Enofirm-Temp/extra-addons/AI/llm_tool_knowledge

LLM Tool Knowledge

Give your AI assistants instant access to your knowledge base with semantic search and source citation capabilities.

Module Type: 🔌 Extension (RAG Tool)

Architecture

┌───────────────────────────────────────────────────────────────┐
│                    AI Consumers                               │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐   │
│  │llm_assistant│  │  llm_letta  │  │   llm_mcp_server    │   │
│  └──────┬──────┘  └──────┬──────┘  └──────────┬──────────┘   │
└─────────┼────────────────┼────────────────────┼──────────────┘
          └────────────────┼────────────────────┘
                           ▼
              ┌───────────────────────────────────────────┐
              │    ★ llm_tool_knowledge (This Module) ★   │
              │         Knowledge Retriever Tool          │
              │  🔍 Semantic Search │ 📚 Source Citations │
              └─────────────────────┬─────────────────────┘
                                    │
                        ┌───────────┴───────────┐
                        ▼                       ▼
    ┌───────────────────────────┐   ┌───────────────────────────┐
    │         llm_tool          │   │       llm_knowledge       │
    │    (Tool Framework)       │   │      (RAG Pipeline)       │
    └───────────────────────────┘   └───────────────────────────┘

Installation

What to Install

For RAG tool access:

odoo-bin -d your_db -i llm_tool_knowledge

Auto-Installed Dependencies

• llm (core infrastructure)
• llm_tool (tool framework)
• llm_knowledge (RAG infrastructure)
• llm_assistant (AI assistant framework)

Why Use This Module?

Feature	llm_tool_knowledge
Search	🔍 Semantic similarity search
Citations	📚 Source document references
Hybrid	🔄 Semantic + keyword search
Dual Use	🤖 Odoo assistants + MCP clients

Common Setups

I want to...	Install
Chat + RAG	llm_assistant + llm_openai + llm_tool_knowledge + llm_pgvector
Claude + RAG	llm_mcp_server + llm_tool_knowledge + llm_pgvector

This module provides RAG (Retrieval-Augmented Generation) tools that enable AI assistants to search documents, cite sources, and answer questions using your actual company data instead of just their training.

Overview

LLM Tool Knowledge extends the Odoo LLM ecosystem with a powerful knowledge_retriever tool that performs semantic search across your knowledge collections. This tool can be used in two ways:

1. With Odoo AI Assistants (llm_assistant module) - Enable your internal Odoo chatbots to search company knowledge
2. With External MCP Clients (llm_mcp_server module) - Expose your knowledge base to external AI tools like Claude Desktop, Continue.dev, and other MCP-compatible applications

Features

Knowledge Retriever Tool

• Semantic Search: Find relevant documents using natural language queries
• Source Citations: AI responses include references to source documents
• Hybrid Search: Combine semantic and keyword search for better accuracy
• Collection-Aware: Search specific collections or across all knowledge
• Configurable Relevance: Set minimum similarity thresholds

Dual Integration

1. Odoo AI Assistants (llm_assistant)

When enabled on an assistant, the AI can automatically search your knowledge base:

User: "What's our refund policy?"
AI: Uses knowledge_retriever tool → searches policy documents → cites sources

2. External MCP Clients (llm_mcp_server)

Expose the knowledge_retriever tool via Model Context Protocol:

Claude Desktop → MCP Server (Odoo) → knowledge_retriever → returns relevant docs

External AI tools can search your Odoo knowledge base securely.

Installation

1. Install dependencies:

   • llm_knowledge module (required)
   • llm_tool module (required)
   • llm_assistant module (required)
   • llm_mcp_server module (optional - for external MCP clients)

2. Install this module:

   # Via Odoo Apps interface
   Apps → Search "LLM Tool Knowledge" → Install
   

3. The tool is automatically registered and ready to use.

Configuration

For Odoo AI Assistants

1. Go to LLM → Assistants → Assistants
2. Open or create an assistant
3. Navigate to the Tools tab
4. Enable the "knowledge_retriever" tool
5. The assistant can now search knowledge collections

For External MCP Clients

1. Install and configure llm_mcp_server module
2. The knowledge_retriever tool is automatically exposed via MCP
3. Configure your MCP client (Claude Desktop, etc.) to connect to Odoo
4. External AI can now search your knowledge base

Usage Examples

Example 1: Odoo Assistant with Knowledge Access

Setup:

• Create knowledge collection with company policies
• Enable knowledge_retriever tool on support assistant

Result:

User: "What's the warranty period for laptops?"
Assistant: [Searches policies collection]
"Based on our Electronics Warranty Policy, laptops have a 2-year warranty covering
hardware defects. (Source: Electronics Warranty Policy, updated Jan 2024)"

