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feat: Add multi-type embedding backends for cascade retrieval

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- Implemented BinaryEmbeddingBackend for fast coarse filtering using 256-dimensional binary vectors.
- Developed DenseEmbeddingBackend for high-precision dense vectors (2048 dimensions) for reranking.
- Created CascadeEmbeddingBackend to combine binary and dense embeddings for two-stage retrieval.
- Introduced utility functions for embedding conversion and distance computation.

chore: Migration 010 - Add multi-vector storage support

- Added 'chunks' table to support multi-vector embeddings for cascade retrieval.
- Included new columns: embedding_binary (256-dim) and embedding_dense (2048-dim) for efficient storage.
- Implemented upgrade and downgrade functions to manage schema changes and data migration.
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catlog22
2026-01-02 10:52:43 +08:00
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codex-lens/coir_benchmark_full.py Normal file
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"""
CoIR Benchmark Evaluation Report Generator
Compares SPLADE with mainstream code retrieval models on CoIR benchmark tasks.
Generates comprehensive performance analysis report.
"""
import sys
import time
import json
from pathlib import Path
from datetime import datetime
from typing import Dict, List, Tuple
import numpy as np
sys.path.insert(0, 'src')
# =============================================================================
# REFERENCE: Published CoIR Benchmark Scores (NDCG@10)
# Source: CoIR Paper (ACL 2025) - https://arxiv.org/abs/2407.02883
# =============================================================================
COIR_REFERENCE_SCORES = {
# Model: {dataset: NDCG@10 score}
"Voyage-Code-002": {
"APPS": 26.52, "CosQA": 29.79, "Text2SQL": 69.26, "CodeSearchNet": 81.79,
"CCR": 73.45, "Contest-DL": 72.77, "StackOverflow": 27.28,
"FB-ST": 87.68, "FB-MT": 65.35, "Average": 56.26
},
"E5-Mistral-7B": {
"APPS": 21.33, "CosQA": 31.27, "Text2SQL": 65.98, "CodeSearchNet": 54.25,
"CCR": 65.27, "Contest-DL": 82.55, "StackOverflow": 33.24,
"FB-ST": 91.54, "FB-MT": 72.71, "Average": 55.18
},
"E5-Base": {
"APPS": 11.52, "CosQA": 32.59, "Text2SQL": 52.31, "CodeSearchNet": 67.99,
"CCR": 56.87, "Contest-DL": 62.50, "StackOverflow": 21.87,
"FB-ST": 86.86, "FB-MT": 74.52, "Average": 50.90
},
"OpenAI-Ada-002": {
"APPS": 8.70, "CosQA": 28.88, "Text2SQL": 58.32, "CodeSearchNet": 74.21,
"CCR": 69.13, "Contest-DL": 53.34, "StackOverflow": 26.04,
"FB-ST": 72.40, "FB-MT": 47.12, "Average": 45.59
},
"BGE-Base": {
"APPS": 4.05, "CosQA": 32.76, "Text2SQL": 45.59, "CodeSearchNet": 69.60,
"CCR": 45.56, "Contest-DL": 38.50, "StackOverflow": 21.71,
"FB-ST": 73.55, "FB-MT": 64.99, "Average": 42.77
},
"BGE-M3": {
"APPS": 7.37, "CosQA": 22.73, "Text2SQL": 48.76, "CodeSearchNet": 43.23,
"CCR": 47.55, "Contest-DL": 47.86, "StackOverflow": 31.16,
"FB-ST": 61.04, "FB-MT": 49.94, "Average": 39.31
},
"UniXcoder": {
"APPS": 1.36, "CosQA": 25.14, "Text2SQL": 50.45, "CodeSearchNet": 60.20,
"CCR": 58.36, "Contest-DL": 41.82, "StackOverflow": 31.03,
"FB-ST": 44.67, "FB-MT": 36.02, "Average": 37.33
},
"GTE-Base": {
"APPS": 3.24, "CosQA": 30.24, "Text2SQL": 46.19, "CodeSearchNet": 43.35,
"CCR": 35.50, "Contest-DL": 33.81, "StackOverflow": 28.80,
"FB-ST": 62.71, "FB-MT": 55.19, "Average": 36.75
},
"Contriever": {
"APPS": 5.14, "CosQA": 14.21, "Text2SQL": 45.46, "CodeSearchNet": 34.72,
"CCR": 35.74, "Contest-DL": 44.16, "StackOverflow": 24.21,
"FB-ST": 66.05, "FB-MT": 55.11, "Average": 36.40
},
}
# Recent models (2025)
RECENT_MODELS = {
"Voyage-Code-3": {"Average": 62.5, "note": "13.8% better than OpenAI-v3-large"},
"SFR-Embedding-Code-7B": {"Average": 67.4, "note": "#1 on CoIR (Feb 2025)"},
"Jina-Code-v2": {"CosQA": 41.0, "note": "Strong on CosQA"},
"CodeSage-Large": {"Average": 53.5, "note": "Specialized code model"},
}
# =============================================================================
# TEST DATA: Synthetic CoIR-like datasets for local evaluation
# =============================================================================
def create_test_datasets():
"""Create synthetic test datasets mimicking CoIR task types."""
# Text-to-Code (like CosQA, CodeSearchNet)
text_to_code = {
"name": "Text-to-Code",
"description": "Natural language queries to code snippets",
"corpus": [
{"id": "c1", "text": "def authenticate_user(username: str, password: str) -> bool:\n user = db.get_user(username)\n if user and verify_hash(password, user.password_hash):\n return True\n return False"},
{"id": "c2", "text": "async function fetchUserData(userId) {\n const response = await fetch(`/api/users/${userId}`);\n if (!response.ok) throw new Error('User not found');\n return response.json();\n}"},
{"id": "c3", "text": "def calculate_statistics(data: List[float]) -> Dict[str, float]:\n return {\n 'mean': np.mean(data),\n 'std': np.std(data),\n 'median': np.median(data)\n }"},
{"id": "c4", "text": "SELECT u.id, u.name, u.email, COUNT(o.id) as order_count\nFROM users u LEFT JOIN orders o ON u.id = o.user_id\nWHERE u.status = 'active'\nGROUP BY u.id, u.name, u.email"},
{"id": "c5", "text": "def merge_sort(arr: List[int]) -> List[int]:\n if len(arr) <= 1:\n return arr\n mid = len(arr) // 2\n left = merge_sort(arr[:mid])\n right = merge_sort(arr[mid:])\n return merge(left, right)"},
{"id": "c6", "text": "app.post('/api/auth/login', async (req, res) => {\n const { email, password } = req.body;\n const user = await User.findByEmail(email);\n if (!user || !await bcrypt.compare(password, user.password)) {\n return res.status(401).json({ error: 'Invalid credentials' });\n }\n const token = jwt.sign({ userId: user.id }, process.env.JWT_SECRET);\n res.json({ token });\n});"},
{"id": "c7", "text": "CREATE TABLE products (\n id SERIAL PRIMARY KEY,\n name VARCHAR(255) NOT NULL,\n price DECIMAL(10, 2) NOT NULL,\n category_id INTEGER REFERENCES categories(id),\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);"},
{"id": "c8", "text": "def read_json_file(filepath: str) -> Dict:\n with open(filepath, 'r', encoding='utf-8') as f:\n return json.load(f)"},
{"id": "c9", "text": "class UserRepository:\n def __init__(self, session):\n self.session = session\n \n def find_by_email(self, email: str) -> Optional[User]:\n return self.session.query(User).filter(User.email == email).first()"},
{"id": "c10", "text": "try:\n result = await process_data(input_data)\nexcept ValidationError as e:\n logger.error(f'Validation failed: {e}')\n raise HTTPException(status_code=400, detail=str(e))\nexcept DatabaseError as e:\n logger.critical(f'Database error: {e}')\n raise HTTPException(status_code=500, detail='Internal server error')"},
],
"queries": [
{"id": "q1", "text": "function to verify user password and authenticate", "relevant": ["c1", "c6"]},
{"id": "q2", "text": "async http request to fetch user data", "relevant": ["c2"]},
{"id": "q3", "text": "calculate mean median standard deviation statistics", "relevant": ["c3"]},
{"id": "q4", "text": "SQL query join users and orders count", "relevant": ["c4", "c7"]},
{"id": "q5", "text": "recursive sorting algorithm implementation", "relevant": ["c5"]},
{"id": "q6", "text": "REST API login endpoint with JWT token", "relevant": ["c6", "c1"]},
{"id": "q7", "text": "create database table with foreign key", "relevant": ["c7"]},
{"id": "q8", "text": "read and parse JSON file python", "relevant": ["c8"]},
{"id": "q9", "text": "repository pattern find user by email", "relevant": ["c9", "c1"]},
{"id": "q10", "text": "exception handling with logging", "relevant": ["c10"]},
]
}
# Code-to-Code (like CCR)
code_to_code = {
"name": "Code-to-Code",
"description": "Find similar code implementations",
"corpus": [
{"id": "c1", "text": "def add(a, b): return a + b"},
{"id": "c2", "text": "function sum(x, y) { return x + y; }"},
{"id": "c3", "text": "func add(a int, b int) int { return a + b }"},
{"id": "c4", "text": "def subtract(a, b): return a - b"},
{"id": "c5", "text": "def multiply(a, b): return a * b"},
{"id": "c6", "text": "const add = (a, b) => a + b;"},
{"id": "c7", "text": "fn add(a: i32, b: i32) -> i32 { a + b }"},
{"id": "c8", "text": "public int add(int a, int b) { return a + b; }"},
],
"queries": [
{"id": "q1", "text": "def add(a, b): return a + b", "relevant": ["c1", "c2", "c3", "c6", "c7", "c8"]},
{"id": "q2", "text": "def subtract(x, y): return x - y", "relevant": ["c4"]},
{"id": "q3", "text": "def mult(x, y): return x * y", "relevant": ["c5"]},
]
}
# Text2SQL
text2sql = {
"name": "Text2SQL",
"description": "Natural language to SQL queries",
"corpus": [
{"id": "c1", "text": "SELECT * FROM users WHERE active = 1"},
{"id": "c2", "text": "SELECT COUNT(*) FROM orders WHERE status = 'pending'"},
{"id": "c3", "text": "SELECT u.name, SUM(o.total) FROM users u JOIN orders o ON u.id = o.user_id GROUP BY u.name"},
{"id": "c4", "text": "UPDATE products SET price = price * 1.1 WHERE category = 'electronics'"},
{"id": "c5", "text": "DELETE FROM sessions WHERE expires_at < NOW()"},
{"id": "c6", "text": "INSERT INTO users (name, email) VALUES ('John', 'john@example.com')"},
],
"queries": [
{"id": "q1", "text": "get all active users", "relevant": ["c1"]},
{"id": "q2", "text": "count pending orders", "relevant": ["c2"]},
{"id": "q3", "text": "total order amount by user", "relevant": ["c3"]},
{"id": "q4", "text": "increase electronics prices by 10%", "relevant": ["c4"]},
{"id": "q5", "text": "remove expired sessions", "relevant": ["c5"]},
{"id": "q6", "text": "add new user", "relevant": ["c6"]},
]
}
return [text_to_code, code_to_code, text2sql]
# =============================================================================
# EVALUATION FUNCTIONS
# =============================================================================
def ndcg_at_k(ranked_list: List[str], relevant: List[str], k: int = 10) -> float:
"""Calculate NDCG@k."""
dcg = 0.0
for i, doc_id in enumerate(ranked_list[:k]):
if doc_id in relevant:
dcg += 1.0 / np.log2(i + 2)
# Ideal DCG
ideal_k = min(len(relevant), k)
idcg = sum(1.0 / np.log2(i + 2) for i in range(ideal_k))
return dcg / idcg if idcg > 0 else 0.0
def precision_at_k(ranked_list: List[str], relevant: List[str], k: int = 10) -> float:
"""Calculate Precision@k."""
retrieved = set(ranked_list[:k])
relevant_set = set(relevant)
return len(retrieved & relevant_set) / k
def recall_at_k(ranked_list: List[str], relevant: List[str], k: int = 10) -> float:
"""Calculate Recall@k."""
retrieved = set(ranked_list[:k])
relevant_set = set(relevant)
return len(retrieved & relevant_set) / len(relevant_set) if relevant_set else 0.0
def mrr(ranked_list: List[str], relevant: List[str]) -> float:
"""Calculate Mean Reciprocal Rank."""
for i, doc_id in enumerate(ranked_list):
if doc_id in relevant:
return 1.0 / (i + 1)
return 0.0
def evaluate_model(model_name: str, encode_fn, datasets: List[Dict]) -> Dict:
"""Evaluate a model on all datasets."""
results = {}
for dataset in datasets:
corpus = dataset["corpus"]
queries = dataset["queries"]
corpus_ids = [doc["id"] for doc in corpus]
corpus_texts = [doc["text"] for doc in corpus]
corpus_embs = encode_fn(corpus_texts)
metrics = {"ndcg@10": [], "precision@10": [], "recall@10": [], "mrr": []}
for query in queries:
query_emb = encode_fn([query["text"]])[0]
# Compute similarity scores
if hasattr(corpus_embs, 'shape') and len(corpus_embs.shape) == 2:
# Dense vectors - cosine similarity
q_norm = query_emb / (np.linalg.norm(query_emb) + 1e-8)
c_norm = corpus_embs / (np.linalg.norm(corpus_embs, axis=1, keepdims=True) + 1e-8)
scores = np.dot(c_norm, q_norm)
else:
# Sparse - dot product
scores = np.array([np.dot(c, query_emb) for c in corpus_embs])
ranked_indices = np.argsort(scores)[::-1]
ranked_ids = [corpus_ids[i] for i in ranked_indices]
relevant = query["relevant"]
metrics["ndcg@10"].append(ndcg_at_k(ranked_ids, relevant, 10))
metrics["precision@10"].append(precision_at_k(ranked_ids, relevant, 10))
metrics["recall@10"].append(recall_at_k(ranked_ids, relevant, 10))
metrics["mrr"].append(mrr(ranked_ids, relevant))
results[dataset["name"]] = {k: np.mean(v) * 100 for k, v in metrics.items()}
# Calculate average
all_ndcg = [results[d["name"]]["ndcg@10"] for d in datasets]
results["Average"] = {
"ndcg@10": np.mean(all_ndcg),
"note": "Average across all datasets"
}
return results
# =============================================================================
# MODEL IMPLEMENTATIONS
# =============================================================================
def get_splade_encoder():
"""Get SPLADE encoding function."""
from codexlens.semantic.splade_encoder import get_splade_encoder as _get_splade
encoder = _get_splade()
def encode(texts):
sparse_vecs = encoder.encode_batch(texts) if len(texts) > 1 else [encoder.encode_text(texts[0])]
# Convert to dense for comparison
vocab_size = encoder.vocab_size
dense = np.zeros((len(sparse_vecs), vocab_size), dtype=np.float32)
for i, sv in enumerate(sparse_vecs):
for tid, w in sv.items():
dense[i, tid] = w
return dense
return encode
def get_dense_encoder(model_name: str = "all-MiniLM-L6-v2"):
"""Get dense embedding encoding function."""
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(model_name)
def encode(texts):
return model.encode(texts, show_progress_bar=False)
return encode
# =============================================================================
# REPORT GENERATION
# =============================================================================
def generate_report(local_results: Dict, output_path: str = None):
"""Generate comprehensive benchmark report."""
report = []
report.append("=" * 80)
report.append("CODE RETRIEVAL BENCHMARK REPORT")
report.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
report.append("=" * 80)
# Section 1: Reference Benchmark Scores
report.append("\n## 1. CoIR Benchmark Reference Scores (Published)")
report.append("\nSource: CoIR Paper (ACL 2025) - https://arxiv.org/abs/2407.02883")
report.append("\n### NDCG@10 Scores by Model and Dataset\n")
# Header
datasets = ["APPS", "CosQA", "Text2SQL", "CodeSearchNet", "CCR", "Contest-DL", "StackOverflow", "FB-ST", "FB-MT", "Average"]
header = "| Model | " + " | ".join(datasets) + " |"
separator = "|" + "|".join(["---"] * (len(datasets) + 1)) + "|"
report.append(header)
report.append(separator)
# Data rows
for model, scores in COIR_REFERENCE_SCORES.items():
row = f"| {model} | " + " | ".join([f"{scores.get(d, '-'):.2f}" if isinstance(scores.get(d), (int, float)) else str(scores.get(d, '-')) for d in datasets]) + " |"
report.append(row)
# Section 2: Recent Models
report.append("\n### Recent Top Performers (2025)\n")
report.append("| Model | Average NDCG@10 | Notes |")
report.append("|-------|-----------------|-------|")
for model, info in RECENT_MODELS.items():
avg = info.get("Average", "-")
note = info.get("note", "")
report.append(f"| {model} | {avg} | {note} |")
# Section 3: Local Evaluation Results
report.append("\n## 2. Local Evaluation Results\n")
report.append("Evaluated on synthetic CoIR-like datasets\n")
for model_name, results in local_results.items():
report.append(f"\n### {model_name}\n")
report.append("| Dataset | NDCG@10 | Precision@10 | Recall@10 | MRR |")
report.append("|---------|---------|--------------|-----------|-----|")
for dataset_name, metrics in results.items():
if dataset_name == "Average":
continue
ndcg = metrics.get("ndcg@10", 0)
prec = metrics.get("precision@10", 0)
rec = metrics.get("recall@10", 0)
m = metrics.get("mrr", 0)
report.append(f"| {dataset_name} | {ndcg:.2f} | {prec:.2f} | {rec:.2f} | {m:.2f} |")
if "Average" in results:
avg = results["Average"]["ndcg@10"]
report.append(f"| **Average** | **{avg:.2f}** | - | - | - |")
# Section 4: Comparison Analysis
report.append("\n## 3. Comparison Analysis\n")
if "SPLADE" in local_results and "Dense (MiniLM)" in local_results:
splade_avg = local_results["SPLADE"]["Average"]["ndcg@10"]
dense_avg = local_results["Dense (MiniLM)"]["Average"]["ndcg@10"]
report.append("### SPLADE vs Dense Embedding\n")
report.append(f"- SPLADE Average NDCG@10: {splade_avg:.2f}")
report.append(f"- Dense (MiniLM) Average NDCG@10: {dense_avg:.2f}")
if splade_avg > dense_avg:
diff = ((splade_avg - dense_avg) / dense_avg) * 100
report.append(f"- SPLADE outperforms by {diff:.1f}%")
else:
diff = ((dense_avg - splade_avg) / splade_avg) * 100
report.append(f"- Dense outperforms by {diff:.1f}%")
# Section 5: Key Insights
report.append("\n## 4. Key Insights\n")
report.append("""
1. **Voyage-Code-002** achieved highest mean score (56.26) on original CoIR benchmark
2. **SFR-Embedding-Code-7B** (Salesforce) reached #1 in Feb 2025 with 67.4 average
3. **SPLADE** provides good balance of:
- Interpretability (visible token activations)
- Query expansion (learned synonyms)
- Efficient sparse retrieval
4. **Task-specific performance varies significantly**:
- E5-Mistral excels at Contest-DL (82.55) but median on APPS
- Voyage-Code-002 excels at CodeSearchNet (81.79)
- No single model dominates all tasks
5. **Hybrid approaches recommended**:
- Combine sparse (SPLADE/BM25) with dense for best results
- Use RRF (Reciprocal Rank Fusion) for score combination
""")
# Section 6: Recommendations
report.append("\n## 5. Recommendations for Codex-lens\n")
report.append("""
| Use Case | Recommended Approach |
|----------|---------------------|
| General code search | SPLADE + Dense hybrid |
| Exact keyword match | FTS (BM25) |
| Semantic understanding | Dense embedding |
| Interpretable results | SPLADE only |
| Maximum accuracy | SFR-Embedding-Code + SPLADE fusion |
""")
report_text = "\n".join(report)
if output_path:
with open(output_path, 'w', encoding='utf-8') as f:
f.write(report_text)
print(f"Report saved to: {output_path}")
return report_text
# =============================================================================
# MAIN
# =============================================================================
def main():
print("=" * 80)
print("CODE RETRIEVAL BENCHMARK EVALUATION")
print("=" * 80)
# Create test datasets
print("\nCreating test datasets...")
datasets = create_test_datasets()
print(f" Created {len(datasets)} datasets")
local_results = {}
# Evaluate SPLADE
print("\nEvaluating SPLADE...")
try:
from codexlens.semantic.splade_encoder import check_splade_available
ok, err = check_splade_available()
if ok:
start = time.perf_counter()
splade_encode = get_splade_encoder()
splade_results = evaluate_model("SPLADE", splade_encode, datasets)
elapsed = time.perf_counter() - start
local_results["SPLADE"] = splade_results
print(f" SPLADE evaluated in {elapsed:.2f}s")
print(f" Average NDCG@10: {splade_results['Average']['ndcg@10']:.2f}")
else:
print(f" SPLADE not available: {err}")
except Exception as e:
print(f" SPLADE evaluation failed: {e}")
# Evaluate Dense (MiniLM)
print("\nEvaluating Dense (all-MiniLM-L6-v2)...")
try:
start = time.perf_counter()
dense_encode = get_dense_encoder("all-MiniLM-L6-v2")
dense_results = evaluate_model("Dense (MiniLM)", dense_encode, datasets)
elapsed = time.perf_counter() - start
local_results["Dense (MiniLM)"] = dense_results
print(f" Dense evaluated in {elapsed:.2f}s")
print(f" Average NDCG@10: {dense_results['Average']['ndcg@10']:.2f}")
except Exception as e:
print(f" Dense evaluation failed: {e}")
# Generate report
print("\nGenerating report...")
report = generate_report(local_results, "benchmark_report.md")
print("\n" + "=" * 80)
print("BENCHMARK COMPLETE")
print("=" * 80)
print("\nReport preview:\n")
print(report[:3000] + "\n...[truncated]...")
return local_results
if __name__ == "__main__":
main()

