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* feat(tools): added google maps and DSPy * updated docs * updated broken import path * updated icon
113 lines
5.4 KiB
Plaintext
113 lines
5.4 KiB
Plaintext
---
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title: DSPy
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description: Run predictions using self-hosted DSPy programs
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---
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import { BlockInfoCard } from "@/components/ui/block-info-card"
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<BlockInfoCard
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type="dspy"
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color="#E0E0E0"
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/>
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{/* MANUAL-CONTENT-START:intro */}
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[DSPy](https://github.com/stanford-oval/dspy) is an open-source framework for programming—rather than prompting—language models. DSPy enables you to build interpretable and modular LLM-powered agents using Python functions, structured modules, and declarative signatures, making it easy to compose, debug, and reliably deploy language model applications.
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With DSPy in Sim, you can:
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- **Run custom predictions**: Connect your self-hosted DSPy server and invoke prediction endpoints for a variety of natural language tasks.
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- **Chain of Thought and ReAct reasoning**: Leverage advanced DSPy modules for step-by-step reasoning, multi-turn dialogs, and action-observation loops.
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- **Integrate with your workflows**: Automate LLM predictions and reasoning as part of any Sim automation or agent routine.
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- **Provide custom endpoints and context**: Flexibly call your own DSPy-powered APIs with custom authentication, endpoints, input fields, and context.
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These features let your Sim agents access modular, interpretable LLM-based programs for tasks like question answering, document analysis, decision support, and more—where you remain in control of the model, data, and logic.
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{/* MANUAL-CONTENT-END */}
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## Usage Instructions
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Integrate with your self-hosted DSPy programs for LLM-powered predictions. Supports Predict, Chain of Thought, and ReAct agents. DSPy is the framework for programming—not prompting—language models.
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## Tools
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### `dspy_predict`
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Run a prediction using a self-hosted DSPy program endpoint
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#### Input
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| Parameter | Type | Required | Description |
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| --------- | ---- | -------- | ----------- |
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| `baseUrl` | string | Yes | Base URL of the DSPy server \(e.g., https://your-dspy-server.com\) |
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| `apiKey` | string | No | API key for authentication \(if required by your server\) |
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| `endpoint` | string | No | API endpoint path \(defaults to /predict\) |
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| `input` | string | Yes | The input text to send to the DSPy program |
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| `inputField` | string | No | Name of the input field expected by the DSPy program \(defaults to "text"\) |
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| `context` | string | No | Additional context to provide to the DSPy program |
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| `additionalInputs` | json | No | Additional key-value pairs to include in the request body |
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#### Output
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| Parameter | Type | Description |
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| --------- | ---- | ----------- |
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| `answer` | string | The main output/answer from the DSPy program |
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| `reasoning` | string | The reasoning or rationale behind the answer \(if available\) |
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| `status` | string | Response status from the DSPy server \(success or error\) |
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| `rawOutput` | json | The complete raw output from the DSPy program \(result.toDict\(\)\) |
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### `dspy_chain_of_thought`
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Run a Chain of Thought prediction using a self-hosted DSPy ChainOfThought program endpoint
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#### Input
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| Parameter | Type | Required | Description |
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| --------- | ---- | -------- | ----------- |
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| `baseUrl` | string | Yes | Base URL of the DSPy server \(e.g., https://your-dspy-server.com\) |
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| `apiKey` | string | No | API key for authentication \(if required by your server\) |
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| `endpoint` | string | No | API endpoint path \(defaults to /predict\) |
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| `question` | string | Yes | The question to answer using chain of thought reasoning |
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| `context` | string | No | Additional context to provide for answering the question |
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#### Output
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| Parameter | Type | Description |
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| --------- | ---- | ----------- |
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| `answer` | string | The answer generated through chain of thought reasoning |
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| `reasoning` | string | The step-by-step reasoning that led to the answer |
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| `status` | string | Response status from the DSPy server \(success or error\) |
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| `rawOutput` | json | The complete raw output from the DSPy program \(result.toDict\(\)\) |
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### `dspy_react`
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Run a ReAct agent using a self-hosted DSPy ReAct program endpoint for multi-step reasoning and action
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#### Input
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| Parameter | Type | Required | Description |
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| --------- | ---- | -------- | ----------- |
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| `baseUrl` | string | Yes | Base URL of the DSPy server \(e.g., https://your-dspy-server.com\) |
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| `apiKey` | string | No | API key for authentication \(if required by your server\) |
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| `endpoint` | string | No | API endpoint path \(defaults to /predict\) |
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| `task` | string | Yes | The task or question for the ReAct agent to work on |
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| `context` | string | No | Additional context to provide for the task |
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| `maxIterations` | number | No | Maximum number of reasoning iterations \(defaults to server setting\) |
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#### Output
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| Parameter | Type | Description |
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| --------- | ---- | ----------- |
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| `answer` | string | The final answer or result from the ReAct agent |
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| `reasoning` | string | The overall reasoning summary from the agent |
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| `trajectory` | array | The step-by-step trajectory of thoughts, actions, and observations |
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| ↳ `thought` | string | The reasoning thought at this step |
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| ↳ `toolName` | string | The name of the tool/action called |
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| ↳ `toolArgs` | json | Arguments passed to the tool |
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| ↳ `observation` | string | The observation/result from the tool execution |
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| `status` | string | Response status from the DSPy server \(success or error\) |
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| `rawOutput` | json | The complete raw output from the DSPy program \(result.toDict\(\)\) |
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