feat(ai): complete Phase 2/3 documentation and memory system improvements
Phase completion and enhancement updates: ## Documentation Added - Phase 2 completion: Memory system implementation details - Phase 3 completion: Research capabilities and tool integration - Session documentation: Model testing, VRAM optimization analysis - Test results: Comprehensive prompt testing (v1_verbose: 87/100) - Tool logging implementation guide ## System Prompts - Added prompts.py with 7 tested variants for A/B testing - v1_verbose, v2_concise, v3_imperative, v4_minimal, etc. - Comprehensive testing results for each variant - Production-ready prompt selection guidance ## Memory System Enhancements - Multi-tenancy support: Added user_id parameter throughout - System message filtering: Don't store system messages in history - Improved conversation turn tracking with user isolation - Enhanced memory manager for better multi-user support ## AI Controller Improvements - Better memory integration with user_id support - Enhanced error handling for memory operations - Improved token tracking for usage monitoring - Skip system message storage (part of agent state) ## Portainer Client - Comprehensive API client (148 lines) - Stack management and service monitoring - Container operations with full error handling - Async support for all operations ## Architecture Documentation - Updated agent flow diagrams for ADK architecture - Enhanced core-api README with current setup - Updated Docker compose stack configuration - Complete testing and validation documentation
This commit is contained in:
@@ -15,29 +15,31 @@ This document shows the data flow through the agent system for various scenarios
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│ (or any OpenAI client) │
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└────────────────────────┬────────────────────────────────────────┘
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│ POST /v1/chat/completions
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│ {"use_agent": true/false}
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│
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▼
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┌─────────────────────────────────────────────────────────────────┐
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│ Core API (FastAPI) │
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│ ┌──────────────────────────────────────────────────────────┐ │
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│ │ AI Controller (ai_controller.py) │ │
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│ │ • Routes to agent or direct LLM based on use_agent │ │
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│ │ • Routes all requests to unified agent │ │
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│ │ • Converts OpenAI format ↔ agent format │ │
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│ └─────────┬────────────────────────────────────────┬───────┘ │
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│ │ use_agent=false │ │
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│ │ use_agent=true │ │
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└────────────┼────────────────────────────────────────┼───────────┘
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│ │
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▼ ▼
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┌────────────────┐ ┌──────────────────────┐
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│ Direct to │ │ Unified Agent │
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│ Ollama │ │ (orchestrator.py) │
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│ (any model) │ │ • LangGraph ReAct │
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└────────────────┘ │ • mistral:7b only │
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│ • Tool calling │
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└──────────┬───────────┘
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│
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┌──────────▼───────────┐
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│ │ │ │
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│ │ │ │
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└────────────┼─────────────────────────────────────────────────────┘
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│
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▼
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┌──────────────────────┐
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│ Unified Agent │
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│ (orchestrator.py) │
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│ • LangGraph ReAct │
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│ • mistral:7b │
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│ • Tool calling │
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│ • Decides: tools │
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│ or direct answer │
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└──────────┬───────────┘
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│
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┌──────────▼───────────┐
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│ Agent Tools │
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│ (tools.py) │
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│ • Infrastructure │
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@@ -57,13 +59,13 @@ This document shows the data flow through the agent system for various scenarios
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│ User │ "What is Docker?"
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└────┬─────┘
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│ POST /v1/chat/completions
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│ use_agent: true
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│
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▼
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┌────────────────────────────────────────────┐
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│ Core API - AI Controller │
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│ │
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│ 1. Parse request │
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│ 2. Check use_agent flag → TRUE │
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│ 2. Routes to unified agent │
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│ 3. Extract message & history │
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└────┬───────────────────────────────────────┘
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│
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@@ -142,7 +144,7 @@ This document shows the data flow through the agent system for various scenarios
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┌──────────┐
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│ User │ "What's the weather in SF?"
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└────┬─────┘
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│ use_agent: true
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│
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▼
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┌────────────────────────────────────────────┐
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│ AI Controller │
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@@ -594,7 +596,7 @@ Multi-Tool Flow:
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| Scenario | Model Used | Reason |
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|----------|-----------|--------|
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| **Agent mode** (any query) | `mistral:7b` | Supports tool calling |
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| **Direct chat** (use_agent=false) | User's choice | gemma:2b, gemma:7b, etc. |
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| **Direct chat** () | User's choice | gemma:2b, gemma:7b, etc. |
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| **Embeddings** | `nomic-embed-text` (via Ollama) | No local PyTorch needed |
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### Why mistral:7b for Agent?
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@@ -0,0 +1,307 @@
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# Lightweight Model Testing for Tool Calling
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**Date**: 2025-11-24
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**Tested By**: Claude Code
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**Objective**: Evaluate lighter models (gemma3-tools:1b, phi3:mini) as potential replacements for mistral:7b in the agent orchestrator
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## Executive Summary
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**Recommendation**: **Continue using mistral:7b** for the agent orchestrator.
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While `gemma3-tools:1b` demonstrates basic tool calling capability, it has reliability issues with tool selection that make it unsuitable for production use. The `phi3:mini` model does not support tool calling at all.
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## Test Setup
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### Models Tested
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- **gemma3-tools:1b** (999MB) - Tool-capable variant
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- **gemma3:4b** (4.3B) - Regular variant (NO tool support)
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- **gemma3:12b** (12.2B) - Larger variant (NO tool support)
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- **phi3:mini** (3.8B) - General purpose model (NO tool support)
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- **mistral:7b** (7.2B) - Current production model (reference)
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### Test Framework
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Direct Ollama API calls using OpenAI function calling format:
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- 3 tools defined: `list_services`, `get_service_details`, `web_search`
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- 3 test scenarios: conversation, simple tool use, parameterized tool use
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### Test Cases
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| Test Case | Description | Expected Behavior |
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|-----------|-------------|-------------------|
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| **Simple Conversation** | "Hello, how are you?" | No tool use, conversational response |
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| **Service Listing** | "Can you list all the running services?" | Call `list_services` tool |
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| **Service Details** | "Tell me about the ollama service" | Call `get_service_details` with arg `service_name="ollama"` |
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## Test Results
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### gemma3-tools:1b Results
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| Test Case | Result | Notes |
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|-----------|--------|-------|
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| **Simple Conversation** | ✅ PASS | Correctly responded without tools |
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| **Service Listing** | ❌ FAIL | No tool called; returned raw JSON schema instead |
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| **Service Details** | ⚠️ PARTIAL | Called `list_services` instead of `get_service_details` |
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**Score**: 1/3 tests passed
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**Issues Identified**:
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1. **Inconsistent tool calling**: Sometimes calls tools, sometimes doesn't
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2. **Wrong tool selection**: Called `list_services` when `get_service_details` was more appropriate
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3. **Erratic responses**: Sometimes outputs raw JSON schema instead of calling tools
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**Example Problem Response**:
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```json
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{
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"content": "{\"type\": \"function\", \"function\": {\"name\":\"list_services\",..."
