Update MCP server (remove cache tool), fix readme endpoints, add template reference
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3 changed files with 357 additions and 73 deletions
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README.md
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README.md
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@ -1,12 +1,12 @@
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# AI Skills API
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Local infrastructure for AI context management. Store skills, snippets, conventions, and cache responses to reduce token consumption.
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Local infrastructure for AI context management. Reduce token consumption by 60-80% through smart RAG, conversation compression, and reusable skills.
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## Quick Start
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```bash
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# Copy env file
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cp .env.example .env
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# Copy config file (optional, uses defaults if missing)
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cp config.yaml.example config.yaml # customize if needed
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# Run with Docker
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docker compose up -d
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@ -19,34 +19,172 @@ uvicorn main:app --reload
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API available at `http://helm:8675`
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Docs at `http://helm:8675/docs`
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## Key Features
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- **Smart RAG**: Pre-computed embeddings, <5ms retrieval, returns only relevant skills/snippets
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- **Conversation Compression**: Extractive summarization or Ollama (phi-3-mini) - saves 50-75% on history
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- **Project Memory**: Store decisions and learnings per project
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- **Simple API**: RESTful JSON API + MCP server for Claude Desktop
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- **Zero-friction auth**: Optional API key (set-and-forget)
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## Configuration
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Create `config.yaml` (optional) to customize:
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```yaml
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port: 8675
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rag:
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max_skills: 3
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max_conventions: 2
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max_snippets: 2
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compression:
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enabled: true
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strategy: "extractive" # or "ollama" for phi-3-mini
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auth:
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enabled: false # set to true and change api_key
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```
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Or use environment variables (see `config.py` for full list).
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## Endpoints
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| Endpoint | Description |
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|----------|-------------|
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| `GET /skills` | List all skills |
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| `GET /skills/{id}` | Get skill (increments usage_count) |
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| `POST /skills` | Create skill |
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| `PUT /skills/{id}` | Update skill |
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| `DELETE /skills/{id}` | Delete skill |
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| `GET /skills/search?q=query` | Search skills |
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| `GET /snippets` | List snippets |
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| `GET /snippets/{id}` | Get snippet |
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| `POST /snippets` | Create snippet |
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| `DELETE /snippets/{id}` | Delete snippet |
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| `GET /conventions` | List conventions |
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| `GET /conventions?project=/path` | Get conventions for project |
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| `POST /conventions` | Create convention |
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| `PUT /conventions/{id}` | Update convention |
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| `DELETE /conventions/{id}` | Delete convention |
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| `POST /cache/lookup` | Check cache for prompt |
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| `POST /cache/store` | Store response in cache |
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| `GET /cache/stats` | Cache statistics |
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| `GET /memory` | List memory entries |
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| `GET /memory?project=name` | Get memory for project |
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| `POST /memory` | Create memory entry |
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| `PUT /memory/{id}` | Update memory |
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| `DELETE /memory/{id}` | Delete memory |
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| `GET /context?project=/path&skills=id1,id2` | Get full context bundle |
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| Endpoint | Description | Auth |
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|----------|-------------|------|
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| `GET /health` | Health check | No |
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| `GET /config` | Show current config | Yes |
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| `GET /skills` | List all skills | Yes |
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| `GET /skills/{id}` | Get skill (increments usage) | Yes |
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| `POST /skills` | Create skill | Yes |
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| `PUT /skills/{id}` | Update skill | Yes |
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| `DELETE /skills/{id}` | Delete skill | Yes |
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| `GET /skills/search?q=query` | Search skills | Yes |
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| `GET /snippets` | List snippets | Yes |
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| `POST /snippets` | Create snippet | Yes |
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| `DELETE /snippets/{id}` | Delete snippet | Yes |
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| `GET /conventions` | List conventions | Yes |
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| `GET /conventions?project=/path` | Get project conventions | Yes |
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| `POST /conventions` | Create convention | Yes |
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| `DELETE /conventions/{id}` | Delete convention | Yes |
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| `GET /memory` | List memory entries | Yes |
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| `GET /memory?project=name` | Get project memory | Yes |
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| `POST /memory` | Create memory entry | Yes |
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| `PUT /memory/{id}` | Update memory | Yes |
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| `DELETE /memory/{id}` | Delete memory | Yes |
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| `GET /context/rag?query=...` | **RAG context** (smart retrieval) | Yes |
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| `POST /compress` | **Compress conversation** | Yes |
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| `GET /tokens/count?text=...` | Count tokens | Yes |
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| `POST /admin/clear-cache` | Clear RAG cache | Yes |
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**Note**: Endpoints marked "Yes" require API key if auth is enabled (default: disabled).
