[
  {
    "text": "The `-e` flag enables **classification mode**. Given comma-separated labels, sgrep classifies each chunk of text against all labels and outputs the best match with a confidence score.",
    "path": "src/pages/docs/classify.mdx",
    "anchor": "zero-shot-classification",
    "heading": "Zero-Shot Classification",
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  {
    "text": "When you use `-e \"label1,label2,label3\"`, sgrep: 1. Embeds all labels as semantic vectors 2. Chunks each input file (by line, paragraph, or whole file) 3. Embeds each chunk and computes cosine similarity against every label 4. Outputs the best-matching label + score for each chunk No training required — works with any labels on any text.",
    "path": "src/pages/docs/classify.mdx",
    "anchor": "how-it-works",
    "heading": "How It Works",
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  {
    "text": "sgrep -e \"bug,feature,refactor,docs\" CHANGELOG.md",
    "path": "src/pages/docs/classify.mdx",
    "anchor": "classify-each-line-of-a-file",
    "heading": "Classify each line of a file",
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    "text": "sgrep -e \"error,warning,info\" app.log --chunk-size 5 ```",
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    "text": "Short labels like \"auth\" may not match \"authentication\" in pure semantic mode. Use `--hybrid` to add keyword matching: ```bash",
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    "text": "Identify different types of code patterns: ```bash",
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    "text": "sgrep -e \"unit test\" src/ ```",
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    "text": "Classify log messages by severity: ```bash",
    "path": "src/pages/docs/classify.mdx",
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    "heading": "Sentiment Analysis",
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    "text": "sgrep -e \"critical failure\" app.log | jq 'select(.score > 0.7)'",
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  {
    "text": "sgrep -e \"warning message\" app.log | jq 'select(.score > 0.6)' ``` > **Tip**: Combine with `jq` for powerful filtering: > ```bash > sgrep -e \"security issue\" src/ | jq 'select(.score > 0.75)' > ```",
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    "text": "Each line outputs a JSON object: ```json ``` - `score`: Float between 0 and 1, higher means more relevant - `text`: The original line that was classified",
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  {
    "text": "find src -name \"*.rs\" | xargs cat | sgrep -e \"authentication\" | jq -r '.text' | sort -u",
    "path": "src/pages/docs/classify.mdx",
    "anchor": "find-all-authentication-related-code",
    "heading": "Find all authentication-related code",
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  {
    "text": "git log --oneline | sgrep -e \"performance improvement\" ```",
    "path": "src/pages/docs/classify.mdx",
    "anchor": "classify-git-commits-by-type",
    "heading": "Classify git commits by type",
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    "text": "Set a minimum similarity threshold: ```bash",
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  {
    "text": "sgrep -e \"security vulnerability\" src/ | jq 'select(.score > 0.7)'",
    "path": "src/pages/docs/classify.mdx",
    "anchor": "only-show-high-confidence-matches-0-7",
    "heading": "Only show high-confidence matches (0.7+)",
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    "text": "sgrep -e \"documentation\" src/ | jq 'select(.score **Warning**: The `-e` flag reads input line-by-line. For multi-line context, consider using `--json` mode instead.",
    "path": "src/pages/docs/classify.mdx",
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    "text": "| Feature | `-e` | `--json` | |---------|------|----------| | Purpose | Classification | Search with metadata | | Output | One JSON per line | Single JSON array | | Scores | Always included | Optional | | Context | Single line | Full match context | Use `-e` when you want to **classify** text. Use `--json` when you want to **search** with structured output.",
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    "text": "Find all error handling patterns: ```bash sgrep \"error handling and recovery\" src/ ```",
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  {
    "text": "Find functions that do similar things: ```bash sgrep \"convert string to number\" src/ --threshold 0.5 ```",
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    "text": "```bash sgrep \"setup instructions\" README.md QUICKSTART.md docs/ ```",
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    "text": "Find related code across your entire project: ```bash sgrep \"recursively walk subdirectories\" src/",
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    "text": "Classify text chunks against labels: ```bash sgrep -e \"bug,feature,refactor,docs\" CHANGELOG.md ```",
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  {
    "text": "> **TL;DR** — Use `--json` to pipe sgrep results into jq, scripts, or other tools. Grep-compatible exit codes included.",
    "path": "src/pages/docs/json-output.mdx",
    "anchor": "json-output",
    "heading": "JSON Output",
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    "text": "```bash sgrep --json \"parse configuration\" src/ ``` Output: ```json [ , ] ```",
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  {
    "text": "sgrep --json \"authentication\" src/ | jq -r '.[0].path' | xargs $EDITOR",
    "path": "src/pages/docs/json-output.mdx",
    "anchor": "open-the-best-match-in-your-editor",
    "heading": "Open the best match in your editor",
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    "text": "sgrep --json \"database query\" src/ | jq -r '.[].path' | xargs rg \"SELECT\" ```",
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    "text": "sgrep follows grep conventions: | Code | Meaning | |------|---------| | `0` | Matches found | | `1` | No matches above threshold | | `2` | Error (bad args, missing files, etc.) | This means you can use sgrep in shell conditionals: ```bash if sgrep --threshold 0.8 \"security vulnerability\" src/ > /dev/null 2>&1; then echo \"Potential security-related code found\" fi ```",
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    "text": "Each result object contains: | Field | Type | Description | |-------|------|-------------| | `path` | string | File path relative to search root | | `line` | number | Line number of the chunk start | | `score` | float | Cosine similarity (0.0–1.0) | | `content` | string | The matched text chunk |",
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  {
    "text": "sgrep Documentation Everything you need to know about semantic code search with sgrep. 🚀 Getting Started Install sgrep and run your first semantic search in under a minute. 📖 Examples Real-world usage patterns: code review, refactoring, documentation search. 🧠 Models Available embedding models — text, image, and cross-modal search. 🔀 Hybrid Search Combine semantic meaning with BM25 keyword precision. 📋 JSON Output Machine-readable output for pipelines, CI, and scripting. ⚙️ CLI Flags Complete reference for all command-line options. 📝 Changelog What's new, what changed, what's coming.",