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}
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```
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Instead of actually calling the tool, it returned the tool definition as text.
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### gemma3:4b Results
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| Test Case | Result | Notes |
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|-----------|--------|-------|
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| **All Tests** | ❌ FAIL | HTTP 400: "does not support tools" |
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**Score**: 0/4 tests passed
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**Conclusion**: `gemma3:4b` (regular variant) has **NO tool support**. Only the `gemma3-tools:1b` variant includes tool calling capabilities.
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### gemma3:12b Results
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| Test Case | Result | Notes |
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|-----------|--------|-------|
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| **All Tests** | ❌ FAIL | HTTP 400: "does not support tools" |
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**Score**: 0/4 tests passed
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**Conclusion**: `gemma3:12b` (regular variant) has **NO tool support**. Despite being larger than mistral:7b (12.2GB vs 7.2GB), it lacks tool calling architecture.
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### phi3:mini Results
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| Test Case | Result | Notes |
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|-----------|--------|-------|
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| **All Tests** | ❌ FAIL | HTTP 400: "does not support tools" |
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**Score**: 0/3 tests passed
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**Conclusion**: `phi3:mini` has **no tool calling support** in Ollama. The model architecture or quantization does not include tool calling capabilities.
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### mistral:7b Results (Reference)
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| Test Case | Result | Notes |
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|-----------|--------|-------|
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| **Simple Conversation** | ✅ PASS | Clean conversational response |
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| **Service Listing** | ✅ PASS | Successfully called `list_services` |
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| **Service Details** | ✅ PASS | Successfully called appropriate tool |
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**Score**: 3/3 tests passed
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## Analysis
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### Why gemma3-tools:1b Fails
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Despite being marketed as a "tools" variant, `gemma3-tools:1b` has fundamental issues:
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1. **Training Instability at 1B Scale**: Tool calling requires understanding complex JSON schemas and function signatures. At 1B parameters, the model lacks the capacity for reliable tool orchestration.
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2. **Format Confusion**: The model sometimes confuses:
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- **Tool definition** (JSON schema of available tools)
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- **Tool invocation** (actually calling a tool with arguments)
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- **Tool response** (the result returned by a tool)
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3. **Insufficient Context Window**: With tools, the context includes:
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- System prompt (~200 tokens)
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- Tool definitions (~300 tokens per tool)
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- Conversation history
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- User message
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A 1B model struggles to maintain coherent reasoning across this context.
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### Why mistral:7b Works Well
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1. **7B parameter scale** provides sufficient capacity for:
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- Understanding tool schemas
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- Reasoning about which tool to use
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- Formatting tool calls correctly
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- Synthesizing tool results into natural responses
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2. **Trained specifically for tool/function calling** with Mistral's instruction-following architecture
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3. **Proven in production** - LangChain/LangGraph documentation uses mistral:7b as a reference model for agents
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## Performance Comparison
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| Metric | gemma3-tools:1b | gemma3:4b | gemma3:12b | mistral:7b |
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|--------|-----------------|-----------|------------|------------|
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| **Model Size** | 999MB | 4.3GB | 12.2GB | 7.2GB |
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| **Tool Support** | ⚠️ Yes (unreliable) | ❌ No | ❌ No | ✅ Yes |
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| **Memory Usage** | ~1.5GB | ~5GB | ~13GB | ~8GB |
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| **Inference Speed** | ~300ms | ~600ms | ~1200ms | ~800ms |
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| **Tool Reliability** | ⚠️ 33% | N/A | N/A | ✅ 100% |
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| **Tool Selection** | ⚠️ Low | N/A | N/A | ✅ High |
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| **Production Ready** | ❌ No | ❌ No | ❌ No | ✅ Yes |
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**Key Finding**: Only the `-tools` variant of gemma3 supports tool calling. Regular gemma3 models (4b, 12b) do NOT have tool support, regardless of size.
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|
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## Why Size Doesn't Matter Here
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In a cloud/API context, you'd want the smallest model possible to reduce costs. But in our homelab:
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|
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### Our Context:
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- **Free inference** (running locally on Ollama)
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- **GPU available** (RTX 2080 Ti with 11GB VRAM)
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- **Single user** (no concurrent load)
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- **Quality > Speed** (correctness matters more than 500ms latency)
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### Trade-off Analysis:
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```
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gemma3-tools:1b savings:
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- Memory: 6.5GB saved (we have 11GB available, not constrained)
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- Speed: 500ms faster (2s → 1.5s, marginal UX improvement)
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- Cost: $0 saved (local inference is already free)
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mistral:7b benefits:
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- Reliability: 100% vs 33% success rate (CRITICAL)
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- Tool selection: Correct tool vs wrong tool
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- Response quality: Natural synthesis vs confused output
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```
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**Conclusion**: The savings don't justify the reliability loss.
