---
title: "Building Context-Aware Search in Python with LLM Embeddings + Metadata"
url: https://daily.dev/posts/building-context-aware-search-in-python-with-llm-embeddings-metadata-dxxo2chqz
source_url: https://machinelearningmastery.com/building-context-aware-search-in-python-with-llm-embeddings-metadata
type: article
source: "Machine Learning Mastery"
published: 2026-05-22T12:40:25.763Z
updated: 2026-05-22T12:40:48.708Z
tags: ["python", "vector-search", "embeddings"]
reading_time: 6
upvotes: 0
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Building Context-Aware Search in Python with LLM Embeddings + Metadata

**[Machine Learning Mastery](https://daily.dev/sources/mlm)** · 6 min read · 0 upvotes · 0 comments

## Summary

A step-by-step guide to building a context-aware semantic search engine in Python using sentence embeddings and metadata filtering. Uses the all-MiniLM-L6-v2 model locally (no API key needed) to generate 384-dimensional vectors, applies metadata pre-filtering by team, status, priority, and date before cosine similarity scoring, and persists the index to disk to avoid re-encoding on restarts. Three example queries demonstrate semantic-only search, filtered search, and cross-team priority-scoped search. Extensions covered include adding documents incrementally, multi-value filters, scaling with FAISS, and hybrid keyword+semantic scoring.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://machinelearningmastery.com/building-context-aware-search-in-python-with-llm-embeddings-metadata>

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---

Tags: [#python](https://daily.dev/tags/python), [#vector-search](https://daily.dev/tags/vector-search), [#embeddings](https://daily.dev/tags/embeddings)

[View this post on daily.dev](https://daily.dev/posts/building-context-aware-search-in-python-with-llm-embeddings-metadata-dxxo2chqz)