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    "text": "sgrep ships with Model2Vec by default — an 8M parameter static embedding model with 256 dimensions. It runs on CPU, requires no GPU, and downloads as a 7.5MB int8-quantized binary.",
    "path": "src/pages/docs/models.mdx",
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  {
    "text": "| Alias | Model | Dims | Size | Notes | |-------|-------|------|------|-------| | `model2vec` (default) | minishlab/potion-base-8M | 256 | 7.5MB (int8) | Best balance of speed and quality | | `code-search` | henrikalbihn/sgrep-code-v1 | 256 | ~8MB (int8) | Fine-tuned for code search | | `bge-small` | BAAI/bge-small-en-v1.5 | 384 | 33MB | Higher quality, slower | | `clip` | openai/clip-vit-base-patch32 | 512 | 350MB | Text + image search | | `simple` | — | — | 0MB | No download, text-only heuristic (for testing) |",
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  {
    "text": "```bash sgrep --model bge-small \"query\" src/ sgrep --model code-search \"query\" src/ ```",
    "path": "src/pages/docs/models.mdx",
    "anchor": "switching-models",
    "heading": "Switching Models",
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    "text": "Model2Vec is a **static embedding** model — no attention layers, no transformer inference. Each token is looked up in an embedding table, then mean-pooled and normalized. This makes it: - **Fast**: sub-10ms for typical queries - **Small**: 7.5MB int8 vs 33MB+ for transformer models - **Offline**: no API calls, no network after first download",
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    "text": "All models are automatically quantized to int8 on download. This reduces size by ~4x with minimal quality loss: - f32 embeddings: ~30MB → int8: ~7.5MB - Quality impact: less than 1% on retrieval benchmarks - The quantization scale factor is stored in `config.json` alongside the model HuggingFace models that ship as f32 are quantized at download time — you don't need to do anything.",
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    "text": "| Feature | sgrep | grep | ripgrep | ag | ast-grep | CodeQL | |---------|-------|------|---------|----|----------|---------| | **Search Type** | Semantic | Text | Text | Text | AST | Semantic | | **Offline** | Yes | Yes | Yes | Yes | Yes | Partial | | **Zero Setup** | Yes | Yes | Yes | Yes | No | No | | **Binary Size** | ~8MB | ~300KB | ~1MB | ~500KB | ~10MB | SDK | | **Speed** | Fast | Fast | Fastest | Fast | Medium | Slow | | **Language Agnostic** | Yes | Yes | Yes | Yes | No* | No | | **Cross-language** | Yes | Yes | Yes | Yes | Yes | No | | **Understanding Meaning** | Yes | No | No | No | Partial | Yes | | **JSON Output** | Yes | No | No | No | Yes | Yes | | **API Calls** | None | None | None | None | None | Optional | *ast-grep supports many languages but requires language-specific grammars.",
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    "text": "sgrep \"future improvements needed\" # Finds \"TODO\", \"FIXME\", etc. ```",
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    "text": "sgrep \"error handling\" # Finds \"Error\", \"Exception\", \"fail\", etc. ```",
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    "anchor": "but-misses-related-concepts",
    "heading": "But misses related concepts",
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    "text": "Early contender in fast grep tools. **Strengths:** - Faster than grep - Respects .gitignore - Clean output format **Weaknesses:** - Less actively maintained than ripgrep - No semantic search **Use when:** You have existing ag scripts or prefer its output format.",
    "path": "src/pages/docs/about/comparison.mdx",
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    "text": "AST-based code search with structural matching. **Strengths:** - Understands code structure - Pattern matching on AST nodes - Language-specific queries - Refactoring support **Weaknesses:** - Requires language parsers - Not truly semantic (matches structure, not meaning) - Heavier setup **Use when:** You need structural code matching or automated refactoring. ```bash",
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  {
    "text": "Powerful semantic analysis framework from GitHub. **Strengths:** - Deep semantic understanding - Taint analysis - Vulnerability detection - Extensive query library - Data flow analysis **Weaknesses:** - Heavy setup (database generation) - Slow (not for quick search) - Language-specific databases - Not ideal for ad-hoc queries **Use when:** You need deep security analysis or complex vulnerability scanning. ```bash",
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    "text": "sgrep \"security vulnerability\" src/ ```",
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    "text": "1. **You don't know exact terms** ```bash # Find database code without knowing ORM name sgrep \"database query\" src/ ``` 2. **Cross-language search** ```bash # Find auth code across Python, Rust, and TypeScript sgrep \"authentication\" src/ ``` 3. **Exploring unfamiliar codebases** ```bash # Understand what modules exist sgrep \"data processing\" src/ ``` 4. **Code review automation** ```bash # Check for similar patterns sgrep --json \"potential bug\" src/ | jq . ``` 5. **Quick semantic queries** ```bash # Faster than setting up CodeQL sgrep \"memory leak pattern\" src/ ```",
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  {
    "text": "| Task | Use This Tool | |------|---------------| | Exact string search | grep / ripgrep | | Regex matching | ripgrep | | Structural refactoring | ast-grep | | Security audit | CodeQL | | Performance profiling | ripgrep (faster) | | Semantic exploration | sgrep |",
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    "heading": "When to use other tools:",
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    "text": "The best approach often combines tools: ```bash",
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    "text": "sgrep \"sql injection\" src/ # quick scan codeql analyze src/ # thorough audit ```",
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    "text": "Traditional tools search for what you type. sgrep searches for what you mean. ``` Query: \"database connection\" grep finds: - \"database connection\" (exact match only) sgrep finds: - \"db.connect()\" - \"open_database()\" - \"ConnectionPool::new()\" - \"psql://localhost\" - \"MongoClient()\" ```",
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    "text": "No API calls, no rate limits, no data leaving your machine: ``` Model: ~7.5MB (downloaded once, cached locally) Inference: Runs on your CPU Privacy: Code never leaves your computer ```",
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    "text": "sgrep \"pattern\" path/",
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    "text": "On a codebase of 100K lines of code: | Operation | sgrep | ripgrep | CodeQL | |-----------|-------|---------|--------| | First run | 15s | 0.5s | 5min | | Subsequent (cached) | 10ms | 0.5s | 30s | | \"find auth code\" | Works | Misses many | Works | | \"function_name\" | Works | Works | Works | sgrep's strength is in semantic queries, not raw text search speed.",
