knowledge-retrieval

# Knowledge Retrieval Perform semantic search over a pre-ingested document collection using Retrieval-Augmented Generation (RAG). Backed by LlamaIndex with ChromaDB or NVIDIA Foundational RAG. ## When to Use - Searching internal or pre-ingested documents and reports - Finding information in PDFs, whitepapers, or technical documentation - Retrieving domain-specific knowledge not available on the open web - This is the **highest priority** source — check the knowledge base first before web or paper searches ## How to Use 1. Formulate a semantic search query describing the information needed 2. Call `knowledge_retrieval` with the query 3. Review returned chunks for relevance 4. Note the citation metadata (filename, page number) for sourcing ## Result Format Results are returned as text chunks with citation metadata: ``` Relevant text passage from the ingested document... Citation: filename.pdf, p.12 ``` ## Constraints - Searches only over documents that have been ingested into the knowledge index - Returns ranked chunks based on semantic similarity - Citation format: `Citation: filename.ext, p.X` - Each call counts toward the researcher's 8-call limit per task ## Backend Options - **LlamaIndex + ChromaDB** — Local vector store with LlamaIndex orchestration - **NVIDIA Foundational RAG** — NVIDIA-hosted RAG service with NeMo Retriever