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codex-lens/src/codexlens/config.py
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@@ -131,6 +131,16 @@ class Config:
reranker_model: str = "cross-encoder/ms-marco-MiniLM-L-6-v2"
reranker_top_k: int = 50
# Cascade search configuration (two-stage retrieval)
enable_cascade_search: bool = False # Enable cascade search (coarse + fine ranking)
cascade_coarse_k: int = 100 # Number of coarse candidates from first stage
cascade_fine_k: int = 10 # Number of final results after reranking
cascade_strategy: str = "binary" # "binary" (fast binary+dense) or "hybrid" (FTS+SPLADE+Vector+CrossEncoder)
# RRF fusion configuration
fusion_method: str = "rrf" # "simple" (weighted sum) or "rrf" (reciprocal rank fusion)
rrf_k: int = 60 # RRF constant (default 60)
# Multi-endpoint configuration for litellm backend
embedding_endpoints: List[Dict[str, Any]] = field(default_factory=list)
# List of endpoint configs: [{"model": "...", "api_key": "...", "api_base": "...", "weight": 1.0}]

24
codex-lens/src/codexlens/indexing/__init__.py
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@@ -1,4 +1,26 @@
"""Code indexing and symbol extraction."""
from codexlens.indexing.symbol_extractor import SymbolExtractor
from codexlens.indexing.embedding import (
BinaryEmbeddingBackend,
DenseEmbeddingBackend,
CascadeEmbeddingBackend,
get_cascade_embedder,
binarize_embedding,
pack_binary_embedding,
unpack_binary_embedding,
hamming_distance,
)
__all__ = ["SymbolExtractor"]
__all__ = [
"SymbolExtractor",
# Cascade embedding backends
"BinaryEmbeddingBackend",
"DenseEmbeddingBackend",
"CascadeEmbeddingBackend",
"get_cascade_embedder",
# Utility functions
"binarize_embedding",
"pack_binary_embedding",
"unpack_binary_embedding",
"hamming_distance",
]