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    "text": "- **grep**: The universal baseline - **ripgrep**: Fastest literal search - **ast-grep**: Structural code matching - **CodeQL**: Deep security analysis - **sgrep**: Fast semantic search Use sgrep when you need to search by meaning, not just text. Combine with other tools for the best workflow.",
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    "text": "sgrep transforms your search query into a semantic journey through your codebase. This document explains the complete pipeline from query to colored results.",
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  {
    "text": "When you run `sgrep \"authenticate user\" src/`, several steps occur:",
    "path": "src/pages/docs/about/how-it-works.mdx",
    "anchor": "query-pipeline",
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  {
    "text": "Your natural language query is tokenized using WordPiece: ``` \"authenticate user\" ↓ [\"authenticate\", \"user\"] ``` WordPiece breaks words into subword units, handling unknown words by splitting them: ``` \"authentication\" → [\"auth\", \"##ent\", \"##ication\"] ```",
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  {
    "text": "Each token is looked up in the pre-computed embedding table: ``` \"authenticate\" → [0.12, -0.34, 0.56, ...] (256 dimensions) \"user\" → [0.78, -0.12, 0.34, ...] ``` These embeddings are averaged (mean pooling) and normalized: ``` query_embedding = mean([token_embeddings]) / ||mean|| ``` Result: A single 256-dimensional vector representing your query's meaning.",
    "path": "src/pages/docs/about/how-it-works.mdx",
    "anchor": "step-2-query-embedding",
    "heading": "Step 2: Query Embedding",
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  {
    "text": "sgrep walks the directory tree to find files: ``` src/ ├── main.rs ├── auth/ │ ├── login.rs │ └── oauth.rs └── db/ └── connection.rs ``` Files are filtered by: - Extension (config language support) - `.gitignore` and `.ignore` files - Binary file detection",
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    "text": "For each line in discovered files, sgrep: 1. **Tokenizes** the line: `fn login(user: User) else } ``` Becomes 7 separate embeddings. **Pros:** Precise location, easier to scan **Cons:** May miss multi-line concepts",
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    "text": "Multiple lines grouped together (planned feature): ```rust fn authenticate_user(...) ```",
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    "text": "In `--hybrid` mode, BM25 keyword results are also cached: ```sql CREATE TABLE bm25_cache ( path TEXT, pattern TEXT, results TEXT, -- JSON array timestamp REAL ); ```",
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  {
    "text": "``` ~/.cache/sgrep/model/sgrep-code-v1/ ├── vocab.json # Tokenizer vocabulary (500KB) ├── embeddings.bin # Pre-computed token embeddings (7.5MB) └── tokenizer.json # WordPiece configuration (1MB) ```",
    "path": "src/pages/docs/about/how-it-works.mdx",
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  {
    "text": "Files are processed in parallel using Rayon: ```rust files.par_iter() .flat_map(|file| search_file(file, query)) .collect() ```",
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  {
    "text": "When available, SIMD instructions accelerate similarity computation: ```rust #[cfg(target_arch = \"x86_64\")] use std::arch::x86_64::*; unsafe fn cosine_simd(a: &[f32], b: &[f32]) -> f32 ```",
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  {
    "text": "Results are streamed, not stored all at once: ```rust search_results .take(top_k) .for_each(|result| print_result(result)); ```",
    "path": "src/pages/docs/about/how-it-works.mdx",
    "anchor": "result-batching",
    "heading": "Result Batching",
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  {
    "text": "```bash $ sgrep \"database query error\" src/ [1. Computing query embedding] \"database query error\" → tokenize → [\"database\", \"query\", \"error\"] → embed → mean([emb_db, emb_query, emb_error]) → normalize → [0.23, -0.45, 0.67, ...] [2. Scanning files] src/main.rs: skip (not matching pattern) src/db/postgres.rs: process... [3. Processing src/db/postgres.rs] Line 1: \"use postgres::Client;\" → embed → [0.12, 0.34, ...] → similarity = 0.32 → below threshold Line 42: \"conn.query(\\\"SELECT * FROM users\\\").await?\" → embed → [0.34, 0.56, ...] → similarity = 0.89 → match! [4. Displaying results] src/db/postgres.rs:42 conn.query(\"SELECT * FROM users\").await? ^^^^ bright red (query!) ``` The entire process typically completes in 10-50ms for medium codebases.",
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    "text": "sgrep combines several key technologies to deliver fast, offline semantic code search. This document explains the technical foundations.",
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    "text": "Traditional transformer models like BERT and CodeBERT run inference through deep neural networks at search time. This is computationally expensive and slow. **Model2Vec** takes a different approach: 1. **Pre-computation**: During model creation, the transformer runs on a massive vocabulary once, computing embeddings for every possible token. 2. **Lookup at runtime**: At search time, we simply look up pre-computed embeddings from a static table. This trades a small amount of quality for massive speed gains and smaller model size. ``` Transformer at search time: query → [attention layers] → embedding (~100ms per query) Model2Vec at search time: query → [tokenize] → [lookup table] → embedding (~1ms per query) ```",
    "path": "src/pages/docs/architecture.mdx",
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    "text": "Given a code snippet or query, sgrep produces a 256-dimensional embedding: ``` \"function authenticate user\" ``` **Step 1: Tokenization (WordPiece)** ``` [\"function\", \"authenticate\", \"user\"] ``` **Step 2: Embedding Lookup** Each token maps to a 256-dimensional vector: ``` \"function\" → [0.12, -0.34, 0.56, ...] (256 values) \"authenticate\" → [-0.23, 0.45, -0.67, ...] \"user\" → [0.78, -0.12, 0.34, ...] ``` **Step 3: Mean Pooling** Average all token embeddings: ``` embedding = mean(token_embeddings) = [0.22, -0.003, 0.08, ...] ``` **Step 4: L2 Normalization** Normalize to unit length for consistent similarity scores: ``` embedding = embedding / ||embedding|| ```",
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    "text": "To find similar code, we compute cosine similarity between query and document embeddings: ``` similarity(A, B) = (A · B) / (||A|| × ||B||) ``` Since embeddings are L2-normalized, this simplifies to: ``` similarity(A, B) = A · B ``` A single dot product operation: extremely fast on modern CPUs.",
    "path": "src/pages/docs/architecture.mdx",
    "anchor": "the-math",
    "heading": "The Math",
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    "text": "- **1.0**: Identical meaning (same direction in vector space) - **0.8-0.9**: Very similar (likely relevant) - **0.6-0.8**: Somewhat related (may be relevant) - **0.5-0.6**: Weakly related (edge cases) - **below 0.5**: Likely unrelated > **Note**: These thresholds vary by domain. Experiment with `--threshold` to find what works for your codebase.",
