-
Notifications
You must be signed in to change notification settings - Fork 120
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Added Anthropic Agent for TS in documentation
- Loading branch information
1 parent
afe9f1b
commit 243b178
Showing
2 changed files
with
233 additions
and
1 deletion.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
231 changes: 231 additions & 0 deletions
231
docs/src/content/docs/agents/built-in/anthropic-agent.mdx
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,231 @@ | ||
| --- | ||
| title: Anthropic Agent | ||
| description: Documentation for the AnthropicAgent in the Multi-Agent Orchestrator | ||
| --- | ||
| ## Overview | ||
|
|
||
| The `AnthropicAgent` is a powerful and flexible agent class in the Multi-Agent Orchestrator System. | ||
| It leverages the [Anthropic API](https://docs.anthropic.com/en/api/getting-started) to interact with various Large Language Models (LLMs) provided by Anthropic, such as Claude. | ||
| This agent can handle a wide range of processing tasks, making it suitable for diverse applications such as conversational AI, question-answering systems, and more. | ||
|
|
||
| ## Key Features | ||
|
|
||
| - Integration with Anthropic's API | ||
| - Support for multiple LLM models available on Anthropic's platform | ||
| - Streaming and non-streaming response options | ||
| - Customizable inference configuration | ||
| - Ability to set and update custom system prompts | ||
| - Optional integration with retrieval systems for enhanced context | ||
| - Support for Tool use within the conversation flow | ||
|
|
||
| ## Creating a AnthropicAgent | ||
|
|
||
| By default, the **AnthropicAgent** uses the `claude-3-5-sonnet-20240620 model`. | ||
|
|
||
| ### Basic Example | ||
|
|
||
| To create a new **Anthropic Agent** with only the required parameters, use the following code: | ||
|
|
||
| import { Tabs, TabItem } from '@astrojs/starlight/components'; | ||
|
|
||
| <Tabs syncKey="runtime"> | ||
| <TabItem label="TypeScript" icon="seti:typescript" color="blue"> | ||
| ```typescript | ||
| import { AnthropicAgent } from 'multi-agent-orchestrator'; | ||
|
|
||
| const agent = new AnthropicAgent({ | ||
| name: 'Tech Agent', | ||
| description: 'Specializes in technology areas including software development, \ | ||
| hardware, AI, cybersecurity, blockchain, cloud computing, \ | ||
| emerging tech innovations,and pricing/costs related to \ | ||
| technology products and services.', | ||
| apiKey: 'your-anthropic-api-key-here' | ||
| }); | ||
| ``` | ||
| </TabItem> | ||
| <TabItem label="Python" icon="seti:python"> | ||
| ```python | ||
| ``` | ||
| </TabItem> | ||
| </Tabs> | ||
|
|
||
| In this basic example, only the `name`, `description` and `apiKey` are provided, which are the only required parameters for creating a AnthropicAgent. | ||
|
|
||
| ### Advanced Example | ||
|
|
||
| For more complex use cases, you can create a **Anthropic Agent** with all available options. All parameters except `name`, `description` and `apiKey` or `client` are optional: | ||
|
|
||
| <Tabs syncKey="runtime"> | ||
| <TabItem label="TypeScript" icon="seti:typescript" color="blue"> | ||
| ```typescript | ||
| import { AnthropicAgent, AnthropicAgentOptions, ParticipantRole } from 'multi-agent-orchestrator'; | ||
| import { Retriever } from '../retrievers/retriever'; | ||
| const options: AnthropicAgentOptions = { | ||
| name: 'My Advanced Anthropic Agent', | ||
| description: 'A versatile agent for complex NLP tasks', | ||
| apiKey: 'your-anthropic-api-key-here', | ||
| modelId: 'claude-3-opus-20240229', | ||
| streaming: true, | ||
| inferenceConfig: { | ||
| maxTokens: 1000, | ||
| temperature: 0.7, | ||
| topP: 0.9, | ||
| stopSequences: ['Human:', 'AI:'] | ||
| }, | ||
| retriever: new Retriever(), // Assuming you have a Retriever class implemented | ||
| toolConfig: { | ||
| tool: [ | ||
| { | ||
| type: 'function', | ||
| function: { | ||
| name: 'get_current_weather', | ||
| description: 'Get the current weather in a given location', | ||
| parameters: { | ||
| type: 'object', | ||
| properties: { | ||
| location: { | ||
| type: 'string', | ||
| description: 'The city and state, e.g. San Francisco, CA' | ||
| }, | ||
| unit: { type: 'string', enum: ['celsius', 'fahrenheit'] } | ||
| }, | ||
| required: ['location'] | ||
| } | ||
| } | ||
| } | ||
| ], | ||
| useToolHandler: (response, conversation) => { | ||