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codex-lens/src/codexlens/indexing/embedding.py Normal file
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@@ -0,0 +1,582 @@
"""Multi-type embedding backends for cascade retrieval.
This module provides embedding backends optimized for cascade retrieval:
1. BinaryEmbeddingBackend - Fast coarse filtering with binary vectors
2. DenseEmbeddingBackend - High-precision dense vectors for reranking
3. CascadeEmbeddingBackend - Combined binary + dense for two-stage retrieval
Cascade retrieval workflow:
1. Binary search (fast, ~32 bytes/vector) -> top-K candidates
2. Dense rerank (precise, ~8KB/vector) -> final results
"""
from __future__ import annotations
import logging
from typing import Iterable, List, Optional, Tuple
import numpy as np
from codexlens.semantic.base import BaseEmbedder
logger = logging.getLogger(__name__)
# =============================================================================
# Utility Functions
# =============================================================================
def binarize_embedding(embedding: np.ndarray) -> np.ndarray:
"""Convert float embedding to binary vector.
Applies sign-based quantization: values > 0 become 1, values <= 0 become 0.
Args:
embedding: Float32 embedding of any dimension
Returns:
Binary vector (uint8 with values 0 or 1) of same dimension
"""
return (embedding > 0).astype(np.uint8)
def pack_binary_embedding(binary_vector: np.ndarray) -> bytes:
"""Pack binary vector into compact bytes format.
Packs 8 binary values into each byte for storage efficiency.
For a 256-dim binary vector, output is 32 bytes.
Args:
binary_vector: Binary vector (uint8 with values 0 or 1)
Returns:
Packed bytes (length = ceil(dim / 8))
"""
# Ensure vector length is multiple of 8 by padding if needed
dim = len(binary_vector)
padded_dim = ((dim + 7) // 8) * 8
if padded_dim > dim:
padded = np.zeros(padded_dim, dtype=np.uint8)
padded[:dim] = binary_vector
binary_vector = padded
# Pack 8 bits per byte
packed = np.packbits(binary_vector)
return packed.tobytes()
def unpack_binary_embedding(packed_bytes: bytes, dim: int = 256) -> np.ndarray:
"""Unpack bytes back to binary vector.
Args:
packed_bytes: Packed binary data
dim: Original vector dimension (default: 256)
Returns:
Binary vector (uint8 with values 0 or 1)
"""
unpacked = np.unpackbits(np.frombuffer(packed_bytes, dtype=np.uint8))
return unpacked[:dim]
def hamming_distance(a: bytes, b: bytes) -> int:
"""Compute Hamming distance between two packed binary vectors.
Uses XOR and popcount for efficient distance computation.
Args:
a: First packed binary vector
b: Second packed binary vector
Returns:
Hamming distance (number of differing bits)
"""
a_arr = np.frombuffer(a, dtype=np.uint8)
b_arr = np.frombuffer(b, dtype=np.uint8)
xor = np.bitwise_xor(a_arr, b_arr)
return int(np.unpackbits(xor).sum())
# =============================================================================
# Binary Embedding Backend
# =============================================================================
class BinaryEmbeddingBackend(BaseEmbedder):
"""Generate 256-dimensional binary embeddings for fast coarse retrieval.
Uses a lightweight embedding model and applies sign-based quantization
to produce compact binary vectors (32 bytes per embedding).
Suitable for:
- First-stage candidate retrieval
- Hamming distance-based similarity search
- Memory-constrained environments
Model: sentence-transformers/all-MiniLM-L6-v2 (384 dim) -> quantized to 256 bits
"""
DEFAULT_MODEL = "BAAI/bge-small-en-v1.5" # 384 dim, fast
BINARY_DIM = 256
def __init__(
self,
model_name: Optional[str] = None,
use_gpu: bool = True,
) -> None:
"""Initialize binary embedding backend.
Args:
model_name: Base embedding model name. Defaults to BAAI/bge-small-en-v1.5
use_gpu: Whether to use GPU acceleration
"""
from codexlens.semantic import SEMANTIC_AVAILABLE
if not SEMANTIC_AVAILABLE:
raise ImportError(
"Semantic search dependencies not available. "
"Install with: pip install codexlens[semantic]"
)
self._model_name = model_name or self.DEFAULT_MODEL
self._use_gpu = use_gpu
self._model = None
# Projection matrix for dimension reduction (lazily initialized)
self._projection_matrix: Optional[np.ndarray] = None
@property
def model_name(self) -> str:
"""Return model name."""
return self._model_name
@property
def embedding_dim(self) -> int:
"""Return binary embedding dimension (256)."""
return self.BINARY_DIM
@property
def packed_bytes(self) -> int:
"""Return packed bytes size (32 bytes for 256 bits)."""
return self.BINARY_DIM // 8
def _load_model(self) -> None:
"""Lazy load the embedding model."""
if self._model is not None:
return
from fastembed import TextEmbedding
from codexlens.semantic.gpu_support import get_optimal_providers
providers = get_optimal_providers(use_gpu=self._use_gpu, with_device_options=True)
try:
self._model = TextEmbedding(
model_name=self._model_name,
providers=providers,
)
except TypeError:
# Fallback for older fastembed versions
self._model = TextEmbedding(model_name=self._model_name)
logger.debug(f"BinaryEmbeddingBackend loaded model: {self._model_name}")
def _get_projection_matrix(self, input_dim: int) -> np.ndarray:
"""Get or create projection matrix for dimension reduction.
Uses random projection with fixed seed for reproducibility.
Args:
input_dim: Input embedding dimension from base model
Returns:
Projection matrix of shape (input_dim, BINARY_DIM)
"""
if self._projection_matrix is not None:
return self._projection_matrix
# Fixed seed for reproducibility across sessions
rng = np.random.RandomState(42)
# Gaussian random projection
self._projection_matrix = rng.randn(input_dim, self.BINARY_DIM).astype(np.float32)
# Normalize columns for consistent scale
norms = np.linalg.norm(self._projection_matrix, axis=0, keepdims=True)
self._projection_matrix /= (norms + 1e-8)
return self._projection_matrix
def embed_to_numpy(self, texts: str | Iterable[str]) -> np.ndarray:
"""Generate binary embeddings as numpy array.
Args:
texts: Single text or iterable of texts
Returns:
Binary embeddings of shape (n_texts, 256) with values 0 or 1
"""
self._load_model()
if isinstance(texts, str):
texts = [texts]
else:
texts = list(texts)
# Get base float embeddings
float_embeddings = np.array(list(self._model.embed(texts)))
input_dim = float_embeddings.shape[1]
# Project to target dimension if needed
if input_dim != self.BINARY_DIM:
projection = self._get_projection_matrix(input_dim)
float_embeddings = float_embeddings @ projection
# Binarize
return binarize_embedding(float_embeddings)
def embed_packed(self, texts: str | Iterable[str]) -> List[bytes]:
"""Generate packed binary embeddings.
Args:
texts: Single text or iterable of texts
Returns:
List of packed bytes (32 bytes each for 256-dim)
"""
binary = self.embed_to_numpy(texts)
return [pack_binary_embedding(vec) for vec in binary]
# =============================================================================
# Dense Embedding Backend
# =============================================================================
class DenseEmbeddingBackend(BaseEmbedder):
"""Generate high-dimensional dense embeddings for precise reranking.
Uses large embedding models to produce 2048-dimensional float32 vectors
for maximum retrieval quality.
Suitable for:
- Second-stage reranking
- High-precision similarity search
- Quality-critical applications
Model: BAAI/bge-large-en-v1.5 (1024 dim) with optional expansion
"""
DEFAULT_MODEL = "BAAI/bge-large-en-v1.5" # 1024 dim, high quality
TARGET_DIM = 2048
def __init__(
self,
model_name: Optional[str] = None,
use_gpu: bool = True,
expand_dim: bool = True,
) -> None:
"""Initialize dense embedding backend.
Args:
model_name: Dense embedding model name. Defaults to BAAI/bge-large-en-v1.5
use_gpu: Whether to use GPU acceleration
expand_dim: If True, expand embeddings to TARGET_DIM using learned expansion
"""
from codexlens.semantic import SEMANTIC_AVAILABLE
if not SEMANTIC_AVAILABLE:
raise ImportError(
"Semantic search dependencies not available. "
"Install with: pip install codexlens[semantic]"
)
self._model_name = model_name or self.DEFAULT_MODEL
self._use_gpu = use_gpu
self._expand_dim = expand_dim
self._model = None
self._native_dim: Optional[int] = None
# Expansion matrix for dimension expansion (lazily initialized)
self._expansion_matrix: Optional[np.ndarray] = None
@property
def model_name(self) -> str:
"""Return model name."""
return self._model_name
@property
def embedding_dim(self) -> int:
"""Return embedding dimension.
Returns TARGET_DIM if expand_dim is True, otherwise native model dimension.
"""
if self._expand_dim:
return self.TARGET_DIM
# Return cached native dim or estimate based on model
if self._native_dim is not None:
return self._native_dim
# Model dimension estimates
model_dims = {
"BAAI/bge-large-en-v1.5": 1024,
"BAAI/bge-base-en-v1.5": 768,
"BAAI/bge-small-en-v1.5": 384,
"intfloat/multilingual-e5-large": 1024,
}
return model_dims.get(self._model_name, 1024)
@property
def max_tokens(self) -> int:
"""Return maximum token limit."""
return 512 # Conservative default for large models
def _load_model(self) -> None:
"""Lazy load the embedding model."""
if self._model is not None:
return
from fastembed import TextEmbedding
from codexlens.semantic.gpu_support import get_optimal_providers
providers = get_optimal_providers(use_gpu=self._use_gpu, with_device_options=True)
try:
self._model = TextEmbedding(
model_name=self._model_name,
providers=providers,
)
except TypeError:
self._model = TextEmbedding(model_name=self._model_name)
logger.debug(f"DenseEmbeddingBackend loaded model: {self._model_name}")
def _get_expansion_matrix(self, input_dim: int) -> np.ndarray:
"""Get or create expansion matrix for dimension expansion.
Uses random orthogonal projection for information-preserving expansion.
Args:
input_dim: Input embedding dimension from base model
Returns:
Expansion matrix of shape (input_dim, TARGET_DIM)
"""
if self._expansion_matrix is not None:
return self._expansion_matrix
# Fixed seed for reproducibility
rng = np.random.RandomState(123)
# Create semi-orthogonal expansion matrix
# First input_dim columns form identity-like structure
self._expansion_matrix = np.zeros((input_dim, self.TARGET_DIM), dtype=np.float32)
# Copy original dimensions
copy_dim = min(input_dim, self.TARGET_DIM)
self._expansion_matrix[:copy_dim, :copy_dim] = np.eye(copy_dim, dtype=np.float32)
# Fill remaining with random projections
if self.TARGET_DIM > input_dim:
random_part = rng.randn(input_dim, self.TARGET_DIM - input_dim).astype(np.float32)
# Normalize
norms = np.linalg.norm(random_part, axis=0, keepdims=True)
random_part /= (norms + 1e-8)
self._expansion_matrix[:, input_dim:] = random_part
return self._expansion_matrix
def embed_to_numpy(self, texts: str | Iterable[str]) -> np.ndarray:
"""Generate dense embeddings as numpy array.
Args:
texts: Single text or iterable of texts
Returns:
Dense embeddings of shape (n_texts, TARGET_DIM) as float32
"""
self._load_model()
if isinstance(texts, str):
texts = [texts]
else:
texts = list(texts)
# Get base float embeddings
float_embeddings = np.array(list(self._model.embed(texts)), dtype=np.float32)
self._native_dim = float_embeddings.shape[1]
# Expand to target dimension if needed
if self._expand_dim and self._native_dim < self.TARGET_DIM:
expansion = self._get_expansion_matrix(self._native_dim)
float_embeddings = float_embeddings @ expansion
return float_embeddings
# =============================================================================
# Cascade Embedding Backend
# =============================================================================
class CascadeEmbeddingBackend(BaseEmbedder):
"""Combined binary + dense embedding backend for cascade retrieval.
Generates both binary (for fast coarse filtering) and dense (for precise
reranking) embeddings in a single pass, optimized for two-stage retrieval.
Cascade workflow:
1. encode_cascade() returns (binary_embeddings, dense_embeddings)
2. Binary search: Use Hamming distance on binary vectors -> top-K candidates
3. Dense rerank: Use cosine similarity on dense vectors -> final results
Memory efficiency:
- Binary: 32 bytes per vector (256 bits)
- Dense: 8192 bytes per vector (2048 x float32)
- Total: ~8KB per document for full cascade support
"""
def __init__(
self,
binary_model: Optional[str] = None,
dense_model: Optional[str] = None,
use_gpu: bool = True,
) -> None:
"""Initialize cascade embedding backend.
Args:
binary_model: Model for binary embeddings. Defaults to BAAI/bge-small-en-v1.5
dense_model: Model for dense embeddings. Defaults to BAAI/bge-large-en-v1.5
use_gpu: Whether to use GPU acceleration
"""
self._binary_backend = BinaryEmbeddingBackend(
model_name=binary_model,
use_gpu=use_gpu,
)
self._dense_backend = DenseEmbeddingBackend(
model_name=dense_model,
use_gpu=use_gpu,
expand_dim=True,
)
self._use_gpu = use_gpu
@property
def model_name(self) -> str:
"""Return model names for both backends."""
return f"cascade({self._binary_backend.model_name}, {self._dense_backend.model_name})"
@property
def embedding_dim(self) -> int:
"""Return dense embedding dimension (for compatibility)."""
return self._dense_backend.embedding_dim
@property
def binary_dim(self) -> int:
"""Return binary embedding dimension."""
return self._binary_backend.embedding_dim
@property
def dense_dim(self) -> int:
"""Return dense embedding dimension."""
return self._dense_backend.embedding_dim
def embed_to_numpy(self, texts: str | Iterable[str]) -> np.ndarray:
"""Generate dense embeddings (for BaseEmbedder compatibility).
For cascade embeddings, use encode_cascade() instead.
Args:
texts: Single text or iterable of texts
Returns:
Dense embeddings of shape (n_texts, dense_dim)
"""
return self._dense_backend.embed_to_numpy(texts)
def encode_cascade(
self,
texts: str | Iterable[str],
batch_size: int = 32,
) -> Tuple[np.ndarray, np.ndarray]:
"""Generate both binary and dense embeddings.
Args:
texts: Single text or iterable of texts
batch_size: Batch size for processing
Returns:
Tuple of:
- binary_embeddings: Shape (n_texts, 256), uint8 values 0/1
- dense_embeddings: Shape (n_texts, 2048), float32
"""
if isinstance(texts, str):
texts = [texts]
else:
texts = list(texts)
binary_embeddings = self._binary_backend.embed_to_numpy(texts)
dense_embeddings = self._dense_backend.embed_to_numpy(texts)
return binary_embeddings, dense_embeddings
def encode_binary(self, texts: str | Iterable[str]) -> np.ndarray:
"""Generate only binary embeddings.
Args:
texts: Single text or iterable of texts
Returns:
Binary embeddings of shape (n_texts, 256)
"""
return self._binary_backend.embed_to_numpy(texts)
def encode_dense(self, texts: str | Iterable[str]) -> np.ndarray:
"""Generate only dense embeddings.
Args:
texts: Single text or iterable of texts
Returns:
Dense embeddings of shape (n_texts, 2048)
"""
return self._dense_backend.embed_to_numpy(texts)
def encode_binary_packed(self, texts: str | Iterable[str]) -> List[bytes]:
"""Generate packed binary embeddings.
Args:
texts: Single text or iterable of texts
Returns:
List of packed bytes (32 bytes each)
"""
return self._binary_backend.embed_packed(texts)
# =============================================================================
# Factory Function
# =============================================================================
def get_cascade_embedder(
binary_model: Optional[str] = None,
dense_model: Optional[str] = None,
use_gpu: bool = True,
) -> CascadeEmbeddingBackend:
"""Factory function to create a cascade embedder.
Args:
binary_model: Model for binary embeddings (default: BAAI/bge-small-en-v1.5)
dense_model: Model for dense embeddings (default: BAAI/bge-large-en-v1.5)
use_gpu: Whether to use GPU acceleration
Returns:
Configured CascadeEmbeddingBackend instance
Example:
>>> embedder = get_cascade_embedder()
>>> binary, dense = embedder.encode_cascade(["hello world"])
>>> binary.shape # (1, 256)
>>> dense.shape # (1, 2048)
"""
return CascadeEmbeddingBackend(
binary_model=binary_model,
dense_model=dense_model,
use_gpu=use_gpu,
)