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    "text": "Floating point embeddings (f32) use 4 bytes per dimension: ``` 256 dimensions × 4 bytes = 1,024 bytes per vector ``` With 50k vocabulary tokens, that's about 50 MB just for embeddings.",
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  {
    "text": "We convert f32 vectors to int8 using a scale factor: ``` int8_value = round(float32_value / scale) ``` The scale factor is computed per embedding vector: ``` scale = max(abs(vector)) / 127 ```",
    "path": "src/pages/docs/architecture.mdx",
    "anchor": "int8-quantization",
    "heading": "Int8 Quantization",
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  {
    "text": "``` 256 dimensions × 1 byte = 256 bytes per vector (4x smaller than f32) ```",
    "path": "src/pages/docs/architecture.mdx",
    "anchor": "size-reduction",
    "heading": "Size Reduction",
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    "text": "When computing similarity, we convert back to f32: ``` float32_value = int8_value × scale ``` This maintains most of the quality while dramatically reducing memory usage and improving cache locality.",
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    "text": "| Aspect | f32 | int8 | |--------|-----|------| | Size | 1KB per vector | 256B per vector | | Precision | Full | Slight loss | | Speed | Good | Better (cache) | | Quality | Baseline | 2-5% MRR loss | For code search, this trade-off is well worth it.",
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    "text": "sgrep can be compiled to WebAssembly for browser-based search: ``` Rust source → wasm32-wasi target → .wasm file ```",
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  {
    "text": "``` sgrep.wasm: ~500KB compressed embeddings.bin: ~12MB compressed (int8 quantized) vocab.json: ~500KB tokenizer.json: ~1MB ``` Total download is under 8MB and cached permanently.",
    "path": "src/pages/docs/architecture.mdx",
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    "heading": "Wasm Size",
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    "text": "When available, sgrep uses Wasm SIMD for faster dot products: ```wasm // Pseudo-code f32x8.dot_product(a, b) -> single instruction for 8 values ``` This provides 2-4x speedup on supported browsers.",
    "path": "src/pages/docs/architecture.mdx",
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  {
    "text": "sgrep highlights tokens based on their contribution to the match. The color gradient uses a quadratic curve: ``` score ∈ [0, 1] t = score t² = normalized score ``` ``` rgb(r, g, b) where: r = 140 + (255 - 140) × t² g = 140 + (50 - 140) × t² b = 140 + (50 - 140) × t² ``` At t=0: gray (140, 140, 140) At t=1: red (255, 50, 50) The quadratic curve means: - Low similarity tokens stay gray - Medium tokens show slight red - High similarity tokens become bright red",
    "path": "src/pages/docs/architecture.mdx",
    "anchor": "score-gradient",
    "heading": "Score Gradient",
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  {
    "text": "Each token's contribution is computed as: ``` token_score = cosine_similarity(query_embedding, token_embedding) ``` Tokens are then normalized: ``` normalized = (score - min_score) / (max_score - min_score) ``` This ensures the gradient always spans the full range.",
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    "text": "sgrep caches embeddings in a local SQLite database using the `sqlite-vec` extension: ```sql CREATE TABLE embeddings ( path TEXT, line INTEGER, hash TEXT UNIQUE, embedding BLOB, -- int8 quantized mtime REAL ); CREATE VIRTUAL TABLE vec_index USING vec0( embedding float_vector(256) ); ```",
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  {
    "text": "Files are re-indexed when: 1. **mtime changes**: File modification time differs from cached value 2. **content hash differs**: SHA-256 of file content changed 3. **manual rebuild**: User runs `sgrep --rebuild-index`",
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  {
    "text": "``` ~/.cache/sgrep/ ├── embeddings.db # SQLite index ├── model/ │ ├── vocab.json │ ├── embeddings.bin │ └── tokenizer.json └── registry/ └── sgrep-code-v1/ ```",
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  {
    "text": "``` Tokenization: ~0.1ms per line Embedding lookup: ~0.05ms per token Similarity computation: ~0.001ms per comparison Total per query: ~5-50ms depending on codebase size ```",
    "path": "src/pages/docs/architecture.mdx",
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  {
    "text": "| Lines of Code | Index Time | Search Time | |---------------|------------|-------------| | 1K | ~1s | ~5ms | | 10K | ~5s | ~10ms | | 100K | ~30s | ~20ms | | 1M | ~5min | ~50ms | Index time is one-time. Subsequent searches use cached embeddings.",
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    "text": "``` Base binary: ~2MB Model (int8): ~7.5MB Per-file overhead: ~line_count × 256 bytes Typical usage: 50-100MB for large projects ``` Memory usage scales linearly with codebase size.",
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    "text": "Add to your `.bashrc` or `.zshrc`: ```bash",
    "path": "src/pages/docs/integrations/shell.mdx",
    "anchor": "bash-zsh",
    "heading": "Bash / Zsh",
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    "text": "alias sgh='sgrep --hybrid'",
    "path": "src/pages/docs/integrations/shell.mdx",
    "anchor": "hybrid-search-bm25-semantic",
    "heading": "Hybrid search (bm25 + semantic)",
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  {
    "text": "alias sgw='sgrep \"$(pwd)\"'",
    "path": "src/pages/docs/integrations/shell.mdx",
    "anchor": "search-current-directory-recursively",
    "heading": "Search current directory recursively",
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    "text": "alias sgj='sgrep --json' ```",
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  {
    "text": "Add to `~/.config/fish/config.fish`: ```fish",
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  {
    "text": "alias sg sgrep alias sgh \"sgrep --hybrid\"",
    "path": "src/pages/docs/integrations/shell.mdx",
    "anchor": "basic-aliases",
    "heading": "Basic aliases",
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  {
    "text": "abbr sgj sgrep --json abbr sgr \"sgrep --json | jq\"",
    "path": "src/pages/docs/integrations/shell.mdx",
    "anchor": "abbreviations-expand-on-space",
    "heading": "Abbreviations expand on space",
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    "text": "function sgw sgrep $argv (pwd) end ```",
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    "text": "def sgj [...args] ```",
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    "text": "Search semantically and browse results interactively: ```bash",
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    "text": "sgf() \" sgrep --json \"$query\" \"$dir\" | \\ jq -r '\"\\(.path)|\\(.line)|\\(.text)\"' | \\ fzf --delimiter=\"|\" \\ --preview='bat --color=always --highlight-line ' \\ --preview-window=+ -/3 } ```",
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    "text": "sgfe() --highlight-line \" ) [ -n \"$result\" ] && $ \"$result\" } ```",
