is enabled\n",
-    "- you have installed the `supabase-py` package\n",
-    "- that you have created a `match_documents` function in your database\n",
-    "- that you have a `documents` table in your `public` schema similar to the one below.\n",
-    "\n",
-    "The following function determines cosine similarity, but you can adjust to your needs.\n",
-    "\n",
-    "```sql\n",
-    "       -- Enable the pgvector extension to work with embedding vectors\n",
-    "       create extension vector;\n",
-    "\n",
-    "       -- Create a table to store your documents\n",
-    "       create table documents (\n",
-    "       id bigserial primary key,\n",
-    "       content text, -- corresponds to Document.pageContent\n",
-    "       metadata jsonb, -- corresponds to Document.metadata\n",
-    "       embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed\n",
-    "       );\n",
-    "\n",
-    "       CREATE FUNCTION match_documents(query_embedding vector(1536), match_count int)\n",
-    "           RETURNS TABLE(\n",
-    "               id bigint,\n",
-    "               content text,\n",
-    "               metadata jsonb,\n",
-    "               -- we return matched vectors to enable maximal marginal relevance searches\n",
-    "               embedding vector(1536),\n",
-    "               similarity float)\n",
-    "           LANGUAGE plpgsql\n",
-    "           AS $$\n",
-    "           # variable_conflict use_column\n",
-    "       BEGIN\n",
-    "           RETURN query\n",
-    "           SELECT\n",
-    "               id,\n",
-    "               content,\n",
-    "               metadata,\n",
-    "               embedding,\n",
-    "               1 -(documents.embedding <=> query_embedding) AS similarity\n",
-    "           FROM\n",
-    "               documents\n",
-    "           ORDER BY\n",
-    "               documents.embedding <=> query_embedding\n",
-    "           LIMIT match_count;\n",
-    "       END;\n",
-    "       $$;\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6bd4498b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# with pip\n",
-    "# !pip install supabase\n",
-    "\n",
-    "# with conda\n",
-    "# !conda install -c conda-forge supabase"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "90afc6df",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# If you're storing your Supabase and OpenAI API keys in a .env file, you can load them with dotenv\n",
-    "from dotenv import load_dotenv\n",
-    "\n",
-    "load_dotenv()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5ce44f7c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from supabase.client import Client, create_client\n",
-    "\n",
-    "supabase_url = os.environ.get(\"SUPABASE_URL\")\n",
-    "supabase_key = os.environ.get(\"SUPABASE_SERVICE_KEY\")\n",
-    "supabase: Client = create_client(supabase_url, supabase_key)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "aac9563e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2023-04-19 20:12:28,593:INFO - NumExpr defaulting to 8 threads.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import SupabaseVectorStore\n",
-    "from langchain.document_loaders import TextLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a3c3999a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "\n",
-    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "docs = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "efec97f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We're using the default `documents` table here. You can modify this by passing in a `table_name` argument to the `from_documents` method.\n",
-    "vector_store = SupabaseVectorStore.from_documents(\n",
-    "    docs, embeddings, client=supabase\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "5eabdb75",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "matched_docs = vector_store.similarity_search(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "4b172de8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(matched_docs[0].page_content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18152965",
-   "metadata": {},
-   "source": [
-    "## Similarity search with score\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "72aaa9c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "matched_docs = vector_store.similarity_search_with_relevance_scores(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "d88e958e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
-       " 0.802509746274066)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "matched_docs[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "794a7552",
-   "metadata": {},
-   "source": [
-    "## Retriever options\n",
-    "\n",
-    "This section goes over different options for how to use SupabaseVectorStore as a retriever.\n",
-    "\n",
-    "### Maximal Marginal Relevance Searches\n",
-    "\n",
-    "In addition to using similarity search in the retriever object, you can also use `mmr`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "96ff911a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = vector_store.as_retriever(search_type=\"mmr\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f00be6d0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "matched_docs = retriever.get_relevant_documents(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "a559c3f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "## Document 0\n",
-      "\n",
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "\n",
-      "## Document 1\n",
-      "\n",
-      "One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. \n",
-      "\n",
-      "When they came home, many of the world’s fittest and best trained warriors were never the same. \n",
-      "\n",
-      "Headaches. Numbness. Dizziness. \n",
-      "\n",
-      "A cancer that would put them in a flag-draped coffin. \n",
-      "\n",
-      "I know. \n",
-      "\n",
-      "One of those soldiers was my son Major Beau Biden. \n",
-      "\n",
-      "We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \n",
-      "\n",
-      "But I’m committed to finding out everything we can. \n",
-      "\n",
-      "Committed to military families like Danielle Robinson from Ohio. \n",
-      "\n",
-      "The widow of Sergeant First Class Heath Robinson.  \n",
-      "\n",
-      "He was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \n",
-      "\n",
-      "Stationed near Baghdad, just yards from burn pits the size of football fields. \n",
-      "\n",
-      "Heath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.\n",
-      "\n",
-      "## Document 2\n",
-      "\n",
-      "And I’m taking robust action to make sure the pain of our sanctions  is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. \n",
-      "\n",
-      "Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.  \n",
-      "\n",
-      "America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies.  \n",
-      "\n",
-      "These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. \n",
-      "\n",
-      "But I want you to know that we are going to be okay. \n",
-      "\n",
-      "When the history of this era is written Putin’s war on Ukraine will have left Russia weaker and the rest of the world stronger. \n",
-      "\n",
-      "While it shouldn’t have taken something so terrible for people around the world to see what’s at stake now everyone sees it clearly.\n",
-      "\n",
-      "## Document 3\n",
-      "\n",
-      "We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n",
-      "\n",
-      "I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n",
-      "\n",
-      "They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n",
-      "\n",
-      "Officer Mora was 27 years old. \n",
-      "\n",
-      "Officer Rivera was 22. \n",
-      "\n",
-      "Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n",
-      "\n",
-      "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n",
-      "\n",
-      "I’ve worked on these issues a long time. \n",
-      "\n",
-      "I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i, d in enumerate(matched_docs):\n",
-    "    print(f\"\\n## Document {i}\\n\")\n",
-    "    print(d.page_content)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "79b1198e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/indexes/vectorstores/examples/weaviate.ipynb