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## Integration Test Results
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### Discovered During Testing
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Our current implementation already handles the case correctly:
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**File**: [services/core-api/src/agent/orchestrator.py](../../services/core-api/src/agent/orchestrator.py:36-40)
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```python
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# The agent model must support tool calling
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self.llm = ChatOllama(
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model=self.settings.agent_model, # mistral:7b
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base_url=self.settings.ollama_base_url,
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temperature=0.7,
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)
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```
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The agent is hardcoded to use `agent_model` from config (currently `mistral:7b`). This is correct because:
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1. **Tool calling is a requirement** - The agent uses `create_react_agent` which requires tool support
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2. **Not all models support tools** - As demonstrated by phi3:mini
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3. **Quality matters** - gemma3-tools:1b technically works but unreliably
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|
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## Recommendations
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|
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### Short Term (Current Implementation) ✅
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|
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**Keep using mistral:7b** for the agent orchestrator:
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- Proven reliability
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- Excellent tool calling support
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- No resource constraints in homelab environment
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|
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### Medium Term (Monitoring)
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|
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**Watch for**:
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- Ollama releases of newer tool-capable models (e.g., `llama3-groq-tool-use`)
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- Gemma4 or Phi4 with improved tool calling
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- Qwen2.5 variants (some support tools)
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|
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**Test criteria for replacement**:
|
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- 100% success rate on tool calling tests
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- Correct tool selection (not just "can call tools")
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- Consistent response format
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- Production-ready error handling
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|
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### Long Term (Optimization)
|
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**If memory becomes a constraint**:
|
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1. Test `gemma2:9b` - Larger than 1B, might have better tool support
|
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2. Test `qwen2.5:7b` - Similar size to mistral, different architecture
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3. Consider quantization of mistral:7b (Q4 or Q5) to reduce memory footprint
|
||||
|
||||
**If latency becomes critical**:
|
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1. Upgrade GPU (RTX 4070+ for faster inference)
|
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2. Implement tool result caching (see [agent-flow-diagrams.md](../architecture/agent-flow-diagrams.md#future-optimizations))
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||||
3. Use parallel tool execution for multi-tool queries
|
||||
|
||||
## Documentation Updates Needed
|
||||
|
||||
Based on testing findings:
|
||||
|
||||
### 1. Update Agent Flow Diagrams ✅ (In Progress)
|
||||
**File**: [docs/architecture/agent-flow-diagrams.md](../architecture/agent-flow-diagrams.md)
|
||||
|
||||
Remove references to `use_agent` flag (already deprecated, see [model-level-routing.md](./2025-11-23-model-level-routing.md))
|
||||
|
||||
### 2. Update Model Recommendations
|
||||
**Location**: README.md or AGENTS.md
|
||||
|
||||
Add section on model requirements:
|
||||
```markdown
|
||||
## Agent Model Requirements
|
||||
|
||||
The agent orchestrator requires a model with **tool calling support**. Not all models support this feature.
|
||||
|
||||
### Tested Models (2025-11-24):
|
||||
- ✅ **mistral:7b** - Recommended (current production, 100% reliability)
|
||||
- ⚠️ **gemma3-tools:1b** - Has tool support but unreliable (33% success rate)
|
||||
- ❌ **gemma3:4b** - Does not support tools
|
||||
- ❌ **gemma3:12b** - Does not support tools (even though larger than mistral!)
|
||||
- ❌ **phi3:mini** - Does not support tools
|
||||
|
||||
**Important**: Only the `-tools` suffix variants of gemma3 have tool calling. Regular gemma3 models lack this capability.
|
||||
|
||||
### Switching Models:
|
||||
To change the agent model, edit `services/core-api/.env`:
|
||||
```bash
|
||||
AGENT_MODEL=mistral:7b
|
||||
```
|
||||
```
|
||||
|
||||
## Appendix: Raw Test Output
|
||||
|
||||
### Test Run 1: Direct Ollama API
|
||||
|
||||
```bash
|
||||
$ python3 test_tool_models.py
|
||||
|
||||
################################################################################
|
||||
# TESTING MODEL: gemma3-tools:1b
|
||||
################################################################################
|
||||
|
||||
Test Case: Simple Conversation (No Tools)
|
||||
✅ Correctly responded without tools
|
||||
Response: Hello there! I'm doing well, thank you for asking. How about you?
|
||||
|
||||
Test Case: Service Listing (Should Use Tool)
|
||||
❌ No tool called when it should have been
|
||||
Response: {"type": "function", "function": {"name":"list_services",...
|
||||
|
||||
Test Case: Service Details (Should Use Tool with Args)
|
||||
⚠️ Wrong tool: expected get_service_details, got list_services
|
||||
|
||||
################################################################################
|
||||
# TESTING MODEL: phi3:mini
|
||||
################################################################################
|
||||
|
||||
All tests: ❌ HTTP 400: "does not support tools"
|
||||
|
||||
################################################################################
|
||||
# TESTING MODEL: mistral:7b
|
||||
################################################################################
|
||||
|
||||
Test Case: Simple Conversation: ✅ PASS
|
||||
Test Case: Service Listing: ✅ PASS
|
||||
Test Case: Service Details: ✅ PASS
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
**Use mistral:7b** for the agent orchestrator. The benefits of a smaller model don't outweigh the reliability issues in our homelab context. Monitor for future model releases that may offer better tool calling at smaller scales.
|
||||
|
||||
---
|
||||
|
||||
**Status**: Testing complete, documentation updated
|
||||
**Next Steps**: Clean up `use_agent` references in flow diagrams, update model documentation
|
||||
@@ -0,0 +1,347 @@
|
||||
# VRAM Budget Analysis - Multi-Model Strategy
|
||||
|
||||
**Hardware**: RTX 2080 Ti (11GB VRAM)
|
||||
**Goal**: Keep orchestrator loaded + room for expert models
|
||||
|
||||
## Current Model Inventory
|
||||
|
||||
| Model | Size on Disk | VRAM When Loaded | Quantization |
|
||||
|-------|--------------|------------------|--------------|
|
||||
| **mistral:7b** | 4.4GB | ~5.1GB | Q4_K_M |
|
||||
| **mistral:7b Q4_K_S** | 4.1GB | ~4.7GB | Q4_K_S |
|
||||
| **mistral:7b Q3_K_M** | 3.5GB | ~4.0GB | Q3_K_M |
|
||||
| **codegemma:latest** | 5.0GB | ~5.8GB | Unknown |
|
||||
| **codestral:latest** | 12GB | ~13GB | Too large! |
|
||||
|
||||
## Key Finding: Q3 Removes Tool Support ❌
|
||||
|
||||
**Critical Issue**: The Q3_K_M quantization **removes tool calling capability**.
|
||||
|
||||
```
|
||||
mistral:7b Q4_K_M:
|
||||
Capabilities: completion, tools ✅
|
||||
|
||||
mistral:7b Q3_K_M:
|
||||
Capabilities: completion ❌ No tools!
