Building Corrective RAG from scratch with open-source, local LLMs
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4 months ago
Published on Apr 22, 2024
This response is partially generated with the help of AI. It may contain inaccuracies.
Table of Contents
Step-by-Step Tutorial: Building Corrective RAG with Open-Source, Local LLMs
1. Setting Up Local LLMs
- Download the AMA application on your laptop to run models locally.
- Choose a model from the model list, such as "mrol instruct," and install it using pip.
- Define a variable for the downloaded model, like
local_llm = mrol_instruct.
2. Creating an Index
- Identify a document or blog post to use as an index for retrieval.
- Split the content into chunks and use GPT embeddings for indexing.
3. Creating a Retriever
- Use a local vector collection tool like
Tav webto create a retriever from the embeddings. - Retrieve relevant documents by calling
get_relevant_documents().
4. Designing the Logical Flow
- Lay out the logical steps and transformations in a graph or dictionary format.
- Implement functions for each node and conditional edge in the graph.
- Use JSON mode from AMA to structure the output for reliable interpretation downstream.
5. Implementing Logical Flow
- Create functions for each node in the graph to modify the state based on the logic.
- Use conditional statements to make decisions based on the grading results.
- Connect the nodes and edges to form the complete logical flow.
6. Running the Logical Flow
- Compile your question and execute the logical flow.
- Monitor the output at each step to ensure the correct processing of documents and decisions.
- Verify the final output for accuracy and relevance.
7. Reviewing the Results
- Analyze the output generated by the logical flow.
- Check the grading of documents and the decision-making process.
- Validate the answers and insights provided by the system.
By following these steps, you can build a Corrective RAG system from scratch using open-source tools and local LLMs for self-reflection and reasoning tasks.