Datasets:

yuricaetano

/

novodataset

like 0

15e1902db202-0

.rst .pdf Welcome to LangChain Contents Getting Started Modules Use Cases Reference Docs LangChain Ecosystem Additional Resources Welcome to LangChain# LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also: Be data-aware: connect a language model to other sources of data Be agentic: allow a language model to interact with its environment The LangChain framework is designed with the above principles in mind. This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see here. For the JavaScript documentation, see here. Getting Started# Checkout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application. Getting Started Documentation Modules# There are several main modules that LangChain provides support for. For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides. These modules are, in increasing order of complexity: Models: The various model types and model integrations LangChain supports. Prompts: This includes prompt management, prompt optimization, and prompt serialization. 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. Indexes: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that. Chains: 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.

https://langchain.readthedocs.io/en/latest/index.html

15e1902db202-1

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. 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: Autonomous agents are long running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI. Agent Simulations: 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: The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data. Question Answering: The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer. Chatbots: Since language models are good at producing text, that makes them ideal for creating chatbots. Querying Tabular Data: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page. Code Understanding: If you want to understand how to use LLMs to query source code from github, you should read this page. Interacting with APIs: Enabling LLMs to interact with APIs is extremely powerful in order to give them more up-to-date information and allow them to take actions. Extraction: Extract structured information from text. Summarization: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.

https://langchain.readthedocs.io/en/latest/index.html

15e1902db202-2

Evaluation: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this. Reference Docs# All of LangChain’s reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain. Reference Documentation LangChain Ecosystem# Guides for how other companies/products can be used with LangChain LangChain Ecosystem Additional Resources# Additional collection of resources we think may be useful as you develop your application! LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents. Glossary: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not! Gallery: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications. Deployments: A collection of instructions, code snippets, and template repositories for deploying LangChain apps. Tracing: A guide on using tracing in LangChain to visualize the execution of chains and agents. Model Laboratory: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so. Discord: Join us on our Discord to discuss all things LangChain! YouTube: A collection of the LangChain tutorials and videos. Production Support: 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. next Quickstart Guide Contents Getting Started Modules Use Cases Reference Docs LangChain Ecosystem Additional Resources By Harrison Chase © Copyright 2023, Harrison Chase.

https://langchain.readthedocs.io/en/latest/index.html

15e1902db202-3

By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Apr 27, 2023.

https://langchain.readthedocs.io/en/latest/index.html