Structured Data Extraction from ChatGPT with LangChain

Discover the two primary approaches to extract structured data from raw language model generations: Functions and Parsing. Dive deep into OpenAI functions, Pydantic library, and output parsers with examples and code snippets.

Code used in this video is in Discord Channel under References:

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[photon]

the amount of effort put into this video is well appreciated!

[lifeoff]

Hey MG,

Just wanted to say a big thank you for your videos! They've helped me a ton in creating my chatbot. You're a real pro, and each video is a goldmine of info.

I'm looking forward to your upcoming content. Keep it up!

Thanks again !!!

[joecok]

Both of you look so alike. Are you twins? There's no way to tell you apart. I really love seeing you both together.

[akrsrivastava]

Your videos should have more views/likes/subscribes.

[hitmusicworldwide]

Great one! I found out ( on the 2nd listening) that my questions were answered!

[Canna_Science_and_Technology]

It would be intriguing to craft a video by employing output parsers and functions during the interaction with a vector store. Engage in querying your own data, then export it as a JSON using OpenAI functions. Utilize the langchain output parser in conjunction with a qnachain to communicate with the vector store.

[souvikguria]

anyone tried this with langchain 0.2 or later versions of lagchain 0.1 ? cz, this code is deprecated

[orlandocastellanos]

how can i integrate a chromadb on azure? is it with blobstorage?

[Shoaibkhan-ojoe]

what if i use pydantic on gpt models which supports function calling, will it make the extraction more powerful or will it lag instead. ? I will be using the extraction on audio transcripts. Also, any way to effectively do dialization on the transcripts? which helps te model to understand who is the person and what details need to be captured even if there are multiple people in the transcript.