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    "text": "sg-fzf() | head -100\" \\ --phony --query \"$query\" \\ --bind \"change:reload($RG_PREFIX || true)\" } ```",
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    "text": "sgrep \"authentication\" src/ | fzf --multi | xargs -r $",
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    "anchor": "edit-matching-files",
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    "text": "sgrep \"bug fix\" src/ | while read -r line; do vim \"+$(echo $line | cut -d: -f2)\" \"$(echo $line | cut -d: -f1)\" done ```",
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    "text": "sgrep --json \"error handling\" src/ | \\ jq -r '.path' | \\ sort | \\ uniq -c | \\ sort -rn",
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    "text": "sgrep --json \".\" src/ | \\ jq -r '.text' | \\ sort | \\ uniq -c | \\ sort -rn | \\ head -20 ```",
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    "text": "sgrep --json \"database\" src/ | \\ jq 'map(select(.score > 0.7))'",
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    "text": "sgrep --json \"change\" src/ | \\ jq -r '- \\(.path):\\(.line): \\(.text)' ```",
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  {
    "text": "sgrep \"function\" src/ | \\ while IFS=: read -r file line text; do echo \"$file:$line:$text\" bat --color=always \"$file\" --highlight-line \"$line\" done | less -R ```",
    "path": "src/pages/docs/integrations/shell.mdx",
    "anchor": "highlighted-preview-with-bat",
    "heading": "Highlighted preview with bat",
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  {
    "text": "for pattern in \"error\" \"warning\" \"todo\"; do echo \"=== $pattern ===\" sgrep \"$pattern\" src/ & done wait ```",
    "path": "src/pages/docs/integrations/shell.mdx",
    "anchor": "search-multiple-patterns-in-parallel",
    "heading": "Search multiple patterns in parallel",
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    "text": "sgrep --json \"security\" src/ | \\ jq 'group_by(.path) | map( ) | sort_by(.count) | reverse'",
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    "text": "sgrep --json \"deprecated\" src/ | \\ jq -r '\"\\(.path):\\(.line)\"' | \\ while IFS=: read -r file line; do sed -i \"$ d\" \"$file\" done",
    "path": "src/pages/docs/integrations/shell.mdx",
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    "heading": "Delete matching lines (careful!)",
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    "text": "sgrep --json \"temp file\" src/ | \\ jq -r '.path' | \\ sort -u | \\ xargs -I mv .bak ```",
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  {
    "text": "rg \"TODO\" src/ -l | xargs sgrep \"urgent task\"",
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  {
    "text": "Create `~/.zsh/completion/_sgrep`: ```zsh #compdef sgrep _sgrep() ```",
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    "heading": "Zsh Completion",
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  {
    "text": "Create `~/.config/fish/completions/sgrep.fish`: ```fish complete -c sgrep -f complete -c sgrep -l help -d \"Show help\" complete -c sgrep -l version -d \"Show version\" complete -c sgrep -l json -d \"Output JSON\" complete -c sgrep -l hybrid -d \"Hybrid BM25 + semantic search\" complete -c sgrep -l model -d \"Model to use\" -x -a \"sgrep-code-v1\" complete -c sgrep -l top-k -d \"Number of results\" -x complete -c sgrep -l threshold -d \"Score threshold\" -x complete -c sgrep -l context -d \"Lines of context\" -x ```",
    "path": "src/pages/docs/integrations/shell.mdx",
    "anchor": "fish-completion",
    "heading": "Fish Completion",
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  {
    "text": "sgrep \"pattern\" # Basic search sgrep --json \"pattern\" # JSON output sgrep --hybrid \"pattern\" # Hybrid search sgrep -e \"category\" **Tip**: Create a `~/scripts/` directory with your custom sgrep wrapper functions and add it to your PATH.",
    "path": "src/pages/docs/integrations/shell.mdx",
    "anchor": "essential-commands",
    "heading": "Essential commands",
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    "text": "sgrep fits naturally into continuous integration pipelines. Use semantic search to enforce code quality standards, detect patterns, and gate deployments.",
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    "text": "Leave a comment on pull requests when issues are found: ```yaml name: Code Review on: pull_request jobs: review: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Install sgrep run: curl -sSf https://sgrep.sh/install.sh | sh - name: Check for TODO comments id: scan run: | result=$(sgrep --json \"TODO or FIXME\" src/) echo \"result $GITHUB_OUTPUT echo \"$result\">$GITHUB_OUTPUT echo EOF>>$GITHUB_OUTPUT - uses: actions/github-script@v7 if: steps.scan.outputs.result != '[]' with: script: | const result = JSON.parse('$ }'); const body = result.map(r => `- **$ :$ **\\n $ `).join('\\n'); github.rest.issues.createComment( TODOs:\\n$ ` }) ```",
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  {
    "text": "```yaml stages: - test security: stage: test image: rust:latest before_script: - cargo install sgrep --git https://github.com/henrikalbihn/sgrep script: # Scan for sensitive patterns - | matches=$(sgrep --json \"password or credential or secret\" src/) if [ \"$matches\" != \"[]\" ]; then echo \"Security issues found:\" echo \"$matches\" | jq -r '.[] | \"\\(.path):\\(.line): \\(.text)\"' exit 1 fi allow_failure: false ```",
    "path": "src/pages/docs/integrations/ci.mdx",
    "anchor": "security-scanning",
    "heading": "Security Scanning",
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    "text": "Track code quality over time: ```yaml quality: stage: test script: - cargo install sgrep # Count complex functions (heuristic) - complex=$(sgrep --json \"complex function or nested logic\" src/ | jq 'length') - echo \"COMPLEX_FUNCTIONS=$complex\" >> metrics.txt - echo \"DETECTED_PATTERNS=$complex\" > metrics.env artifacts: reports: metrics: metrics.txt ```",
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    "heading": "Quality Metrics",
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    "text": "```groovy pipeline } stage('Code Scan') deprecated API uses\") } } } } } } ```",
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    "text": "The `--json` flag outputs structured data that's easy to parse: ```bash",
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  {
    "text": "count=$(sgrep --json \"pattern\" src/ | jq 'length')",
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    "anchor": "count-total-matches",
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    "text": "sgrep --json \"pattern\" src/ | jq -r '.[].path' | sort -u",
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    "text": "sgrep --json \"pattern\" src/ | jq -r '.[] | \"\\(.path):\\(.line)\"'",
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    "text": "public_funcs=$(grep -r \"^pub fn\" src/ | wc -l) doc_comments=$(sgrep --json \"public function documentation\" src/ | jq 'length') if [ \"$doc_comments\" -lt \"$public_funcs\" ]; then echo \"Some public functions lack documentation\" exit 1 fi ```",
