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DeepSeek Stream Search RAG

This solution is not about a single capability. It is about the combined chain of "streaming output + web search + private-domain RAG".

1. When it fits​

  • You need to return results while the model is still generating
  • You need to query both the public web and a private knowledge base
  • You want an augmented response chain of "search first, then retrieve, then answer"

It is closer to a composite response system than to a pure chat or pure RAG baseline.

2. Core module combination​

The main chain is typically:

  • Streaming model output
  • ChatWithWebSearchEnhance or a web-search-augmented chain
  • RagService
  • Final response assembly

The point is not to force every capability into one prompt, but to separate clearly:

  • Time-sensitive information comes from web search
  • Private-domain knowledge comes from vector retrieval
  • The final answer is then organized in a unified pass

3. The value of this solution​

  • Public web information and private-domain knowledge can both enter the response chain
  • Streaming output suits front-end interactive experiences
  • It is easier to explain "which part came from search, which part came from internal knowledge"

4. When you don't need this much weight​

If you only have one of these needs:

5. Mainline pages to read first​

  1. Core SDK / Search & RAG
  2. Core SDK / Model Access
  3. SearXNG Web Search

6. Going deeper into implementation​

If you want to see:

  • Combined code
  • How to wire up streaming
  • How to split responsibilities between search and RAG

Continue to the deep page:

7. Key objects​

This solution typically lands on the following kinds of objects:

  • A streaming Chat or Responses client
  • ChatWithWebSearchEnhance
  • RagService
  • Upstream result-assembly logic

They carry streaming consumption, web augmentation, private-domain retrieval, and final response assembly respectively.

8. Where this combined chain breaks most easily​

Where this combined chain breaks most easily
  • Mixing web search results and private-domain knowledge into a single context layer
  • Streaming the output first without defining a citation and evidence-display strategy
  • Leaving the search chain, retrieval chain, and response chain unlayered, which makes troubleshooting hard

So the focus of this solution is not how many capabilities you stack, but how cleanly the layers are separated.

9. When it is worth upgrading to​

This page is only truly valuable when you meet all three of these needs at once:

  1. You need front-end streaming interaction
  2. You need real-time public web information
  3. You need private-domain knowledge base support

If any one is missing, there is usually a lighter solution to ship first.