context-retrieval

# Context Retrieval Context retrieval is the process of finding and assembling the most relevant pieces of information from a knowledge base to ground an AI agent's responses in factual, up-to-date data. It is the backbone of Retrieval Augmented Generation (RAG) and ensures that generated outputs are accurate and verifiable rather than hallucinated. ## Workflow 1. **Embed the Query**: Convert the user's natural-language query into a dense vector representation using an embedding model (e.g., OpenAI `text-embedding-3-small`, Cohere `embed-v3`, or an open-source model like `bge-large`). The embedding captures the semantic meaning of the query so it can be compared against stored documents. 2. **Search the Vector Store**: Send the query embedding to a vector database (Pinecone, Weaviate, Qdrant, Chroma, etc.) and perform an approximate nearest-neighbor (ANN) search. Request the top-k candidate chunks, typically k = 10–20 to give the reranker enough material to work with. 3. **Rerank the Results**: Pass the candidate chunks through a cross-encoder reranker (e.g., Cohere Rerank, `bge-reranker-large`, or a ColBERT model). The reranker scores each chunk against the original query with full attention, producing much more accurate relevance scores than cosine similarity alone. Keep the top-n results (typically n = 3–5). 4. **Assemble the Context Window**: Concatenate the selected chunks into a single context block, ordered by relevance score descending. Prepend source metadata (...