@@ -75,8 +75,7 @@
     "            \"vectorizer\": \"text2vec-openai\",\n",
     "              \"moduleConfig\": {\n",
     "                \"text2vec-openai\": {\n",
-    "                  \"model\": \"ada\",\n",
-    "                  \"modelVersion\": \"002\",\n",
+    "                  \"model\": \"babbage\",\n",
     "                  \"type\": \"text\"\n",
     "                }\n",
     "              },\n",

docs/modules/memory/types/entity_summary_memory.ipynb

@@ -161,7 +161,7 @@
       "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
       "\n",
       "Context:\n",
-      "{'Deven': 'Deven is working on a hackathon project with Sam.', 'Sam': 'Sam is working on a hackathon project with Deven.'}\n",
+      "{'Deven': '', 'Sam': ''}\n",
       "\n",
       "Current conversation:\n",
       "\n",
@@ -189,29 +189,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "id": "0269f513",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon.',\n",
+       "{'Deven': 'Deven is working on a hackathon project with Sam.',\n",
        " 'Sam': 'Sam is working on a hackathon project with Deven.'}"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "conversation.memory.entity_store.store"
+    "conversation.memory.store"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "id": "46324ca8",
    "metadata": {},
    "outputs": [
@@ -232,7 +232,7 @@
       "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
       "\n",
       "Context:\n",
-      "{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon.', 'Sam': 'Sam is working on a hackathon project with Deven.', 'Langchain': ''}\n",
+      "{'Deven': 'Deven is working on a hackathon project with Sam.', 'Sam': 'Sam is working on a hackathon project with Deven.', 'Langchain': ''}\n",
       "\n",
       "Current conversation:\n",
       "Human: Deven & Sam are working on a hackathon project\n",
@@ -250,7 +250,7 @@
        "' That sounds like an interesting project! What kind of memory structures are they trying to add?'"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "ff2ebf6b",
    "metadata": {},
    "outputs": [
@@ -282,7 +282,7 @@
       "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
       "\n",
       "Context:\n",
-      "{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain.', 'Langchain': 'Langchain is a project that is trying to add more complex memory structures.', 'Key-Value Store': ''}\n",
+      "{'Deven': 'Deven is working on a hackathon project with Sam, attempting to add more complex memory structures to Langchain.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain.', 'Langchain': 'Langchain is a project that is trying to add more complex memory structures.', 'Key-Value Store': ''}\n",
       "\n",
       "Current conversation:\n",
       "Human: Deven & Sam are working on a hackathon project\n",
@@ -299,10 +299,10 @@
     {
      "data": {
       "text/plain": [
-       "' That sounds like a great idea! How will the key-value store help with the project?'"
+       "' That sounds like a great idea! How will the key-value store work?'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "id": "56cfd4ba",
    "metadata": {},
    "outputs": [
@@ -334,7 +334,7 @@
       "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
       "\n",
       "Context:\n",
-      "{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.'}\n",
+      "{'Deven': 'Deven is working on a hackathon project with Sam, attempting to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.'}\n",
       "\n",
       "Current conversation:\n",
       "Human: Deven & Sam are working on a hackathon project\n",
@@ -342,7 +342,7 @@
       "Human: They are trying to add more complex memory structures to Langchain\n",
       "AI:  That sounds like an interesting project! What kind of memory structures are they trying to add?\n",
       "Human: They are adding in a key-value store for entities mentioned so far in the conversation.\n",
-      "AI:  That sounds like a great idea! How will the key-value store help with the project?\n",
+      "AI:  That sounds like a great idea! How will the key-value store work?\n",
       "Last line:\n",
       "Human: What do you know about Deven & Sam?\n",
       "You:\u001b[0m\n",
@@ -353,10 +353,10 @@
     {
      "data": {
       "text/plain": [
-       "' Deven and Sam are working on a hackathon project together, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be working hard on this project and have a great idea for how the key-value store can help.'"
+       "' Deven and Sam are working on a hackathon project together, attempting to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.'"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "id": "038b4d3f",
    "metadata": {},
    "outputs": [
@@ -384,34 +384,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur.',\n",
-      " 'Deven': 'Deven is working on a hackathon project with Sam, which they are '\n",
-      "          'entering into a hackathon. They are trying to add more complex '\n",
-      "          'memory structures to Langchain, including a key-value store for '\n",
-      "          'entities mentioned so far in the conversation, and seem to be '\n",
-      "          'working hard on this project with a great idea for how the '\n",
-      "          'key-value store can help.',\n",
-      " 'Key-Value Store': 'A key-value store is being added to the project to store '\n",
-      "                    'entities mentioned in the conversation.',\n",
+      "{'Deven': 'Deven is working on a hackathon project with Sam, attempting to add '\n",
+      "          'more complex memory structures to Langchain, including a key-value '\n",
+      "          'store for entities mentioned so far in the conversation.',\n",
+      " 'Key-Value Store': 'A key-value store that stores entities mentioned in the '\n",
+      "                    'conversation.',\n",
       " 'Langchain': 'Langchain is a project that is trying to add more complex '\n",
       "              'memory structures, including a key-value store for entities '\n",
       "              'mentioned so far in the conversation.',\n",
-      " 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more '\n",
-      "        'complex memory structures to Langchain, including a key-value store '\n",
-      "        'for entities mentioned so far in the conversation. They seem to have '\n",
-      "        'a great idea for how the key-value store can help, and Sam is also '\n",
-      "        'the founder of a company called Daimon.'}\n"
+      " 'Sam': 'Sam is working on a hackathon project with Deven, attempting to add '\n",
+      "        'more complex memory structures to Langchain, including a key-value '\n",
+      "        'store for entities mentioned so far in the conversation.'}\n"
      ]
     }
    ],
    "source": [
     "from pprint import pprint\n",
-    "pprint(conversation.memory.entity_store.store)"
+    "pprint(conversation.memory.store)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 8,
    "id": "2df4800e",
    "metadata": {},
    "outputs": [
@@ -432,16 +426,15 @@
       "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
       "\n",
       "Context:\n",
-      "{'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to have a great idea for how the key-value store can help, and Sam is also the founder of a company called Daimon.'}\n",
+      "{'Daimon': '', 'Sam': 'Sam is working on a hackathon project with Deven to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.'}\n",
       "\n",
       "Current conversation:\n",
+      "Human: They are trying to add more complex memory structures to Langchain\n",
+      "AI:  That sounds like an interesting project! What kind of memory structures are they trying to add?\n",
       "Human: They are adding in a key-value store for entities mentioned so far in the conversation.\n",
-      "AI:  That sounds like a great idea! How will the key-value store help with the project?\n",
+      "AI:  That sounds like a great idea! How will the key-value store work?\n",
       "Human: What do you know about Deven & Sam?\n",
-      "AI:  Deven and Sam are working on a hackathon project together, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be working hard on this project and have a great idea for how the key-value store can help.\n",
-      "Human: Sam is the founder of a company called Daimon.\n",
-      "AI: \n",
-      "That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?\n",
+      "AI:  Deven and Sam are working on a hackathon project to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be very motivated and passionate about their project, and are working hard to make it a success.\n",
       "Last line:\n",
       "Human: Sam is the founder of a company called Daimon.\n",
       "You:\u001b[0m\n",
@@ -452,10 +445,10 @@
     {
      "data": {
       "text/plain": [
-       "\" That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?\""
+       "\"\\nThat's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?\""
       ]
      },
-     "execution_count": 22,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -466,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 9,
    "id": "ebe9e36f",
    "metadata": {},
    "outputs": [
@@ -474,36 +467,32 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur, who '\n",
-      "           'is working on a hackathon project with Deven to add more complex '\n",
-      "           'memory structures to Langchain.',\n",
-      " 'Deven': 'Deven is working on a hackathon project with Sam, which they are '\n",
-      "          'entering into a hackathon. They are trying to add more complex '\n",
-      "          'memory structures to Langchain, including a key-value store for '\n",
-      "          'entities mentioned so far in the conversation, and seem to be '\n",
-      "          'working hard on this project with a great idea for how the '\n",
-      "          'key-value store can help.',\n",
-      " 'Key-Value Store': 'A key-value store is being added to the project to store '\n",
-      "                    'entities mentioned in the conversation.',\n",
-      " 'Langchain': 'Langchain is a project that is trying to add more complex '\n",
-      "              'memory structures, including a key-value store for entities '\n",
-      "              'mentioned so far in the conversation.',\n",
-      " 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more '\n",
-      "        'complex memory structures to Langchain, including a key-value store '\n",
-      "        'for entities mentioned so far in the conversation. They seem to have '\n",
-      "        'a great idea for how the key-value store can help, and Sam is also '\n",
-      "        'the founder of a successful company called Daimon.'}\n"
+      "{'Daimon': 'Daimon is a company founded by Sam.',\n",
+      " 'Deven': 'Deven is working on a hackathon project with Sam to add more '\n",
+      "          'complex memory structures to Langchain, including a key-value store '\n",
+      "          'for entities mentioned so far in the conversation.',\n",
+      " 'Key-Value Store': 'Key-Value Store: A data structure that stores values '\n",
+      "                    'associated with a unique key, allowing for efficient '\n",
+      "                    'retrieval of values. Deven and Sam are adding a key-value '\n",
+      "                    'store for entities mentioned so far in the conversation.',\n",
+      " 'Langchain': 'Langchain is a project that seeks to add more complex memory '\n",
+      "              'structures, including a key-value store for entities mentioned '\n",
+      "              'so far in the conversation.',\n",
+      " 'Sam': 'Sam is working on a hackathon project with Deven to add more complex '\n",
+      "        'memory structures to Langchain, including a key-value store for '\n",
+      "        'entities mentioned so far in the conversation. He is also the founder '\n",
+      "        'of a company called Daimon.'}\n"
      ]
     }
    ],
    "source": [
     "from pprint import pprint\n",
-    "pprint(conversation.memory.entity_store.store)"
+    "pprint(conversation.memory.store)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "id": "dd547144",
    "metadata": {},
    "outputs": [
@@ -524,16 +513,16 @@
       "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
       "\n",
       "Context:\n",
-      "{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation, and seem to be working hard on this project with a great idea for how the key-value store can help.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to have a great idea for how the key-value store can help, and Sam is also the founder of a successful company called Daimon.', 'Langchain': 'Langchain is a project that is trying to add more complex memory structures, including a key-value store for entities mentioned so far in the conversation.', 'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur, who is working on a hackathon project with Deven to add more complex memory structures to Langchain.'}\n",
+      "{'Sam': 'Sam is working on a hackathon project with Deven to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. He is also the founder of a company called Daimon.', 'Daimon': 'Daimon is a company founded by Sam.'}\n",
       "\n",
       "Current conversation:\n",
+      "Human: They are adding in a key-value store for entities mentioned so far in the conversation.\n",
+      "AI:  That sounds like a great idea! How will the key-value store work?\n",
       "Human: What do you know about Deven & Sam?\n",
-      "AI:  Deven and Sam are working on a hackathon project together, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be working hard on this project and have a great idea for how the key-value store can help.\n",
+      "AI:  Deven and Sam are working on a hackathon project to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be very motivated and passionate about their project, and are working hard to make it a success.\n",
       "Human: Sam is the founder of a company called Daimon.\n",
       "AI: \n",
       "That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?\n",
-      "Human: Sam is the founder of a company called Daimon.\n",
-      "AI:  That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?\n",
       "Last line:\n",
       "Human: What do you know about Sam?\n",
       "You:\u001b[0m\n",
@@ -544,10 +533,10 @@
     {
      "data": {
       "text/plain": [
-       "' Sam is the founder of a successful company called Daimon. He is also working on a hackathon project with Deven to add more complex memory structures to Langchain. They seem to have a great idea for how the key-value store can help.'"
+       "' Sam is the founder of a company called Daimon. He is also working on a hackathon project with Deven to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. He seems to be very motivated and passionate about his project, and is working hard to make it a success.'"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -581,7 +570,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/models/chat/integrations/anthropic.ipynb