Example 2: Claude Desktop Accessing Odoo Knowledge

Setup:

• Configure llm_mcp_server with your Odoo instance
• Add server to Claude Desktop MCP settings
• Index product documentation in Odoo knowledge

Result:

Claude Desktop → uses knowledge_retriever tool → searches Odoo docs
Returns: Relevant product specs with source citations from your knowledge base

Example 3: Continue.dev with Company Codebase

Setup:

• Index code documentation in Odoo knowledge collection
• Expose via MCP server
• Configure Continue.dev to use Odoo MCP server

Result: Developer asks Continue.dev about internal APIs → searches indexed docs → provides accurate answers from your actual documentation.

How It Works

Tool Input Schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Search query to find relevant knowledge"
    },
    "collection_id": {
      "type": "string",
      "description": "ID of knowledge collection to search (optional)"
    },
    "top_k": {
      "type": "integer",
      "description": "Number of results to return (default: 5)"
    },
    "min_similarity": {
      "type": "number",
      "description": "Minimum similarity score 0-1 (default: 0.7)"
    }
  },
  "required": ["query"]
}

Tool Execution Flow

1. Receive query: AI assistant or external tool calls knowledge_retriever
2. Vector search: Query is embedded and searched against knowledge chunks
3. Filter results: Apply similarity threshold and top_k limit
4. Return sources: Chunks with metadata, similarity scores, and source references
5. AI uses context: Assistant incorporates results into response with citations

Technical Details

Tool Registration

Defined in data/llm_tool_data.xml:

<record id="llm_tool_knowledge_retriever" model="llm.tool">
    <field name="name">knowledge_retriever</field>
    <field
    name="description"
  >Retrieves relevant knowledge from document database
    using semantic search...</field>
    <field name="implementation">knowledge_retriever</field>
    <field name="active" eval="True" />
</record>

Implementation

Located in models/llm_tool_knowledge_retriever.py:

class LLMToolKnowledgeRetriever(models.Model):
    _inherit = "llm.tool"

    @api.model
    def _get_available_implementations(self):
        implementations = super()._get_available_implementations()
        return implementations + [("knowledge_retriever", "Knowledge Retriever")]

Search Process

1. Embed query using collection's embedding model
2. Perform vector similarity search in vector store (pgvector/Qdrant/Chroma)
3. Filter by min_similarity threshold
4. Return top_k most relevant chunks
5. Include source document metadata

Use Cases

Internal Odoo Assistants

• Customer Support: Search FAQ, policies, product docs
• HR Assistant: Search employee handbook, HR policies
• Sales Assistant: Search product specs, pricing, competitor analysis
• IT Helpdesk: Search technical documentation, troubleshooting guides

External MCP Integration

• Developer Tools: Continue.dev, Cursor accessing code documentation
• Claude Desktop: Personal assistant with access to company knowledge
• Custom AI Apps: Build external apps that query Odoo knowledge
• Multi-Tool Workflows: Chain knowledge search with other MCP tools

Security

Access Control

• Tool: Requires llm.group_llm_user to execute
• Collections: Respects Odoo record rules and access rights
• MCP Server: Separate authentication for external access

Data Privacy

• Knowledge searches respect user permissions
• External MCP access requires explicit configuration
• No knowledge is shared unless explicitly indexed in collections

Best Practices

1. Organize Collections: Create topic-specific collections for better search accuracy
2. Update Regularly: Keep knowledge collections current with latest information
3. Set Thresholds: Adjust min_similarity based on precision/recall needs
4. Limit Scope: Use collection_id parameter to search specific domains
5. Monitor Usage: Track which queries are most common to improve indexing

Troubleshooting

Tool not appearing

1. Verify module is installed and active
2. Check llm_knowledge module is installed
3. Refresh assistants or MCP client

Search returns no results

1. Check collection has processed resources (state=ready)
2. Verify embeddings are generated
3. Lower min_similarity threshold
4. Check vector store is configured correctly

MCP connection fails

1. Verify llm_mcp_server is installed and configured
2. Check MCP client configuration matches Odoo URL
3. Review authentication credentials
4. Check Odoo is accessible from MCP client network

Requirements

• Odoo: 18.0+
• Python: 3.11+
• Dependencies:
  • llm_knowledge module (semantic search, vector storage)
  • llm_tool module (tool framework)
  • llm_assistant module (AI assistants)
  • llm_mcp_server module (optional - for external MCP clients)

License

LGPL-3

Author

Apexive Solutions LLC

• Website: https://github.com/apexive/odoo-llm
• Email: info@apexive.com

Contributing

Issues and pull requests welcome at https://github.com/apexive/odoo-llm