Building Corrective RAG from scratch with open-source, local LLMs

Summary

The transcript discusses building self-reflective retrieval-augmented generation (RAG) applications using open-source and local models. It highlights the concept of self-reflection in RAG, where the system grades the relevance of retrieved documents and performs knowledge refinement. The speaker introduces 'langra', a tool for implementing these ideas locally, and demonstrates its use with a local LLM and a CPU-optimized embedding model. The process involves creating a local index, grading documents, and using conditional logic to decide whether to perform web searches or generate responses. The transcript emphasizes the potential of logical flows and local models for complex reasoning tasks without the need for an agent.

Takeaways

• 🌟 The concept of self-reflection in RAG (Retrieval-Augmented Generation) is gaining popularity, allowing for more dynamic and relevant information retrieval and generation based on feedback loops.
• 📚 The 'Corrective RAG' (C-RAG) paper demonstrates a straightforward approach to self-reflection, by grading retrieved documents and refining knowledge based on their relevance and correctness.
• 💡 Implementing self-reflective RAG apps can be achieved using open-source and local models, which can run efficiently on a laptop without the need for large-scale, API-gated models.
• 🛠️ The LangChain team has developed a tool called 'langra' which facilitates the implementation of self-reflective RAG using local LLMs (Language Models).
• 🔍 For local information retrieval, the use of no embeddings and GPT for all embeddings from Nomic is suggested for its CPU optimization and effectiveness.
• 📈 The process of building a RAG app involves creating a graph of logical steps, where each node represents a specific operation or function, and the state is propagated through these steps.
• 🔗 The use of AMA (Alama) with the mraw 7B model is highlighted for its ability to run large-scale models locally and its support for JSON mode, which structures the model's output for easy interpretation and flow control.
• 🔄 The concept of logical gates is introduced, where the output from one step (e.g., document grading) determines the next step in the process (e.g., appending relevant documents or performing a web search).
• 🔍 The demonstration showcases a multi-step logical flow in action, including retrieval, grading, web search, question transformation, and generation, all running locally and seamlessly integrated.
• 🚀 The potential of using local models in a constrained, step-by-step manner is emphasized over using them as agent executors, which can lead to more reliable and effective logical reasoning tasks.

Q & A

What is the main focus of the Lang Chain team's discussion?

The main focus of the Lang Chain team's discussion is building self-reflective rag (retrieval-augmented generation) applications from scratch, using only open source and local models that run strictly on a laptop.

What is the significance of self-reflection in rag research?

Self-reflection in rag research is significant as it allows the system to perform retrieval based on a question from an index, assess the relevance or quality of the retrieved documents, and perform reasoning to potentially retry various steps, leading to more accurate and refined outputs.

How does the concept of self-reflection improve the rag process?

The concept of self-reflection improves the rag process by allowing the system to not just perform a single-shot retrieval and generation but to also self-reflect, reason, and retry steps from alternative sources, leading to enhanced accuracy and relevance in the final output.

What is the role of local LLMs in the discussed approach?

Local LLMs play a crucial role in the discussed approach as they are smaller and more manageable models that run locally on a system, allowing for efficient and fast processing without relying on API-gated, large-scale models.

How does the 'corrective rag' paper contribute to the self-reflection idea?

The 'corrective rag' paper contributes to the self-reflection idea by demonstrating a method where the system performs retrieval, grades the documents based on relevance, refines knowledge when documents are correct, and performs a web search to supplement retrieval when documents are ambiguous or incorrect.

What is the benefit of using open source tools like AMA and Langra for local model implementation?

The benefit of using open source tools like AMA and Langra for local model implementation is that they provide an easy, efficient, and seamless way to run models locally, enabling users to leverage powerful machine learning capabilities without the need for extensive infrastructure or API access.

How does the use of GPT for all embeddings from nomic enhance the local indexing process?

The use of GPT for all embeddings from nomic enhances the local indexing process by providing a CPU-optimized, contrastively trained embedding model that works well locally, ensuring fast and efficient document indexing without relying on external APIs or cloud services.

What is the purpose of the conditional edge in the logical flow of the rag process?

The purpose of the conditional edge in the logical flow of the rag process is to make decisions based on the output of certain nodes, such as the grading step, to determine the next course of action, like whether to append a relevant document or perform a web search to supplement the retrieval.

How does the JSON mode in AMA help in constraining the output of the local LLM?

The JSON mode in AMA helps in constraining the output of the local LLM by enforcing a specific output format, such as a binary yes/no score in JSON, which makes it easier to interpret and process the model's output within the logical flow of the rag application.

What is the key takeaway from the discussion on building logical flows using local models and Lang graph?

The key takeaway is that building logical flows using local models and Lang graph allows for the creation of reliable and efficient rag applications by breaking down the process into a series of logical steps, each performed by the local model, without the need for a complex agent executor. This approach enhances the reliability and manageability of the system.