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    "text": "sgrep --json \"deprecated or obsolete\" src/ | jq -r '.[] | \"\\(.path):\\(.line): \\(.text)\"' ```",
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    "text": "- Use `--json` output for efficient parsing - Cache the model directory for faster builds - Run sgrep on specific directories, not the entire repo - Combine with `jq` for powerful filtering and aggregation - Consider parallelizing scans for large codebases ```bash",
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    "text": "find src -type d | parallel -j4 'sgrep --json \"pattern\" ' ```",
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  {
    "text": "While sgrep doesn't have native editor plugins yet, its CLI-first design makes it easy to integrate with any editor through configuration. Below are setups for popular editors.",
    "path": "src/pages/docs/integrations/editors.mdx",
    "anchor": "editor-plugins",
    "heading": "Editor Plugins",
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  {
    "text": "Add semantic search tasks to your workspace: ```json \", \"$ \" ], \"problemMatcher\": [], \"presentation\": }, \", \"$ \" ], \"problemMatcher\": [] } ], \"inputs\": [ ] } ```",
    "path": "src/pages/docs/integrations/editors.mdx",
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    "heading": "tasks.json Configuration",
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    "text": "Add a keybinding to trigger sgrep quickly: ```json ```",
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  {
    "text": "Use with any terminal extension like: - **code-runner**: Configure custom command - **multi-command**: Chain sgrep with other tools - **output-colorizer**: Highlight results",
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  {
    "text": "Create a VS Code extension that uses Quick Pick: ```typescript // Extension that wraps sgrep const query = await vscode.window.showInputBox( ); if (query) \" $ `); } ```",
    "path": "src/pages/docs/integrations/editors.mdx",
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    "heading": "Quick Pick",
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    "text": "Add to your `init.lua`: ```lua -- Semantic search function vim.api.nvim_create_user_command('Sgrep', function(opts) local query = opts.args if query == '' then query = vim.fn.input('Search: ') end local file = vim.fn.expand('%:p') vim.cmd('term sgrep \"' .. query .. '\" ' .. file) end, ) -- Keybinding vim.keymap.set('n', ' sg', ':Sgrep ', ) ```",
    "path": "src/pages/docs/integrations/editors.mdx",
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  {
    "text": "Set sgrep as your grep program: ```vim \" In your .vimrc or init.vim set grepprg=sgrep\\ $* set grepformat=%f:%l:%m \" Usage :grep \"database query\" src/ \" Open quickfix automatically autocmd QuickFixCmdPost grep copen ```",
    "path": "src/pages/docs/integrations/editors.mdx",
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    "heading": "Using grepprg",
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    "text": "If you use Telescope.nvim: ```lua local telescope = require('telescope') local pickers = require('telescope.pickers') local finders = require('telescope.finders') local conf = require('telescope.config').values vim.api.nvim_create_user_command('TelescopeSgrep', function(opts) local query = opts.args if query == '' then query = vim.fn.input('Search: ') end pickers.new( , end, entry_maker = function(line) local ok, data = pcall(vim.json.decode, line) if ok and data.score then return end end }), sorter = conf.generic_sorter(), previewer = conf.grep_previewer(), }):find() end, ) ```",
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    "text": "```vim command! -nargs=1 FzfSgrep call fzf#run( ) ```",
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    "text": "Go to **Settings → Tools → External Tools** and add: **Name**: sgrep Search **Program**: `/path/to/sgrep` (or just `sgrep` if in PATH) **Arguments**: `$Prompt$ $FilePath$` **Working directory**: `$ProjectFileDir$`",
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    "text": "**Name**: sgrep Workspace **Program**: `sgrep` **Arguments**: `$Prompt$ $ProjectFileDir$` **Working directory**: `$ProjectFileDir$`",
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    "anchor": "external-tool-for-workspace",
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    "text": "Assign a keyboard shortcut in **Settings → Keymap**: Search for \"sgrep\" and bind to your preferred keys.",
    "path": "src/pages/docs/integrations/editors.mdx",
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    "heading": "Keyboard Shortcut",
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    "text": "Set up a file watcher to run sgrep on save: 1. Go to **Settings → Tools → File Watchers** 2. Add new watcher for your file type 3. Program: `sgrep` 4. Arguments: `\"potential bug\" $FilePath$` 5. Output: Parse with regex for highlighting",
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    "text": "```elisp (defun sgrep-search (query) \"Run sgrep with QUERY on current file.\" (interactive \"sSearch query: \") (let ((file (buffer-file-name))) (compile (format \"sgrep \\\"%s\\\" %s\" query file)))) ;; Keybinding (global-set-key (kbd \"C-c s\") 'sgrep-search) ```",
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    "text": "Create a transient menu for sgrep: ```elisp (require 'transient) (transient-define-prefix sgrep-transient () \"Sgrep commands\" [\"Sgrep\" [(\"s\" \"Search File\" sgrep-search-file) (\"w\" \"Search Workspace\" sgrep-search-workspace) (\"j\" \"JSON Output\" sgrep-json-search)]]) (global-set-key (kbd \"C-c g\") 'sgrep-transient) ```",
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  {
    "text": "If you use Consult: ```elisp (defun consult-sgrep (query) \"Search with sgrep using Consult.\" (interactive \"sSearch: \") (consult--grep \"sgrep\" (list \"sgrep\" \"--json\" query) nil)) (define-key global-map (kbd \"C-c g\") 'consult-sgrep) ```",
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    "text": "Parse sgrep JSON output in compilation mode: ```elisp (defun sgrep-compilation (query directory) (interactive \"sQuery: \\nDDirectory: \") (let ((buf (compile (format \"sgrep --json \\\"%s\\\" %s\" query directory)))) (with-current-buffer buf (setq-local compilation-error-regexp-alist '(sgrep)) (setq-local compilation-error-regexp-alist-alist '((sgrep \"^ ] } ```",
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  {
    "text": "Add a command to `Packages/User/Default.sublime-commands`: ```json [ } ] ```",
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    "anchor": "command-palette",
    "heading": "Command Palette",
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    "text": "define-command sgrep -params 1 % }",
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    "heading": "Define sgrep command",
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  },
  {
    "text": "map global user s ':sgrep ' -docstring 'sgrep search' ```",
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    "text": "Add to `~/.config/helix/config.toml`: ```toml [[keys]] key = \"space\" mode = \"n\" commands = [ \"] } ] ``` > **Tip**: Most editors support running external commands. If your editor isn't listed, check its documentation for \"external tools\" or \"shell commands.\"",