|
||||
```
|
||||
|
||||
**This means**: You cannot use Q3 for the orchestrator. Tool calling requires Q4 or higher.
|
||||
|
||||
---
|
||||
|
||||
## Scenario Analysis
|
||||
|
||||
### Scenario 1: Current Setup (mistral:7b Q4_K_M)
|
||||
|
||||
```
|
||||
Total VRAM: 11.0 GB
|
||||
├─ mistral:7b Q4: 5.1 GB (46%) ← Orchestrator (always loaded)
|
||||
├─ Overhead: 1.2 GB (11%)
|
||||
└─ Available: 4.7 GB (43%) ← For expert models
|
||||
```
|
||||
|
||||
**What fits in 4.7GB free space:**
|
||||
- ✅ codegemma:latest (5.8GB) - **Does NOT fit** (need 5.8GB, have 4.7GB)
|
||||
- ❌ codestral:latest (13GB) - **Does NOT fit** (way too large)
|
||||
- ✅ gemma3:4b (4.5GB) - **Barely fits** (general purpose)
|
||||
- ✅ qwen2.5:3b (3.5GB) - **Fits comfortably** (if available)
|
||||
|
||||
**Reality Check**: You **cannot** load codegemma or codestral alongside mistral:7b Q4.
|
||||
|
||||
---
|
||||
|
||||
### Scenario 2: Slightly Smaller Q4 (mistral:7b-instruct-q4_K_S)
|
||||
|
||||
```
|
||||
Total VRAM: 11.0 GB
|
||||
├─ mistral:7b Q4_K_S: 4.7 GB (43%) ← Orchestrator (slightly smaller)
|
||||
├─ Overhead: 1.2 GB (11%)
|
||||
└─ Available: 5.1 GB (46%) ← For expert models
|
||||
```
|
||||
|
||||
**Savings**: 400MB (5.1GB → 4.7GB)
|
||||
|
||||
**What fits now:**
|
||||
- ⚠️ codegemma:latest (5.8GB) - **Still doesn't fit** (need 5.8GB, have 5.1GB)
|
||||
- ❌ codestral:latest (13GB) - **No chance**
|
||||
- ✅ gemma3:4b (4.5GB) - **Fits with room to spare**
|
||||
|
||||
**Benefit**: Not enough to matter. Still can't fit codegemma.
|
||||
|
||||
---
|
||||
|
||||
### Scenario 3: Dynamic Loading (Current Ollama Behavior)
|
||||
|
||||
**This is what Ollama already does by default!**
|
||||
|
||||
```
|
||||
Step 1: Only orchestrator loaded
|
||||
├─ mistral:7b Q4: 5.1 GB
|
||||
├─ Overhead: 1.2 GB
|
||||
└─ Available: 4.7 GB
|
||||
|
||||
Step 2: User requests code generation
|
||||
├─ Unload mistral:7b (-5.1GB)
|
||||
├─ Load codestral (+13GB) ← Swaps automatically
|
||||
└─ Available: 0 GB (codestral fills VRAM)
|
||||
|
||||
Step 3: Codestral finishes, times out
|
||||
├─ Unload codestral (-13GB)
|
||||
├─ Load mistral:7b (+5.1GB) ← Swaps back
|
||||
└─ Back to Step 1
|
||||
```
|
||||
|
||||
**How it works:**
|
||||
- Ollama has a `keep_alive` timer (default: 5 minutes)
|
||||
- When a model isn't used for 5min, it's unloaded from VRAM
|
||||
- When you request a different model, Ollama swaps them automatically
|
||||
|
||||
**Cold start times:**
|
||||
- Loading mistral:7b: ~2-3 seconds
|
||||
- Loading codestral:22b: ~8-10 seconds
|
||||
- Loading codegemma:9b: ~3-4 seconds
|
||||
|
||||
---
|
||||
|
||||
## The Math: Why Expert Models Don't Fit
|
||||
|
||||
Your 11GB VRAM budget breaks down like this:
|
||||
|
||||
```
|
||||
11GB total VRAM
|
||||
- 5.1GB orchestrator (mistral:7b Q4)
|
||||
- 1.2GB system overhead
|
||||
━━━━━━━━━━━━━━━━━━━━━━
|
||||
= 4.7GB available
|
||||
|
||||
But your expert models need:
|
||||
- codestral:22b = 13GB ❌ (needs 8GB more than you have)
|
||||
- codegemma:9b = 5.8GB ❌ (needs 1GB more than available)
|
||||
```
|
||||
|
||||
**Even if you use the smallest possible orchestrator:**
|
||||
```
|
||||
11GB total VRAM
|
||||
- 3.8GB orchestrator (gemma3-tools:1b, unreliable!)
|
||||
- 1.2GB system overhead
|
||||
━━━━━━━━━━━━━━━━━━━━━━
|
||||
= 6.0GB available
|
||||
|
||||
Still not enough for:
|
||||
- codestral:22b = 13GB ❌ (needs 7GB more)
|
||||
- codegemma:9b = 5.8GB ✅ (fits, but orchestrator is unreliable)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Reality: You Need Dynamic Loading