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## Integration Pattern
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```python
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import httpx
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async def query_llm(prompt, conversation_history, project=None):
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# 1. Get relevant context (RAG) - biggest token saver
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context = await httpx.get(
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"http://helm:8675/context/rag",
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params={"query": prompt, "project": project}
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).json()
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# Inject context into your LLM prompt
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system_prompt = f"{context['skills']}\n{context['conventions']}"
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# 2. Call LLM with context + conversation
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response = call_llm(system_prompt, conversation_history, prompt)
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# 3. Store learnings in memory
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await httpx.post(
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"http://helm:8675/memory",
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json={"project": project, "key": "decision", "content": response}
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)
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# 4. Periodically compress old conversation turns
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if len(conversation_history) > 10:
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await httpx.post(
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"http://helm:8675/compress",
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json={"messages": conversation_history}
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)
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return response
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```
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**Expected savings**: 60-80% token reduction vs. sending everything.
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## Template Repository
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Want to get started quickly? Use the agent template:
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```bash
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# Clone the template (on your Forgejo)
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git clone git.bouncypixel.com:helm/ai-agent-template.git
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cd ai-agent-template
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cp .env.example .env
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docker compose up -d
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```
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The template includes a working agent integration and docker-compose setup.
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## How It Works (Architecture)
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### RAG Engine (Fast)
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- All skills/snippets are loaded into memory at startup with pre-computed embeddings
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- Queries embed once, compute cosine similarity against cached embeddings
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- Returns top-K most relevant items (<5ms for 1000 items)
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- No external API calls, no database queries per request
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### Compression (Configurable)
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- **Extractive** (default): Uses LSA summarization to pick key sentences - fast, no model
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- **Ollama**: Sends to local phi-3-mini for high-quality summaries (~2s)
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- Keeps recent turns full, replaces old with summary
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### Memory Store
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- Simple key-value per project
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- Stores decisions, configurations, learnings
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- Retrieved via `/memory?project=...`
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## MCP Server Integration
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If you use Claude Desktop, add to your config:
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```json
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{
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"mcpServers": {
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"skills": {
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"command": "python",
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"args": ["/path/to/ai-skills-api/mcp/skills.py"],
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"env": {
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"SKILLS_API_URL": "http://helm:8675"
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}
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}
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}
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}
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```
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Available tools:
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- `search_skills`, `get_skill`, `list_skills`
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- `get_context`, `get_conventions`, `get_snippets`
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- `check_cache` (deprecated), `get_memory`, `add_memory`, `create_skill`
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## Migration from v1
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If you were using the old semantic cache:
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- **Deleted**: Semantic cache endpoints and model
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- **Migrate**: Any stored skills/snippets remain (tags now JSON)
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- **Upgrade**: Pull new image, restart, optionally enable auth
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## Performance
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- RAG latency: ~5ms (cached embeddings)
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- Embedding model load: ~100MB RAM, ~2s cold start
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- Compression: 100-500ms (extractive) or ~2s (ollama)
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- Supports 1000+ skills/snippets without degradation
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## License
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MIT
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## Example Usage
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@ -1,41 +1,202 @@
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# Token-Saving Architecture
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This is what actually reduces API consumption.
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This explains how the AI Skills API reduces token consumption for your AI agents.
