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MIGRATE.md
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MIGRATE.md
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# Migrating to `langchain_experimental`
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# Migrating
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## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
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In an effort to make `langchain` leaner and safer, we are moving select chains to `langchain_experimental`.
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This migration has already started, but we are remaining backwards compatible until 7/28.
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On that date, we will remove functionality from `langchain`.
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Read more about the motivation and the progress [here](https://github.com/langchain-ai/langchain/discussions/8043).
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### Migrating to `langchain_experimental`
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We are moving any experimental components of LangChain, or components with vulnerability issues, into `langchain_experimental`.
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This guide covers how to migrate.
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## Installation
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### Installation
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Previously:
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@@ -13,7 +22,7 @@ Now (only if you want to access things in experimental):
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`pip install -U langchain langchain_experimental`
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## Things in `langchain.experimental`
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### Things in `langchain.experimental`
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Previously:
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@@ -23,7 +32,7 @@ Now:
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`from langchain_experimental import ...`
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## PALChain
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### PALChain
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Previously:
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`from langchain_experimental.pal_chain import PALChain`
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## SQLDatabaseChain
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### SQLDatabaseChain
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Previously:
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@@ -47,7 +56,7 @@ Alternatively, if you are just interested in using the query generation part of
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`from langchain.chains import create_sql_query_chain`
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## `load_prompt` for Python files
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### `load_prompt` for Python files
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Note: this only applies if you want to load Python files as prompts.
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If you want to load json/yaml files, no change is needed.
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96
README.md
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README.md
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[](https://libraries.io/github/langchain-ai/langchain)
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[](https://github.com/langchain-ai/langchain/issues)
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Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
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[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.
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Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team
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## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
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In an effort to make `langchain` leaner and safer, we are moving select chains to `langchain_experimental`.
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This migration has already started, but we are remaining backwards compatible until 7/28.
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On that date, we will remove functionality from `langchain`.
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Read more about the motivation and the progress [here](https://github.com/langchain-ai/langchain/discussions/8043).
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Read how to migrate your code [here](MIGRATE.md).
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Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team.
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## Quick Install
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`pip install langchain`
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or
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`pip install langsmith && conda install langchain -c conda-forge`
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With pip:
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```bash
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pip install langchain
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```
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## 🤔 What is this?
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With conda:
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```bash
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pip install langsmith && conda install langchain -c conda-forge
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```
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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.
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## 🤔 What is LangChain?
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This library aims to assist in the development of those types of applications. Common examples of these applications include:
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**LangChain** is a framework for developing applications powered by language models. It enables applications that:
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- **Are context-aware**: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
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- **Reason**: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)
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**❓ Question Answering over specific documents**
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This framework consists of several parts.
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- **LangChain Libraries**: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
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- **[LangChain Templates](templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.
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- **[LangServe](https://github.com/langchain-ai/langserve)**: A library for deploying LangChain chains as a REST API.
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- **[LangSmith](https://smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
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**This repo contains the `langchain` ([here](libs/langchain)), `langchain-experimental` ([here](libs/experimental)), and `langchain-cli` ([here](libs/cli)) Python packages, as well as [LangChain Templates](templates).**
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## 🧱 What can you build with LangChain?
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**❓ Retrieval augmented generation**
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- [Documentation](https://python.langchain.com/docs/use_cases/question_answering/)
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- End-to-end Example: [Question Answering over Notion Database](https://github.com/hwchase17/notion-qa)
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- End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
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**💬 Chatbots**
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**💬 Analyzing structured data**
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- [Documentation](https://python.langchain.com/docs/use_cases/chatbots/)
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- End-to-end Example: [Chat-LangChain](https://github.com/langchain-ai/chat-langchain)
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- [Documentation](https://python.langchain.com/docs/use_cases/qa_structured/sql)
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- End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain/tree/master/templates/sql-llama2)
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**🤖 Agents**
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**🤖 Chatbots**
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- [Documentation](https://python.langchain.com/docs/modules/agents/)
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- End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
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- [Documentation](https://python.langchain.com/docs/use_cases/chatbots)
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- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
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## 📖 Documentation
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And much more! Head to the [Use cases](https://python.langchain.com/docs/use_cases/) section of the docs for more.
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Please see [here](https://python.langchain.com) for full documentation on:
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## 🚀 How does LangChain help?
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The main value props of the LangChain libraries are:
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1. **Components**: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
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2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks
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- Getting started (installation, setting up the environment, simple examples)
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- How-To examples (demos, integrations, helper functions)
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- Reference (full API docs)
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- Resources (high-level explanation of core concepts)
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Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
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## 🚀 What can this help with?
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Components fall into the following **modules**:
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There are six main areas that LangChain is designed to help with.
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These are, in increasing order of complexity:
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**📃 LLMs and Prompts:**
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**📃 Model I/O:**
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This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
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**🔗 Chains:**
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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.
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**📚 Data Augmented Generation:**
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**📚 Retrieval:**
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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.
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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.
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**🧠 Memory:**
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## 📖 Documentation
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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.
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Please see [here](https://python.langchain.com) for full documentation, which includes:
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**🧐 Evaluation:**
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- [Getting started](https://python.langchain.com/docs/get_started/introduction): installation, setting up the environment, simple examples
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- Overview of the [interfaces](https://python.langchain.com/docs/expression_language/), [modules](https://python.langchain.com/docs/modules/) and [integrations](https://python.langchain.com/docs/integrations/providers)
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- [Use case](https://python.langchain.com/docs/use_cases/qa_structured/sql) walkthroughs and best practice [guides](https://python.langchain.com/docs/guides/adapters/openai)
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- [LangSmith](https://python.langchain.com/docs/langsmith/), [LangServe](https://python.langchain.com/docs/langserve), and [LangChain Template](https://python.langchain.com/docs/templates/) overviews
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- [Reference](https://api.python.langchain.com): full API docs
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[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is by using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
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For more information on these concepts, please see our [full documentation](https://python.langchain.com).
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## 💁 Contributing
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