Check out these new guides for 13 popular LLM use-cases. Part of a major community effort to improve the @LangChainAI docs + add CoLabs prototyping.
1/13: Open source LLMs
How to use many open source LLMs on your device
python.langchain.com/docs/guides/lo…
2/13: Agents
How to quickly test various types of agents
python.langchain.com/docs/use_cases…
3/13: RAG (retrieval augmented generation)
How to do RAG at multiple levels of abstraction
python.langchain.com/docs/use_cases…
4/13: Fine-tuning
How to fine tune LLMs (gpt-3.5, LLaMA, etc)
blog.langchain.dev/using-langsmit…
5/13: Private RAG
How to do RAG w/ local LLM/embeddings/vectorstore
python.langchain.com/docs/use_cases…
6/13: Web scraping
How to do web scraping / research w/ LLMs
python.langchain.com/docs/use_cases…
7/13: Summarization
How to summarize small or large docs w/ LLMs
python.langchain.com/docs/use_cases…
8/13: Tagging
How to annotate/tag content w/ LLMs
python.langchain.com/docs/use_cases…
9/13: Text-to-SQL
How to interact w/ SQL DBs using LLMs
python.langchain.com/docs/use_cases…
10/13: Extraction
How to extract structured output from LLMs
python.langchain.com/docs/use_cases…
11/13: Code understanding
How to perform QA on code bases w/ LLMs
python.langchain.com/docs/use_cases…
12/13: Chatbots
How to build LLM-powered chatbots
python.langchain.com/docs/use_cases…
13/13: APIs
How to connect LLMs w/ external APIs
python.langchain.com/docs/use_cases…
All include LangSmith traces to visualize what is going on under the hood (chain information flow + prompts) + most include CoLab notebooks for prototyping.
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