GPT-4 Codes near perfect Llama Index code using Vector DB from documentation
We will learn about how to crawl a documentation website. Extract self sufficient code blocks along with generated explanations and create a dataset of the entire Llama-Index documentation. Then we will embed it and search over it using similarity search and let GPT-4 generate entire Llama index applications from simple descriptions
Extracting self sufficient code chunks cost $30 in API usage. You can download them below ⬇️⬇️⬇️
Code files with extracted code dataset and Vectordb download:
/ 88848022
Download code files without extracted code and embeddings csv:
/ 88848023
Langchain Dynamo video: • GPT-4 Codes near perfect Langchain code us...
Code extraction for Langchain costs $75 You can download it below⬇️⬇️
Langchain Dynamo full code files: / all-files-for-4-87972378
Langchain Dynamo Code without embeddings and extracted code:
/ code-files-for-4-87972389
Source code for AUTO AGI: / code-files-for-87530987
Search 140+ echohive videos and code download links:
https://www.echohive.live/
Chat with us on Discord:
/ discord
LLM Paper Summaries:
https://llmpapers.up.railway.app/
Try the GPT-4 Auto Coder app:
https://gpt4autocoder.streamlit.app/
CHAPTERS:
00:00 Intro
00:34 DEMO
02:43 Code Download
03:34 Documentation Crawler Code overview
04:09 Docs cleaner code overview
07:11 Code Extractor explanation and review
09:45 With vs Without API reference?
10:26 Embedding file code overview
11.18 echohive AI Academy website
11:46 Building Llama index applications file review
#llamaindex #gpt4 #openai #openaiembeddings
#vectordb #artificialintelligence #pythonprojects #similaritysearch #gpt