@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
-   "metadata": {},
-   "source": [
-    "# Anthropic\n",
-    "\n",
-    "This notebook covers how to get started with Anthropic chat models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatAnthropic\n",
-    "from langchain.prompts.chat import (\n",
-    "    ChatPromptTemplate,\n",
-    "    SystemMessagePromptTemplate,\n",
-    "    AIMessagePromptTemplate,\n",
-    "    HumanMessagePromptTemplate,\n",
-    ")\n",
-    "from langchain.schema import (\n",
-    "    AIMessage,\n",
-    "    HumanMessage,\n",
-    "    SystemMessage\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "chat = ChatAnthropic()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" J'aime programmer. \", additional_kwargs={})"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
-    "]\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
-   "metadata": {},
-   "source": [
-    "## `ChatAnthropic` also supports async and streaming functionality:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks.base import CallbackManager\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\" J'aime la programmation.\", generation_info=None, message=AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}))]], llm_output={})"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await chat.agenerate([messages])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " J'adore programmer."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" J'adore programmer.\", additional_kwargs={})"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat = ChatAnthropic(streaming=True, verbose=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "df45f59f",
-   "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/models/llms/examples/llm_caching.ipynb

@@ -785,9 +785,7 @@
    "id": "9df0dab8",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "!rm .langchain.db sqlite.db"
-   ]
+   "source": []
   }
  ],
  "metadata": {

docs/modules/models/llms/integrations/ai21.ipynb

@@ -6,55 +6,14 @@
    "metadata": {},
    "source": [
     "# AI21\n",
-    "\n",
-    "[AI21 Studio](https://docs.ai21.com/) provides API access to `Jurassic-2` large language models.\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with [AI21 models](https://docs.ai21.com/docs/jurassic-2-models)."
+    "This example goes over how to use LangChain to interact with AI21 models"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "02be122d-04e8-4ec6-84d1-f1d8961d6828",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# install the package:\n",
-    "!pip install ai21"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4229227e-6ca2-41ad-a3c3-5f29e3559091",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get AI21_API_KEY. Use https://studio.ai21.com/account/account\n",
-    "\n",
-    "from getpass import getpass\n",
-    "AI21_API_KEY  = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "id": "6fb585dd",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.llms import AI21\n",
@@ -63,11 +22,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "id": "035dea0f",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "template = \"\"\"Question: {question}\n",
@@ -79,23 +36,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "id": "3f3458d9",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "llm = AI21(ai21_api_key=AI21_API_KEY)"
+    "llm = AI21()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "id": "a641dbd9",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "llm_chain = LLMChain(prompt=prompt, llm=llm)"
@@ -103,23 +56,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "9f0b1960",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\n1. What year was Justin Bieber born?\\nJustin Bieber was born in 1994.\\n2. What team won the Super Bowl in 1994?\\nThe Dallas Cowboys won the Super Bowl in 1994.'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
     "\n",
@@ -151,7 +91,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/models/llms/integrations/aleph_alpha.ipynb