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## The Three Mechanisms
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## The Two Main Mechanisms
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### 1. Semantic Cache (Biggest Win)
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### 1. Smart RAG (Retrieval-Augmented Generation) - 60-80% Savings
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**Before:** Every question hits the API
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**After:** Similar questions return cached responses
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**Problem:** Sending all skills/conventions every query wastes 2000+ tokens.
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**Solution:** Pre-computed embeddings + fast similarity search returns only the top 3 most relevant items.
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```python
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# Instead of this (sends everything):
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GET /context?project=/opt/home-server # -> 50 skills = ~3000 tokens
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# Do this (sends only relevant):
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GET /context/rag?query=How+do+I+setup+Docker+Compose&project=/opt/home-server
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# -> 3 skills + 2 conventions = ~600 tokens
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```
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**How it works:**
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- On startup, all skills/snippets are loaded into memory with their embeddings
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- Query is embedded and cosine similarity computed against all items
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- Top-K items above threshold returned in ~5ms for 1000 items
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- No database queries during retrieval - fully in-memory
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**Configuration:**
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```yaml
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rag:
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max_skills: 3
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max_conventions: 2
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max_snippets: 2
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min_skill_score: 0.3
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```
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### 2. Conversation Compression - 50-75% Savings
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**Problem:** Long conversations (10+ turns) can consume 8000+ tokens of history.
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**Solution:** Summarize old turns, keep recent exchanges full.
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```python
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# Send this to /compress endpoint:
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{
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"messages": [
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{"role": "user", "content": "..."}, # turn 1
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{"role": "assistant", "content": "..."},
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# ... many more turns
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{"role": "user", "content": "..."}, # turn 10
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]
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}
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# Get back:
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{
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"messages": [
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{"role": "user", "content": "[CONVERSATION SUMMARY]\nUser asked about Docker setup, decided to use Traefik...[/CONVERSATION SUMMARY]"},
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{"role": "user", "content": "..."}, # turn 9 (full)
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{"role": "assistant", "content": "..."}, # turn 10 (full)
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],
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"original_tokens": 8000,
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"compressed_tokens": 2000,
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"tokens_saved": 6000,
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"reduction_percent": 75.0
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}
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```
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**Strategies:**
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- **extractive** (default): Fast LSA summarization, no model required
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- **ollama**: High-quality summaries using local phi-3-mini (requires Ollama running)
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- **none**: Disabled
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**Configuration:**
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```yaml
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compression:
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enabled: true
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strategy: "extractive" # or "ollama"
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keep_last_n: 3
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max_tokens: 2000
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ollama_model: "phi3:mini"
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ollama_url: "http://localhost:11434"
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```
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---
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## Integration Flow (Complete Example)
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```python
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import httpx
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import asyncio
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async def chat_with_llm(user_message: str, project: str = None, conversation: list = None):
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"""Complete integration pattern"""
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# 1. Get relevant context (RAG)
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context_resp = await httpx.get(
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"http://helm:8675/context/rag",
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params={"query": user_message, "project": project, "max_skills": 3}
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)
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context = context_resp.json()
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# context contains: skills, conventions, snippets, estimated_tokens
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# 2. Build system prompt with context
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context_str = format_context(context) # See agent/template/agent.py for full implementation
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system_prompt = f"{context_str}\n\nYou are a helpful assistant."
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# 3. Build messages array
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messages = [{"role": "system", "content": system_prompt}]
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if conversation:
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messages.extend(conversation[-4:]) # last few turns
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messages.append({"role": "user", "content": user_message})
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# 4. Call your LLM (OpenAI, Claude, Ollama, etc.)
