Yes, DeepSeek-V3.2 can absolutely be connected to Milvus-based vector search, but this happens at the application and tool-calling layer rather than inside the model itself. V3.2-Exp is a text-to-text LLM with strong long-context reasoning and explicit support for function (tool) calling via an OpenAI-compatible API.:contentReference[oaicite:18]{index=18} To integrate it with a vector database such as Milvus or its managed equivalent Zilliz Cloud, you define tools like semantic_search, fetch_by_ids, or upsert_vectors whose implementations call Milvus/Zilliz over HTTP/gRPC or via official SDKs. From the model’s perspective, these are just JSON-described functions; your code handles the actual index scans and document retrieval.
A typical RAG pipeline with DeepSeek-V3.2 and Milvuslooks like this: during ingestion, you chunk documents, compute embeddings using a dedicated embedding model, and write vectors plus metadata into Milvus or Zilliz Cloud collections. At query time, you send the user’s question and the chat history to DeepSeek-V3.2 along with a function schema like semantic_search(collection, query, top_k, filters). When the model decides it needs external knowledge, it emits a tool call with structured arguments; your service executes a Milvussearch, retrieves top-k matches and metadata, and feeds those back to the model in a follow-up message. Because V3.2’s training explicitly includes “agentic search” and long-context reasoning, it is comfortable taking several retrieval steps, refining queries, or combining multiple tool results into a final answer.:contentReference[oaicite:19]{index=19}
To make this integration robust, lean on both JSON mode and good schema design. Use DeepSeek’s JSON Output (response_format: { "type": "json_object" }) to force tool arguments into strict JSON, and keep your tool contracts narrow—e.g., one tool that only does vector search, another that only updates user preferences, and so on.:contentReference[oaicite:20]{index=20} For high-traffic use cases, let Milvus or Zilliz Cloud handle most of the “memory,” and keep the LLM context window short by only passing the top retrieved passages plus a small chat history. This plays nicely with V3.2’s sparse attention design, which is optimized for long contexts when you need them but still benefits from lean prompts in day-to-day traffic. In practice, the combination of DeepSeek-V3.2’s tool-aware reasoning and a scalable vector backend like Milvus or Zilliz Cloud gives you a clean path to build large-scale, cost-effective RAG and agent systems without overloading the model with raw text.