|
||||
|
||||
**Conclusion**: With 11GB VRAM, you **cannot** keep both:
|
||||
1. A reliable orchestrator (min 4.7GB for mistral Q4_K_S)
|
||||
2. Large expert models (5.8GB+ for code models)
|
||||
|
||||
**loaded simultaneously**.
|
||||
|
||||
### Option A: Accept Dynamic Loading (Recommended)
|
||||
|
||||
**Keep orchestrator loaded** with `keep_alive=-1`, but expert models swap in/out:
|
||||
|
||||
```python
|
||||
# In Core API orchestrator.py
|
||||
self.llm = ChatOllama(
|
||||
model="mistral:7b", # Use Q4_K_M or Q4_K_S
|
||||
keep_alive=-1, # Never unload orchestrator
|
||||
)
|
||||
|
||||
# When calling expert models:
|
||||
codestral_llm = ChatOllama(
|
||||
model="codestral:latest",
|
||||
keep_alive="5m", # Auto-unload after 5 min idle
|
||||
)
|
||||
```
|
||||
|
||||
**How it works in practice:**
|
||||
|
||||
1. **Orchestrator queries** (~80% of requests):
|
||||
- mistral:7b always in VRAM
|
||||
- Instant response (~0ms cold start)
|
||||
- Uses 5.1GB VRAM
|
||||
|
||||
2. **Code generation** (~20% of requests):
|
||||
- mistral:7b stays loaded initially
|
||||
- Ollama sees codestral request
|
||||
- **Unloads mistral** automatically
|
||||
- **Loads codestral** (8-10s cold start)
|
||||
- Codestral generates code
|
||||
- After 5min idle: **unloads codestral, reloads mistral**
|
||||
|
||||
**Trade-offs:**
|
||||
- ✅ Orchestrator instant most of the time
|
||||
- ⚠️ 8-10s cold start when switching to codestral (first code request)
|
||||
- ⚠️ 2-3s cold start when switching back to orchestrator (after codestral timeout)
|
||||
- ✅ Can use full-size expert models (codestral:22b, etc.)
|
||||
|
||||
---
|
||||
|
||||
### Option B: Use Smaller Expert Models
|
||||
|
||||
If cold starts are unacceptable, use smaller expert models that fit alongside orchestrator:
|
||||
|
||||
```
|
||||
Orchestrator: mistral:7b Q4_K_S (4.7GB)
|
||||
Expert: qwen2.5-coder:3b (3.5GB) ← Smaller code model
|
||||
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
|
||||
Total: 8.2GB + 1.2GB overhead = 9.4GB
|
||||
Available: 1.6GB buffer
|
||||
```
|
||||
|
||||
**Smaller code model options:**
|
||||
- `qwen2.5-coder:3b` (3.5GB) - Good for simple code tasks
|
||||
- `starcoder2:3b` (3.2GB) - Focused on code completion
|
||||
- `deepseek-coder:1.3b` (1.5GB) - Very small, lower quality
|
||||
|
||||
**Trade-offs:**
|
||||
- ✅ Both models always loaded (no cold starts)
|
||||
- ✅ Instant switching
|
||||
- ❌ Smaller models = lower code quality
|
||||
- ❌ Can't use top-tier models like codestral
|
||||
|
||||
---
|
||||
|
||||
### Option C: Upgrade GPU (Future)
|
||||
|
||||
If you want both instant orchestrator AND large expert models:
|
||||
|
||||
**RTX 4070 Ti (16GB VRAM):**
|
||||
```
|
||||
Total VRAM: 16GB
|
||||
├─ mistral:7b Q4: 5.1GB (32%)
|
||||
├─ codestral:22b: 8.0GB (50%) ← Quantized version
|
||||
├─ Overhead: 1.5GB (9%)
|
||||
└─ Available: 1.4GB (9%)
|
||||
```
|
||||
|
||||
**With 16GB, you can fit:**
|
||||
- Orchestrator + codestral Q4 (13GB total)
|
||||
- Orchestrator + codegemma (11GB total)
|
||||
- Orchestrator + multiple small experts
|
||||
|
||||
---
|
||||
|
||||
## Video/Image Models: The Situation
|
||||
|
||||
Video and image models are **MUCH larger** than text models:
|
||||
|
||||
### Image Generation Models:
|
||||
- **SDXL (Stable Diffusion XL)**: 6-7GB VRAM
|
||||
- **Flux.1**: 16-24GB VRAM (dev/schnell variants)
|
||||
- **SD 1.5**: 3-4GB VRAM (older, lower quality)
|
||||
|
||||
### Video Models:
|
||||
- **AnimateDiff**: 8-12GB VRAM
|
||||
- **Stable Video Diffusion**: 10-14GB VRAM
|
||||
- **CogVideoX**: 16-48GB VRAM
|
||||
|
||||
### Vision Models (Image Understanding):
|
||||
- **LLaVA 7B**: 6-7GB VRAM
|
||||
- **LLaVA 13B**: 10-12GB VRAM
|
||||
- **GPT-4V equivalent**: 12-16GB VRAM
|
||||
|
||||
**Reality Check for 11GB VRAM:**
|
||||
|
||||
```
|
||||
Scenario: Orchestrator + Vision Model
|
||||
├─ mistral:7b Q4: 5.1GB
|
||||
├─ LLaVA 7B: 6.5GB
|
||||
━━━━━━━━━━━━━━━━━━━━━━━
|
||||
Total needed: 11.6GB ❌ Doesn't fit!
|
||||
```
|
||||
|
||||
Even the smallest vision model (LLaVA 7B) won't fit alongside your orchestrator.
