Packs the best snippets into the prompt window.
Context assembly takes the highest-ranked passages after retrieval and reranking and prepares them as context for the model. This includes formatting the text, removing redundancies, trimming for length to fit token limits, organizing snippets in a logical order, and structuring the prompt to clearly separate context from the query.
The LLM's answer will only be as good as the context it receives. Even with perfect retrieval, poorly assembled context can confuse the model or cause it to miss critical information. Well-assembled context gives the model a clear, digestible set of facts to work with and helps prevent it from veering off-course.
contextAssembly.subtitle
Vector databases are specialized database systems designed to store and query high-dimensional vector data efficiently. Unlike traditional databases that excel at exact matches, vector databases are optimized for similarity search using techniques like approximate nearest neighbor (ANN) algorithms.
Approximate nearest neighbor (ANN) algorithms are fundamental to vector search. These algorithms trade perfect accuracy for dramatic speed improvements, making them practical for large-scale applications. Popular ANN algorithms include HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), and PQ (Product Quantization).
Vector databases are optimized for similarity search rather than exact matching. They store embeddings - numerical representations of data - and find similar items by calculating distance in vector space. This makes them ideal for semantic search, recommendation systems, and other AI applications.
When implementing a vector database, you need to consider several factors: the dimensionality of your vectors, the distance metric (cosine, Euclidean, dot product), the indexing algorithm, and hardware requirements. These choices significantly impact search performance and accuracy.
Popular vector database options include Pinecone, Weaviate, Milvus, Qdrant, and pgvector (a PostgreSQL extension). Each has different strengths: some excel at scale, others at ease of use, and some offer unique features like filtering or hybrid search capabilities.
Vector databases store and query high-dimensional vector data efficiently, making them essential for modern AI applications. They enable similarity search across millions or billions of vectors in milliseconds, powering use cases from semantic search to recommendation engines.
contextAssembly.simpleStrategyNote
contextAssembly.basicTemplateNote
contextAssembly.contextAssemblyDescription
Building a robust Context Assembly solution is challenging. Respeak's Enterprise RAG Platform handles this complexity for you.