rag-system-builder-advanced-hybrid-search-bm25-vector
Sub-skill of rag-system-builder: Advanced: Hybrid Search (BM25 + Vector).
Best use case
rag-system-builder-advanced-hybrid-search-bm25-vector is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of rag-system-builder: Advanced: Hybrid Search (BM25 + Vector).
Teams using rag-system-builder-advanced-hybrid-search-bm25-vector should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/advanced-hybrid-search-bm25-vector/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How rag-system-builder-advanced-hybrid-search-bm25-vector Compares
| Feature / Agent | rag-system-builder-advanced-hybrid-search-bm25-vector | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of rag-system-builder: Advanced: Hybrid Search (BM25 + Vector).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Advanced: Hybrid Search (BM25 + Vector)
## Advanced: Hybrid Search (BM25 + Vector)
Combine keyword and semantic search for better results:
```python
import sqlite3
from rank_bm25 import BM25Okapi
import numpy as np
class HybridSearch:
def __init__(self, db_path, embedding_model):
self.db_path = db_path
self.model = embedding_model
self._build_bm25_index()
def _build_bm25_index(self):
"""Build BM25 index from chunks."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute('SELECT id, chunk_text FROM chunks')
self.chunk_ids = []
tokenized_corpus = []
for chunk_id, text in cursor.fetchall():
self.chunk_ids.append(chunk_id)
tokenized_corpus.append(text.lower().split())
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