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Vector storage Qdrant initialization and configuration

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idchlife
2026-02-04 01:10:07 +03:00
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services/rag/llamaindex/QWEN.md
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@@ -84,10 +84,11 @@ This is a Retrieval Augmented Generation (RAG) solution built using LlamaIndex a
- [x] Required loader libraries installation
### Phase 3: Vector Storage Setup
- [ ] Qdrant library installation
- [ ] Vector storage initialization module
- [ ] Embedding model configuration with Ollama
- [ ] Collection creation strategy
- [x] Qdrant library installation
- [x] Vector storage initialization module
- [x] Collection creation strategy for "documents_llamaindex"
- [x] Ollama embedding model configuration
- [x] Optional OpenAI embedding via OpenRouter (commented)
### Phase 4: Document Enrichment
- [ ] Document loading module with appropriate loaders
@@ -109,7 +110,7 @@ This is a Retrieval Augmented Generation (RAG) solution built using LlamaIndex a
llamaindex/
├── venv/ # Python virtual environment
├── cli.py # CLI entry point
├── vector_storage.py # Vector storage configuration (to be created)
├── vector_storage.py # Vector storage configuration
├── enrichment.py # Document loading and processing (to be created)
├── retrieval.py # Search and retrieval functionality (to be created)
├── agent.py # Chat agent implementation (to be created)