repointel 0.1.1

Local-first codebase-aware AI: index any repo and ask questions about it.

RepoScope

A local-first tool for querying codebases in plain English. Point it at any repository, build an index, and ask questions — no cloud sync, no background services, no mandatory API keys.

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How it works

Without --embed (default):

repo → file discovery → chunking → JSON index → BM25 retrieval → LLM answer

With --embed (semantic search):

repo → file discovery → chunking → JSON index + .npy embeddings → BM25 + vector → RRF merge → LLM answer

RepoScope walks your project and breaks files into chunks — by function, class, or method boundary for Python, JS/TS, and C# (using regex-based detection), and by fixed line windows for everything else. Chunks are stored in a local JSON index and ranked with BM25 scoring at query time. If you run index --embed, it also generates sentence embeddings and merges the two results with Reciprocal Rank Fusion for better semantic matches.

If an LLM key is configured, ask feeds the top-ranked chunks to the model and returns a cited answer. If not, it falls back to showing the top matches with previews.

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Installation

Recommended — pipx (all platforms, including Windows)

pipx installs CLI tools into isolated environments and automatically puts the command on your PATH — no manual PATH configuration needed.

pipx install repointel

To add optional features:

pipx install "repointel[embed]"   # semantic search (downloads ~80 MB model on first use)
pipx install "repointel[claude]"  # Anthropic Claude for LLM answers
pipx install "repointel[gemini]"  # Google Gemini for LLM answers
pipx install "repointel[openai]"  # OpenAI for LLM answers and embeddings
pipx install "repointel[all]"     # everything

Install pipx itself with pip install pipx or brew install pipx (macOS).

Alternative — pip

pip install repointel

Windows note: pip installs the repointel script to your Python Scripts\ directory, which may not be on your PATH. If repointel is not found after installing, either add that directory to PATH or use the module fallback instead:

python -m repointel configure

To add optional features:

pip install "repointel[embed]"
pip install "repointel[claude]"
pip install "repointel[gemini]"
pip install "repointel[openai]"
pip install "repointel[all]"

From source

git clone https://github.com/nirakar24/reposcope.git
cd reposcope
pip install -e ".[all]"

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Setting up an LLM API key

index and search work with no API key. ask needs one to generate answers.

Interactive setup (recommended)

repointel configure

Prompts for your provider and key (input is hidden), then saves to ~/.config/repointel/.env. Picked up automatically in every session and directory from then on.

Which provider do you want to use for 'ask' answers?
  1) Anthropic Claude
  2) Google Gemini
  3) OpenAI

Enter 1, 2, or 3: 2

Google Gemini API key (input hidden):
Saved GEMINI_API_KEY to ~/.config/repointel/.env

Environment variable

Add to your shell profile (~/.bashrc, ~/.zshrc, etc.):

export ANTHROPIC_API_KEY="sk-ant-..."
export GEMINI_API_KEY="AIza..."
export OPENAI_API_KEY="sk-..."

.env file

Create a .env in your project directory or any parent. RepoScope walks up from the current directory automatically:

GEMINI_API_KEY=AIza...

Provider priority and model overrides

If multiple keys are present, the order is: Claude → Gemini → OpenAI.

Override the default model with environment variables:

REPOSCOPE_CLAUDE_MODEL=claude-sonnet-4-6
REPOSCOPE_GEMINI_MODEL=gemini-2.5-flash
REPOSCOPE_OPENAI_MODEL=gpt-4.1-mini

Where to get keys

Provider	Free tier	Key page
Google Gemini	Yes	https://aistudio.google.com/apikey
Anthropic Claude	No	https://console.anthropic.com
OpenAI	No	https://platform.openai.com/api-keys

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Quick start

# Index your project
repointel index /path/to/project

# Search (instant, no LLM)
repointel search "where is authentication handled"

# Ask a question (requires an LLM key)
repointel ask "how does the database schema relate to the API routes?"

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Commands

configure

Interactive first-time setup. Saves your LLM API key to ~/.config/repointel/.env.

repointel configure

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index

Walks a directory, chunks its files, and writes a JSON index.

repointel index /path/to/project

Add --embed to generate sentence embeddings alongside the index. Once present, search and ask automatically switch to hybrid retrieval — no extra flag needed at query time.

repointel index /path/to/project --embed

Use --index-file (before the subcommand) to control where the index is written. Useful for keeping separate indexes per project:

repointel --index-file .reposcope/backend.json index ./backend --embed

The default path is .reposcope/index.json in the current directory.

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search

Retrieves the most relevant chunks for a query. Instant — no network call.

Uses BM25 by default. Automatically switches to hybrid BM25 + vector search if embeddings exist for the current index.

repointel search "JWT token validation"
repointel search "database migration" --top-k 5
repointel search "controller routes" --json

Flag	Default	Description
--top-k	8	Number of results to return
--json	off	Emit results as a JSON array

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ask

Retrieves top chunks and sends them to an LLM for a cited answer.

repointel ask "how does authentication work?"
repointel ask "what entities exist in the database?" --top-k 12

Falls back to listing top matches with text previews if no LLM key is set.

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stats

Prints a breakdown of the current index.

repointel stats

{
  "files_indexed": 72,
  "chunks_indexed": 428,
  "languages": { "csharp": 160, "javascript": 25, "json": 221 },
  "kinds": { "method": 117, "block": 247, "class": 25 },
  "embeddings": ".reposcope/index.npy"
}

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Multiple projects

Use --index-file to maintain separate indexes. The flag goes before the subcommand.

repointel --index-file .reposcope/frontend.json index ./frontend
repointel --index-file .reposcope/backend.json  index ./backend

repointel --index-file .reposcope/frontend.json ask "how is routing configured?"
repointel --index-file .reposcope/backend.json  ask "what database tables exist?"

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Optional REST API

pip install -e ".[api]"
uvicorn reposcope.api:app --reload

Method	Endpoint	Body
POST	/index	{ "path": "/abs/path/to/repo", "embed": false }
POST	/search	{ "query": "...", "top_k": 8 }
POST	/ask	{ "query": "...", "top_k": 8 }
GET	/stats	—

Docs at http://localhost:8000/docs.

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Supported languages

Language	Chunking
Python	Regex-based: splits at def / async def / class boundaries
JavaScript / TypeScript / JSX / TSX	Regex-based: splits at function, class, and arrow function boundaries
C#	Regex-based: splits at class, interface, record, struct, and method boundaries
SQL, Markdown, JSON, YAML, TOML, CSS, SCSS, HTML, Dockerfile, Makefile	Fixed 80-line windows with 15-line overlap

Files over 750 KB and generated lock files (package-lock.json, yarn.lock, pnpm-lock.yaml) are skipped.

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Ignored directories

node_modules, .git, dist, build, bin, obj, .venv, __pycache__, .next, .nuxt, coverage, target, temp, vendor, and other standard build/cache directories are excluded automatically.

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Roadmap

• Tree-sitter chunking for true AST-level boundaries (replacing the regex approach)
• Incremental re-indexing on file change
• Cross-repo index merging for monorepos
• Qdrant / Chroma backend for repositories with >50k chunks

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License

MIT