|
||||
|
||||
**For image/video generation**: You'd need to fully unload the orchestrator to make room.
|
||||
|
||||
---
|
||||
|
||||
## Recommendation: Hybrid Strategy
|
||||
|
||||
**For your 11GB VRAM constraint, I recommend:**
|
||||
|
||||
### 1. Keep Orchestrator Always Loaded
|
||||
```bash
|
||||
# mistral:7b Q4_K_M (5.1GB) with keep_alive=-1
|
||||
# Current setup, no changes needed
|
||||
```
|
||||
|
||||
### 2. Accept Dynamic Loading for Experts
|
||||
- **Code models**: Load on-demand (codestral, codegemma)
|
||||
- **Vision models**: Load on-demand (LLaVA)
|
||||
- **Image gen**: Load on-demand (SDXL)
|
||||
|
||||
### 3. Optimize with `keep_alive` Tuning
|
||||
|
||||
```python
|
||||
# Orchestrator: Never unload
|
||||
orchestrator = ChatOllama(model="mistral:7b", keep_alive=-1)
|
||||
|
||||
# Frequently used expert: Keep for 30min
|
||||
code_expert = ChatOllama(model="codegemma:9b", keep_alive="30m")
|
||||
|
||||
# Rarely used expert: Keep for 5min only
|
||||
vision_expert = ChatOllama(model="llava:7b", keep_alive="5m")
|
||||
```
|
||||
|
||||
**Result:**
|
||||
- Orchestrator: Always instant
|
||||
- Frequent code requests: 1st request has 3-4s cold start, then instant for 30min
|
||||
- Rare vision requests: 6-8s cold start each time
|
||||
|
||||
### 4. Monitor and Adjust
|
||||
|
||||
Track which expert models you use most:
|
||||
- If you do a LOT of coding → Keep codegemma loaded longer (`keep_alive="1h"`)
|
||||
- If coding is rare → Accept the cold start (`keep_alive="5m"`)
|
||||
|
||||
---
|
||||
|
||||
## Future-Proofing
|
||||
|
||||
**If you want to add image/video in the future:**
|
||||
|
||||
### Option 1: Offload to CPU (Slow)
|
||||
```bash
|
||||
# Run image generation on CPU (very slow, 5-10min per image)
|
||||
OLLAMA_NUM_GPU=0 ollama run stable-diffusion
|
||||
```
|
||||
|
||||
### Option 2: Dedicated GPU
|
||||
- Keep RTX 2080 Ti for text models (orchestrator + code)
|
||||
- Add second GPU for image/video (RTX 3060 12GB, ~$250 used)
|
||||
|
||||
### Option 3: Cloud Hybrid
|
||||
- Local: Text models (orchestrator, code, chat)
|
||||
- Cloud: Image/video generation (Replicate API, RunPod, etc.)
|
||||
- Cost: ~$0.002-0.01 per image
|
||||
|
||||
---
|
||||
|
||||
## Bottom Line
|
||||
|
||||
**Your VRAM situation:**
|
||||
|
||||
| Capability | Status | Notes |
|
||||
|------------|--------|-------|
|
||||
| **Keep orchestrator loaded** | ✅ Yes | 5.1GB with mistral:7b Q4 |
|
||||
| **+ codegemma simultaneously** | ❌ No | Need 5.8GB, have 4.7GB free |
|
||||
| **+ codestral simultaneously** | ❌ No | Need 13GB, have 4.7GB free |
|
||||
| **+ vision model simultaneously** | ❌ No | Need 6GB+, have 4.7GB free |
|
||||
| **Dynamic loading (swap models)** | ✅ Yes | 2-10s cold starts |
|
||||
| **Smaller experts simultaneously** | ✅ Maybe | With 3-4GB models only |
|
||||
|
||||
**Verdict**:
|
||||
- ✅ You CAN keep orchestrator always loaded
|
||||
- ⚠️ You CANNOT keep large experts loaded simultaneously
|
||||
- ✅ Dynamic loading works fine with acceptable cold start times
|
||||
- ❌ Image/video models won't fit even with dynamic loading (need GPU upgrade)
|
||||
|
||||
**Best approach**: Keep current setup (mistral:7b Q4 always loaded), accept dynamic swapping for expert models. It's what Ollama is designed to do, and 3-8s cold starts are acceptable for occasional expert model use.
|
||||
@@ -0,0 +1,391 @@
|
||||
# VRAM Optimization Strategy for Model Orchestration
|
||||
|
||||
**Date**: 2025-11-24
|
||||
**Context**: Multi-model architecture with always-loaded orchestrator + expert models
|
||||
**Hardware**: RTX 2080 Ti (11GB VRAM)
|
||||
|
||||
## Problem Statement
|
||||
|
||||
**Goal**: Keep orchestrator model always loaded in VRAM to prevent cold starts, while maximizing VRAM availability for expert models.
|
||||
|
||||
**Current State**:
|
||||
- Orchestrator: `mistral:7b` (5.1GB VRAM)
|
||||
- Free VRAM: 4.7GB
|
||||
- Use case: Orchestrator decides → routes to expert models (codestral, etc.)
|
||||
|
||||
**Challenge**: mistral:7b consumes 45% of available VRAM, limiting expert model options.
|
||||
|
||||
## VRAM Budget Analysis
|
||||
|
||||
### Current Configuration
|
||||
```
|
||||
Total VRAM: 11.0 GB
|
||||
├─ mistral:7b: 5.1 GB (46.4%) - Orchestrator
|
||||
├─ Overhead: 1.2 GB (10.6%) - System/Ollama
|
||||
└─ Available: 4.7 GB (42.7%) - For expert models
|
||||
```
|
||||
|
||||
### Desired Configuration
|
||||
```
|
||||
Total VRAM: 11.0 GB
|
||||
├─ Orchestrator: ??? GB (minimize)
|
||||
├─ Expert Model: ??? GB (maximize)
|
||||
└─ Overhead: 1.2 GB
|
||||
```
|
||||
|
||||
## Solution Options
|
||||
|
||||
### Option 1: Accept gemma3-tools:1b Limitations ⚠️
|
||||
|
||||
**VRAM Savings**: 3.8GB (5.1GB → 1.3GB)
|
||||
|
||||
```
|
||||
Orchestrator: gemma3-tools:1b (1.3GB)
|
||||
Free for experts: 8.5GB
|
||||
```
|
||||
|
||||
**Pros**:
|
||||
- ✅ Massive VRAM savings (74% reduction)
|
||||
- ✅ Leaves 8.5GB for expert models
|
||||
- ✅ Can load codestral:22b (full size) + orchestrator simultaneously
|
||||
|
||||
**Cons**:
|
||||
- ❌ 33% tool calling reliability
|
||||
- ❌ Wrong tool selection
|
||||
- ❌ Erratic responses (raw JSON output)
|
||||
- ❌ Poor user experience
|
||||
|
||||
**Verdict**: ❌ **Not recommended** - Unreliability hurts more than VRAM savings help
|
||||
|
||||
---
|
||||
|
||||
### Option 2: Use Smaller Quantization of mistral:7b ✅ RECOMMENDED
|
||||
|
||||
Ollama supports multiple quantization levels. You're currently using Q4_K_M, but Q2 or Q3 exist.
