langchain vector storage connection and confguration

services/rag/langchain/QWEN.md

@@ -27,6 +27,7 @@ rag-solution/services/rag/langchain/
 ├── PLANNING.md        # Development roadmap and phases
 ├── QWEN.md            # Current file - project context
 ├── requirements.txt   # Python dependencies
+├── vector_storage.py  # Vector storage module with Qdrant and Ollama embeddings
 └── venv/              # Virtual environment
 ```
 
@@ -57,10 +58,10 @@ The project is organized into 6 development phases as outlined in `PLANNING.md`:
 - [x] Configure environment variables
 
 ### Phase 3: Vector Storage Setup
-- [ ] Install Qdrant client library
-- [ ] Create `vector_storage.py` for initialization
-- [ ] Configure Ollama embeddings using `OLLAMA_EMBEDDING_MODEL`
-- [ ] Prepare OpenAI fallback (commented)
+- [x] Install Qdrant client library
+- [x] Create `vector_storage.py` for initialization
+- [x] Configure Ollama embeddings using `OLLAMA_EMBEDDING_MODEL`
+- [x] Prepare OpenAI fallback (commented)
 
 ### Phase 4: Document Loading Module
 - [ ] Create `enrichment.py` for loading documents to vector storage