140 lines
4.1 KiB
Python
140 lines
4.1 KiB
Python
# retrieve.py
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# Hybrid verbatim chunk retrieval: BM25 + vector search + cross-encoder, no LLM.
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#
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# Same hybrid retrieval as query_hybrid.py but outputs raw chunk text
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# instead of LLM synthesis. Useful for inspecting what the hybrid pipeline
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# retrieves.
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#
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# Each chunk is annotated with its source (vector, BM25, or both) so you can
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# see which retriever nominated it.
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#
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# E.M.F. February 2026
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# Environment vars must be set before importing huggingface/transformers
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# libraries, because huggingface_hub.constants evaluates HF_HUB_OFFLINE
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# at import time.
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import os
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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os.environ["SENTENCE_TRANSFORMERS_HOME"] = "./models"
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os.environ["HF_HUB_OFFLINE"] = "1"
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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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Settings,
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)
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from llama_index.core.postprocessor import SentenceTransformerRerank
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from llama_index.retrievers.bm25 import BM25Retriever
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import sys
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import textwrap
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#
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# Globals
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#
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# Embedding model (must match build_store.py)
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EMBED_MODEL = HuggingFaceEmbedding(cache_folder="./models", model_name="BAAI/bge-large-en-v1.5", local_files_only=True)
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# Cross-encoder model for re-ranking (cached in ./models/)
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RERANK_MODEL = "cross-encoder/ms-marco-MiniLM-L-12-v2"
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RERANK_TOP_N = 15
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# Retrieval parameters
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VECTOR_TOP_K = 20
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BM25_TOP_K = 20
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# Output formatting
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WRAP_WIDTH = 80
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def main():
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# No LLM needed -- set embed model only
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Settings.embed_model = EMBED_MODEL
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# Load persisted vector store
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storage_context = StorageContext.from_defaults(persist_dir="./store")
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index = load_index_from_storage(storage_context)
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# --- Retrievers ---
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vector_retriever = index.as_retriever(similarity_top_k=VECTOR_TOP_K)
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bm25_retriever = BM25Retriever.from_defaults(
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index=index,
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similarity_top_k=BM25_TOP_K,
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)
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# Cross-encoder re-ranker
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reranker = SentenceTransformerRerank(
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model=RERANK_MODEL,
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top_n=RERANK_TOP_N,
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)
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# Query
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if len(sys.argv) < 2:
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print("Usage: python retrieve_hybrid_raw.py QUERY_TEXT")
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sys.exit(1)
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q = " ".join(sys.argv[1:])
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# Retrieve from both sources
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vector_nodes = vector_retriever.retrieve(q)
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bm25_nodes = bm25_retriever.retrieve(q)
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# Track which retriever found each node
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vector_ids = {n.node.node_id for n in vector_nodes}
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bm25_ids = {n.node.node_id for n in bm25_nodes}
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# Merge and deduplicate by node ID
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seen_ids = set()
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merged = []
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for node in vector_nodes + bm25_nodes:
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node_id = node.node.node_id
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if node_id not in seen_ids:
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seen_ids.add(node_id)
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merged.append(node)
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# Re-rank merged candidates
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reranked = reranker.postprocess_nodes(merged, query_str=q)
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# Retrieval stats
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n_both = len(vector_ids & bm25_ids)
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n_vector_only = len(vector_ids - bm25_ids)
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n_bm25_only = len(bm25_ids - vector_ids)
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print(f"\nQuery: {q}")
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print(f"Vector: {len(vector_nodes)}, BM25: {len(bm25_nodes)}, "
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f"overlap: {n_both}, merged: {len(merged)}, re-ranked to: {len(reranked)}")
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print(f" vector-only: {n_vector_only}, bm25-only: {n_bm25_only}, both: {n_both}\n")
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# Output re-ranked chunks with source annotation
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for i, node in enumerate(reranked, 1):
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meta = getattr(node, "metadata", None) or node.node.metadata
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score = getattr(node, "score", None)
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file_name = meta.get("file_name", "unknown")
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text = node.get_content()
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node_id = node.node.node_id
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# Annotate source
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in_vector = node_id in vector_ids
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in_bm25 = node_id in bm25_ids
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if in_vector and in_bm25:
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source = "vector+bm25"
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elif in_bm25:
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source = "bm25-only"
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else:
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source = "vector-only"
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print("=" * WRAP_WIDTH)
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print(f"=== [{i}] {file_name} (score: {score:.3f}) [{source}]")
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print("=" * WRAP_WIDTH)
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for line in text.splitlines():
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if line.strip():
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print(textwrap.fill(line, width=WRAP_WIDTH))
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else:
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print()
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print()
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if __name__ == "__main__":
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main()
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