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DeepSearcher: A local open-source Deep Research
In contrast to OpenAI’s Deep Research, this example ran locally, using only open-source models and tools like Milvus and LangChain.
These tools differ in architecture and methodology although sharing an objective: iteratively research a topic or question by surfing the web or internal documents and output a detailed, informed, and well-structured report. This approach has the advantage over our prototype, which analyzed each question separately and simply concatenated the output, of producing a report where all sections are consistent with each other, i.e., containing no repeated or contradictory information. A more complex system could combine aspects of both, using a conditional execution flow to structure the report, summarize, rewrite, reflect and pivot, and so on, which we leave for future work.
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