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    "text": "- **Int8 quantized model**: default model is now 7.5MB (down from 29MB f32) - **Auto-quantization**: f32/f16 models from HuggingFace are automatically quantized to int8 on download - **Token-level coloring**: search results highlight individual tokens with a relevance gradient (gray → red) - **WASM search demo**: live in-browser semantic search at sgrep.sh, powered by the same model compiled to WebAssembly - **Heading-chunked docs search**: ⌘K search links directly to the relevant section heading - **Context lines**: `-C`, `-B`, `-A` flags for showing surrounding lines (grep-compatible) - **Caching**: sqlite-vec index for instant repeated searches (`--index`, `--reindex`, `--no-cache`) - **Hybrid search**: combine BM25 keyword matching with semantic similarity (`--hybrid`, `--alpha`) - **Zero-shot classification**: label text chunks against arbitrary categories (`-e`) - **Code search model**: `sgrep-code-v1` fine-tuned for code search - **Mobile docs**: responsive layout with hamburger menu and semantic search - **curl installer**: `curl -fsSL sgrep.sh/install.sh | sh`",
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  {
    "text": "```bash curl -fsSL https://sgrep.sh/install.sh | sh ``` This downloads a pre-built binary for your platform (macOS, Linux) and installs it to `~/.local/bin`.",
    "path": "src/pages/docs/getting-started.mdx",
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    "heading": "Install",
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  {
    "text": "```bash sgrep \"handle authentication errors\" src/ ``` On first run, sgrep downloads an 8M parameter embedding model (~7.5MB, int8 quantized) from sgrep.sh and caches it in `~/.cache/sgrep/models/`. After that, everything runs offline.",
    "path": "src/pages/docs/getting-started.mdx",
    "anchor": "first-search",
    "heading": "First Search",
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  {
    "text": "1. Your query was converted to a 256-dimensional vector via Model2Vec 2. Every chunk of code in `src/` was also embedded 3. Results ranked by cosine similarity — highest meaning-match first",
    "path": "src/pages/docs/getting-started.mdx",
    "anchor": "what-just-happened",
    "heading": "What Just Happened?",
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    "text": "Combine semantic understanding with keyword precision: ```bash sgrep --hybrid \"database connection pool\" src/ ```",
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    "text": "```bash sgrep [FLAGS] ```",
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  {
    "text": "| Flag | Default | Description | |------|---------|-------------| | `--threshold` | 0.5 | Minimum similarity score (0.0–1.0) | | `--top-k` | 10 | Maximum results to show | | `--model` | auto | Embedding model alias | | `--hybrid` | off | Enable BM25 + semantic hybrid search | | `--alpha` | 0.7 | Hybrid blend (0.0 = pure BM25, 1.0 = pure semantic) | | `--json` | off | JSON output for piping | | `--color` | auto | Color mode: auto, always, never | | `-e, --classify` | — | Zero-shot classification labels (comma-separated) | | `-C, --context` | — | Show N lines of context before and after | | `-B, --before-context` | — | Show N lines before each result | | `-A, --after-context` | — | Show N lines after each result | | `--chunk-size` | — | Lines per chunk (overrides --granularity) | | `--granularity` | line | Chunking: line or paragraph | | `-r, --recursive` | off | Recursive directory search | | `-l, --filenames-only` | off | Show filenames only | | `-c, --count` | off | Count matches per file | | `--no-score` | off | Hide similarity scores | | `--no-filter` | off | Disable junk line filtering | | `--no-ignore` | off | Disable .gitignore filtering | | `--hidden` | off | Include hidden files | | `--files` | off | Search file paths instead of contents | | `--verbose` | off | Show backend, model, and timing info | | `--model-path` | — | Path to a local model directory | | `--dtype` | f32 | Neural model precision: f32 or f16 | | `--index` | `.sgrep/index.db` | Path to sqlite-vec inde",
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    "text": "sgrep --index /tmp/my-index.db \"query\" src/ ```",
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    "text": "> **TL;DR** — Combine semantic meaning with keyword precision. Best results when you know *some* of the words but not the exact phrasing. Pure semantic search excels when you don't know the exact terminology. But sometimes you *do* know a keyword — a function name, an error code, a library name. Hybrid mode gives you both.",
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    "text": "Hybrid search runs two searches in parallel and blends their scores: 1. **Semantic** — Model2Vec embeds your query and every code chunk, ranks by cosine similarity 2. **BM25** — built-in keyword matching with tf-idf scoring 3. **Fusion** — scores are combined with a configurable weight (default 70% semantic, 30% keyword)",
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    "text": "```bash sgrep --hybrid \"database connection pool\" src/ ```",
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    "text": "| Scenario | Mode | Why | |----------|------|-----| | Know the concept, not the code | Semantic (default) | No keywords to match | | Know some keywords + concept | **Hybrid** | Keywords boost precision | | Know exact string | Just use `grep`/`rg` | Exact match is faster |",
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  {
    "text": "Common issues and solutions when using sgrep.",
    "path": "src/pages/docs/troubleshooting.mdx",
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  {
    "text": "If you manually downloaded the `sgrep` binary on macOS, you may see a security warning from Gatekeeper. > **Note:** This only affects manual binary downloads. If you installed via `curl -fsSL https://sgrep.sh/install.sh | sh`, the installer handles this automatically.",
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  {
    "text": "xattr -d com.apple.quarantine $(which sgrep) 2>/dev/null codesign -s - -f $(which sgrep) ```",
    "path": "src/pages/docs/troubleshooting.mdx",
    "anchor": "remove-quarantine-flag-and-ad-hoc-sign",
    "heading": "Remove quarantine flag and ad-hoc sign",
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  {
    "text": "1. Try running `sgrep --version` in your terminal (this triggers the security block) 2. Open **System Settings** → **Privacy & Security** 3. Scroll down and click **\"Allow Anyway\"** next to the sgrep warning 4. Run `sgrep --version` again and click **\"Open Anyway\"**",
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    "heading": "GUI Fix",
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    "text": "On first run, sgrep downloads a 7.5MB model from sgrep.sh. If this fails: ```bash",
    "path": "src/pages/docs/troubleshooting.mdx",
    "anchor": "model-download-fails",
    "heading": "Model Download Fails",
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    "text": "curl -I https://sgrep.sh/model/embeddings.bin",
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    "anchor": "check-connectivity",
