Tutorial-Codebase-Knowledge/output/DSPy/index.md

Tutorial: DSPy

DSPy helps you build and optimize programs that use Language Models (LMs) and Retrieval Models (RMs). Think of it like composing Lego bricks (Modules) where each brick performs a specific task (like generating text or retrieving information). Signatures define what each Module does (its inputs and outputs), and Teleprompters automatically tune these modules (like optimizing prompts or examples) to get the best performance on your data.

Source Repository: https://github.com/stanfordnlp/dspy/tree/7cdfe988e6404289b896d946d957f17bb4d9129b/dspy

flowchart TD
    A0["Module / Program"]
    A1["Signature"]
    A2["Predict"]
    A3["LM (Language Model Client)"]
    A4["RM (Retrieval Model Client)"]
    A5["Teleprompter / Optimizer"]
    A6["Example"]
    A7["Evaluate"]
    A8["Adapter"]
    A9["Settings"]
    A0 -- "Contains / Composes" --> A0
    A0 -- "Uses (via Retrieve)" --> A4
    A1 -- "Defines structure for" --> A6
    A2 -- "Implements" --> A1
    A2 -- "Calls" --> A3
    A2 -- "Uses demos from" --> A6
    A2 -- "Formats prompts using" --> A8
    A5 -- "Optimizes" --> A0
    A5 -- "Fine-tunes" --> A3
    A5 -- "Uses training data from" --> A6
    A5 -- "Uses metric from" --> A7
    A7 -- "Tests" --> A0
    A7 -- "Evaluates on dataset of" --> A6
    A8 -- "Translates" --> A1
    A8 -- "Formats demos from" --> A6
    A9 -- "Configures default" --> A3
    A9 -- "Configures default" --> A4
    A9 -- "Configures default" --> A8

Chapters

1. Module / Program
2. Signature
3. Example
4. Predict
5. LM (Language Model Client)
6. RM (Retrieval Model Client)
7. Evaluate
8. Teleprompter / Optimizer
9. Adapter
10. Settings

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