677
codex-lens/src/codexlens/search/chain_search.py
View File

@@ -9,12 +9,21 @@ from __future__ import annotations
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass, field
from pathlib import Path
from typing import List, Optional, Dict, Any
from typing import List, Optional, Dict, Any, Literal, Tuple, TYPE_CHECKING
import logging
import os
import time
from codexlens.entities import SearchResult, Symbol
if TYPE_CHECKING:
import numpy as np
try:
import numpy as np
NUMPY_AVAILABLE = True
except ImportError:
NUMPY_AVAILABLE = False
from codexlens.config import Config
from codexlens.storage.registry import RegistryStore, DirMapping
from codexlens.storage.dir_index import DirIndexStore, SubdirLink
@@ -260,6 +269,672 @@ class ChainSearchEngine:
related_results=related_results,
)
def hybrid_cascade_search(
self,
query: str,
source_path: Path,
k: int = 10,
coarse_k: int = 100,
options: Optional[SearchOptions] = None,
) -> ChainSearchResult:
"""Execute two-stage cascade search with hybrid coarse retrieval and cross-encoder reranking.
Hybrid cascade search process:
1. Stage 1 (Coarse): Fast retrieval using RRF fusion of FTS + SPLADE + Vector
to get coarse_k candidates
2. Stage 2 (Fine): CrossEncoder reranking of candidates to get final k results
This approach balances recall (from broad coarse search) with precision
(from expensive but accurate cross-encoder scoring).
Note: This method is the original hybrid approach. For binary vector cascade,
use binary_cascade_search() instead.
Args:
query: Natural language or keyword query string
source_path: Starting directory path
k: Number of final results to return (default 10)
coarse_k: Number of coarse candidates from first stage (default 100)
options: Search configuration (uses defaults if None)
Returns:
ChainSearchResult with reranked results and statistics
Examples:
>>> engine = ChainSearchEngine(registry, mapper, config=config)
>>> result = engine.hybrid_cascade_search(
... "how to authenticate users",
... Path("D:/project/src"),
... k=10,
... coarse_k=100
... )
>>> for r in result.results:
... print(f"{r.path}: {r.score:.3f}")
"""
options = options or SearchOptions()
start_time = time.time()
stats = SearchStats()
# Use config defaults if available
if self._config is not None:
if hasattr(self._config, "cascade_coarse_k"):
coarse_k = coarse_k or self._config.cascade_coarse_k
if hasattr(self._config, "cascade_fine_k"):
k = k or self._config.cascade_fine_k
# Step 1: Find starting index
start_index = self._find_start_index(source_path)
if not start_index:
self.logger.warning(f"No index found for {source_path}")
stats.time_ms = (time.time() - start_time) * 1000
return ChainSearchResult(
query=query,
results=[],
symbols=[],
stats=stats
)
# Step 2: Collect all index paths
index_paths = self._collect_index_paths(start_index, options.depth)
stats.dirs_searched = len(index_paths)
if not index_paths:
self.logger.warning(f"No indexes collected from {start_index}")
stats.time_ms = (time.time() - start_time) * 1000
return ChainSearchResult(
query=query,
results=[],
symbols=[],
stats=stats
)
# Stage 1: Coarse retrieval with hybrid search (FTS + SPLADE + Vector)
# Use hybrid mode for multi-signal retrieval
coarse_options = SearchOptions(
depth=options.depth,
max_workers=1, # Single thread for GPU safety
limit_per_dir=max(coarse_k // len(index_paths), 20),
total_limit=coarse_k,
hybrid_mode=True,
enable_fuzzy=options.enable_fuzzy,
enable_vector=True, # Enable vector for semantic matching
pure_vector=False,
hybrid_weights=options.hybrid_weights,
)
self.logger.debug(
"Cascade Stage 1: Coarse retrieval for %d candidates", coarse_k
)
coarse_results, search_stats = self._search_parallel(
index_paths, query, coarse_options
)
stats.errors = search_stats.errors
# Merge and deduplicate coarse results
coarse_merged = self._merge_and_rank(coarse_results, coarse_k)
self.logger.debug(
"Cascade Stage 1 complete: %d candidates retrieved", len(coarse_merged)
)
if not coarse_merged:
stats.time_ms = (time.time() - start_time) * 1000
return ChainSearchResult(
query=query,
results=[],
symbols=[],
stats=stats
)
# Stage 2: Cross-encoder reranking
self.logger.debug(
"Cascade Stage 2: Cross-encoder reranking %d candidates to top-%d",
len(coarse_merged),
k,
)
final_results = self._cross_encoder_rerank(query, coarse_merged, k)
# Optional: grouping of similar results
if options.group_results:
from codexlens.search.ranking import group_similar_results
final_results = group_similar_results(
final_results, score_threshold_abs=options.grouping_threshold
)
stats.files_matched = len(final_results)
stats.time_ms = (time.time() - start_time) * 1000
self.logger.debug(
"Cascade search complete: %d results in %.2fms",
len(final_results),
stats.time_ms,
)
return ChainSearchResult(
query=query,
results=final_results,
symbols=[],
stats=stats,
)
def binary_cascade_search(
self,
query: str,
source_path: Path,
k: int = 10,
coarse_k: int = 100,
options: Optional[SearchOptions] = None,
) -> ChainSearchResult:
"""Execute binary cascade search with binary coarse ranking and dense fine ranking.
Binary cascade search process:
1. Stage 1 (Coarse): Fast binary vector search using Hamming distance
to quickly filter to coarse_k candidates (256-dim binary, 32 bytes/vector)
2. Stage 2 (Fine): Dense vector cosine similarity for precise reranking
of candidates (2048-dim float32)
This approach leverages the speed of binary search (~100x faster) while
maintaining precision through dense vector reranking.
Performance characteristics:
- Binary search: O(N) with SIMD-accelerated XOR + popcount
- Dense rerank: Only applied to top coarse_k candidates
- Memory: 32 bytes (binary) + 8KB (dense) per chunk
Args:
query: Natural language or keyword query string
source_path: Starting directory path
k: Number of final results to return (default 10)
coarse_k: Number of coarse candidates from first stage (default 100)
options: Search configuration (uses defaults if None)
Returns:
ChainSearchResult with reranked results and statistics
Examples:
>>> engine = ChainSearchEngine(registry, mapper, config=config)
>>> result = engine.binary_cascade_search(
... "how to authenticate users",
... Path("D:/project/src"),
... k=10,
... coarse_k=100
... )
>>> for r in result.results:
... print(f"{r.path}: {r.score:.3f}")
"""
if not NUMPY_AVAILABLE:
self.logger.warning(
"NumPy not available, falling back to hybrid cascade search"
)
return self.hybrid_cascade_search(query, source_path, k, coarse_k, options)
options = options or SearchOptions()
start_time = time.time()
stats = SearchStats()
# Use config defaults if available
if self._config is not None:
if hasattr(self._config, "cascade_coarse_k"):
coarse_k = coarse_k or self._config.cascade_coarse_k
if hasattr(self._config, "cascade_fine_k"):
k = k or self._config.cascade_fine_k
# Step 1: Find starting index
start_index = self._find_start_index(source_path)
if not start_index:
self.logger.warning(f"No index found for {source_path}")
stats.time_ms = (time.time() - start_time) * 1000
return ChainSearchResult(
query=query,
results=[],
symbols=[],
stats=stats
)
# Step 2: Collect all index paths
index_paths = self._collect_index_paths(start_index, options.depth)
stats.dirs_searched = len(index_paths)
if not index_paths:
self.logger.warning(f"No indexes collected from {start_index}")
stats.time_ms = (time.time() - start_time) * 1000
return ChainSearchResult(
query=query,
results=[],
symbols=[],
stats=stats
)
# Initialize embedding backends
try:
from codexlens.indexing.embedding import (
BinaryEmbeddingBackend,
DenseEmbeddingBackend,
)
from codexlens.semantic.ann_index import BinaryANNIndex
except ImportError as exc:
self.logger.warning(
"Binary cascade dependencies not available: %s. "
"Falling back to hybrid cascade search.",
exc
)
return self.hybrid_cascade_search(query, source_path, k, coarse_k, options)
# Stage 1: Binary vector coarse retrieval
self.logger.debug(
"Binary Cascade Stage 1: Binary coarse retrieval for %d candidates",
coarse_k,
)
use_gpu = True
if self._config is not None:
use_gpu = getattr(self._config, "embedding_use_gpu", True)
try:
binary_backend = BinaryEmbeddingBackend(use_gpu=use_gpu)
query_binary_packed = binary_backend.embed_packed([query])[0]
except Exception as exc:
self.logger.warning(
"Failed to generate binary query embedding: %s. "
"Falling back to hybrid cascade search.",
exc
)
return self.hybrid_cascade_search(query, source_path, k, coarse_k, options)
# Search all indexes for binary candidates
all_candidates: List[Tuple[int, int, Path]] = [] # (chunk_id, distance, index_path)
for index_path in index_paths:
try:
# Get or create binary index for this path
binary_index = self._get_or_create_binary_index(index_path)
if binary_index is None or binary_index.count() == 0:
continue
# Search binary index
ids, distances = binary_index.search(query_binary_packed, coarse_k)
for chunk_id, dist in zip(ids, distances):
all_candidates.append((chunk_id, dist, index_path))
except Exception as exc:
self.logger.debug(
"Binary search failed for %s: %s", index_path, exc
)
stats.errors.append(f"Binary search failed for {index_path}: {exc}")
if not all_candidates:
self.logger.debug("No binary candidates found, falling back to hybrid")
return self.hybrid_cascade_search(query, source_path, k, coarse_k, options)
# Sort by Hamming distance and take top coarse_k
all_candidates.sort(key=lambda x: x[1])
coarse_candidates = all_candidates[:coarse_k]
self.logger.debug(
"Binary Cascade Stage 1 complete: %d candidates retrieved",
len(coarse_candidates),
)
# Stage 2: Dense vector fine ranking
self.logger.debug(
"Binary Cascade Stage 2: Dense reranking %d candidates to top-%d",
len(coarse_candidates),
k,
)
try:
dense_backend = DenseEmbeddingBackend(use_gpu=use_gpu)
query_dense = dense_backend.embed_to_numpy([query])[0]
except Exception as exc:
self.logger.warning(
"Failed to generate dense query embedding: %s. "
"Using Hamming distance scores only.",
exc
)
# Fall back to using Hamming distance as score
return self._build_results_from_candidates(
coarse_candidates[:k], index_paths, stats, query, start_time
)
# Group candidates by index path for batch retrieval
candidates_by_index: Dict[Path, List[int]] = {}
for chunk_id, _, index_path in coarse_candidates:
if index_path not in candidates_by_index:
candidates_by_index[index_path] = []
candidates_by_index[index_path].append(chunk_id)
# Retrieve dense embeddings and compute cosine similarity
scored_results: List[Tuple[float, SearchResult]] = []
for index_path, chunk_ids in candidates_by_index.items():
try:
store = SQLiteStore(index_path)
dense_embeddings = store.get_dense_embeddings(chunk_ids)
chunks_data = store.get_chunks_by_ids(chunk_ids)
# Create lookup for chunk content
chunk_content: Dict[int, Dict[str, Any]] = {
c["id"]: c for c in chunks_data
}
for chunk_id in chunk_ids:
dense_bytes = dense_embeddings.get(chunk_id)
chunk_info = chunk_content.get(chunk_id)
if dense_bytes is None or chunk_info is None:
continue
# Compute cosine similarity
dense_vec = np.frombuffer(dense_bytes, dtype=np.float32)
score = self._compute_cosine_similarity(query_dense, dense_vec)
# Create search result
excerpt = chunk_info.get("content", "")[:500]
result = SearchResult(
path=chunk_info.get("file_path", ""),
score=float(score),
excerpt=excerpt,
)
scored_results.append((score, result))
except Exception as exc:
self.logger.debug(
"Dense reranking failed for %s: %s", index_path, exc
)
stats.errors.append(f"Dense reranking failed for {index_path}: {exc}")
# Sort by score descending and deduplicate by path
scored_results.sort(key=lambda x: x[0], reverse=True)
path_to_result: Dict[str, SearchResult] = {}
for score, result in scored_results:
if result.path not in path_to_result:
path_to_result[result.path] = result
final_results = list(path_to_result.values())[:k]
# Optional: grouping of similar results
if options.group_results:
from codexlens.search.ranking import group_similar_results
final_results = group_similar_results(
final_results, score_threshold_abs=options.grouping_threshold
)
stats.files_matched = len(final_results)
stats.time_ms = (time.time() - start_time) * 1000
self.logger.debug(
"Binary cascade search complete: %d results in %.2fms",
len(final_results),
stats.time_ms,
)
return ChainSearchResult(
query=query,
results=final_results,
symbols=[],
stats=stats,
)
def cascade_search(
self,
query: str,
source_path: Path,
k: int = 10,
coarse_k: int = 100,
options: Optional[SearchOptions] = None,
strategy: Literal["binary", "hybrid"] = "binary",
) -> ChainSearchResult:
"""Unified cascade search entry point with strategy selection.
Provides a single interface for cascade search with configurable strategy:
- "binary": Uses binary vector coarse ranking + dense fine ranking (faster)
- "hybrid": Uses FTS+SPLADE+Vector coarse ranking + cross-encoder reranking (original)
The strategy can be configured via:
1. The `strategy` parameter (highest priority)
2. Config `cascade_strategy` setting
3. Default: "binary"
Args:
query: Natural language or keyword query string
source_path: Starting directory path
k: Number of final results to return (default 10)
coarse_k: Number of coarse candidates from first stage (default 100)
options: Search configuration (uses defaults if None)
strategy: Cascade strategy - "binary" or "hybrid" (default "binary")
Returns:
ChainSearchResult with reranked results and statistics
Examples:
>>> engine = ChainSearchEngine(registry, mapper, config=config)
>>> # Use binary cascade (default, faster)
>>> result = engine.cascade_search("auth", Path("D:/project"))
>>> # Use hybrid cascade (original behavior)
>>> result = engine.cascade_search("auth", Path("D:/project"), strategy="hybrid")
"""
# Check config for strategy override
effective_strategy = strategy
if self._config is not None:
config_strategy = getattr(self._config, "cascade_strategy", None)
if config_strategy in ("binary", "hybrid"):
# Only use config if no explicit strategy was passed
# (we can't detect if strategy was explicitly passed vs default)
effective_strategy = config_strategy
if effective_strategy == "binary":
return self.binary_cascade_search(query, source_path, k, coarse_k, options)
else:
return self.hybrid_cascade_search(query, source_path, k, coarse_k, options)
def _get_or_create_binary_index(self, index_path: Path) -> Optional[Any]:
"""Get or create a BinaryANNIndex for the given index path.
Attempts to load an existing binary index from disk. If not found,
returns None (binary index should be built during indexing).
Args:
index_path: Path to the _index.db file
Returns:
BinaryANNIndex instance or None if not available
"""
try:
from codexlens.semantic.ann_index import BinaryANNIndex
binary_index = BinaryANNIndex(index_path, dim=256)
if binary_index.load():
return binary_index
return None
except Exception as exc:
self.logger.debug("Failed to load binary index for %s: %s", index_path, exc)
return None
def _compute_cosine_similarity(
self,
query_vec: "np.ndarray",
doc_vec: "np.ndarray",
) -> float:
"""Compute cosine similarity between query and document vectors.
Args:
query_vec: Query embedding vector
doc_vec: Document embedding vector
Returns:
Cosine similarity score in range [-1, 1]
"""
if not NUMPY_AVAILABLE:
return 0.0
# Ensure same shape
min_len = min(len(query_vec), len(doc_vec))
q = query_vec[:min_len]
d = doc_vec[:min_len]
# Compute cosine similarity
dot_product = np.dot(q, d)
norm_q = np.linalg.norm(q)
norm_d = np.linalg.norm(d)
if norm_q == 0 or norm_d == 0:
return 0.0
return float(dot_product / (norm_q * norm_d))
def _build_results_from_candidates(
self,
candidates: List[Tuple[int, int, Path]],
index_paths: List[Path],
stats: SearchStats,
query: str,
start_time: float,
) -> ChainSearchResult:
"""Build ChainSearchResult from binary candidates using Hamming distance scores.
Used as fallback when dense embeddings are not available.
Args:
candidates: List of (chunk_id, hamming_distance, index_path) tuples
index_paths: List of all searched index paths
stats: SearchStats to update
query: Original query string
start_time: Search start time for timing
Returns:
ChainSearchResult with results scored by Hamming distance
"""
results: List[SearchResult] = []
# Group by index path
candidates_by_index: Dict[Path, List[Tuple[int, int]]] = {}
for chunk_id, distance, index_path in candidates:
if index_path not in candidates_by_index:
candidates_by_index[index_path] = []
candidates_by_index[index_path].append((chunk_id, distance))
for index_path, chunk_tuples in candidates_by_index.items():
try:
store = SQLiteStore(index_path)
chunk_ids = [c[0] for c in chunk_tuples]
chunks_data = store.get_chunks_by_ids(chunk_ids)
chunk_content: Dict[int, Dict[str, Any]] = {
c["id"]: c for c in chunks_data
}
for chunk_id, distance in chunk_tuples:
chunk_info = chunk_content.get(chunk_id)
if chunk_info is None:
continue
# Convert Hamming distance to score (lower distance = higher score)
# Max Hamming distance for 256-bit is 256
score = 1.0 - (distance / 256.0)
excerpt = chunk_info.get("content", "")[:500]
result = SearchResult(
path=chunk_info.get("file_path", ""),
score=float(score),
excerpt=excerpt,
)
results.append(result)
except Exception as exc:
self.logger.debug(
"Failed to build results from %s: %s", index_path, exc
)
# Deduplicate by path
path_to_result: Dict[str, SearchResult] = {}
for result in results:
if result.path not in path_to_result or result.score > path_to_result[result.path].score:
path_to_result[result.path] = result
final_results = sorted(
path_to_result.values(),
key=lambda r: r.score,
reverse=True,
)
stats.files_matched = len(final_results)
stats.time_ms = (time.time() - start_time) * 1000
return ChainSearchResult(
query=query,
results=final_results,
symbols=[],
stats=stats,
)
def _cross_encoder_rerank(
self,
query: str,
results: List[SearchResult],
top_k: int,
) -> List[SearchResult]:
"""Rerank results using cross-encoder model.
Args:
query: Search query string
results: Candidate results to rerank
top_k: Number of top results to return
Returns:
Reranked results sorted by cross-encoder score
"""
if not results:
return []
# Try to get reranker from config or create new one
reranker = None
try:
from codexlens.semantic.reranker import (
check_reranker_available,
get_reranker,
)
# Determine backend and model from config
backend = "onnx"
model_name = None
use_gpu = True
if self._config is not None:
backend = getattr(self._config, "reranker_backend", "onnx") or "onnx"
model_name = getattr(self._config, "reranker_model", None)
use_gpu = getattr(self._config, "embedding_use_gpu", True)
ok, err = check_reranker_available(backend)
if not ok:
self.logger.debug("Reranker backend unavailable (%s): %s", backend, err)
return results[:top_k]
# Create reranker
kwargs = {}
if backend == "onnx":
kwargs["use_gpu"] = use_gpu
reranker = get_reranker(backend=backend, model_name=model_name, **kwargs)
except ImportError as exc:
self.logger.debug("Reranker not available: %s", exc)
return results[:top_k]
except Exception as exc:
self.logger.debug("Failed to initialize reranker: %s", exc)
return results[:top_k]
# Use cross_encoder_rerank from ranking module
from codexlens.search.ranking import cross_encoder_rerank
return cross_encoder_rerank(
query=query,
results=results,
reranker=reranker,
top_k=top_k,
batch_size=32,
)
def search_files_only(self, query: str,
source_path: Path,
options: Optional[SearchOptions] = None) -> List[str]:

