Qdrant Memory Skill

Qdrant Memory Skill

Token optimization engine using Qdrant vector database for semantic caching and intelligent memory retrieval.

Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                      USER QUERY                              │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│  1. SEMANTIC CACHE CHECK (Cache Hit = 100% Token Savings)   │
│  ┌─────────────────┐    ┌─────────────────────────────────┐ │
│  │   Embed Query   │───▶│  Search Qdrant (similarity>0.9) │ │
│  └─────────────────┘    └─────────────────────────────────┘ │
│                                      │                       │
│                    ┌─────────────────┴──────────────────┐    │
│                    ▼                                    ▼    │
│            [CACHE HIT]                          [CACHE MISS] │
│            Return cached                        Continue to  │
│            response                             LLM          │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│  2. CONTEXT RETRIEVAL (RAG - 80-95% Context Reduction)      │
│  ┌─────────────────┐    ┌─────────────────────────────────┐ │
│  │  Identify Need  │───▶│  Retrieve Top-K Relevant Chunks │ │
│  └─────────────────┘    └─────────────────────────────────┘ │
│         Instead of 20K tokens ───▶ Only 500-1000 tokens     │
└─────────────────────────────────────────────────────────────┘

---

Prerequisites

Qdrant (Vector Database)

# Option 1: Docker (recommended)
docker run -d -p 6333:6333 -v qdrant_storage:/​qdrant/​storage qdrant/​qdrant

# Option 2: Docker Compose (persistent)
# See references/​complete_guide.md for docker-compose.yml

Embeddings Provider

Choose based on your needs:

Ollama Setup (M3 Mac Optimized)

# 1. Install Ollama (if not already installed)
brew install ollama

# 2. Start server (choose one option)
ollama serve              # Foreground (Ctrl+C to stop)
ollama serve &            # Background (current terminal)
nohup ollama serve &      # Background (survives terminal close)

# 3. Pull embedding model (768 dimensions, excellent quality)
ollama pull nomic-embed-text

# 4. Verify server is running
curl http://localhost:11434/api/tags

# 5. Test embedding generation
curl http://localhost:11434/api/embeddings -d '{"model":"nomic-embed-text","prompt":"hello"}'

Tip: To auto-start Ollama on login, add ollama serve & to your ~/​.zshrc or use brew services start ollama.

Note: For Ollama, use --dimension 768 when creating collections.

Amazon Bedrock Setup (AWS/​Kiro Subscription)

Uses your existing AWS credentials - no secrets stored in code.

# 1. Ensure AWS CLI is configured (uses ~/​.aws/​credentials)
aws configure  # Or set AWS_PROFILE for specific profile

# 2. Install boto3 if not present
pip install boto3

# 3. Set environment variables
export EMBEDDING_PROVIDER=bedrock
export AWS_REGION=eu-west-1  # Default region

# 4. Test authentication
python3 skills/​qdrant-memory/​scripts/​embedding_utils.py

Models Available (cheapest first):

Note: For Bedrock Titan V2, use --dimension 1024 when creating collections.

OpenAI Setup (Cloud)

export OPENAI_API_KEY="sk-..."

---

Quick Start

MCP Server Configuration

{
  "qdrant-mcp": {
    "command": "npx",
    "args": ["-y", "@qdrant/​mcp-server-qdrant"],
    "env": {
      "QDRANT_URL": "http://localhost:6333",
      "QDRANT_API_KEY": "${QDRANT_API_KEY}",
      "COLLECTION_NAME": "agent_memory"
    }
  }
}

Initialize Memory Collection

Run scripts/​init_collection.py to create the optimized collection:

# For Ollama (nomic-embed-text - 768 dimensions)
python3 scripts/​init_collection.py --collection agent_memory --dimension 768

# For OpenAI (text-embedding-3-small - 1536 dimensions)
python3 scripts/​init_collection.py --collection agent_memory --dimension 1536

---

Core Capabilities

1. Semantic Cache (Maximum Token Savings)

Purpose: Avoid LLM calls entirely for semantically similar queries.