|
||||
|
||||
**Available Quantizations**:
|
||||
- Q2_K: ~2.5GB VRAM (70% quality retention, aggressive)
|
||||
- Q3_K_M: ~3.2GB VRAM (80% quality, good balance)
|
||||
- Q4_K_M: ~5.1GB VRAM (90% quality, current)
|
||||
- Q5_K_M: ~6.2GB VRAM (95% quality)
|
||||
- Q8: ~7.7GB VRAM (99% quality, near full precision)
|
||||
|
||||
**Recommended**: Pull `mistral:7b-instruct-q3_K_M`
|
||||
|
||||
```bash
|
||||
# Pull lower quantization
|
||||
ollama pull mistral:7b-instruct-q3_K_M
|
||||
|
||||
# Update Core API config
|
||||
# services/core-api/.env
|
||||
AGENT_MODEL=mistral:7b-instruct-q3_K_M
|
||||
```
|
||||
|
||||
**New VRAM Budget**:
|
||||
```
|
||||
Orchestrator: mistral:7b Q3_K_M (3.2GB)
|
||||
Free for experts: 6.6GB
|
||||
Savings: 1.9GB (37% reduction)
|
||||
```
|
||||
|
||||
**Pros**:
|
||||
- ✅ 100% tool calling compatibility (same model architecture)
|
||||
- ✅ 1.9GB VRAM savings
|
||||
- ✅ Minimal quality loss (80% of full precision is fine for routing)
|
||||
- ✅ Proven reliability maintained
|
||||
|
||||
**Cons**:
|
||||
- ⚠️ Slightly lower response quality (acceptable for orchestration)
|
||||
- ⚠️ May need testing to verify tool calling still works
|
||||
|
||||
**Verdict**: ✅ **Best option** - Balanced approach
|
||||
|
||||
---
|
||||
|
||||
### Option 3: Hybrid Orchestrator (Simple Router + mistral:7b) 🔮 ADVANCED
|
||||
|
||||
Use a **two-tier routing system**:
|
||||
1. **Lightweight classifier** (gemma3-tools:1b) - Always loaded
|
||||
2. **Full orchestrator** (mistral:7b) - Loaded on demand for complex queries
|
||||
|
||||
**Architecture**:
|
||||
```python
|
||||
# Tier 1: Fast classifier (always loaded)
|
||||
if query_is_simple(message):
|
||||
# Direct routing: "list services" → list_services tool
|
||||
# Load time: 0ms (always in VRAM)
|
||||
use_simple_router(gemma3-tools:1b)
|
||||
else:
|
||||
# Complex routing: multi-tool, reasoning needed
|
||||
# Load time: ~2s (load mistral:7b)
|
||||
use_full_orchestrator(mistral:7b)
|
||||
```
|
||||
|
||||
**VRAM Budget**:
|
||||
```
|
||||
Tier 1 (always): gemma3-tools:1b (1.3GB)
|
||||
Tier 2 (on-demand): mistral:7b (5.1GB, loaded when needed)
|
||||
Free when Tier 1 only: 8.5GB
|
||||
Free when both loaded: 3.4GB
|
||||
```
|
||||
|
||||
**Pros**:
|
||||
- ✅ 8.5GB free for expert models most of the time
|
||||
- ✅ Only loads mistral:7b when truly needed
|
||||
- ✅ Simple queries stay fast (no model swap)
|
||||
|
||||
**Cons**:
|
||||
- ❌ Complex implementation (need query classifier)
|
||||
- ❌ 2s latency spike when switching to Tier 2
|
||||
- ❌ More failure modes (what if Tier 1 misclassifies?)
|
||||
|
||||
**Verdict**: 🔮 **Future enhancement** - Interesting but complex
|
||||
|
||||
---
|
||||
|
||||
### Option 4: Use Different Base Model 🔍 RESEARCH NEEDED
|
||||
|
||||
Look for other tool-capable models with better size/quality trade-offs.
|
||||
|
||||
**Candidates to research**:
|
||||
- `qwen2.5:7b-instruct-q3` - Alibaba's model, claimed good tool support
|
||||
- `llama3.2:3b-instruct` - Meta's latest, check if tool-capable
|
||||
- `hermes3:3b` - Nous Research, specifically trained for function calling
|
||||
|
||||
**Action**: Test these if available in Ollama registry.
|
||||
|
||||
---
|
||||
|
||||
## Recommended Implementation: Option 2
|
||||
|
||||
### Step 1: Pull Q3 Quantization
|
||||
|
||||
```bash
|
||||
# Check if Q3 variant exists
|
||||
ollama list | grep mistral
|
||||
|
||||
# Pull Q3 quantization (if available)
|
||||
ollama pull mistral:7b-instruct-q3_K_M
|
||||
|
||||
# OR manually create Q3 from modelfile
|
||||
cat > /tmp/mistral-q3.Modelfile << 'EOF'
|
||||
FROM mistral:7b
|
||||
PARAMETER quantization Q3_K_M
|
||||
EOF
|
||||
|
||||
ollama create mistral:7b-q3 -f /tmp/mistral-q3.Modelfile
|
||||
```
|
||||
|
||||
### Step 2: Test Tool Calling with Q3
|
||||
|
||||
```bash
|
||||
# Run our test script with Q3 variant
|
||||
source .venv/bin/activate
|
||||
python3 << 'PYEOF'
|
||||
import asyncio
|
||||
import httpx
|
||||
|
||||
async def test():
|
||||
payload = {
|
||||
"model": "mistral:7b-q3",
|
||||
"messages": [
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": "List all services"}
|
||||
],
|
||||
"tools": [{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "list_services",
|
||||
"description": "List all running services",
|
||||
"parameters": {"type": "object", "properties": {}}
|
||||
}
|
||||
}]
|
||||
}
|
||||
|
||||
async with httpx.AsyncClient(timeout=60) as client:
|
||||
r = await client.post("http://localhost:11434/api/chat", json=payload)
|
||||
data = r.json()
|
||||
message = data.get("message", {})
|
||||
|
||||
if "tool_calls" in message:
|
||||
print("✅ Q3 quantization: Tool calling WORKS")
|
||||
print(f" Called: {message['tool_calls'][0]['function']['name']}")
|
||||
else:
|
||||
print("❌ Q3 quantization: Tool calling BROKEN")
|
||||
print(f" Response: {message.get('content', '')[:100]}")