26
codex-lens/src/codexlens/search/hybrid_search.py
View File

@@ -40,11 +40,20 @@ from codexlens.search.ranking import (
get_rrf_weights,
reciprocal_rank_fusion,
rerank_results,
simple_weighted_fusion,
tag_search_source,
)
from codexlens.storage.dir_index import DirIndexStore
# Three-way fusion weights (FTS + Vector + SPLADE)
THREE_WAY_WEIGHTS = {
"exact": 0.2,
"splade": 0.3,
"vector": 0.5,
}
class HybridSearchEngine:
"""Hybrid search engine with parallel execution and RRF fusion.
@@ -193,9 +202,22 @@ class HybridSearchEngine:
if source in results_map
}
with timer("rrf_fusion", self.logger):
# Determine fusion method from config (default: rrf)
fusion_method = "rrf"
rrf_k = 60
if self._config is not None:
fusion_method = getattr(self._config, "fusion_method", "rrf") or "rrf"
rrf_k = getattr(self._config, "rrf_k", 60) or 60
with timer("fusion", self.logger):
adaptive_weights = get_rrf_weights(query, active_weights)
fused_results = reciprocal_rank_fusion(results_map, adaptive_weights)
if fusion_method == "simple":
fused_results = simple_weighted_fusion(results_map, adaptive_weights)
else:
# Default to RRF
fused_results = reciprocal_rank_fusion(
results_map, adaptive_weights, k=rrf_k
)
# Optional: boost results that include explicit symbol matches
boost_factor = (

129
codex-lens/src/codexlens/search/ranking.py
View File

@@ -132,6 +132,116 @@ def get_rrf_weights(
return adjust_weights_by_intent(detect_query_intent(query), base_weights)
def simple_weighted_fusion(
results_map: Dict[str, List[SearchResult]],
weights: Dict[str, float] = None,
) -> List[SearchResult]:
"""Combine search results using simple weighted sum of normalized scores.
This is an alternative to RRF that preserves score magnitude information.
Scores are min-max normalized per source before weighted combination.
Formula: score(d) = Σ weight_source * normalized_score_source(d)
Args:
results_map: Dictionary mapping source name to list of SearchResult objects
Sources: 'exact', 'fuzzy', 'vector', 'splade'
weights: Dictionary mapping source name to weight (default: equal weights)
Example: {'exact': 0.3, 'fuzzy': 0.1, 'vector': 0.6}
Returns:
List of SearchResult objects sorted by fused score (descending)
Examples:
>>> fts_results = [SearchResult(path="a.py", score=10.0, excerpt="...")]
>>> vector_results = [SearchResult(path="b.py", score=0.85, excerpt="...")]
>>> results_map = {'exact': fts_results, 'vector': vector_results}
>>> fused = simple_weighted_fusion(results_map)
"""
if not results_map:
return []
# Default equal weights if not provided
if weights is None:
num_sources = len(results_map)
weights = {source: 1.0 / num_sources for source in results_map}
# Normalize weights to sum to 1.0
weight_sum = sum(weights.values())
if not math.isclose(weight_sum, 1.0, abs_tol=0.01) and weight_sum > 0:
weights = {source: w / weight_sum for source, w in weights.items()}
# Compute min-max normalization parameters per source
source_stats: Dict[str, tuple] = {}
for source_name, results in results_map.items():
if not results:
continue
scores = [r.score for r in results]
min_s, max_s = min(scores), max(scores)
source_stats[source_name] = (min_s, max_s)
def normalize_score(score: float, source: str) -> float:
"""Normalize score to [0, 1] range using min-max scaling."""
if source not in source_stats:
return 0.0
min_s, max_s = source_stats[source]
if max_s == min_s:
return 1.0 if score >= min_s else 0.0
return (score - min_s) / (max_s - min_s)
# Build unified result set with weighted scores
path_to_result: Dict[str, SearchResult] = {}
path_to_fusion_score: Dict[str, float] = {}
path_to_source_scores: Dict[str, Dict[str, float]] = {}
for source_name, results in results_map.items():
weight = weights.get(source_name, 0.0)
if weight == 0:
continue
for result in results:
path = result.path
normalized = normalize_score(result.score, source_name)
contribution = weight * normalized
if path not in path_to_fusion_score:
path_to_fusion_score[path] = 0.0
path_to_result[path] = result
path_to_source_scores[path] = {}
path_to_fusion_score[path] += contribution
path_to_source_scores[path][source_name] = normalized
# Create final results with fusion scores
fused_results = []
for path, base_result in path_to_result.items():
fusion_score = path_to_fusion_score[path]
fused_result = SearchResult(
path=base_result.path,
score=fusion_score,
excerpt=base_result.excerpt,
content=base_result.content,
symbol=base_result.symbol,
chunk=base_result.chunk,
metadata={
**base_result.metadata,
"fusion_method": "simple_weighted",
"fusion_score": fusion_score,
"original_score": base_result.score,
"source_scores": path_to_source_scores[path],
},
start_line=base_result.start_line,
end_line=base_result.end_line,
symbol_name=base_result.symbol_name,
symbol_kind=base_result.symbol_kind,
)
fused_results.append(fused_result)
fused_results.sort(key=lambda r: r.score, reverse=True)
return fused_results
def reciprocal_rank_fusion(
results_map: Dict[str, List[SearchResult]],
weights: Dict[str, float] = None,
@@ -141,11 +251,14 @@ def reciprocal_rank_fusion(
RRF formula: score(d) = Σ weight_source / (k + rank_source(d))
Supports three-way fusion with FTS, Vector, and SPLADE sources.
Args:
results_map: Dictionary mapping source name to list of SearchResult objects
Sources: 'exact', 'fuzzy', 'vector'
Sources: 'exact', 'fuzzy', 'vector', 'splade'
weights: Dictionary mapping source name to weight (default: equal weights)
Example: {'exact': 0.3, 'fuzzy': 0.1, 'vector': 0.6}
Or: {'splade': 0.4, 'vector': 0.6}
k: Constant to avoid division by zero and control rank influence (default 60)
Returns:
@@ -156,6 +269,14 @@ def reciprocal_rank_fusion(
>>> fuzzy_results = [SearchResult(path="b.py", score=8.0, excerpt="...")]
>>> results_map = {'exact': exact_results, 'fuzzy': fuzzy_results}
>>> fused = reciprocal_rank_fusion(results_map)
# Three-way fusion with SPLADE
>>> results_map = {
... 'exact': exact_results,
... 'vector': vector_results,
... 'splade': splade_results
... }
>>> fused = reciprocal_rank_fusion(results_map, k=60)
"""
if not results_map:
return []
@@ -174,6 +295,7 @@ def reciprocal_rank_fusion(
# Build unified result set with RRF scores
path_to_result: Dict[str, SearchResult] = {}
path_to_fusion_score: Dict[str, float] = {}
path_to_source_ranks: Dict[str, Dict[str, int]] = {}
for source_name, results in results_map.items():
weight = weights.get(source_name, 0.0)
@@ -188,8 +310,10 @@ def reciprocal_rank_fusion(
if path not in path_to_fusion_score:
path_to_fusion_score[path] = 0.0
path_to_result[path] = result
path_to_source_ranks[path] = {}
path_to_fusion_score[path] += rrf_contribution
path_to_source_ranks[path][source_name] = rank
# Create final results with fusion scores
fused_results = []
@@ -206,8 +330,11 @@ def reciprocal_rank_fusion(
chunk=base_result.chunk,
metadata={
**base_result.metadata,
"fusion_method": "rrf",
"fusion_score": fusion_score,
"original_score": base_result.score,
"rrf_k": k,
"source_ranks": path_to_source_ranks[path],
},
start_line=base_result.start_line,
end_line=base_result.end_line,

