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#!/usr/bin/env python3
import sys
import subprocess
from pathlib import Path
from typing import List
import json
from tqdm import tqdm
from sentence_transformers import SentenceTransformer
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document
from langchain.embeddings.base import Embeddings
from langchain_community.vectorstores import FAISS
def load_settings(path: Path):
if not path.exists():
print(f"Settings file not found: {path}", file=sys.stderr)
sys.exit(1)
return json.loads(path.read_text(encoding='utf-8'))
def clone_repo(repo_url: str, local_path: Path) -> None:
if not local_path.exists():
print(f"Cloning repo {repo_url} into {local_path}...")
subprocess.run(["git", "clone", repo_url, str(local_path)], check=True)
else:
print(f"Repository already exists at {local_path}")
def extract_repo_files(repo_path: Path) -> List[Document]:
docs: List[Document] = []
allowed_extensions = {'.cs', '.cpp', '.c', '.h', '.hpp'}
all_files = [p for p in repo_path.rglob('*') if p.is_file() and p.suffix in allowed_extensions]
for path in tqdm(all_files, desc="Reading repo files"):
try:
text = path.read_text(encoding='utf-8', errors='ignore')
docs.append(Document(page_content=text, metadata={'source': str(path)}))
except Exception as e:
print(f"Warning: could not read {path}: {e}", file=sys.stderr)
return docs
def build_embeddings_index(
repo_path: Path,
index_path: Path,
embed_model_name: str
) -> None:
splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=64)
raw_docs = extract_repo_files(repo_path)
chunks: List[Document] = []
for doc in raw_docs:
splits = splitter.split_text(doc.page_content)
for chunk_text in splits:
chunks.append(Document(page_content=chunk_text, metadata=doc.metadata))
embedder = SentenceTransformer(embed_model_name)
class BTEmbeddings(Embeddings):
def embed_documents(self, texts: List[str]) -> List[List[float]]:
return embedder.encode(texts, show_progress_bar=True)
def embed_query(self, text: str) -> List[float]:
return embedder.encode([text])[0]
embedding = BTEmbeddings()
if not index_path.exists():
print("Building FAISS index...")
vectorstore = FAISS.from_documents(chunks, embedding)
vectorstore.save_local(str(index_path))
print("FAISS index built and saved.")
else:
print(f"FAISS index already exists at {index_path}.")
def main():
# Configuration
BASE_DIR = Path(__file__).resolve().parent
SETTINGS_PATH = BASE_DIR.parent / 'settings.json'
# Load settings
settings = load_settings(SETTINGS_PATH)
EMBED_MODEL = settings['embed_model']
OUT_DIR = BASE_DIR.parent / 'data' / 'rag'
OUT_DIR.mkdir(parents=True, exist_ok=True)
repo_url = settings['repository']
local_repo = OUT_DIR / 'repo'
vector_index_path = OUT_DIR / 'faiss_index'
clone_repo(repo_url, local_repo)
build_embeddings_index(local_repo, vector_index_path, EMBED_MODEL)
if __name__ == '__main__':
main()
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