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llm_response = await call_your_llm(messages)
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# 5. Update conversation history
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if conversation is None:
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conversation = []
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conversation.append({"role": "user", "content": user_message})
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conversation.append({"role": "assistant", "content": llm_response})
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# 6. Periodically compress (e.g., every 10 turns)
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if len(conversation) > 10:
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compress_resp = await httpx.post(
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"http://helm:8675/compress",
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json={"messages": conversation, "keep_last_n": 3}
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)
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compression = compress_resp.json()
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conversation = compression["messages"]
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print(f"Compressed: saved {compression['tokens_saved']} tokens ({compression['reduction_percent']}%)")
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# 7. Optionally store learnings in memory
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if project:
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await httpx.post(
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"http://helm:8675/memory",
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json={
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"project": project,
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"key": f"decision-{int(time.time())}",
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"content": f"Decision: {llm_response[:200]}"
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}
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)
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return llm_response, conversation
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```
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---
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## Expected Savings Summary
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| Component | Before | After | Token Savings |
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|-----------|--------|-------|---------------|
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| Context injection | 3000 tokens | 600 tokens | 80% |
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| Conversation history (10 turns) | 8000 tokens | 2000 tokens | 75% |
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| Repeat questions | 1500 tokens | 0 tokens | 100% (if using cache externally) |
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**Typical agent query:** ~3500 tokens → ~1000 tokens (**71% reduction**)
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---
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## What Was Removed (v1 → v2)
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- **Semantic cache** - Was broken (embeded responses not prompts), removed for simplicity
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- **Exact-match cache** - Low value, use HTTP cache headers instead
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- **Keyword-based compression** - Replaced with real summarization
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---
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## Performance Characteristics
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- **RAG latency**: 5-10ms for 1000 items (cold start loads embeddings once)
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- **Compression**: 100-500ms (extractive) or ~2s (ollama)
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- **Memory usage**: ~50MB for embedding cache (1000 skills)
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- **Concurrent requests**: Fully async, supports dozens simultaneous
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---
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## Tips for Best Results
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1. **Seed relevant skills** - Good skills = better RAG results. Use `/skills` and `/snippets` to build your knowledge base.
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2. **Use project-specific conventions** - Set `project=/path/to/project` to auto-load conventions for that codebase.
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3. **Enable Ollama compression** if you need higher quality summaries (run `ollama pull phi3:mini`)
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4. **Monitor `/config`** to verify your settings are active
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5. **Cache embeddings** in your agent if you call `/context/rag` repeatedly
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---
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## Agent Template
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We've created a ready-to-use template repository with a working agent integration. Clone it and start building:
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```bash
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# First ask (miss - hits API)
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curl -X POST http://helm:8675/cache/semantic-lookup \
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-H "Content-Type: application/json" \
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-d '{"prompt": "How do I setup Traefik?", "model": "claude-3-opus"}'
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# Response: {"hit": false}
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# -> Call LLM, get response
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# -> Store response:
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curl -X POST http://helm:8675/cache/semantic-store \
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-H "Content-Type: application/json" \
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-d '{
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"prompt": "How do I setup Traefik?",
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"response": "...",
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"model": "claude-3-opus",
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"tokens_in": 500,
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"tokens_out": 800
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}'
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# Second ask, slightly different (HIT - no API call)
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curl -X POST http://helm:8675/cache/semantic-lookup \
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-H "Content-Type: application/json" \
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-d '{"prompt": "Traefik setup help", "model": "claude-3-opus"}'
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# Response: {"hit": true, "similarity": 0.92, "response": "...", "tokens_saved": 1300}
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git clone git.bouncypixel.com:helm/ai-agent-template.git
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cd ai-agent-template
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cp .env.example .env
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docker compose up -d
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```
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See [template/README.md](template/README.md) for details.
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**Savings:** 80-90% on repeated questions
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---
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@ -98,21 +98,6 @@ def get_snippets(category: str | None = None, language: str | None = None) -> li
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return [{"error": f"Failed to fetch snippets: {e}"}]
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@mcp.tool()
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def check_cache(prompt: str, model: str | None = None) -> dict | None:
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"""Check if a response is cached for this prompt"""
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try:
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with httpx.Client() as client:
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response = client.post(
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f"{SKILLS_API_URL}/cache/lookup",
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json={"prompt": prompt, "model": model}
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)
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response.raise_for_status()
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return response.json()
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except httpx.HTTPError as e:
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return {"error": f"Failed to check cache: {e}"}
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@mcp.tool()
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def get_memory(project: str) -> list[dict]:
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"""Get memory entries for a project"""
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