rag_search_ddgs
Search the web with DuckDuckGo and return context scored by semantic similarity to prioritize relevant results.
Instructions
Search the web for a given query using DuckDuckGo. Returns context to the LLM with RAG-like similarity scoring to prioritize the most relevant results.
This tool fetches web search results, scores them by semantic similarity to the query using text embeddings, and returns the top-ranked content as markdown text.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query. Use natural language questions or keywords. Example: "latest developments in quantum computing" | |
| num_results | No | Number of initial search results to fetch from DuckDuckGo. More results provide better coverage but increase processing time. Default: 10 | |
| top_k | No | Number of top-scored results to include in the final output. These are the most semantically relevant results after scoring. Default: 5 | |
| include_urls | No | Whether to include source URLs in the results. If True, each result includes its URL for citation. Default: True |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||