Enhance LLM Factuality with Self-RAG
Enhance LLM factuality with Self-Reflective Retrieval-Augmented Generation. Improves quality and accuracy by combining retrieval and self-reflection.
Why it matters
Improve the quality and factuality of LLM responses by integrating retrieval and self-reflection mechanisms. This pack enables LLMs to generate more accurate and reliable information.
Outcomes
What it gets done
Implement Self-Reflective Retrieval-Augmented Generation (Self-RAG).
Combine retrieval and self-reflection for enhanced LLM output.
Adapt existing Self-RAG implementations for practical use.
Provide a short-form pack for easy integration.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/li-pack-packs-self-rag | bash Steps
Steps in the chain
Use llamaindex-cli to download the SelfRAGPack: llamaindex-cli download-llamapack SelfRAGPack --download-dir ./self_rag_pack
Install the huggingface-hub package: pip3 install -q huggingface-hub
Download the selfrag_llama2_7b model using huggingface-cli: huggingface-cli download m4r1/selfrag_llama2_7b-GGUF selfrag_llama2_7b.q4_k_m.gguf --local-dir "<DIR_PATH>" --local-dir-use-symlinks False
Use download_llama_pack to download and install dependencies: SelfRAGPack = download_llama_pack("SelfRAGPack", "./self_rag_pack")
Create a SelfRAGQueryEngine instance with model_path, retriever, and verbose parameters: query_engine = SelfRAGQueryEngine(model_path=model_path, retriever=retriever, verbose=True)
Execute a query using the query engine: response = query_engine.query("Who won best Director in the 1972 Academy Awards?")
Overview
Simple self-RAG short form pack
What it does
This LlamaPack implements a short-form version of the Self-Reflective Retrieval-Augmented Generation (Self-RAG) framework. It enhances the quality and factuality of Large Language Models (LLMs) by integrating retrieval and self-reflection mechanisms.
How it connects
Use this pack to improve the factual accuracy and overall quality of LLM-generated responses, especially when grounding information in retrieved documents is critical. It is ideal for applications demanding higher reliability in AI outputs.
Source README
Description pending for li-pack-packs-self-rag.
Step 1: Download the SelfRAGPack using CLI
Use llamaindex-cli to download the SelfRAGPack: llamaindex-cli download-llamapack SelfRAGPack --download-dir ./self_rag_pack
Step 2: Install huggingface-hub dependency
Install the huggingface-hub package: pip3 install -q huggingface-hub
Step 3: Download the SelfRAG model
Download the selfrag_llama2_7b model using huggingface-cli: huggingface-cli download m4r1/selfrag_llama2_7b-GGUF selfrag_llama2_7b.q4_k_m.gguf --local-dir "<DIR_PATH>" --local-dir-use-symlinks False
Step 4: Download and install the LlamaPack
Use download_llama_pack to download and install dependencies: SelfRAGPack = download_llama_pack("SelfRAGPack", "./self_rag_pack")Step 5: Initialize the SelfRAGQueryEngine
Create a SelfRAGQueryEngine instance with model_path, retriever, and verbose parameters: query_engine = SelfRAGQueryEngine(model_path=model_path, retriever=retriever, verbose=True)
Step 6: Query the engine
Execute a query using the query engine: response = query_engine.query("Who won best Director in the 1972 Academy Awards?")Discussion
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