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46 lines
1.7 KiB
Markdown
46 lines
1.7 KiB
Markdown
---
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layout: default
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title: "DSPy"
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nav_order: 9
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has_children: true
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---
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# Tutorial: DSPy
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> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
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DSPy<sup>[View Repo](https://github.com/stanfordnlp/dspy/tree/7cdfe988e6404289b896d946d957f17bb4d9129b/dspy)</sup> helps you build and optimize *programs* that use **Language Models (LMs)** and **Retrieval Models (RMs)**.
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Think of it like composing Lego bricks (**Modules**) where each brick performs a specific task (like generating text or retrieving information).
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**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.
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```mermaid
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flowchart TD
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A0["Module / Program"]
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A1["Signature"]
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A2["Predict"]
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A3["LM (Language Model Client)"]
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A4["RM (Retrieval Model Client)"]
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A5["Teleprompter / Optimizer"]
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A6["Example"]
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A7["Evaluate"]
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A8["Adapter"]
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A9["Settings"]
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A0 -- "Contains / Composes" --> A0
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A0 -- "Uses (via Retrieve)" --> A4
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A1 -- "Defines structure for" --> A6
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A2 -- "Implements" --> A1
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A2 -- "Calls" --> A3
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A2 -- "Uses demos from" --> A6
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A2 -- "Formats prompts using" --> A8
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A5 -- "Optimizes" --> A0
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A5 -- "Fine-tunes" --> A3
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A5 -- "Uses training data from" --> A6
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A5 -- "Uses metric from" --> A7
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A7 -- "Tests" --> A0
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A7 -- "Evaluates on dataset of" --> A6
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A8 -- "Translates" --> A1
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A8 -- "Formats demos from" --> A6
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A9 -- "Configures default" --> A3
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A9 -- "Configures default" --> A4
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A9 -- "Configures default" --> A8
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``` |