@@ -1,61 +1,20 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "9597802c",
    "metadata": {},
    "source": [
     "# Aleph Alpha\n",
-    "\n",
-    "[The Luminous series](https://docs.aleph-alpha.com/docs/introduction/luminous/) is a family of large language models.\n",
-    "\n",
     "This example goes over how to use LangChain to interact with Aleph Alpha models"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "fe1bf9fb-e9fa-49f3-a768-8f603225ccce",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Install the package\n",
-    "!pip install aleph-alpha-client"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0cb0f937-b610-42a2-b765-336eed037031",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create a new token: https://docs.aleph-alpha.com/docs/account/#create-a-new-token\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "ALEPH_ALPHA_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "6fb585dd",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.llms import AlephAlpha\n",
@@ -64,11 +23,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "f81a230d",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "template = \"\"\"Q: {question}\n",
@@ -80,23 +37,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "f0d26e48",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "llm = AlephAlpha(model=\"luminous-extended\", maximum_tokens=20, stop_sequences=[\"Q:\"], aleph_alpha_api_key=ALEPH_ALPHA_API_KEY)"
+    "llm = AlephAlpha(model=\"luminous-extended\", maximum_tokens=20, stop_sequences=[\"Q:\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "id": "6811d621",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "llm_chain = LLMChain(prompt=prompt, llm=llm)"
@@ -104,11 +57,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "3058e63f",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -116,7 +67,7 @@
        "' Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.\\n'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -130,7 +81,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -144,7 +95,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/models/llms/integrations/anthropic_example.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9597802c",
+   "metadata": {},
+   "source": [
+    "# Anthropic\n",
+    "This example goes over how to use LangChain to interact with Anthropic models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6fb585dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import Anthropic\n",
+    "from langchain import PromptTemplate, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "035dea0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3f3458d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = Anthropic()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a641dbd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9f844993",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" Step 1: Justin Beiber was born on March 1, 1994\\nStep 2: The NFL season ends with the Super Bowl in January/February\\nStep 3: Therefore, the Super Bowl that occurred closest to Justin Beiber's birth would be Super Bowl XXIX in 1995\\nStep 4: The San Francisco 49ers won Super Bowl XXIX in 1995\\n\\nTherefore, the answer is the San Francisco 49ers won the Super Bowl in the year Justin Beiber was born.\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "\n",
+    "llm_chain.run(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4797d719",
+   "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/models/llms/integrations/azure_openai_example.ipynb

@@ -5,7 +5,7 @@
    "id": "9e9b7651",
    "metadata": {},
    "source": [
-    "# Azure OpenAI\n",
+    "# Azure OpenAI LLM Example\n",
     "\n",
     "This notebook goes over how to use Langchain with [Azure OpenAI](https://aka.ms/azure-openai).\n",
     "\n",
@@ -49,18 +49,6 @@
     "```\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "89fdb593-5a42-4098-87b7-1496fa511b1c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install openai"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -158,7 +146,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/models/llms/integrations/banana.ipynb

@@ -5,50 +5,19 @@
    "metadata": {},
    "source": [
     "# Banana\n",
-    "\n",
-    "\n",
-    "[Banana](https://www.banana.dev/about-us) is focused on building the machine learning infrastructure.\n",
-    "\n",
     "This example goes over how to use LangChain to interact with Banana models"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Install the package  https://docs.banana.dev/banana-docs/core-concepts/sdks/python\n",
-    "!pip install banana-dev"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# get new tokens: https://app.banana.dev/\n",
-    "# We need two tokens, not just an `api_key`: `BANANA_API_KEY` and `YOUR_MODEL_KEY`\n",
-    "\n",
     "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"BANANA_API_KEY\"] = \"YOUR_API_KEY\"\n",
-    "# OR\n",
-    "# BANANA_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "from langchain.llms import Banana\n",
-    "from langchain import PromptTemplate, LLMChain"
+    "from langchain import PromptTemplate, LLMChain\n",
+    "os.environ[\"BANANA_API_KEY\"] = \"YOUR_API_KEY\""
    ]
   },
   {
@@ -96,22 +65,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.12 ('palm')",
    "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"
+   "version": "3.9.12"
   },
+  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
@@ -119,5 +81,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/models/llms/integrations/cerebriumai_example.ipynb

@@ -4,10 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# CerebriumAI\n",
-    "\n",
-    "`Cerebrium` is an AWS Sagemaker alternative. It also provides API access to [several LLM models](https://docs.cerebrium.ai/cerebrium/prebuilt-models/deployment).\n",
-    "\n",
+    "# CerebriumAI LLM Example\n",
     "This notebook goes over how to use Langchain with [CerebriumAI](https://docs.cerebrium.ai/introduction)."
    ]
   },
@@ -16,7 +13,7 @@
    "metadata": {},
    "source": [
     "## Install cerebrium\n",
-    "The `cerebrium` package is required to use the `CerebriumAI` API. Install `cerebrium` using `pip3 install cerebrium`."
+    "The `cerebrium` package is required to use the CerebriumAI API. Install `cerebrium` using `pip3 install cerebrium`."
    ]
   },
   {
@@ -25,8 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install the package\n",
-    "!pip3 install cerebrium"
+    "$ pip3 install cerebrium"
    ]
   },
   {
@@ -52,7 +48,7 @@
    "metadata": {},
    "source": [
     "## Set the Environment API Key\n",
-    "Make sure to get your API key from CerebriumAI. See [here](https://dashboard.cerebrium.ai/login). You are given a 1 hour free of serverless GPU compute to test different models."
+    "Make sure to get your API key from CerebriumAI. You are given a 1 hour free of serverless GPU compute to test different models."
    ]
   },
   {
@@ -140,22 +136,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.12 ('palm')",
    "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"
+   "version": "3.9.12"
   },
+  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
@@ -163,5 +152,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/models/llms/integrations/cohere.ipynb

@@ -6,56 +6,14 @@
    "metadata": {},
    "source": [
     "# Cohere\n",
-    "\n",
-    "[Cohere](https://cohere.ai/about) is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with `Cohere` [models](https://docs.cohere.ai/docs/generation-card)."
+    "This example goes over how to use LangChain to interact with Cohere models"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "91ea14ce-831d-409a-a88f-30353acdabd1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Install the package\n",
-    "!pip install cohere"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "3f5dc9d7-65e3-4b5b-9086-3327d016cfe0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get a new token: https://dashboard.cohere.ai/\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "COHERE_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "6fb585dd",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.llms import Cohere\n",
@@ -64,11 +22,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "035dea0f",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "template = \"\"\"Question: {question}\n",
@@ -80,23 +36,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "3f3458d9",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "llm = Cohere(cohere_api_key=COHERE_API_KEY)"
+    "llm = Cohere()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "a641dbd9",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "llm_chain = LLMChain(prompt=prompt, llm=llm)"
@@ -150,7 +102,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/models/llms/integrations/deepinfra_example.ipynb

