DSPy RAG with LlamaIndex — Programming LLMs over Prompting
Fellow Developer: Hi Phil, when exactly should I use DSPy over LlamaIndex or LangChain when building LM apps over my data?
Me (Phil): Actually, they are not mutually exclusive. You can use DSPy and LlamaIndex or LangChain in one app. However, the philosophy and abstraction of DSPy differ so significantly from that of LlamaIndex and Langchain that it is usually straightforward to decide when DSPy is (or isn’t) the right framework for your use case.
From my experience, LangChain and LlamaIndex are amazing libraries for high-level application development, especially when all you need are generic, off-the-shelf prompts for question answering over PDFs or standard text-to-SQL and rich ecosystems for integrations.
However, DSPy shines over LangChain and LlamaIndex when you seek a more tailored, adaptable approach to LM application development. DSPy offers powerful, general-purpose modules that dynamically adjust prompts or finetune LMs within your pipeline. It provides unmatched modularity and automatic optimization, ideal for achieving high-quality results with minimal effort.
DSPy introduces an automatic compiler that teaches LMs how to conduct the declarative steps in your program. The DSPy compiler will internally trace your program and then craft high-quality prompts for large LMs (or train automatic finetunes for small LMs) to teach them the steps of your task.
Here is a step-by-step illustration of how DSPy and LlamaIndex can coexist, and how DSPy uses training datasets, optimizers, and bootstrapping within a standard DNN (Deep Neural Network) supervised learning setup to optimize LM outputs in a RAG system. Full code here
- Setting up modules and set env variables
Combining LlamaIndex and DSPy enhances RAG systems by leveraging LlamaIndex’s document handling and DSPy’s LM optimization. Choosing the default BootstrapFewShot teleprompter in DSPy ensures efficient program compilation with minimal setup, making it an ideal choice for initiating simple RAG workflows.
Step 2: Build a simple RAG with Query Engine as an Index
In DSPy systems, indexes streamline data retrieval, enhancing LM and RM performance. DSPy supports popular vector databases like Pinecone, Chroma, and Weaviate, ensuring versatile integration. LlamaParse stands as the premier PDF parsing tool, enriching the system’s document processing capabilities.
Step 3: Define Signatures, Modules, and Predictor
In DSPy, defining signatures is pivotal as each LM call necessitates one. A signature succinctly describes the task, input, and output fields, facilitating clear task delineation.
Declaratively defining modules in DSPy promotes a modular approach, focusing on pipeline information flow. DSPy optimizes LM usage automatically, tailored to each pipeline, enhancing efficiency.
Defining predictors in DSPy is essential. Predictors encapsulate LM usage for a given signature, learning to adapt behavior to tasks.
Step 4: Set up Validation Logic and curate training Dataset
The validation logic ensures accurate prediction by verifying the correctness of answers and contextual relevance. In DSPy RAGs, this logic is integral for maintaining model fidelity. Diverse training datasets, like the one prepared, ensure robust learning and generalization. Larger datasets enhance model proficiency, covering varied scenarios and improving performance across tasks.
Step 5: Choose Teleprompter, Compile and Query the System
DSPy’s teleprompters, like BootstrapFewShot, excel in optimizing programs by learning to select effective prompts for modules, promoting efficient LM usage. With more bootstrapped traces, the system gains deeper insights, enhancing adaptability and improving performance across diverse tasks, demonstrating DSPy’s versatility and robustness.
Conclusion
Overall, DSPy’s philosophy of programming over prompting offers composable and declarative modules in Python syntax. Its automatic compiler crafts high-quality prompts for LMs based on program steps, enhancing accuracy and versatility in RAG systems through unified techniques and minimalistic operations. Check the full code here 👉 Code
