Introduces a comprehensive, multi-tiered memory system to provide conversation history and context for the AI agent. This lays the foundation for more stateful and intelligent interactions.
Key components of this implementation:
- **Multi-Tiered Memory Architecture:**
- **Tier 1 (Working Memory):** A fast, in-memory buffer (`ConversationBufferMemory`) that holds the most recent turns of a conversation for immediate access.
- **Tier 3 (Long-Term Memory):** A persistent, semantic search-based memory store using Qdrant (`QdrantConversationMemory`). It stores all conversation turns as vector embeddings, enabling long-term recall and similarity search.
- **Qdrant Integration:**
- The `qdrant-client` is added to manage collections and perform vector search operations.
- Each user is assigned a dedicated Qdrant collection for multi-tenancy.
- **Ollama Embedding Client:**
- A new `OllamaEmbeddingClient` generates text embeddings via the Ollama API, replacing the need for local sentence-transformer models. This significantly reduces the service's dependency footprint.
- **Configuration and Stack Updates:**
- The `config.py` and `core-ai.yml` stack file are updated with new settings for enabling memory, configuring Qdrant, and specifying the embedding model.
- **Utility and Schema Additions:**
- New Pydantic schemas (`memory/schemas.py`) define the data structures for conversation turns and memory management.
- Utility functions (`utils.py`) are added for user ID sanitization and collection naming.
This feature enhances the agent's capabilities by allowing it to maintain context across multiple turns and sessions, leading to more coherent and relevant responses.
jpmschweitzer
8487b2a366