@@ -1,13 +1,11 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# DeepInfra\n",
-    "\n",
-    "`DeepInfra` provides [several LLMs](https://deepinfra.com/models).\n",
-    "\n",
+    "# DeepInfra LLM Example\n",
     "This notebook goes over how to use Langchain with [DeepInfra](https://deepinfra.com)."
    ]
   },
@@ -20,10 +18,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
@@ -36,44 +32,17 @@
    "metadata": {},
    "source": [
     "## Set the Environment API Key\n",
-    "Make sure to get your API key from DeepInfra. You have to [Login](https://deepinfra.com/login?from=%2Fdash) and get a new token.\n",
-    "\n",
-    "You are given a 1 hour free of serverless GPU compute to test different models. (see [here](https://github.com/deepinfra/deepctl#deepctl))\n",
+    "Make sure to get your API key from DeepInfra. You are given a 1 hour free of serverless GPU compute to test different models.\n",
     "You can print your token with `deepctl auth token`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get a new token: https://deepinfra.com/login?from=%2Fdash\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "DEEPINFRA_API_TOKEN = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "os.environ[\"DEEPINFRA_API_TOKEN\"] = DEEPINFRA_API_TOKEN"
+    "os.environ[\"DEEPINFRA_API_TOKEN\"] = \"YOUR_KEY_HERE\""
    ]
   },
   {
@@ -81,7 +50,7 @@
    "metadata": {},
    "source": [
     "## Create the DeepInfra instance\n",
-    "Make sure to deploy your model first via `deepctl deploy create -m google/flat-t5-xl` (see [here](https://github.com/deepinfra/deepctl#deepctl))"
+    "Make sure to deploy your model first via `deepctl deploy create -m google/flat-t5-xl` (for example)"
    ]
   },
   {
@@ -152,22 +121,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.12 ('palm')",
    "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"
+   "version": "3.9.12"
   },
+  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
@@ -175,5 +137,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/models/llms/integrations/forefrontai_example.ipynb

@@ -4,12 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# ForefrontAI\n",
-    "\n",
-    "\n",
-    "The `Forefront` platform gives you the ability to fine-tune and use [open source large language models](https://docs.forefront.ai/forefront/master/models).\n",
-    "\n",
-    "This notebook goes over how to use Langchain with [ForefrontAI](https://www.forefront.ai/).\n"
+    "# ForefrontAI LLM Example\n",
+    "This notebook goes over how to use Langchain with [ForefrontAI](https://www.forefront.ai/)."
    ]
   },
   {
@@ -44,20 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# get a new token: https://docs.forefront.ai/forefront/api-reference/authentication\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "FOREFRONTAI_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.environ[\"FOREFRONTAI_API_KEY\"] = FOREFRONTAI_API_KEY"
+    "os.environ[\"FOREFRONTAI_API_KEY\"] = \"YOUR_KEY_HERE\""
    ]
   },
   {
@@ -136,22 +119,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.12 ('palm')",
    "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"
+   "version": "3.9.12"
   },
+  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
@@ -159,5 +135,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/models/llms/integrations/gooseai_example.ipynb

@@ -4,11 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# GooseAI\n",
-    "\n",
-    "`GooseAI` is a fully managed NLP-as-a-Service, delivered via API. GooseAI provides access to [these models](https://goose.ai/docs/models).\n",
-    "\n",
-    "This notebook goes over how to use Langchain with [GooseAI](https://goose.ai/).\n"
+    "# GooseAI LLM Example\n",
+    "This notebook goes over how to use Langchain with [GooseAI](https://goose.ai/)."
    ]
   },
   {
@@ -60,18 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from getpass import getpass\n",
-    "\n",
-    "GOOSEAI_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.environ[\"GOOSEAI_API_KEY\"] = GOOSEAI_API_KEY"
+    "os.environ[\"GOOSEAI_API_KEY\"] = \"YOUR_KEY_HERE\""
    ]
   },
   {
@@ -150,22 +136,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.12 ('palm')",
    "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"
+   "version": "3.9.12"
   },
+  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
@@ -173,5 +152,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/models/llms/integrations/gpt4all.ipynb

@@ -6,36 +6,22 @@
    "source": [
     "# GPT4All\n",
     "\n",
-    "[GitHub:nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with `GPT4All` models."
+    "This example goes over how to use LangChain to interact with GPT4All models"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%pip install pyllamacpp > /dev/null"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain import PromptTemplate, LLMChain\n",
@@ -46,10 +32,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "template = \"\"\"Question: {question}\n",
@@ -67,10 +51,6 @@
     "\n",
     "To run locally, download a compatible ggml-formatted model. For more info, visit https://github.com/nomic-ai/pyllamacpp\n",
     "\n",
-    "For full installation instructions go [here](https://gpt4all.io/index.html).\n",
-    "\n",
-    "The GPT4All Chat installer needs to decompress a 3GB LLM model during the installation process!\n",
-    "\n",
     "Note that new models are uploaded regularly - check the link above for the most recent `.bin` URL"
    ]
   },
@@ -166,9 +146,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/models/llms/integrations/huggingface_hub.ipynb