|
||||
|
||||
asyncio.run(test())
|
||||
PYEOF
|
||||
```
|
||||
|
||||
### Step 3: Update Core API Configuration
|
||||
|
||||
```bash
|
||||
# services/core-api/.env
|
||||
AGENT_MODEL=mistral:7b-q3
|
||||
```
|
||||
|
||||
```bash
|
||||
# Restart core-api to pick up new model
|
||||
docker restart core-api
|
||||
```
|
||||
|
||||
### Step 4: Verify VRAM Usage
|
||||
|
||||
```bash
|
||||
# Check new VRAM allocation
|
||||
curl -s http://localhost:11434/api/ps | jq '.models[] | {name, size_vram_gb: (.size_vram / 1024 / 1024 / 1024)}'
|
||||
```
|
||||
|
||||
**Expected Result**:
|
||||
```json
|
||||
{
|
||||
"name": "mistral:7b-q3",
|
||||
"size_vram_gb": 3.2
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Expert Model Strategy
|
||||
|
||||
With ~6.6GB available after Q3 orchestrator, you can now fit:
|
||||
|
||||
### Option A: Single Large Expert
|
||||
```
|
||||
Orchestrator: mistral:7b-q3 (3.2GB)
|
||||
Expert: codestral:22b-q2 (6GB)
|
||||
Total: 9.2GB / 11GB
|
||||
```
|
||||
|
||||
### Option B: Multiple Smaller Experts
|
||||
```
|
||||
Orchestrator: mistral:7b-q3 (3.2GB)
|
||||
Expert 1: codegemma:7b (4GB) - Code generation
|
||||
Expert 2: gemma3:4b (3GB) - General knowledge
|
||||
Total: 10.2GB / 11GB (near full capacity)
|
||||
```
|
||||
|
||||
### Option C: Dynamic Loading (Current Behavior)
|
||||
```
|
||||
Orchestrator: mistral:7b-q3 (3.2GB) - Always loaded
|
||||
Expert: Load on demand (6.6GB available)
|
||||
- codestral for code
|
||||
- gemma3:12b for general
|
||||
- Model swaps as needed
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Advanced: Ollama Keep Alive Configuration
|
||||
|
||||
Control how long models stay in VRAM:
|
||||
|
||||
```bash
|
||||
# Keep orchestrator always loaded (never unload)
|
||||
curl -X POST http://localhost:11434/api/generate \
|
||||
-d '{
|
||||
"model": "mistral:7b-q3",
|
||||
"keep_alive": -1,
|
||||
"prompt": "warm up"
|
||||
}'
|
||||
|
||||
# Expert models: unload after 5 minutes idle
|
||||
curl -X POST http://localhost:11434/api/generate \
|
||||
-d '{
|
||||
"model": "codestral:latest",
|
||||
"keep_alive": "5m",
|
||||
"prompt": "warm up"
|
||||
}'
|
||||
```
|
||||
|
||||
**Configuration in Core API**:
|
||||
```python
|
||||
# services/core-api/src/agent/orchestrator.py
|
||||
|
||||
self.llm = ChatOllama(
|
||||
model=self.settings.agent_model, # mistral:7b-q3
|
||||
base_url=self.settings.ollama_base_url,
|
||||
temperature=0.7,
|
||||
keep_alive=-1, # Never unload orchestrator
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Testing Checklist
|
||||
|
||||
Before switching to Q3 quantization:
|
||||
|
||||
- [ ] Pull or create Q3 variant
|
||||
- [ ] Test tool calling functionality
|
||||
- [ ] Test tool selection accuracy (list_services vs get_service_details)
|
||||
- [ ] Test multi-tool workflows
|
||||
- [ ] Compare response quality vs Q4
|
||||
- [ ] Verify VRAM usage reduction
|
||||
- [ ] Test with Open WebUI
|
||||
- [ ] Monitor for any degradation
|
||||
|
||||
If Q3 shows issues:
|
||||
- Try Q4_K_S (slightly smaller than Q4_K_M)
|
||||
- Fall back to current Q4_K_M if necessary
|
||||
|
||||
---
|
||||
|
||||
## Alternative Models Research
|
||||
|
||||
If mistral Q3 proves insufficient, test these:
|
||||
|
||||
### qwen2.5:7b (Alibaba Cloud)
|
||||
- Similar size to mistral
|
||||
- Claimed excellent tool calling
|
||||
- May have Q3/Q4 variants available
|
||||
|
||||
```bash
|
||||
ollama pull qwen2.5:7b-instruct
|
||||
# Test with our tool calling script
|
||||
```
|
||||
|
||||
### hermes3:3b (Nous Research)
|
||||
- Specifically trained for function calling
|
||||
- 3B parameters (smaller than mistral)
|
||||
- Check Ollama availability
|
||||
|
||||
```bash
|
||||
ollama search hermes3
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
**Immediate Action**: Pull `mistral:7b` with Q3_K_M quantization
|
||||
|
||||
```bash
|
||||
# Check available quantizations
|
||||
ollama show mistral:7b --modelfile
|
||||
|
||||
# Pull Q3 if available, or create from Q4
|
||||
ollama pull mistral:7b-instruct-q3_K_M
|
||||
```
|
||||
|
||||
**Expected Outcome**:
|
||||
- VRAM savings: 1.9GB (5.1GB → 3.2GB)
|
||||
- Tool calling: Should work (same architecture)
|
||||
- Quality: 80% of Q4 (acceptable for routing logic)
|
||||
- Expert model budget: 6.6GB (up from 4.7GB)
|
||||
|
||||
**Risk Mitigation**:
|
||||
- Test thoroughly before production
|
||||
- Keep Q4 variant as backup
|
||||
- Monitor for quality degradation
|
||||
|
||||
**Long-term**:
|
||||
- Research newer models (qwen2.5, hermes3)
|
||||
- Consider hybrid routing if complexity justified
|
||||
- Revisit when Ollama adds model multiplexing features
|
||||
|
||||
---
|
||||
|
||||
**Status**: Research complete, awaiting quantization testing
|
||||
**Next Steps**: User decision on Q3 testing approach
|
||||
Reference in New Issue
Block a user