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985496f4be
Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
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921 B
Chatbots
Since language models are good at producing text, that makes them ideal for creating chatbots.
Aside from the base prompts/LLMs, an important concept to know for Chatbots is memory
.
Most chat based applications rely on remembering what happened in previous interactions, which is memory
is designed to help with.
The following resources exist:
- ChatGPT Clone: A notebook walking through how to recreate a ChatGPT-like experience with LangChain.
- Conversation Memory: A notebook walking through how to use different types of conversational memory.
Additional related resources include:
- Memory Key Concepts: Explanation of key concepts related to memory.
- Memory Examples: A collection of how-to examples for working with memory.