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docs: update "quickstart" tutorial #28096

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docs: update "quickstart" tutorial #28096

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b6e68a5
update
ccurme Nov 13, 2024
548fea7
update cassettes
ccurme Nov 13, 2024
c3d0722
fix broken link
ccurme Nov 13, 2024
e6b01e7
Merge branch 'master' into cc/docs_quickstart
ccurme Nov 14, 2024
360d59d
remove deployment section
ccurme Nov 14, 2024
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2 changes: 1 addition & 1 deletion docs/cassettes/llm_chain_1b2481f0.msgpack.zlib
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Expand Up @@ -6,15 +6,15 @@ sidebar_class_name: hidden

New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications.

If you're looking to get up and running quickly with [chat models](/docs/integrations/chat/), [vector stores](/docs/integrations/vectorstores/),
If you're looking to get started with [chat models](/docs/integrations/chat/), [vector stores](/docs/integrations/vectorstores/),
or other LangChain components from a specific provider, check out our supported [integrations](/docs/integrations/providers/).

Refer to the [how-to guides](/docs/how_to) for more detail on using common LangChain components.

See the [conceptual documentation](/docs/concepts) for high level explanations of all LangChain concepts.

## Basics
- [LLM applications](/docs/tutorials/llm_chain): Build and deploy a simple LLM application.
- [LLM applications](/docs/tutorials/llm_chain): Build a simple LLM application with prompt templates and chat models.
- [Chatbots](/docs/tutorials/chatbot): Build a chatbot that incorporates memory.
- [Vector stores](/docs/tutorials/retrievers): Build vector stores and use them to retrieve data.
- [Agents](/docs/tutorials/agents): Build an agent that interacts with external tools.
Expand Down
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