Beginners workshop to fine-tune OpenAI LLM model with enterprise web data in Python
This hands-on workshop shows how to use Langchain LLM application framework with Chroma embedding database to fine-tune an OpenAI GPT-3.5-Turbo LLM model on web data. The final solution returns ChatGPT like interface to your customer web data.
You will also learn:
- Why it is important to fine-tune LLM models with ad-hoc data
- How to use open-source libraries i.e Langchain, ChromaDB
The workshop code:
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== Video Timeline ==
(00:00) Content Intro
(00:58) The Problem
(04:12) The Solution
(06:13) Working Solution Demo
(09:10) Understanding Solution
(12:12) Open-source libs
(14:22) Web Data as source content
(12:30) Testing UI (without action)
(15:58) Python Libs Installed
(17:08) Python Coding Starts
(17:27) Setting OpenAI API Key
(18:16) Setting Embeddings
(20:12) Setting Chunk Splitter
(21:02) Setting embeddings Model
(21:43) Create & Persist Embeddings
(23:30) Test Embeddings Code
(26:08) Setting Langchain App
(27:30) Adding Query to KB DB
(29:18) Testing Query with KB
(30:24) Continuous Queries
(32:08) Opensource Libs Review
(32:41) OpenAI Billing
(33:34) Source code
(34:00) Recap
=== Open-source Libraries Used: ===
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Content Creator:
Avkash Chauhan (
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