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Updated tech spec
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1 changed files with 100 additions and 35 deletions
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@ -25,9 +25,9 @@ This is a two-tier model, analogous to MCP tools:
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- MCP: MCP server defines the tool interface → Tool config references it
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- Tool Services: Tool service defines the Pulsar interface → Tool config references it
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## Current Architecture
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## Background: Existing Tools
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### Existing Tool Implementation
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### Built-in Tool Implementation
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Tools are currently defined in `trustgraph-flow/trustgraph/agent/react/tools.py` with typed implementations:
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@ -55,7 +55,7 @@ elif impl_id == "text-completion":
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# ... etc
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```
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## Proposed Architecture
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## Architecture
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### Two-Tier Model
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@ -134,38 +134,34 @@ Multiple tools can reference the same service with different configurations:
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When a tool is invoked, the request to the tool service includes:
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- `user`: From the agent request (multi-tenancy)
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- Config values: From the tool descriptor (e.g., `collection`)
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- `arguments`: From the LLM
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- `config`: JSON-encoded config values from the tool descriptor
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- `arguments`: JSON-encoded arguments from the LLM
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```json
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{
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"user": "alice",
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"collection": "customers",
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"arguments": {
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"question": "What are the top customer complaints?"
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}
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"config": "{\"collection\": \"customers\"}",
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"arguments": "{\"question\": \"What are the top customer complaints?\"}"
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}
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```
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The tool service receives these as parsed dicts in the `invoke` method.
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### Generic Tool Service Implementation
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A `ToolServiceImpl` class invokes tool services based on configuration:
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```python
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class ToolServiceImpl:
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def __init__(self, service_topic, config_values, context):
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self.service_topic = service_topic
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def __init__(self, context, request_queue, response_queue, config_values, arguments, processor):
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self.request_queue = request_queue
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self.response_queue = response_queue
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self.config_values = config_values # e.g., {"collection": "customers"}
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self.context = context
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# ...
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async def invoke(self, user, **arguments):
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client = self.context(self.service_topic)
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request = {
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"user": user,
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**self.config_values,
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"arguments": arguments,
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}
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response = await client.call(request)
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async def invoke(self, **arguments):
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client = await self._get_or_create_client()
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response = await client.call(user, self.config_values, arguments)
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if isinstance(response, str):
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return response
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else:
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@ -245,8 +241,8 @@ This follows the existing pattern used throughout the codebase (e.g., `agent_ser
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Tool services can return streaming responses:
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- Multiple response messages with the same `id` in properties
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- Each response includes `end_of_message: bool` field
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- Final response has `end_of_message: True`
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- Each response includes `end_of_stream: bool` field
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- Final response has `end_of_stream: True`
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This matches the pattern used in `AgentResponse` and other streaming services.
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@ -269,7 +265,88 @@ Tool services follow the same contract:
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This keeps the descriptor simple and places responsibility on the service to return an appropriate text response for the agent.
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## Implementation
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## Configuration Guide
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To add a new tool service, two configuration items are required:
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### 1. Tool Service Configuration
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Stored under the `tool-service` config key. Defines the Pulsar queues and available config parameters.
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| Field | Required | Description |
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|-------|----------|-------------|
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| `id` | Yes | Unique identifier for the tool service |
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| `request-queue` | Yes | Full Pulsar topic for requests (e.g., `non-persistent://tg/request/joke`) |
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| `response-queue` | Yes | Full Pulsar topic for responses (e.g., `non-persistent://tg/response/joke`) |
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| `config-params` | No | Array of config parameters the service accepts |
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Each config param can specify:
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- `name`: Parameter name (required)
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- `required`: Whether the parameter must be provided by tools (default: false)
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Example:
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```json
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{
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"id": "joke-service",
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"request-queue": "non-persistent://tg/request/joke",
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"response-queue": "non-persistent://tg/response/joke",
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"config-params": [
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{"name": "style", "required": false}
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]
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}
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```
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### 2. Tool Configuration
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Stored under the `tool` config key. Defines a tool that the agent can use.
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| Field | Required | Description |
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|-------|----------|-------------|
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| `type` | Yes | Must be `"tool-service"` |
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| `name` | Yes | Tool name exposed to the LLM |
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| `description` | Yes | Description of what the tool does (shown to LLM) |
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| `service` | Yes | ID of the tool-service to invoke |
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| `arguments` | No | Array of argument definitions for the LLM |
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| *(config params)* | Varies | Any config params defined by the service |
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Each argument can specify:
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- `name`: Argument name (required)
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- `type`: Data type, e.g., `"string"` (required)
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- `description`: Description shown to the LLM (required)
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Example:
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```json
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{
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"type": "tool-service",
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"name": "tell-joke",
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"description": "Tell a joke on a given topic",
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"service": "joke-service",
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"style": "pun",
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"arguments": [
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{
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"name": "topic",
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"type": "string",
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"description": "The topic for the joke (e.g., programming, animals, food)"
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}
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]
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}
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```
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### Loading Configuration
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Use `tg-put-config-item` to load configurations:
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```bash
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# Load tool-service config
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tg-put-config-item tool-service/joke-service < joke-service.json
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# Load tool config
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tg-put-config-item tool/tell-joke < tell-joke.json
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```
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The agent-manager must be restarted to pick up new configurations.
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## Implementation Details
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### Schema
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@ -323,18 +400,6 @@ class ToolServiceImpl:
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return response if isinstance(response, str) else json.dumps(response)
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```
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### Configuration Structure
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Two config sections:
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```
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tool-service/
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joke-service: {"id": "joke-service", "request-queue": "non-persistent://tg/request/joke", "response-queue": "non-persistent://tg/response/joke", "config-params": [...]}
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tool/
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tell-joke: {"type": "tool-service", "service": "joke-service", ...}
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```
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### Files
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| File | Purpose |
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