@@ -7,243 +7,41 @@
    "source": [
     "# Hugging Face Hub\n",
     "\n",
-    "The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
-    "\n",
     "This example showcases how to connect to the Hugging Face Hub."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "4c1b8450-5eaf-4d34-8341-2d785448a1ff",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "To use, you should have the ``huggingface_hub`` python [package installed](https://huggingface.co/docs/huggingface_hub/installation)."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "d772b637-de00-4663-bd77-9bc96d798db2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install huggingface_hub > /dev/null"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d597a792-354c-4ca5-b483-5965eec5d63d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get a token: https://huggingface.co/docs/api-inference/quicktour#get-your-api-token\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "HUGGINGFACEHUB_API_TOKEN = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b8c5b88c-e4b8-4d0d-9a35-6e8f106452c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = HUGGINGFACEHUB_API_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84dd44c1-c428-41f3-a911-520281386c94",
-   "metadata": {},
-   "source": [
-    "**Select a Model**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "39c7eeac-01c4-486b-9480-e828a9e73e78",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain import HuggingFaceHub\n",
-    "\n",
-    "repo_id = \"google/flan-t5-xl\" # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options\n",
-    "\n",
-    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "3acf0069",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The FIFA World Cup is a football tournament that is played every 4 years. The year 1994 was the 44th FIFA World Cup. The final answer: Brazil.\n"
+     ]
+    }
+   ],
    "source": [
-    "from langchain import PromptTemplate, LLMChain\n",
+    "from langchain import PromptTemplate, HuggingFaceHub, LLMChain\n",
     "\n",
     "template = \"\"\"Question: {question}\n",
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":0, \"max_length\":64}))\n",
     "\n",
     "question = \"Who won the FIFA World Cup in the year 1994? \"\n",
     "\n",
     "print(llm_chain.run(question))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ddaa06cf-95ec-48ce-b0ab-d892a7909693",
-   "metadata": {},
-   "source": [
-    "## Examples\n",
-    "\n",
-    "Below are some examples of models you can access through the Hugging Face Hub integration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4fa9337e-ccb5-4c52-9b7c-1653148bc256",
-   "metadata": {},
-   "source": [
-    "### StableLM, by Stability AI\n",
-    "\n",
-    "See [Stability AI's](https://huggingface.co/stabilityai) organization page for a list of available models."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "36a1ce01-bd46-451f-8ee6-61c8f4bd665a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"stabilityai/stablelm-tuned-alpha-3b\"\n",
-    "# Others include stabilityai/stablelm-base-alpha-3b\n",
-    "# as well as 7B parameter versions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b5654cea-60b0-4f40-ab34-06ba1eca810d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2f19d0dc-c987-433f-a8d6-b1214e8ee067",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reuse the prompt and question from above.\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a5c97af-89bc-4e59-95c1-223742a9160b",
-   "metadata": {},
-   "source": [
-    "### Dolly, by DataBricks\n",
-    "\n",
-    "See [DataBricks](https://huggingface.co/databricks) organization page for a list of available models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "521fcd2b-8e38-4920-b407-5c7d330411c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import HuggingFaceHub\n",
-    "\n",
-    "repo_id = \"databricks/dolly-v2-3b\"\n",
-    "\n",
-    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9907ec3a-fe0c-4543-81c4-d42f9453f16c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Reuse the prompt and question from above.\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03f6ae52-b5f9-4de6-832c-551cb3fa11ae",
-   "metadata": {},
-   "source": [
-    "### Camel, by Writer\n",
-    "\n",
-    "See [Writer's](https://huggingface.co/Writer) organization page for a list of available models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "257a091d-750b-4910-ac08-fe1c7b3fd98b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain import HuggingFaceHub\n",
-    "\n",
-    "repo_id = \"Writer/camel-5b-hf\" # See https://huggingface.co/Writer for other options\n",
-    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b06f6838-a11a-4d6a-88e3-91fa1747a2b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reuse the prompt and question from above.\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bf838eb-1083-402f-b099-b07c452418c8",
-   "metadata": {},
-   "source": [
-    "**And many more!**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "18c78880-65d7-41d0-9722-18090efb60e9",
+   "id": "843a3837",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -265,7 +63,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,

docs/modules/models/llms/integrations/huggingface_pipelines.ipynb

@@ -1,145 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "959300d4",
-   "metadata": {},
-   "source": [
-    "# Hugging Face Local Pipelines\n",
-    "\n",
-    "Hugging Face models can be run locally through the `HuggingFacePipeline` class.\n",
-    "\n",
-    "The [Hugging Face Model Hub](https://huggingface.co/models) hosts over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
-    "\n",
-    "These can be called from LangChain either through this local pipeline wrapper or by calling their hosted inference endpoints through the HuggingFaceHub class. For more information on the hosted pipelines, see the [HuggingFaceHub](huggingface_hub.ipynb) notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c1b8450-5eaf-4d34-8341-2d785448a1ff",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "To use, you should have the ``transformers`` python [package installed](https://pypi.org/project/transformers/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d772b637-de00-4663-bd77-9bc96d798db2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install transformers > /dev/null"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91ad075f-71d5-4bc8-ab91-cc0ad5ef16bb",
-   "metadata": {},
-   "source": [
-    "### Load the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "165ae236-962a-4763-8052-c4836d78a5d2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Failed to default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x1117f9790>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain import HuggingFacePipeline\n",
-    "\n",
-    "llm = HuggingFacePipeline.from_model_id(model_id=\"bigscience/bloom-1b7\", task=\"text-generation\", model_kwargs={\"temperature\":0, \"max_length\":64})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00104b27-0c15-4a97-b198-4512337ee211",
-   "metadata": {},
-   "source": [
-    "### Integrate the model in an LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3acf0069",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/wfh/code/lc/lckg/.venv/lib/python3.11/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (64) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
-      "  warnings.warn(\n",
-      "WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x144d06910>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " First, we need to understand what is an electroencephalogram. An electroencephalogram is a recording of brain activity. It is a recording of brain activity that is made by placing electrodes on the scalp. The electrodes are placed\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain import PromptTemplate,  LLMChain\n",
-    "\n",
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
-    "\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "\n",
-    "question = \"What is electroencephalography?\"\n",
-    "\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "843a3837",
-   "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/models/llms/integrations/llamacpp.ipynb

@@ -6,52 +6,32 @@
    "source": [
     "# Llama-cpp\n",
     "\n",
-    "[llama-cpp](https://github.com/abetlen/llama-cpp-python) is a Python binding for [llama.cpp](https://github.com/ggerganov/llama.cpp). \n",
-    "It supports [several LLMs](https://github.com/ggerganov/llama.cpp).\n",
-    "\n",
-    "This notebook goes over how to run `llama-cpp` within LangChain."
+    "This notebook goes over how to run llama-cpp within LangChain"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "!pip install llama-cpp-python"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Make sure you are following all instructions to [install all necessary model files](https://github.com/ggerganov/llama.cpp).\n",
-    "\n",
-    "You don't need an `API_TOKEN`!"
+    "from langchain.llms import LlamaCpp\n",
+    "from langchain import PromptTemplate, LLMChain"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.llms import LlamaCpp\n",
-    "from langchain import PromptTemplate, LLMChain\n",
-    "from langchain.callbacks.base import CallbackManager\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "template = \"\"\"Question: {question}\n",
@@ -64,19 +44,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Callbacks support token-wise streaming\n",
-    "callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])\n",
-    "# Verbose is required to pass to the callback manager\n",
-    "\n",
-    "# Make sure the model path is correct for your system!\n",
-    "llm = LlamaCpp(\n",
-    "    model_path=\"./ggml-model-q4_0.bin\", callback_manager=callback_manager, verbose=True\n",
-    ")"
+    "llm = LlamaCpp(model_path=\"./ggml-model-q4_0.bin\")"
    ]
   },
   {
@@ -93,17 +64,10 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " First we need to identify what year Justin Beiber was born in. A quick google search reveals that he was born on March 1st, 1994. Now we know when the Super Bowl was played in, so we can look up which NFL team won it. The NFL Superbowl of the year 1994 was won by the San Francisco 49ers against the San Diego Chargers."
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "' First we need to identify what year Justin Beiber was born in. A quick google search reveals that he was born on March 1st, 1994. Now we know when the Super Bowl was played in, so we can look up which NFL team won it. The NFL Superbowl of the year 1994 was won by the San Francisco 49ers against the San Diego Chargers.'"
+       "'\\n\\nWe know that Justin Bieber is currently 25 years old and that he was born on March 1st, 1994 and that he is a singer and he has an album called Purpose, so we know that he was born when Super Bowl XXXVIII was played between Dallas and Seattle and that it took place February 1st, 2004 and that the Seattle Seahawks won 24-21, so Seattle is our answer!'"
       ]
      },
      "execution_count": 6,
@@ -134,9 +98,9 @@
    "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/models/llms/integrations/manifest.ipynb