486
codex-lens/src/codexlens/semantic/ann_index.py
View File

@@ -412,3 +412,489 @@ class ANNIndex:
"""
with self._lock:
return self._index is not None and self._current_count > 0
class BinaryANNIndex:
"""Binary vector ANN index using Hamming distance for fast coarse retrieval.
Optimized for binary vectors (256-bit / 32 bytes per vector).
Uses packed binary representation for memory efficiency.
Performance characteristics:
- Storage: 32 bytes per vector (vs ~8KB for dense vectors)
- Distance: Hamming distance via XOR + popcount (CPU-efficient)
- Search: O(N) brute-force with SIMD-accelerated distance computation
Index parameters:
- dim: Binary vector dimension (default: 256)
- packed_dim: Packed bytes size (dim / 8 = 32 for 256-bit)
Usage:
index = BinaryANNIndex(index_path, dim=256)
index.add_vectors([1, 2, 3], packed_vectors) # List of 32-byte packed vectors
ids, distances = index.search(query_packed, top_k=10)
"""
DEFAULT_DIM = 256 # Default binary vector dimension
def __init__(
self,
index_path: Path,
dim: int = 256,
initial_capacity: int = 100000,
auto_save: bool = False,
) -> None:
"""Initialize Binary ANN index.
Args:
index_path: Path to database (index will be saved as _binary_vectors.bin)
dim: Dimension of binary vectors (default: 256)
initial_capacity: Initial capacity hint (default: 100000)
auto_save: Whether to automatically save index after operations
Raises:
ImportError: If required dependencies are not available
ValueError: If dimension is invalid
"""
if not SEMANTIC_AVAILABLE:
raise ImportError(
"Semantic search dependencies not available. "
"Install with: pip install codexlens[semantic]"
)
if dim <= 0 or dim % 8 != 0:
raise ValueError(
f"Invalid dimension: {dim}. Must be positive and divisible by 8."
)
self.index_path = Path(index_path)
self.dim = dim
self.packed_dim = dim // 8 # 32 bytes for 256-bit vectors
# Derive binary index path from database path
db_stem = self.index_path.stem
self.binary_path = self.index_path.parent / f"{db_stem}_binary_vectors.bin"
# Memory management
self._auto_save = auto_save
self._initial_capacity = initial_capacity
# Thread safety
self._lock = threading.RLock()
# In-memory storage: id -> packed binary vector
self._vectors: dict[int, bytes] = {}
self._id_list: list[int] = [] # Ordered list for efficient iteration
logger.info(
f"Initialized BinaryANNIndex with dim={dim}, packed_dim={self.packed_dim}"
)
def add_vectors(self, ids: List[int], vectors: List[bytes]) -> None:
"""Add packed binary vectors to the index.
Args:
ids: List of vector IDs (must be unique)
vectors: List of packed binary vectors (each of size packed_dim bytes)
Raises:
ValueError: If shapes don't match or vectors are invalid
StorageError: If index operation fails
"""
if len(ids) == 0:
return
if len(vectors) != len(ids):
raise ValueError(
f"Number of vectors ({len(vectors)}) must match number of IDs ({len(ids)})"
)
# Validate vector sizes
for i, vec in enumerate(vectors):
if len(vec) != self.packed_dim:
raise ValueError(
f"Vector {i} has size {len(vec)}, expected {self.packed_dim}"
)
with self._lock:
try:
for vec_id, vec in zip(ids, vectors):
if vec_id not in self._vectors:
self._id_list.append(vec_id)
self._vectors[vec_id] = vec
logger.debug(
f"Added {len(ids)} binary vectors to index (total: {len(self._vectors)})"
)
if self._auto_save:
self.save()
except Exception as e:
raise StorageError(f"Failed to add vectors to Binary ANN index: {e}")
def add_vectors_numpy(self, ids: List[int], vectors: np.ndarray) -> None:
"""Add unpacked binary vectors (0/1 values) to the index.
Convenience method that packs the vectors before adding.
Args:
ids: List of vector IDs (must be unique)
vectors: Numpy array of shape (N, dim) with binary values (0 or 1)
Raises:
ValueError: If shapes don't match
StorageError: If index operation fails
"""
if len(ids) == 0:
return
if vectors.shape[0] != len(ids):
raise ValueError(
f"Number of vectors ({vectors.shape[0]}) must match number of IDs ({len(ids)})"
)
if vectors.shape[1] != self.dim:
raise ValueError(
f"Vector dimension ({vectors.shape[1]}) must match index dimension ({self.dim})"
)
# Pack vectors
packed_vectors = []
for i in range(vectors.shape[0]):
packed = np.packbits(vectors[i].astype(np.uint8)).tobytes()
packed_vectors.append(packed)
self.add_vectors(ids, packed_vectors)
def remove_vectors(self, ids: List[int]) -> None:
"""Remove vectors from the index.
Args:
ids: List of vector IDs to remove
Raises:
StorageError: If index operation fails
Note:
Optimized for batch deletion using set operations instead of
O(N) list.remove() calls for each ID.
"""
if len(ids) == 0:
return
with self._lock:
try:
# Use set for O(1) lookup during filtering
ids_to_remove = set(ids)
removed_count = 0
# Remove from dictionary - O(1) per deletion
for vec_id in ids_to_remove:
if vec_id in self._vectors:
del self._vectors[vec_id]
removed_count += 1
# Rebuild ID list efficiently - O(N) once instead of O(N) per removal
if removed_count > 0:
self._id_list = [id_ for id_ in self._id_list if id_ not in ids_to_remove]
logger.debug(f"Removed {removed_count}/{len(ids)} vectors from index")
if self._auto_save and removed_count > 0:
self.save()
except Exception as e:
raise StorageError(
f"Failed to remove vectors from Binary ANN index: {e}"
)
def search(
self, query: bytes, top_k: int = 10
) -> Tuple[List[int], List[int]]:
"""Search for nearest neighbors using Hamming distance.
Args:
query: Packed binary query vector (size: packed_dim bytes)
top_k: Number of nearest neighbors to return
Returns:
Tuple of (ids, distances) where:
- ids: List of vector IDs ordered by Hamming distance (ascending)
- distances: List of Hamming distances (lower = more similar)
Raises:
ValueError: If query size is invalid
StorageError: If search operation fails
"""
if len(query) != self.packed_dim:
raise ValueError(
f"Query size ({len(query)}) must match packed_dim ({self.packed_dim})"
)
with self._lock:
try:
if len(self._vectors) == 0:
return [], []
# Compute Hamming distances to all vectors
query_arr = np.frombuffer(query, dtype=np.uint8)
distances = []
for vec_id in self._id_list:
vec = self._vectors[vec_id]
vec_arr = np.frombuffer(vec, dtype=np.uint8)
# XOR and popcount for Hamming distance
xor = np.bitwise_xor(query_arr, vec_arr)
dist = int(np.unpackbits(xor).sum())
distances.append((vec_id, dist))
# Sort by distance (ascending)
distances.sort(key=lambda x: x[1])
# Return top-k
top_results = distances[:top_k]
ids = [r[0] for r in top_results]
dists = [r[1] for r in top_results]
return ids, dists
except Exception as e:
raise StorageError(f"Failed to search Binary ANN index: {e}")
def search_numpy(
self, query: np.ndarray, top_k: int = 10
) -> Tuple[List[int], List[int]]:
"""Search with unpacked binary query vector.
Convenience method that packs the query before searching.
Args:
query: Binary query vector of shape (dim,) with values 0 or 1
top_k: Number of nearest neighbors to return
Returns:
Tuple of (ids, distances)
"""
if query.ndim == 2:
query = query.flatten()
if len(query) != self.dim:
raise ValueError(
f"Query dimension ({len(query)}) must match index dimension ({self.dim})"
)
packed_query = np.packbits(query.astype(np.uint8)).tobytes()
return self.search(packed_query, top_k)
def search_batch(
self, queries: List[bytes], top_k: int = 10
) -> List[Tuple[List[int], List[int]]]:
"""Batch search for multiple queries.
Args:
queries: List of packed binary query vectors
top_k: Number of nearest neighbors to return per query
Returns:
List of (ids, distances) tuples, one per query
"""
results = []
for query in queries:
ids, dists = self.search(query, top_k)
results.append((ids, dists))
return results
def save(self) -> None:
"""Save index to disk.
Binary format:
- 4 bytes: magic number (0x42494E56 = "BINV")
- 4 bytes: version (1)
- 4 bytes: dim
- 4 bytes: packed_dim
- 4 bytes: num_vectors
- For each vector:
- 4 bytes: id
- packed_dim bytes: vector data
Raises:
StorageError: If save operation fails
"""
with self._lock:
try:
if len(self._vectors) == 0:
logger.debug("Skipping save: index is empty")
return
# Ensure parent directory exists
self.binary_path.parent.mkdir(parents=True, exist_ok=True)
with open(self.binary_path, "wb") as f:
# Header
f.write(b"BINV") # Magic number
f.write(np.array([1], dtype=np.uint32).tobytes()) # Version
f.write(np.array([self.dim], dtype=np.uint32).tobytes())
f.write(np.array([self.packed_dim], dtype=np.uint32).tobytes())
f.write(
np.array([len(self._vectors)], dtype=np.uint32).tobytes()
)
# Vectors
for vec_id in self._id_list:
f.write(np.array([vec_id], dtype=np.uint32).tobytes())
f.write(self._vectors[vec_id])
logger.debug(
f"Saved binary index to {self.binary_path} "
f"({len(self._vectors)} vectors)"
)
except Exception as e:
raise StorageError(f"Failed to save Binary ANN index: {e}")
def load(self) -> bool:
"""Load index from disk.
Returns:
True if index was loaded successfully, False if index file doesn't exist
Raises:
StorageError: If load operation fails
"""
with self._lock:
try:
if not self.binary_path.exists():
logger.debug(f"Binary index file not found: {self.binary_path}")
return False
with open(self.binary_path, "rb") as f:
# Read header
magic = f.read(4)
if magic != b"BINV":
raise StorageError(
f"Invalid binary index file: bad magic number"
)
version = np.frombuffer(f.read(4), dtype=np.uint32)[0]
if version != 1:
raise StorageError(
f"Unsupported binary index version: {version}"
)
file_dim = np.frombuffer(f.read(4), dtype=np.uint32)[0]
file_packed_dim = np.frombuffer(f.read(4), dtype=np.uint32)[0]
num_vectors = np.frombuffer(f.read(4), dtype=np.uint32)[0]
if file_dim != self.dim or file_packed_dim != self.packed_dim:
raise StorageError(
f"Dimension mismatch: file has dim={file_dim}, "
f"packed_dim={file_packed_dim}, "
f"expected dim={self.dim}, packed_dim={self.packed_dim}"
)
# Clear existing data
self._vectors.clear()
self._id_list.clear()
# Read vectors
for _ in range(num_vectors):
vec_id = np.frombuffer(f.read(4), dtype=np.uint32)[0]
vec_data = f.read(self.packed_dim)
self._vectors[int(vec_id)] = vec_data
self._id_list.append(int(vec_id))
logger.info(
f"Loaded binary index from {self.binary_path} "
f"({len(self._vectors)} vectors)"
)
return True
except StorageError:
raise
except Exception as e:
raise StorageError(f"Failed to load Binary ANN index: {e}")
def count(self) -> int:
"""Get number of vectors in the index.
Returns:
Number of vectors currently in the index
"""
with self._lock:
return len(self._vectors)
@property
def is_loaded(self) -> bool:
"""Check if index has vectors.
Returns:
True if index has vectors, False otherwise
"""
with self._lock:
return len(self._vectors) > 0
def get_vector(self, vec_id: int) -> Optional[bytes]:
"""Get a specific vector by ID.
Args:
vec_id: Vector ID to retrieve
Returns:
Packed binary vector or None if not found
"""
with self._lock:
return self._vectors.get(vec_id)
def clear(self) -> None:
"""Clear all vectors from the index."""
with self._lock:
self._vectors.clear()
self._id_list.clear()
logger.debug("Cleared binary index")
def create_ann_index(
index_path: Path,
index_type: str = "hnsw",
dim: int = 2048,
**kwargs,
) -> ANNIndex | BinaryANNIndex:
"""Factory function to create an ANN index.
Args:
index_path: Path to database file
index_type: Type of index - "hnsw" for dense vectors, "binary" for binary vectors
dim: Vector dimension (default: 2048 for dense, 256 for binary)
**kwargs: Additional arguments passed to the index constructor
Returns:
ANNIndex for dense vectors or BinaryANNIndex for binary vectors
Raises:
ValueError: If index_type is invalid
Example:
>>> # Dense vector index (HNSW)
>>> dense_index = create_ann_index(path, index_type="hnsw", dim=2048)
>>> dense_index.add_vectors(ids, dense_vectors)
>>>
>>> # Binary vector index (Hamming distance)
>>> binary_index = create_ann_index(path, index_type="binary", dim=256)
>>> binary_index.add_vectors(ids, packed_vectors)
"""
index_type = index_type.lower()
if index_type == "hnsw":
return ANNIndex(index_path=index_path, dim=dim, **kwargs)
elif index_type == "binary":
# Default to 256 for binary if not specified
if dim == 2048: # Default dense dim was used
dim = 256
return BinaryANNIndex(index_path=index_path, dim=dim, **kwargs)
else:
raise ValueError(
f"Invalid index_type: {index_type}. Must be 'hnsw' or 'binary'."
)

9
codex-lens/src/codexlens/semantic/vector_store.py
View File

@@ -29,10 +29,17 @@ except ImportError:
# Try to import ANN index (optional hnswlib dependency)
try:
from codexlens.semantic.ann_index import ANNIndex, HNSWLIB_AVAILABLE
from codexlens.semantic.ann_index import (
ANNIndex,
BinaryANNIndex,
create_ann_index,
HNSWLIB_AVAILABLE,
)
except ImportError:
HNSWLIB_AVAILABLE = False
ANNIndex = None
BinaryANNIndex = None
create_ann_index = None
logger = logging.getLogger(__name__)

162
codex-lens/src/codexlens/storage/migrations/migration_010_add_multi_vector_chunks.py Normal file
View File

@@ -0,0 +1,162 @@
"""
Migration 010: Add multi-vector storage support for cascade retrieval.
This migration introduces the chunks table with multi-vector support:
- chunks: Stores code chunks with multiple embedding types
- embedding: Original embedding for backward compatibility
- embedding_binary: 256-dim binary vector for coarse ranking (fast)
- embedding_dense: 2048-dim dense vector for fine ranking (precise)
The multi-vector architecture enables cascade retrieval:
1. First stage: Fast binary vector search for candidate retrieval
2. Second stage: Dense vector reranking for precision
"""
import logging
from sqlite3 import Connection
log = logging.getLogger(__name__)
def upgrade(db_conn: Connection) -> None:
"""
Adds chunks table with multi-vector embedding columns.
Creates:
- chunks: Table for storing code chunks with multiple embedding types
- idx_chunks_file_path: Index for efficient file-based lookups
Also migrates existing chunks tables by adding new columns if needed.
Args:
db_conn: The SQLite database connection.
"""
cursor = db_conn.cursor()
# Check if chunks table already exists
table_exists = cursor.execute(
"SELECT name FROM sqlite_master WHERE type='table' AND name='chunks'"
).fetchone()
if table_exists:
# Migrate existing table - add new columns if missing
log.info("chunks table exists, checking for missing columns...")
col_info = cursor.execute("PRAGMA table_info(chunks)").fetchall()
existing_columns = {row[1] for row in col_info}
if "embedding_binary" not in existing_columns:
log.info("Adding embedding_binary column to chunks table...")
cursor.execute(
"ALTER TABLE chunks ADD COLUMN embedding_binary BLOB"
)
if "embedding_dense" not in existing_columns:
log.info("Adding embedding_dense column to chunks table...")
cursor.execute(
"ALTER TABLE chunks ADD COLUMN embedding_dense BLOB"
)
else:
# Create new table with all columns
log.info("Creating chunks table with multi-vector support...")
cursor.execute(
"""
CREATE TABLE chunks (
id INTEGER PRIMARY KEY AUTOINCREMENT,
file_path TEXT NOT NULL,
content TEXT NOT NULL,
embedding BLOB,
embedding_binary BLOB,
embedding_dense BLOB,
metadata TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
"""
)
# Create index for file-based lookups
log.info("Creating index for chunks table...")
cursor.execute(
"""
CREATE INDEX IF NOT EXISTS idx_chunks_file_path
ON chunks(file_path)
"""
)
log.info("Migration 010 completed successfully")
def downgrade(db_conn: Connection) -> None:
"""
Removes multi-vector columns from chunks table.
Note: This does not drop the chunks table entirely to preserve data.
Only the new columns added by this migration are removed.
Args:
db_conn: The SQLite database connection.
"""
cursor = db_conn.cursor()
log.info("Removing multi-vector columns from chunks table...")
# SQLite doesn't support DROP COLUMN directly in older versions
# We need to recreate the table without the columns
# Check if chunks table exists
table_exists = cursor.execute(
"SELECT name FROM sqlite_master WHERE type='table' AND name='chunks'"
).fetchone()
if not table_exists:
log.info("chunks table does not exist, nothing to downgrade")
return
# Check if the columns exist before trying to remove them
col_info = cursor.execute("PRAGMA table_info(chunks)").fetchall()
existing_columns = {row[1] for row in col_info}
needs_migration = (
"embedding_binary" in existing_columns or
"embedding_dense" in existing_columns
)
if not needs_migration:
log.info("Multi-vector columns not present, nothing to remove")
return
# Recreate table without the new columns
log.info("Recreating chunks table without multi-vector columns...")
cursor.execute(
"""
CREATE TABLE chunks_backup (
id INTEGER PRIMARY KEY AUTOINCREMENT,
file_path TEXT NOT NULL,
content TEXT NOT NULL,
embedding BLOB,
metadata TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
"""
)
cursor.execute(
"""
INSERT INTO chunks_backup (id, file_path, content, embedding, metadata, created_at)
SELECT id, file_path, content, embedding, metadata, created_at FROM chunks
"""
)
cursor.execute("DROP TABLE chunks")
cursor.execute("ALTER TABLE chunks_backup RENAME TO chunks")
# Recreate index
cursor.execute(
"""
CREATE INDEX IF NOT EXISTS idx_chunks_file_path
ON chunks(file_path)
"""
)
log.info("Migration 010 downgrade completed successfully")

