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Qdrant stores vector representations produced by the configured embedding provider. The agent uses it for knowledge retrieval and a high-confidence FAQ fast path.

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Stored context

Each point combines an embedding with payload metadata such as organization, document/source identity, source type, chunk content, and FAQ answer fields. Search always needs an organization filter so one tenant’s knowledge cannot appear in another tenant’s prompt.

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Retrieval modes

The knowledge_retrieval tool requests the top relevant chunks, limited by RAG_TOP_K, and returns their text and metadata to the model.

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Model changes

Vector collections have a fixed dimension. Defaults in source include 1024 for Bedrock Titan v2, 768 for Ollama nomic-embed-text, 384 for Hugging Face MiniLM, and 1536 for OpenAI text-embedding-3-small; configuration can override these.
When changing embedding provider, model, or dimension, rebuild the collection and reindex every source. Mixing embeddings from incompatible models produces invalid comparisons even when dimensions happen to match.
Use the agent’s inspect:vectors, debug:qdrant, and reset:vectors scripts for controlled diagnostics. Resetting vectors is destructive and must be followed by reindexing.
Last modified on July 17, 2026