query_embs = model.encode(queries, normalize_embeddings=True) doc_embs = model.encode(documents, normalize_embeddings=True)
If you are building a Retrieval-Augmented Generation pipeline with a local LLM (e.g., Mistral 7B or Llama 3), your retrieval model is often the bottleneck. Here is why AllPile V7 3B shines in RAG scenarios:
queries = ["What is the capital of France?", "Explain quantum computing"] documents = ["Paris is the capital of France.", "Quantum computing uses qubits."]
: Supports analysis for steel, concrete, and timber piles, as well as tower foundations for transmission towers and wireless antennas. Vertical & Lateral Methods
Define piles with varying diameters, materials, or wall thicknesses at different depths.
Evaluates the interaction between multiple piles, considering spacing and layout. Core Features of Version 7.3b