    "heading": "Check connectivity",
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  {
    "text": "rm -rf ~/.cache/sgrep/models/potion-base-8M sgrep \"test\" . ```",
    "path": "src/pages/docs/troubleshooting.mdx",
    "anchor": "force-re-download",
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  {
    "text": "If you're behind a corporate proxy, set the standard environment variables: ```bash export HTTP_PROXY=http://proxy.example.com:8080 export HTTPS_PROXY=http://proxy.example.com:8080 sgrep \"test\" . ```",
    "path": "src/pages/docs/troubleshooting.mdx",
    "anchor": "behind-a-proxy",
    "heading": "Behind a Proxy",
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    "text": "If sgrep returns no results when you expect matches:",
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    "heading": "No Results Found",
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    "text": "sgrep searches the paths you specify. Make sure you're pointing at the right directory: ```bash",
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    "text": "sgrep \"query\" src/ ```",
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  {
    "text": "Hybrid mode combines semantic search with keyword matching. If you're searching for concepts rather than specific keywords, use regular (non-hybrid) mode: ```bash",
    "path": "src/pages/docs/troubleshooting.mdx",
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    "heading": "\"No keyword matches found\"",
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    "text": "sgrep \"handle errors gracefully\" src/",
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    "text": "sgrep --hybrid \"ConnectionPool timeout\" src/ ```",
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    "text": "If results seem outdated after editing files: ```bash",
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    "heading": "Stale Results",
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    "text": "sgrep --reindex \"query\" src/",
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    "anchor": "force-re-embedding",
    "heading": "Force re-embedding",
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    "text": "sgrep --no-cache \"query\" src/ ```",
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    "heading": "Or skip cache entirely",
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    "text": "Short labels like \"auth\" may not match \"authentication\" in pure semantic mode. Use `--hybrid` for better matching with abbreviations: ```bash",
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    "text": "sgrep \"different query\" src/ ```",
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    "text": "For very large repos, you can limit the search scope: ```bash",
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  {
    "text": "sgrep --top-k 5 \"query\" src/ ```",
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    "heading": "Use --top-k to limit results",
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  {
    "text": "- **Docs:** sgrep.sh/docs - **GitHub Issues:** github.com/henrikalbihn/sgrep/issues - **Chat:** Use the chat assistant on any docs page (bottom-right bubble)",
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  {
    "text": "404 Page not found The page you're looking for doesn't exist. Maybe try searching for it? Home Docs $ sgrep \"what you're looking for\" docs/",
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    "text": "v0.1 — Pure Rust, zero dependencies Find code by meaning , not just text. Grep finds exact text. Ripgrep does it fast. sgrep finds what you mean . Zero API calls. Zero Python. One binary, one 8M parameter model. $ curl -fsSL https://sgrep.sh/install.sh | sh Get Started → View on GitHub 8M params model · Zero API calls · &lt;10ms search · Pure Rust $ curl -fsSL sgrep.sh/install.sh | sh Try it in your browser Real semantic search powered by sgrep compiled to WebAssembly. Same model, same results as the CLI. Live Demo → Why sgrep? Built for developers who think in concepts, not keywords. 🧠 Semantic Search Search by meaning, not keywords. Ask for \"recursively walk subdirectories\" and find recursive_dir_search . ✈️ Fully Offline No API keys. No network after first model download (~7.5MB, cached). Everything runs locally on your machine. ⚡ Instant Results Model2Vec embeddings in pure Rust via Candle. Sub-10ms search on most codebases. Faster than grep on cold cache. 🔀 Hybrid Mode Combine semantic + BM25 keyword search via ripgrep integration. Best of both worlds in one flag. 🦀 Pure Rust No Python. No Node. Single static binary. Cross-compile anywhere Rust runs. Zero runtime deps. 🔧 Pipeline-Ready JSON output, configurable thresholds, grep-compatible exit codes. Pipe it anywhere, script everything. How It Works Three steps. No configuration. No API keys. Just meaning. 1 Embed Your query and every code chunk get converted to 256-dimensional vectors via Model2Vec. 8M parameter mod",
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    "text": "Install sgrep One command. Zero dependencies. All platforms. OS ))} Architecture ARM64 x86_64 bash $ curl -fsSL https://sgrep.sh/install.sh | sh or cargo install sgrep Download Binary macOS (Apple Silicon) • sgrep-darwin-aarch64 Manual installation instructions $ chmod +x sgrep-darwin-aarch64 # Make executable $ sudo mv sgrep-darwin-aarch64 /usr/local/bin/sgrep # Move to PATH $ sgrep --version # Verify What happens next? 1. First search downloads the model (~7.5MB, cached) 2. After that, everything runs fully offline 3. sg alias is created automatically $ sgrep \"handle authentication\" src/ Need help? Check out the getting started guide or CLI reference . Download Started! Your binary is downloading now @keyframes bounceIn 50% 70% 100% } #success-checkmark.animate-bounce-in @keyframes float-1 50% } @keyframes float-2 50% } @keyframes float-3 50% } .animate-float-1 .animate-float-2 .animate-float-3 import confetti from 'canvas-confetti'; (window as any).confetti = confetti; const platformConfig = , x86_64: } }, linux: , aarch64: } }, windows: } } }; let currentOS = 'darwin'; let currentArch = 'aarch64'; let isAnimating = false; // Rate limiting const MAX_DOWNLOADS = 5; const RATE_WINDOW = 5 * 60 * 1000; function checkRateLimit() '); const now = Date.now(); const recent = data.t.filter(t => now - t = MAX_DOWNLOADS) return false; data.t = [...recent, now]; sessionStorage.setItem('sgrepDl', JSON.stringify(data)); return true; } function detectPlatform() ; } function updateArchButt",
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    "text": "Live demo — runs in your browser Try sgrep right now Real semantic search powered by sgrep compiled to WebAssembly. Same model, same algorithm as the CLI. No server, no API. Try these queries sgrep \"error handling\" sgrep \"install from source\" sgrep \"hybrid search\" What's happening Your query is tokenized, embedded into a 256-dim vector, then compared against all doc pages using cosine similarity. All in ~10ms. Same as the CLI This WASM build uses the same int8 quantized model and HuggingFace tokenizer as the Rust binary. Token-level coloring included.",
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