langchain/docs/use_cases/chatbots.md

# 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](../modules/memory/examples/chatgpt_clone.ipynb): A notebook walking through how to recreate a ChatGPT-like experience with LangChain.
- [Conversation Memory](../modules/memory/getting_started.ipynb): A notebook walking through how to use different types of conversational memory.
- [Conversation Agent](../modules/memory/examples/conversational_agent.ipynb): A notebook walking through how to create an agent optimized for conversation.


Additional related resources include:
- [Memory Key Concepts](../modules/memory/key_concepts.md): Explanation of key concepts related to memory.
- [Memory Examples](../modules/memory/how_to_guides.rst): A collection of how-to examples for working with memory.
Docs refactor (#480) 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> 1 year ago			`# 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](../modules/memory/examples/chatgpt_clone.ipynb): A notebook walking through how to recreate a ChatGPT-like experience with LangChain.`
			`- [Conversation Memory](../modules/memory/getting_started.ipynb): A notebook walking through how to use different types of conversational memory.`
bump to 0058 (#556) 1 year ago			`- [Conversation Agent](../modules/memory/examples/conversational_agent.ipynb): A notebook walking through how to create an agent optimized for conversation.`
Docs refactor (#480) 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> 1 year ago

			`Additional related resources include:`
			`- [Memory Key Concepts](../modules/memory/key_concepts.md): Explanation of key concepts related to memory.`
			`- [Memory Examples](../modules/memory/how_to_guides.rst): A collection of how-to examples for working with memory.`