Flow:

1. Embed incoming query
2. Search Qdrant for similar past queries (threshold > 0.9)
3. If match found → return cached response (100% token savings)
4. If no match → proceed to LLM, then cache result

Implementation:

# Cache check before LLM call
from scripts.semantic_cache import check_cache, store_response

# Check cache first
cached = check_cache(query, similarity_threshold=0.92)
if cached:
    return cached["response"]  # 100% token savings

# Generate response with LLM
response = llm.generate(query)

# Store for future cache hits
store_response(query, response, metadata={
    "type": "cache",
    "model": "gpt-4",
    "tokens_saved": len(response.split())
})

Collection Schema:

{
  "collection": "semantic_cache",
  "vectors": {
    "size": 1536,
    "distance": "Cosine"
  },
  "payload_schema": {
    "query": "keyword",
    "response": "text",
    "timestamp": "datetime",
    "model": "keyword",
    "token_count": "integer"
  }
}

2. Long-Term Memory (Context Optimization)

Purpose: Retrieve only relevant context instead of full conversation history.

Problem: 20,000 token conversation history → Expensive + Confuses model Solution: Query Qdrant → Return only top 3-5 relevant chunks (500-1000 tokens)

Memory Types:

Implementation:

from scripts.memory_retrieval import retrieve_context

# Instead of passing 20K tokens of history:
relevant_chunks = retrieve_context(
    query="What did we decide about the database architecture?",
    filters={"type": "decision"},
    top_k=5,
    score_threshold=0.7
)

# Build optimized prompt with only relevant context
prompt = f"""
Relevant Context:
{relevant_chunks}

User Question: {user_query}
"""
# Now only ~1000 tokens instead of 20,000

3. Hybrid Search (Vector + Keyword)

Purpose: Combine semantic similarity with exact keyword matching for technical queries.

When to use: Error codes, variable names, specific identifiers

from scripts.hybrid_search import hybrid_query

results = hybrid_query(
    text_query="kubernetes deployment failed",
    keyword_filters={
        "error_code": "ImagePullBackOff",
        "namespace": "production"
    },
    fusion_weights={"text": 0.7, "keyword": 0.3}
)

---

MCP Tools Reference

Store Memory

{
  "tool": "qdrant_store_memory",
  "arguments": {
    "content": "We decided to use PostgreSQL for user data due to ACID compliance requirements",
    "metadata": {
      "type": "decision",
      "project": "api-catalogue",
      "date": "2026-01-22",
      "tags": ["database", "architecture"]
    }
  }
}

Search Memory

{
  "tool": "qdrant_search_memory",
  "arguments": {
    "query": "database architecture decisions",
    "filter": {
      "must": [{ "key": "type", "match": { "value": "decision" } }]
    },
    "limit": 5,
    "score_threshold": 0.7
  }
}

---

Payload Filtering Patterns

Filter by Type

{
  "filter": {
    "must": [{ "key": "type", "match": { "value": "technical" } }]
  }
}

Filter by Project + Date Range

{
  "filter": {
    "must": [
      { "key": "project", "match": { "value": "api-catalogue" } },
      { "key": "timestamp", "range": { "gte": "2026-01-01" } }
    ]
  }
}

Exclude Certain Tags

{
  "filter": {
    "must_not": [
      { "key": "tags", "match": { "any": ["deprecated", "archived"] } }
    ]
  }
}

---

Collection Design Patterns

Single Collection (Simple)

agent_memory/
├── type: "cache" | "decision" | "code" | "error" | "conversation"
├── project: "<project_name>"
├── timestamp: "<ISO8601>"
└── content: "<text>"

Multi-Collection (Advanced)

---

Token Savings Metrics

Track savings with metadata:

{
    "tokens_input_saved": 15000,
    "tokens_output_saved": 2000,
    "cost_saved_usd": 0.27,
    "cache_hit": True,
    "retrieval_latency_ms": 45
}

Expected Savings:

---

Best Practices

Embedding Model Selection

Cache Invalidation

• Time-based: Expire cache entries after N days
• Manual: Clear cache when underlying data changes
• Version-based: Include model version in metadata

Memory Hygiene

1. Deduplicate: Check similarity before storing
2. Prune: Remove low-value memories periodically
3. Compress: Summarize long conversations before storing

---

References

• See references/​complete_guide.md for full setup, testing, and troubleshooting
• See references/​collection_schemas.md for complete schema definitions
• See references/​embedding_models.md for model comparisons
• See references/​advanced_patterns.md for RAG optimization patterns

AGI Framework Integration

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/​memory_manager.py auto --query "<task summary>"

Decision Tree:

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/​memory_manager.py store \
  --content "Description of what was decided/​solved" \
  --type decision \
  --tags qdrant-memory <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration- Strategy: This skill communicates via the shared memory system.

• Orchestration: Invoked by orchestrator via intelligent routing.
• Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns