e9fc99ddc6
Vector search with cross-encoder re-ranking, hybrid BM25+vector retrieval, incremental index updates, and multiple LLM backends (Ollama local, OpenAI API).
27 lines
No EOL
731 B
Python
27 lines
No EOL
731 B
Python
# vs_metrics.py
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# Quantify vector store properties and performance
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#
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# E.M.F. August 2025
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# Read in vector store
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# What are properties of the vector store?
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# - number of vectors
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# - distribution of distances
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# - clustering?
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from llama_index.core import (
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StorageContext,
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load_index_from_storage,
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ServiceContext,
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Settings,
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)
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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# Load embedding model (same as used for vector store)
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embed_model = HuggingFaceEmbedding(model_name="all-mpnet-base-v2")
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Settings.embed_model = embed_model
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# Load persisted vector store + metadata
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storage_context = StorageContext.from_defaults(persist_dir="./storage")
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index = load_index_from_storage(storage_context) |