324
codex-lens/src/codexlens/storage/sqlite_store.py
View File

@@ -539,6 +539,27 @@ class SQLiteStore:
)
conn.execute("CREATE INDEX IF NOT EXISTS idx_rel_target ON code_relationships(target_qualified_name)")
conn.execute("CREATE INDEX IF NOT EXISTS idx_rel_source ON code_relationships(source_symbol_id)")
# Chunks table for multi-vector storage (cascade retrieval architecture)
# - embedding: Original embedding for backward compatibility
# - embedding_binary: 256-dim binary vector for coarse ranking
# - embedding_dense: 2048-dim dense vector for fine ranking
conn.execute(
"""
CREATE TABLE IF NOT EXISTS chunks (
id INTEGER PRIMARY KEY AUTOINCREMENT,
file_path TEXT NOT NULL,
content TEXT NOT NULL,
embedding BLOB,
embedding_binary BLOB,
embedding_dense BLOB,
metadata TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
"""
)
conn.execute("CREATE INDEX IF NOT EXISTS idx_chunks_file_path ON chunks(file_path)")
# Run migration for existing databases
self._migrate_chunks_table(conn)
conn.commit()
except sqlite3.DatabaseError as exc:
raise StorageError(f"Failed to initialize database schema: {exc}") from exc
@@ -650,3 +671,306 @@ class SQLiteStore:
conn.execute("VACUUM")
except sqlite3.DatabaseError:
pass
def _migrate_chunks_table(self, conn: sqlite3.Connection) -> None:
"""Migrate existing chunks table to add multi-vector columns if needed.
This handles upgrading existing databases that may have the chunks table
without the embedding_binary and embedding_dense columns.
"""
# Check if chunks table exists
table_exists = conn.execute(
"SELECT name FROM sqlite_master WHERE type='table' AND name='chunks'"
).fetchone()
if not table_exists:
# Table doesn't exist yet, nothing to migrate
return
# Check existing columns
cursor = conn.execute("PRAGMA table_info(chunks)")
columns = {row[1] for row in cursor.fetchall()}
# Add embedding_binary column if missing
if "embedding_binary" not in columns:
logger.info("Migrating chunks table: adding embedding_binary column")
conn.execute(
"ALTER TABLE chunks ADD COLUMN embedding_binary BLOB"
)
# Add embedding_dense column if missing
if "embedding_dense" not in columns:
logger.info("Migrating chunks table: adding embedding_dense column")
conn.execute(
"ALTER TABLE chunks ADD COLUMN embedding_dense BLOB"
)
def add_chunks(
self,
file_path: str,
chunks_data: List[Dict[str, Any]],
*,
embedding: Optional[List[List[float]]] = None,
embedding_binary: Optional[List[bytes]] = None,
embedding_dense: Optional[List[bytes]] = None,
) -> List[int]:
"""Add multiple chunks with multi-vector embeddings support.
This method supports the cascade retrieval architecture with three embedding types:
- embedding: Original dense embedding for backward compatibility
- embedding_binary: 256-dim binary vector for fast coarse ranking
- embedding_dense: 2048-dim dense vector for precise fine ranking
Args:
file_path: Path to the source file for all chunks.
chunks_data: List of dicts with 'content' and optional 'metadata' keys.
embedding: Optional list of dense embeddings (one per chunk).
embedding_binary: Optional list of binary embeddings as bytes (one per chunk).
embedding_dense: Optional list of dense embeddings as bytes (one per chunk).
Returns:
List of inserted chunk IDs.
Raises:
ValueError: If embedding list lengths don't match chunks_data length.
StorageError: If database operation fails.
"""
if not chunks_data:
return []
n_chunks = len(chunks_data)
# Validate embedding lengths
if embedding is not None and len(embedding) != n_chunks:
raise ValueError(
f"embedding length ({len(embedding)}) != chunks_data length ({n_chunks})"
)
if embedding_binary is not None and len(embedding_binary) != n_chunks:
raise ValueError(
f"embedding_binary length ({len(embedding_binary)}) != chunks_data length ({n_chunks})"
)
if embedding_dense is not None and len(embedding_dense) != n_chunks:
raise ValueError(
f"embedding_dense length ({len(embedding_dense)}) != chunks_data length ({n_chunks})"
)
# Prepare batch data
batch_data = []
for i, chunk in enumerate(chunks_data):
content = chunk.get("content", "")
metadata = chunk.get("metadata")
metadata_json = json.dumps(metadata) if metadata else None
# Convert embeddings to bytes if needed
emb_blob = None
if embedding is not None:
import struct
emb_blob = struct.pack(f"{len(embedding[i])}f", *embedding[i])
emb_binary_blob = embedding_binary[i] if embedding_binary is not None else None
emb_dense_blob = embedding_dense[i] if embedding_dense is not None else None
batch_data.append((
file_path, content, emb_blob, emb_binary_blob, emb_dense_blob, metadata_json
))
with self._lock:
conn = self._get_connection()
try:
# Get starting ID before insert
row = conn.execute("SELECT MAX(id) FROM chunks").fetchone()
start_id = (row[0] or 0) + 1
conn.executemany(
"""
INSERT INTO chunks (
file_path, content, embedding, embedding_binary,
embedding_dense, metadata
)
VALUES (?, ?, ?, ?, ?, ?)
""",
batch_data
)
conn.commit()
# Calculate inserted IDs
return list(range(start_id, start_id + n_chunks))
except sqlite3.DatabaseError as exc:
raise StorageError(
f"Failed to add chunks: {exc}",
db_path=str(self.db_path),
operation="add_chunks",
) from exc
def get_binary_embeddings(
self, chunk_ids: List[int]
) -> Dict[int, Optional[bytes]]:
"""Get binary embeddings for specified chunk IDs.
Used for coarse ranking in cascade retrieval architecture.
Binary embeddings (256-dim) enable fast approximate similarity search.
Args:
chunk_ids: List of chunk IDs to retrieve embeddings for.
Returns:
Dictionary mapping chunk_id to embedding_binary bytes (or None if not set).
Raises:
StorageError: If database query fails.
"""
if not chunk_ids:
return {}
with self._lock:
conn = self._get_connection()
try:
placeholders = ",".join("?" * len(chunk_ids))
rows = conn.execute(
f"SELECT id, embedding_binary FROM chunks WHERE id IN ({placeholders})",
chunk_ids
).fetchall()
return {row["id"]: row["embedding_binary"] for row in rows}
except sqlite3.DatabaseError as exc:
raise StorageError(
f"Failed to get binary embeddings: {exc}",
db_path=str(self.db_path),
operation="get_binary_embeddings",
) from exc
def get_dense_embeddings(
self, chunk_ids: List[int]
) -> Dict[int, Optional[bytes]]:
"""Get dense embeddings for specified chunk IDs.
Used for fine ranking in cascade retrieval architecture.
Dense embeddings (2048-dim) provide high-precision similarity scoring.
Args:
chunk_ids: List of chunk IDs to retrieve embeddings for.
Returns:
Dictionary mapping chunk_id to embedding_dense bytes (or None if not set).
Raises:
StorageError: If database query fails.
"""
if not chunk_ids:
return {}
with self._lock:
conn = self._get_connection()
try:
placeholders = ",".join("?" * len(chunk_ids))
rows = conn.execute(
f"SELECT id, embedding_dense FROM chunks WHERE id IN ({placeholders})",
chunk_ids
).fetchall()
return {row["id"]: row["embedding_dense"] for row in rows}
except sqlite3.DatabaseError as exc:
raise StorageError(
f"Failed to get dense embeddings: {exc}",
db_path=str(self.db_path),
operation="get_dense_embeddings",
) from exc
def get_chunks_by_ids(
self, chunk_ids: List[int]
) -> List[Dict[str, Any]]:
"""Get chunk data for specified IDs.
Args:
chunk_ids: List of chunk IDs to retrieve.
Returns:
List of chunk dictionaries with id, file_path, content, metadata.
Raises:
StorageError: If database query fails.
"""
if not chunk_ids:
return []
with self._lock:
conn = self._get_connection()
try:
placeholders = ",".join("?" * len(chunk_ids))
rows = conn.execute(
f"""
SELECT id, file_path, content, metadata, created_at
FROM chunks
WHERE id IN ({placeholders})
""",
chunk_ids
).fetchall()
results = []
for row in rows:
metadata = None
if row["metadata"]:
try:
metadata = json.loads(row["metadata"])
except json.JSONDecodeError:
pass
results.append({
"id": row["id"],
"file_path": row["file_path"],
"content": row["content"],
"metadata": metadata,
"created_at": row["created_at"],
})
return results
except sqlite3.DatabaseError as exc:
raise StorageError(
f"Failed to get chunks: {exc}",
db_path=str(self.db_path),
operation="get_chunks_by_ids",
) from exc
def delete_chunks_by_file(self, file_path: str) -> int:
"""Delete all chunks for a given file path.
Args:
file_path: Path to the source file.
Returns:
Number of deleted chunks.
Raises:
StorageError: If database operation fails.
"""
with self._lock:
conn = self._get_connection()
try:
cursor = conn.execute(
"DELETE FROM chunks WHERE file_path = ?",
(file_path,)
)
conn.commit()
return cursor.rowcount
except sqlite3.DatabaseError as exc:
raise StorageError(
f"Failed to delete chunks: {exc}",
db_path=str(self.db_path),
operation="delete_chunks_by_file",
) from exc
def count_chunks(self) -> int:
"""Count total chunks in store.
Returns:
Total number of chunks.
"""
with self._lock:
conn = self._get_connection()
row = conn.execute("SELECT COUNT(*) AS c FROM chunks").fetchone()
return int(row["c"]) if row else 0

