Manual prompting in AI is becoming obsolete, with DSPy offering a revolutionary approach to optimize language model (LM) prompts algorithmically. DSPy uses signatures, modules, metrics, and optimizers to attain consistent and reproducible results across different LMs. This guide details the step-by-step process of integrating DSPy with Qdrant for vector databases and Ollama for local LLM deployments. Highlights include dataset loading, creating a vector database, and implementing a Chain of Thought Reasoning with a RAG model.
Table of contents
Building a Chain of Thought RAG Model with DSPy, Qdrant and OllamaPart 1. SetupPart 2. DatasetPart 3: Initialize Qdrant Client and Encode TextsPart 4. Initialize Llama2 Model Using DSPy-Ollama IntegrationPart 5: Define Signatures for Input and OutputPart 6: Create a DSPy CoT ModulePart 7: Generate AnswersSort: