First steps with llama index to use as graph/vector semantic search with reranking, sub-queries, ...
The intent of this code is to provide a simple example of how to use the llama index to perform semantic search on a graph of documents.
Most importantly it serves me as a playground to compare different models and approaches with each other through the overview written to CSV.
Files are in llamaindex_simple_graph_rag.py and lib/*.
This Python script is a driver for a system that processes data using language models. It uses environment variables for configuration, initializes a Retrieval-Augmented Generation (RAG) system, and runs a process for each specified language model.
The process includes keyword and vector tools for answering questions about relationships and semantic similarity, respectively. It also uses various selectors and a response synthesizer for data handling.
Errors are logged and written to an error file. The script is used for running various language models in a specific scenario to analyze text data.
Usage: ./build_run.sh <type> <model> <ai_model> <ident>
Examples:
./build_run.sh together mixtral-together mistralai/Mixtral-8x7B-Instruct-v0.1 AY-yahoo-content-no_sentiment-40
./build_run.sh ollama codeup ignore AY-yahoo-content-no_sentiment-40
Status: Draft
This script is designed to analyze and report the method calls made within each function across multiple Python files. Its primary purpose is to provide an overview of how different functions interact with other parts of the code, specifically focusing on which methods are called within each function.
This analysis can be useful for understanding code structure, debugging, or for refactoring purposes. Essentially, it creates a map of method usage throughout the given Python files.