| // Process tool response | ||
| // Return processed response | ||
| return {role: ParticipantRole.USER, content: { | ||
| "type": "tool_result", | ||
| "tool_use_id": "tool_user_id", | ||
| "content": "Response from the tool" | ||
| }} | ||
| } | ||
| } | ||
| }; | ||
| const agent = new AnthropicAgent(options); | ||
| ``` | ||
| </TabItem> | ||
| <TabItem label="Python" icon="seti:python"> | ||
| ```python | ||
| ``` | ||
| </TabItem> | ||
| </Tabs> | ||
|
|
||
| ### Option Explanations | ||
|
|
||
| - `name` and `description`: Identify and describe the agent's purpose. | ||
| - `apiKey`: Your Anthropic API key for authentication. | ||
| - `modelId`: Specifies the LLM model to use (e.g., Claude 3 Sonnet). | ||
| - `streaming`: Enables streaming responses for real-time output. | ||
| - `inferenceConfig`: Fine-tunes the model's output characteristics. | ||
| - `retriever`: Integrates a retrieval system for enhanced context. | ||
| - `toolConfig`: Defines tools the agent can use and how to handle their responses. | ||
|
|
||
| ## Setting a New Prompt | ||
|
|
||
| You can dynamically set or update the system prompt for the agent: | ||
|
|
||
| <Tabs syncKey="runtime"> | ||
| <TabItem label="TypeScript" icon="seti:typescript" color="blue"> | ||
| ```typescript | ||
| agent.setSystemPrompt( | ||
| `You are an AI assistant specialized in {{DOMAIN}}. | ||
| Your main goal is to {{GOAL}}. | ||
| Always maintain a {{TONE}} tone in your responses.`, | ||
| { | ||
| DOMAIN: "cybersecurity", | ||
| GOAL: "help users understand and mitigate potential security threats", | ||
| TONE: "professional and reassuring" | ||
| } | ||
| ); | ||
| ``` | ||
| </TabItem> | ||
| <TabItem label="Python" icon="seti:python"> | ||
| ```python | ||
| agent.set_system_prompt( | ||
| """You are an AI assistant specialized in {{DOMAIN}}. | ||
| Your main goal is to {{GOAL}}. | ||
| Always maintain a {{TONE}} tone in your responses.""", | ||
| { | ||
| "DOMAIN": "cybersecurity", | ||
| "GOAL": "help users understand and mitigate potential security threats", | ||
| "TONE": "professional and reassuring" | ||
| } | ||
| ) | ||
| ``` | ||
| </TabItem> | ||
| </Tabs> | ||
|
|
||
| This method allows you to dynamically change the agent's behavior and focus without creating a new instance. | ||
|
|
||
| ## Adding the Agent to the Orchestrator | ||
|
|
||
| To integrate the **Anthropic Agent** into your orchestrator, follow these steps: | ||
|
|
||
| 1. First, ensure you have created an instance of the orchestrator: | ||
|
|
||
| <Tabs syncKey="runtime"> | ||
| <TabItem label="TypeScript" icon="seti:typescript" color="blue"> | ||
| ```typescript | ||
| import { MultiAgentOrchestrator } from "multi-agent-orchestrator"; | ||
| const orchestrator = new MultiAgentOrchestrator(); | ||
| ``` | ||
| </TabItem> | ||
| <TabItem label="Python" icon="seti:python"> | ||
| ```python | ||
| from multi_agent_orchestrator.orchestrator import MultiAgentOrchestrator | ||
| orchestrator = MultiAgentOrchestrator() | ||
| ``` | ||
| </TabItem> | ||
| </Tabs> | ||
|
|
||
| 2. Then, add the agent to the orchestrator: | ||
|
|
||
| <Tabs syncKey="runtime"> | ||
| <TabItem label="TypeScript" icon="seti:typescript" color="blue"> | ||
| ```typescript | ||
| orchestrator.addAgent(agent); | ||
| ``` | ||
| </TabItem> | ||
| <TabItem label="Python" icon="seti:python"> | ||
| ```python | ||
| orchestrator.add_agent(agent) | ||
| ``` | ||
| </TabItem> | ||
| </Tabs> | ||
|
|
||
| 3. Now you can use the orchestrator to route requests to the appropriate agent, including your Anthropic agent: | ||
|
|
||
| <Tabs syncKey="runtime"> | ||
| <TabItem label="TypeScript" icon="seti:typescript" color="blue"> | ||
| ```typescript | ||
| const response = await orchestrator.routeRequest( | ||
| "What is the base rate interest for 30 years?", | ||
| "user123", | ||
| "session456" | ||
| ); | ||
| ``` | ||
| </TabItem> | ||
| <TabItem label="Python" icon="seti:python"> | ||
| ```python | ||
| response = await orchestrator.route_request( | ||
| "What is the base rate interest for 30 years?", | ||
| "user123", | ||
| "session456" | ||
| ) | ||
| ``` | ||
| </TabItem> | ||
| </Tabs> | ||
|
|
||
| --- | ||
|
|
||
| By leveraging the **AnthropicAgent**, you can create sophisticated, context-aware AI agents capable of handling a wide range of tasks and interactions, all powered by the latest LLM models available through Anthropic's platform. |