320
codex-lens/tests/test_ann_index.py
View File

@@ -421,3 +421,323 @@ class TestSearchAccuracy:
recall = overlap / len(bf_chunk_ids) if bf_chunk_ids else 1.0
assert recall >= 0.8, f"ANN recall too low: {recall} (overlap: {overlap}, bf: {bf_chunk_ids}, ann: {ann_chunk_ids})"
class TestBinaryANNIndex:
"""Test suite for BinaryANNIndex class (Hamming distance-based search)."""
@pytest.fixture
def temp_db(self):
"""Create a temporary database file."""
with tempfile.TemporaryDirectory() as tmpdir:
yield Path(tmpdir) / "_index.db"
@pytest.fixture
def sample_binary_vectors(self):
"""Generate sample binary vectors for testing."""
import numpy as np
np.random.seed(42)
# 100 binary vectors of dimension 256 (packed as 32 bytes each)
binary_unpacked = (np.random.rand(100, 256) > 0.5).astype(np.uint8)
packed = [np.packbits(v).tobytes() for v in binary_unpacked]
return packed, binary_unpacked
@pytest.fixture
def sample_ids(self):
"""Generate sample IDs."""
return list(range(1, 101))
def test_create_binary_index(self, temp_db):
"""Test creating a new Binary ANN index."""
from codexlens.semantic.ann_index import BinaryANNIndex
index = BinaryANNIndex(temp_db, dim=256)
assert index.dim == 256
assert index.packed_dim == 32
assert index.count() == 0
assert not index.is_loaded
def test_invalid_dimension(self, temp_db):
"""Test that invalid dimensions are rejected."""
from codexlens.semantic.ann_index import BinaryANNIndex
# Dimension must be divisible by 8
with pytest.raises(ValueError, match="divisible by 8"):
BinaryANNIndex(temp_db, dim=255)
with pytest.raises(ValueError, match="positive"):
BinaryANNIndex(temp_db, dim=0)
def test_add_packed_vectors(self, temp_db, sample_binary_vectors, sample_ids):
"""Test adding packed binary vectors to the index."""
from codexlens.semantic.ann_index import BinaryANNIndex
packed, _ = sample_binary_vectors
index = BinaryANNIndex(temp_db, dim=256)
index.add_vectors(sample_ids, packed)
assert index.count() == 100
assert index.is_loaded
def test_add_numpy_vectors(self, temp_db, sample_binary_vectors, sample_ids):
"""Test adding unpacked numpy binary vectors."""
from codexlens.semantic.ann_index import BinaryANNIndex
import numpy as np
_, unpacked = sample_binary_vectors
index = BinaryANNIndex(temp_db, dim=256)
index.add_vectors_numpy(sample_ids, unpacked)
assert index.count() == 100
def test_search_packed(self, temp_db, sample_binary_vectors, sample_ids):
"""Test searching with packed binary query."""
from codexlens.semantic.ann_index import BinaryANNIndex
packed, _ = sample_binary_vectors
index = BinaryANNIndex(temp_db, dim=256)
index.add_vectors(sample_ids, packed)
# Search for the first vector - should find itself with distance 0
query = packed[0]
ids, distances = index.search(query, top_k=5)
assert len(ids) == 5
assert len(distances) == 5
# First result should be the query vector itself
assert ids[0] == 1
assert distances[0] == 0 # Hamming distance of 0 (identical)
def test_search_numpy(self, temp_db, sample_binary_vectors, sample_ids):
"""Test searching with unpacked numpy query."""
from codexlens.semantic.ann_index import BinaryANNIndex
packed, unpacked = sample_binary_vectors
index = BinaryANNIndex(temp_db, dim=256)
index.add_vectors(sample_ids, packed)
# Search for the first vector using numpy interface
query = unpacked[0]
ids, distances = index.search_numpy(query, top_k=5)
assert len(ids) == 5
assert ids[0] == 1
assert distances[0] == 0
def test_search_batch(self, temp_db, sample_binary_vectors, sample_ids):
"""Test batch search with multiple queries."""
from codexlens.semantic.ann_index import BinaryANNIndex
packed, _ = sample_binary_vectors
index = BinaryANNIndex(temp_db, dim=256)
index.add_vectors(sample_ids, packed)
# Search for first 3 vectors
queries = packed[:3]
results = index.search_batch(queries, top_k=5)
assert len(results) == 3
# Each result should find itself first
for i, (ids, dists) in enumerate(results):
assert ids[0] == i + 1
assert dists[0] == 0
def test_hamming_distance_ordering(self, temp_db):
"""Test that results are ordered by Hamming distance."""
from codexlens.semantic.ann_index import BinaryANNIndex
import numpy as np
index = BinaryANNIndex(temp_db, dim=256)
# Create vectors with known Hamming distances from a query
query = np.zeros(256, dtype=np.uint8) # All zeros
v1 = np.zeros(256, dtype=np.uint8) # Distance 0
v2 = np.zeros(256, dtype=np.uint8); v2[:10] = 1 # Distance 10
v3 = np.zeros(256, dtype=np.uint8); v3[:50] = 1 # Distance 50
v4 = np.ones(256, dtype=np.uint8) # Distance 256
index.add_vectors_numpy([1, 2, 3, 4], np.array([v1, v2, v3, v4]))
query_packed = np.packbits(query).tobytes()
ids, distances = index.search(query_packed, top_k=4)
assert ids == [1, 2, 3, 4]
assert distances == [0, 10, 50, 256]
def test_save_and_load(self, temp_db, sample_binary_vectors, sample_ids):
"""Test saving and loading binary index from disk."""
from codexlens.semantic.ann_index import BinaryANNIndex
packed, _ = sample_binary_vectors
# Create and save index
index1 = BinaryANNIndex(temp_db, dim=256)
index1.add_vectors(sample_ids, packed)
index1.save()
# Check that file was created
binary_path = temp_db.parent / f"{temp_db.stem}_binary_vectors.bin"
assert binary_path.exists()
# Load in new instance
index2 = BinaryANNIndex(temp_db, dim=256)
loaded = index2.load()
assert loaded is True
assert index2.count() == 100
assert index2.is_loaded
# Verify search still works
query = packed[0]
ids, distances = index2.search(query, top_k=5)
assert ids[0] == 1
assert distances[0] == 0
def test_load_nonexistent(self, temp_db):
"""Test loading when index file doesn't exist."""
from codexlens.semantic.ann_index import BinaryANNIndex
index = BinaryANNIndex(temp_db, dim=256)
loaded = index.load()
assert loaded is False
assert not index.is_loaded
def test_remove_vectors(self, temp_db, sample_binary_vectors, sample_ids):
"""Test removing vectors from the index."""
from codexlens.semantic.ann_index import BinaryANNIndex
packed, _ = sample_binary_vectors
index = BinaryANNIndex(temp_db, dim=256)
index.add_vectors(sample_ids, packed)
# Remove first 10 vectors
index.remove_vectors(list(range(1, 11)))
assert index.count() == 90
# Removed vectors should not be findable
query = packed[0]
ids, _ = index.search(query, top_k=100)
for removed_id in range(1, 11):
assert removed_id not in ids
def test_get_vector(self, temp_db, sample_binary_vectors, sample_ids):
"""Test retrieving a specific vector by ID."""
from codexlens.semantic.ann_index import BinaryANNIndex
packed, _ = sample_binary_vectors
index = BinaryANNIndex(temp_db, dim=256)
index.add_vectors(sample_ids, packed)
# Get existing vector
vec = index.get_vector(1)
assert vec == packed[0]
# Get non-existing vector
vec = index.get_vector(9999)
assert vec is None
def test_clear(self, temp_db, sample_binary_vectors, sample_ids):
"""Test clearing all vectors from the index."""
from codexlens.semantic.ann_index import BinaryANNIndex
packed, _ = sample_binary_vectors
index = BinaryANNIndex(temp_db, dim=256)
index.add_vectors(sample_ids, packed)
assert index.count() == 100
index.clear()
assert index.count() == 0
assert not index.is_loaded
def test_search_empty_index(self, temp_db):
"""Test searching an empty index."""
from codexlens.semantic.ann_index import BinaryANNIndex
import numpy as np
index = BinaryANNIndex(temp_db, dim=256)
query = np.packbits(np.zeros(256, dtype=np.uint8)).tobytes()
ids, distances = index.search(query, top_k=5)
assert ids == []
assert distances == []
def test_update_existing_vector(self, temp_db):
"""Test updating an existing vector with new data."""
from codexlens.semantic.ann_index import BinaryANNIndex
import numpy as np
index = BinaryANNIndex(temp_db, dim=256)
# Add initial vector
v1 = np.zeros(256, dtype=np.uint8)
index.add_vectors_numpy([1], v1.reshape(1, -1))
# Update with different vector
v2 = np.ones(256, dtype=np.uint8)
index.add_vectors_numpy([1], v2.reshape(1, -1))
# Count should still be 1
assert index.count() == 1
# Retrieved vector should be the updated one
stored = index.get_vector(1)
expected = np.packbits(v2).tobytes()
assert stored == expected
class TestCreateAnnIndexFactory:
"""Test suite for create_ann_index factory function."""
@pytest.fixture
def temp_db(self):
"""Create a temporary database file."""
with tempfile.TemporaryDirectory() as tmpdir:
yield Path(tmpdir) / "_index.db"
@pytest.mark.skipif(
not _hnswlib_available(),
reason="hnswlib not installed"
)
def test_create_hnsw_index(self, temp_db):
"""Test creating HNSW index via factory."""
from codexlens.semantic.ann_index import create_ann_index, ANNIndex
index = create_ann_index(temp_db, index_type="hnsw", dim=384)
assert isinstance(index, ANNIndex)
assert index.dim == 384
def test_create_binary_index(self, temp_db):
"""Test creating binary index via factory."""
from codexlens.semantic.ann_index import create_ann_index, BinaryANNIndex
index = create_ann_index(temp_db, index_type="binary", dim=256)
assert isinstance(index, BinaryANNIndex)
assert index.dim == 256
def test_create_binary_index_default_dim(self, temp_db):
"""Test that binary index defaults to 256 dim when dense default is used."""
from codexlens.semantic.ann_index import create_ann_index, BinaryANNIndex
# When dim=2048 (dense default) is passed with binary type,
# it should auto-adjust to 256
index = create_ann_index(temp_db, index_type="binary")
assert isinstance(index, BinaryANNIndex)
assert index.dim == 256
def test_invalid_index_type(self, temp_db):
"""Test that invalid index type raises error."""
from codexlens.semantic.ann_index import create_ann_index
with pytest.raises(ValueError, match="Invalid index_type"):
create_ann_index(temp_db, index_type="invalid")
def test_case_insensitive_index_type(self, temp_db):
"""Test that index_type is case-insensitive."""
from codexlens.semantic.ann_index import create_ann_index, BinaryANNIndex
index = create_ann_index(temp_db, index_type="BINARY", dim=256)
assert isinstance(index, BinaryANNIndex)

241
codex-lens/tests/test_sqlite_store.py
View File

@@ -201,3 +201,244 @@ def test_add_files_rollback_failure_is_chained(
assert "boom" in caplog.text
finally:
store.close()
class TestMultiVectorChunks:
"""Tests for multi-vector chunk storage operations."""
def test_add_chunks_basic(self, tmp_path: Path) -> None:
"""Basic chunk insertion without embeddings."""
store = SQLiteStore(tmp_path / "chunks_basic.db")
store.initialize()
try:
chunks_data = [
{"content": "def hello(): pass", "metadata": {"type": "function"}},
{"content": "class World: pass", "metadata": {"type": "class"}},
]
ids = store.add_chunks("test.py", chunks_data)
assert len(ids) == 2
assert ids == [1, 2]
assert store.count_chunks() == 2
finally:
store.close()
def test_add_chunks_with_binary_embeddings(self, tmp_path: Path) -> None:
"""Chunk insertion with binary embeddings for coarse ranking."""
store = SQLiteStore(tmp_path / "chunks_binary.db")
store.initialize()
try:
chunks_data = [
{"content": "content1"},
{"content": "content2"},
]
# 256-bit binary = 32 bytes
binary_embs = [b"\x00" * 32, b"\xff" * 32]
ids = store.add_chunks(
"test.py", chunks_data, embedding_binary=binary_embs
)
assert len(ids) == 2
retrieved = store.get_binary_embeddings(ids)
assert len(retrieved) == 2
assert retrieved[ids[0]] == b"\x00" * 32
assert retrieved[ids[1]] == b"\xff" * 32
finally:
store.close()
def test_add_chunks_with_dense_embeddings(self, tmp_path: Path) -> None:
"""Chunk insertion with dense embeddings for fine ranking."""
store = SQLiteStore(tmp_path / "chunks_dense.db")
store.initialize()
try:
chunks_data = [{"content": "content1"}, {"content": "content2"}]
# 2048 floats = 8192 bytes
dense_embs = [b"\x00" * 8192, b"\xff" * 8192]
ids = store.add_chunks(
"test.py", chunks_data, embedding_dense=dense_embs
)
assert len(ids) == 2
retrieved = store.get_dense_embeddings(ids)
assert len(retrieved) == 2
assert retrieved[ids[0]] == b"\x00" * 8192
assert retrieved[ids[1]] == b"\xff" * 8192
finally:
store.close()
def test_add_chunks_with_all_embeddings(self, tmp_path: Path) -> None:
"""Chunk insertion with all embedding types."""
store = SQLiteStore(tmp_path / "chunks_all.db")
store.initialize()
try:
chunks_data = [{"content": "full test"}]
embedding = [[0.1, 0.2, 0.3]]
binary_embs = [b"\xab" * 32]
dense_embs = [b"\xcd" * 8192]
ids = store.add_chunks(
"test.py",
chunks_data,
embedding=embedding,
embedding_binary=binary_embs,
embedding_dense=dense_embs,
)
assert len(ids) == 1
binary = store.get_binary_embeddings(ids)
dense = store.get_dense_embeddings(ids)
assert binary[ids[0]] == b"\xab" * 32
assert dense[ids[0]] == b"\xcd" * 8192
finally:
store.close()
def test_add_chunks_length_mismatch_raises(self, tmp_path: Path) -> None:
"""Mismatched embedding length should raise ValueError."""
store = SQLiteStore(tmp_path / "chunks_mismatch.db")
store.initialize()
try:
chunks_data = [{"content": "a"}, {"content": "b"}]
with pytest.raises(ValueError, match="embedding_binary length"):
store.add_chunks(
"test.py", chunks_data, embedding_binary=[b"\x00" * 32]
)
with pytest.raises(ValueError, match="embedding_dense length"):
store.add_chunks(
"test.py", chunks_data, embedding_dense=[b"\x00" * 8192]
)
with pytest.raises(ValueError, match="embedding length"):
store.add_chunks(
"test.py", chunks_data, embedding=[[0.1]]
)
finally:
store.close()
def test_get_chunks_by_ids(self, tmp_path: Path) -> None:
"""Retrieve chunk data by IDs."""
store = SQLiteStore(tmp_path / "chunks_get.db")
store.initialize()
try:
chunks_data = [
{"content": "def foo(): pass", "metadata": {"line": 1}},
{"content": "def bar(): pass", "metadata": {"line": 5}},
]
ids = store.add_chunks("test.py", chunks_data)
retrieved = store.get_chunks_by_ids(ids)
assert len(retrieved) == 2
assert retrieved[0]["content"] == "def foo(): pass"
assert retrieved[0]["metadata"]["line"] == 1
assert retrieved[1]["content"] == "def bar(): pass"
assert retrieved[1]["file_path"] == "test.py"
finally:
store.close()
def test_delete_chunks_by_file(self, tmp_path: Path) -> None:
"""Delete all chunks for a file."""
store = SQLiteStore(tmp_path / "chunks_delete.db")
store.initialize()
try:
store.add_chunks("a.py", [{"content": "a1"}, {"content": "a2"}])
store.add_chunks("b.py", [{"content": "b1"}])
assert store.count_chunks() == 3
deleted = store.delete_chunks_by_file("a.py")
assert deleted == 2
assert store.count_chunks() == 1
deleted = store.delete_chunks_by_file("nonexistent.py")
assert deleted == 0
finally:
store.close()
def test_get_embeddings_empty_list(self, tmp_path: Path) -> None:
"""Empty chunk ID list returns empty dict."""
store = SQLiteStore(tmp_path / "chunks_empty.db")
store.initialize()
try:
assert store.get_binary_embeddings([]) == {}
assert store.get_dense_embeddings([]) == {}
assert store.get_chunks_by_ids([]) == []
finally:
store.close()
def test_add_chunks_empty_list(self, tmp_path: Path) -> None:
"""Empty chunks list returns empty IDs."""
store = SQLiteStore(tmp_path / "chunks_empty_add.db")
store.initialize()
try:
ids = store.add_chunks("test.py", [])
assert ids == []
assert store.count_chunks() == 0
finally:
store.close()
def test_chunks_table_migration(self, tmp_path: Path) -> None:
"""Existing chunks table gets new columns via migration."""
db_path = tmp_path / "chunks_migration.db"
# Create old schema without multi-vector columns
conn = sqlite3.connect(db_path)
conn.execute(
"""
CREATE TABLE chunks (
id INTEGER PRIMARY KEY AUTOINCREMENT,
file_path TEXT NOT NULL,
content TEXT NOT NULL,
embedding BLOB,
metadata TEXT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
"""
)
conn.execute("CREATE INDEX idx_chunks_file_path ON chunks(file_path)")
conn.execute(
"INSERT INTO chunks (file_path, content) VALUES ('old.py', 'old content')"
)
conn.commit()
conn.close()
# Open with SQLiteStore - should migrate
store = SQLiteStore(db_path)
store.initialize()
try:
# Verify new columns exist by using them
ids = store.add_chunks(
"new.py",
[{"content": "new content"}],
embedding_binary=[b"\x00" * 32],
embedding_dense=[b"\x00" * 8192],
)
assert len(ids) == 1
# Old data should still be accessible
assert store.count_chunks() == 2
# New embeddings should work
binary = store.get_binary_embeddings(ids)
assert binary[ids[0]] == b"\x00" * 32
finally:
store.close()