Best Practices for RAG System Integration
Integrating Deeptrain into an existing RAG architecture requires attention to three key integration points:
- data ingestion layer: Submitting video URLs through the /v1/ingest interface, it is recommended to set callback_url to receive the results asynchronously.
- vector database layer: directly use the JSON-LD format embeddings output by Deeptrain (with timestamp metadata already included)
- retrieval enhancement layer: Implement multimodal retrieval using the video_context field, example SPARQL query:
SELECT ?frame WHERE { ?frame deeptrain:contains "keyword"@zh }
Performance Optimization Recommendations::
1. Enabling QVC compression for long videos reduces the vector index volume by a factor of 4
2. Use of language filter parameters to avoid mixing of embeddings in unrelated languages
3. Combining vector databases such as Milvus/Pinecone that support multimodal searching
The platform provides SDK packaging for Python/Node.js, and basic integration can be completed in 5 lines of code. The test environment can handle 20 hours of video content per minute, meeting most enterprise-level requirements.
This answer comes from the articleDeeptrain: converting video content into large model retrievable informationThe