@@ -15,30 +15,14 @@
    "id": "59fcaebc",
    "metadata": {},
    "source": [
-    "For more detailed information on `manifest`, and how to use it with local hugginface models like in this example, see https://github.com/HazyResearch/manifest\n",
-    "\n",
-    "Another example of [using Manifest with Langchain](https://github.com/HazyResearch/manifest/blob/main/examples/langchain_chatgpt.ipynb)."
+    "For more detailed information on `manifest`, and how to use it with local hugginface models like in this example, see https://github.com/HazyResearch/manifest"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1205d1e4-e6da-4d67-a0c7-b7e8fd1e98d5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install manifest-ml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "04a0170a",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from manifest import Manifest\n",
@@ -47,12 +31,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "de250a6a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'model_name': 'bigscience/T0_3B', 'model_path': 'bigscience/T0_3B'}\n"
+     ]
+    }
+   ],
    "source": [
     "manifest = Manifest(\n",
     "    client_name = \"huggingface\",\n",
@@ -212,7 +202,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {

docs/modules/models/llms/integrations/modal.ipynb

@@ -5,60 +5,7 @@
    "metadata": {},
    "source": [
     "# Modal\n",
-    "\n",
-    "The [Modal Python Library](https://modal.com/docs/guide) provides convenient, on-demand access to serverless cloud compute from Python scripts on your local computer. \n",
-    "The `Modal` itself does not provide any LLMs but only the infrastructure.\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with `Modal`.\n",
-    "\n",
-    "[Here](https://modal.com/docs/guide/ex/potus_speech_qanda) is another example how to use LangChain to interact with `Modal`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install modal-client"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[?25lLaunching login page in your browser window\u001b[33m...\u001b[0m\n",
-      "\u001b[2KIf this is not showing up, please copy this URL into your web browser manually:\n",
-      "\u001b[2Km⠙\u001b[0m Waiting for authentication in the web browser...\n",
-      "\u001b]8;id=417802;https://modal.com/token-flow/tf-ptEuGecm7T1T5YQe42kwM1\u001b\\\u001b[4;94mhttps://modal.com/token-flow/tf-ptEuGecm7T1T5YQe42kwM1\u001b[0m\u001b]8;;\u001b\\\n",
-      "\n",
-      "\u001b[2K\u001b[32m⠙\u001b[0m Waiting for authentication in the web browser...\n",
-      "\u001b[1A\u001b[2K^C\n",
-      "\n",
-      "\u001b[31mAborted.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "# register and get a new token\n",
-    "\n",
-    "!modal token new"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Follow [these instructions](https://modal.com/docs/guide/secrets) to deal with secrets."
+    "This example goes over how to use LangChain to interact with Modal models"
    ]
   },
   {
@@ -116,22 +63,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.12 ('palm')",
    "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"
+   "version": "3.9.12"
   },
+  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
@@ -139,5 +79,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/models/llms/integrations/nlpcloud.ipynb

@@ -1,171 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9597802c",
-   "metadata": {},
-   "source": [
-    "# NLP Cloud\n",
-    "\n",
-    "The [NLP Cloud](https://nlpcloud.io) serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, grammar and spelling correction, keywords and keyphrases extraction, chatbot, product description and ad generation, intent classification, text generation, image generation, blog post generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API.\n",
-    "\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with `NLP Cloud` [models](https://docs.nlpcloud.com/#models)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8e94b1ca-6e84-44c4-91ca-df7364c007f0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install nlpcloud"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ea7adb58-cabe-4a2c-b0a2-988fc3aac012",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get a token: https://docs.nlpcloud.com/#authentication\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "NLPCLOUD_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9cc2d68f-52a8-4a11-ba34-bb6c068e0b6a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"NLPCLOUD_API_KEY\"] = NLPCLOUD_API_KEY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6fb585dd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.llms import NLPCloud\n",
-    "from langchain import PromptTemplate, LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "035dea0f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "3f3458d9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm = NLPCloud()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a641dbd9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9f844993",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' Justin Bieber was born in 1994, so the team that won the Super Bowl that year was the San Francisco 49ers.'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
-    "\n",
-    "llm_chain.run(question)"
-   ]
-  }
- ],
- "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": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/models/llms/integrations/openai.ipynb

@@ -6,57 +6,14 @@
    "metadata": {},
    "source": [
     "# OpenAI\n",
-    "\n",
-    "[OpenAI](https://platform.openai.com/docs/introduction) offers a spectrum of models with different levels of power suitable for different tasks.\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with `OpenAI` [models](https://platform.openai.com/docs/models)"
+    "This example goes over how to use LangChain to interact with OpenAI models"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "5d71df86-8a17-4283-83d7-4e46e7c06c44",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get a token: https://platform.openai.com/account/api-keys\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "OPENAI_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5472a7cd-af26-48ca-ae9b-5f6ae73c74d2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "6fb585dd",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.llms import OpenAI\n",
@@ -65,11 +22,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "035dea0f",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "template = \"\"\"Question: {question}\n",
@@ -81,11 +36,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "3f3458d9",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "llm = OpenAI()"
@@ -93,11 +46,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "id": "a641dbd9",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "llm_chain = LLMChain(prompt=prompt, llm=llm)"
@@ -105,19 +56,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "9f844993",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "' Justin Bieber was born in 1994, so we are looking for the Super Bowl winner from that year. The Super Bowl in 1994 was Super Bowl XXVIII, and the winner was the Dallas Cowboys.'"
+       "' Justin Bieber was born in 1994, so the NFL team that won the Super Bowl in that year was the Dallas Cowboys.'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -145,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {

docs/modules/models/llms/integrations/petals_example.ipynb

@@ -4,10 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Petals\n",
-    "\n",
-    "`Petals` runs 100B+ language models at home, BitTorrent-style.\n",
-    "\n",
+    "# Petals LLM Example\n",
     "This notebook goes over how to use Langchain with [Petals](https://github.com/bigscience-workshop/petals)."
    ]
   },
@@ -25,7 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip3 install petals"
+    "$ pip3 install petals"
    ]
   },
   {
@@ -37,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,37 +48,16 @@
    "metadata": {},
    "source": [
     "## Set the Environment API Key\n",
-    "Make sure to get [your API key](https://huggingface.co/docs/api-inference/quicktour#get-your-api-token) from Huggingface."
+    "Make sure to get your API key from Huggingface."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "from getpass import getpass\n",
-    "\n",
-    "HUGGINGFACE_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "os.environ[\"HUGGINGFACE_API_KEY\"] = HUGGINGFACE_API_KEY"
+    "os.environ[\"HUGGINGFACE_API_KEY\"] = \"YOUR_KEY_HERE\""
    ]
   },
   {
@@ -96,18 +72,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading:   1%|▏                        | 40.8M/7.19G [00:24<15:44, 7.57MB/s]"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# this can take several minutes to download big files!\n",
-    "\n",
     "llm = Petals(model_name=\"bigscience/bloom-petals\")"
    ]
   },
@@ -184,7 +150,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {
@@ -193,5 +159,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

