LangChain Indexing API in Production - Indexing with LangChain & FastAPI

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Dive into the practical use of LangChain and FastAPI in this tutorial! We’ll be discussing how these technologies, coupled with the PgVector database, can be used to create a streamlined and efficient indexing process in production.

0:00 Why index with FastAPI
1:06 Code explanation
7:41 Use the Indexing endpoint
  1. Coding Crash Courses
  2. fastapi
  3. langchain
  4. embedding
  5. indexing api
  6. rest
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Комментарии
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This is very useful. Thank you very much for your work!😊

RealEstateD
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Thanks for the good videos, keep them coming!

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Hii awesome content man!
I’ve been following your tutorials to learn to build with Langchain, currently I’m working on a project that queries an api and returns a response which I loaded, splitted and indexed into PG vector like this, I also shipped to a server using LCEL chain.
My question is can I create a python script outside the Server which calls the server and returns asynchronously a list of output answer for every indexed embeddings without adding the async function to my LCEL chain?

oluwaseunakinropo
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Hey there, very helpful thank you and exactly what I'm looking for! How could I adapt this to query the embeddings directly without creating a prompt and going by gpt? Currently it is making things up to fill in the gaps

frazic
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Nice . May I know the vs code theme used in this video

karthikb.s.k.