e9fc99ddc6
Vector search with cross-encoder re-ranking, hybrid BM25+vector retrieval, incremental index updates, and multiple LLM backends (Ollama local, OpenAI API).
123 lines
3.8 KiB
Python
123 lines
3.8 KiB
Python
# query_topk_prompt.py
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# Run a querry on a vector store
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#
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# This version from query_rewrite_hyde.py, but removing hyde and using a custom prompt
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# This verison implements a prompt and uses the build_exp.py vector store with BAAI/bge-large-en-v1.5
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# Based on query_exp.py->query_topk.py->query_rewrite_hyde.py
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# The results are as good as with HyDE.
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#
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# E.M.F. August 2025
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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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from llama_index.llms.ollama import Ollama
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from llama_index.core.prompts import PromptTemplate
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import os
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#
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# Globals
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#
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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# Embedding model used in vector store (this should match the one in build_exp.py or equivalent)
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# embed_model = HuggingFaceEmbedding(model_name="all-mpnet-base-v2")
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embed_model = HuggingFaceEmbedding(cache_folder="./models",model_name="BAAI/bge-large-en-v1.5")
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# LLM model to use in query transform and generation
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# command-r7b generates about as quickly as llama3.1:8B, but provides results that stick better
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# to the provided context
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llm="command-r7b"
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# Other models tried:
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#llm="llama3.1:8B"
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#llm="deepseek-r1:8B"
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#llm="gemma3:1b"
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#
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# Custom prompt for the query engine
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#
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PROMPT = PromptTemplate(
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"""You are an expert research assistant. You are given top-ranked writing excerpts (CONTEXT) and a user's QUERY.
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Instructions:
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- Base your response *only* on the CONTEXT.
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- The snippets are ordered from most to least relevant—prioritize insights from earlier (higher-ranked) snippets.
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- Aim to reference *as many distinct* relevant files as possible (up to 10).
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- Do not invent or generalize; refer to specific passages or facts only.
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- If a passage only loosely matches, deprioritize it.
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Format your answer in two parts:
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1. **Summary Theme**
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Summarize the dominant theme from the relevant context in a few sentences.
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2. **Matching Files**
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Make a list of 10 matching files. The format for each should be:
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<filename> -
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<rationale tied to content. Include date or section hints if available.>
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CONTEXT:
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{context_str}
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QUERY:
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{query_str}
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Now provide the theme and list of matching files."""
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)
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#
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# Main program routine
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#
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def main():
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# Use a local model to generate -- in this case using Ollama
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Settings.llm = Ollama(
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model=llm, # First model tested
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request_timeout=360.0,
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context_window=8000
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)
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# Load embedding model (same as used for vector store)
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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_exp")
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index = load_index_from_storage(storage_context)
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# Build regular query engine with custom prompt
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query_engine = index.as_query_engine(
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similarity_top_k=15, # pull wide
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#response_mode="compact" # concise synthesis
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text_qa_template=PROMPT, # custom prompt
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# node_postprocessors=[
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# SimilarityPostprocessor(similarity_cutoff=0.75) # keep strong hits; makes result count flexible
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# ],
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)
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# Query
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while True:
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q = input("\nEnter a search topic or question (or 'exit'): ").strip()
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if q.lower() in ("exit", "quit"):
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break
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print()
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# Generate the response by querying the engine
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# This performes the similarity search and then applies the prompt
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response = query_engine.query(q)
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# Return the query response and source documents
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print(response.response)
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print("\nSource documents:")
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for node in response.source_nodes:
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meta = getattr(node, "metadata", None) or node.node.metadata
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print(f"{meta.get('file_name')} {meta.get('file_path')} {getattr(node, 'score', None)}")
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if __name__ == "__main__":
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main()
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