docs/modules/models/llms/integrations/pipelineai_example.ipynb

@@ -1,171 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# PipelineAI\n",
-    "\n",
-    "PipelineAI allows you to run your ML models at scale in the cloud. It also provides API access to [several LLM models](https://pipeline.ai).\n",
-    "\n",
-    "This notebook goes over how to use Langchain with [PipelineAI](https://docs.pipeline.ai/docs)."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Install pipeline-ai\n",
-    "The `pipeline-ai` library is required to use the `PipelineAI` API, AKA `Pipeline Cloud`. Install `pipeline-ai` using `pip install pipeline-ai`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the package\n",
-    "!pip install pipeline-ai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from langchain.llms import PipelineAI\n",
-    "from langchain import PromptTemplate, LLMChain"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set the Environment API Key\n",
-    "Make sure to get your API key from PipelineAI. Check out the [cloud quickstart guide](https://docs.pipeline.ai/docs/cloud-quickstart). You'll be given a 30 day free trial with 10 hours of serverless GPU compute to test different models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.environ[\"PIPELINE_API_KEY\"] = \"YOUR_API_KEY_HERE\""
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create the PipelineAI instance\n",
-    "When instantiating PipelineAI, you need to specify the id or tag of the pipeline you want to use, e.g. `pipeline_key = \"public/gpt-j:base\"`. You then have the option of passing additional pipeline-specific keyword arguments:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = PipelineAI(pipeline_key=\"YOUR_PIPELINE_KEY\", pipeline_kwargs={...})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a Prompt Template\n",
-    "We will create a prompt template for Question and Answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initiate the LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run the LLMChain\n",
-    "Provide a question and run the LLMChain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
-    "\n",
-    "llm_chain.run(question)"
-   ]
-  }
- ],
- "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": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/modules/models/llms/integrations/predictionguard.ipynb

@@ -1,155 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# PredictionGuard\n",
-    "\n",
-    "How to use PredictionGuard wrapper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "3RqWPav7AtKL"
-   },
-   "outputs": [],
-   "source": [
-    "! pip install predictionguard langchain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "id": "2xe8JEUwA7_y"
-   },
-   "outputs": [],
-   "source": [
-    "import predictionguard as pg\n",
-    "from langchain.llms import PredictionGuard"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mesCTyhnJkNS"
-   },
-   "source": [
-    "## Basic LLM usage\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Ua7Mw1N4HcER"
-   },
-   "outputs": [],
-   "source": [
-    "pgllm = PredictionGuard(name=\"default-text-gen\", token=\"<your access token>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Qo2p5flLHxrB"
-   },
-   "outputs": [],
-   "source": [
-    "pgllm(\"Tell me a joke\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "v3MzIUItJ8kV"
-   },
-   "source": [
-    "## Chaining"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "pPegEZExILrT"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain import PromptTemplate, LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "suxw62y-J-bg"
-   },
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=pgllm, 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": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "l2bc26KHKr7n"
-   },
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
-    "\n",
-    "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "I--eSa2PLGqq"
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

docs/modules/models/llms/integrations/promptlayer_openai.ipynb

@@ -1,23 +1,18 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "959300d4",
    "metadata": {},
    "source": [
     "# PromptLayer OpenAI\n",
     "\n",
-    "`PromptLayer` is the first platform that allows you to track, manage, and share your GPT prompt engineering. `PromptLayer` acts a middleware between your code and `OpenAI’s` python library.\n",
-    "\n",
-    "`PromptLayer` records all your `OpenAI API` requests, allowing you to search and explore request history in the `PromptLayer` dashboard.\n",
-    "\n",
-    "\n",
-    "This example showcases how to connect to [PromptLayer](https://www.promptlayer.com) to start recording your OpenAI requests.\n",
-    "\n",
-    "Another example is [here](https://python.langchain.com/en/latest/ecosystem/promptlayer.html)."
+    "This example showcases how to connect to [PromptLayer](https://www.promptlayer.com) to start recording your OpenAI requests."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6a45943e",
    "metadata": {},
@@ -31,14 +26,13 @@
    "execution_count": null,
    "id": "dbe09bd8",
    "metadata": {
-    "tags": [],
     "vscode": {
      "languageId": "powershell"
     }
    },
    "outputs": [],
    "source": [
-    "!pip install promptlayer"
+    "pip install promptlayer"
    ]
   },
   {
@@ -51,11 +45,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "c16da3b5",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
@@ -64,6 +56,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8564ce7d",
    "metadata": {},
@@ -71,80 +64,21 @@
     "## Set the Environment API Key\n",
     "You can create a PromptLayer API Key at [www.promptlayer.com](https://www.promptlayer.com) by clicking the settings cog in the navbar.\n",
     "\n",
-    "Set it as an environment variable called `PROMPTLAYER_API_KEY`.\n",
-    "\n",
-    "You also need an OpenAI Key, called `OPENAI_API_KEY`."
+    "Set it as an environment variable called `PROMPTLAYER_API_KEY`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "1df96674-a9fb-4126-bb87-541082782240",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "from getpass import getpass\n",
-    "\n",
-    "PROMPTLAYER_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "46ba25dc",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "os.environ[\"PROMPTLAYER_API_KEY\"] = PROMPTLAYER_API_KEY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9aa68c46-4d88-45ba-8a83-18fa41b4daed",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "from getpass import getpass\n",
-    "\n",
-    "OPENAI_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "6023b6fa-d9db-49d6-b713-0e19686119b0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
+    "os.environ[\"PROMPTLAYER_API_KEY\"] = \"********\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "bf0294de",
    "metadata": {},
@@ -155,18 +89,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "3acf0069",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' to go outside\\n\\nUnfortunately, cats cannot go outside without being supervised by a human. Going outside can be dangerous for cats, as they may come into contact with cars, other animals, or other dangers. If you want to go outside, ask your human to take you on a supervised walk or to a safe, enclosed outdoor space.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "llm = PromptLayerOpenAI(pl_tags=[\"langchain\"])\n",
     "llm(\"I am a cat and I want\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "a2d76826",
    "metadata": {},
@@ -175,6 +119,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "05e9e2fe",
    "metadata": {},
@@ -199,6 +144,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7eb19139",
    "metadata": {},
@@ -210,7 +156,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -224,7 +170,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.8 (default, Apr 13 2021, 12:59:45) \n[Clang 10.0.0 ]"
   },
   "vscode": {
    "interpreter": {