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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>
15 lines
921 B
Markdown
15 lines
921 B
Markdown
# Chatbots
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Since language models are good at producing text, that makes them ideal for creating chatbots.
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Aside from the base prompts/LLMs, an important concept to know for Chatbots is `memory`.
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Most chat based applications rely on remembering what happened in previous interactions, which is `memory` is designed to help with.
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The following resources exist:
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- [ChatGPT Clone](../modules/memory/examples/chatgpt_clone.ipynb): A notebook walking through how to recreate a ChatGPT-like experience with LangChain.
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- [Conversation Memory](../modules/memory/getting_started.ipynb): A notebook walking through how to use different types of conversational memory.
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Additional related resources include:
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- [Memory Key Concepts](../modules/memory/key_concepts.md): Explanation of key concepts related to memory.
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- [Memory Examples](../modules/memory/how_to_guides.rst): A collection of how-to examples for working with memory.
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