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import typer
from src.embedder import embed_documents
from src.retriever import search_documents
from src.generator import generate_commit_message, fallback_commit_message
from src.diff_analyzer import get_staged_diff_chunks
from src.config_loader import load_config
# Initialize Typer app
app = typer.Typer()
config = load_config("config.yaml")
print("Config loaded:", config)
# ===== CLI Commands =====
# `init` — Index Documents
@app.command()
def init(path: str = typer.Argument(...)):
embeddings = embed_documents(path, config)
if embeddings:
typer.echo(f"Documents indexed successfully: {len(embeddings)} documents")
else:
typer.echo("No documents were indexed.")
# `search` — Semantic Lookup
@app.command()
def search(query: str):
"""
Perform a semantic search on indexed documents.
Args:
query (str): The search query string.
"""
results = search_documents(query, config)
for i, result in enumerate(results):
typer.echo(f"[{i+1}] {result}")
# `ask` — Retrieval-Augmented Generation (RAG)
@app.command()
def ask(query: str):
"""
Perform a retrieval-augmented generation (RAG) process to answer a user query.
Args:
query (str): The user query string.
"""
context = search_documents(query, config)
prompt = "\n".join(context) + f"\n\nUser question: {query}"
try:
response = generate_commit_message(prompt, config)
except (
RuntimeError
): # Replace with the specific exception type raised by generate_commit_message
response = fallback_commit_message([])
typer.echo(response)
# `commit` — Git Diff + Message Generation
@app.command()
def commit(preview: bool = True, apply: bool = False, dry_run: bool = False):
"""
Generate a commit message based on staged Git diff chunks.
Args:
preview (bool): If True, display the suggested commit message.
apply (bool): If True, apply the commit with the generated message.
dry_run (bool): If True, display staged files and chunks without generating a message.
"""
file_list, diff_chunks = get_staged_diff_chunks()
if dry_run:
typer.echo(f"Files staged: {len(file_list)}")
typer.echo(f"Chunks: {len(diff_chunks)}")
typer.echo(
f"Estimated tokens: {sum(len(chunk.split()) for chunk in diff_chunks)}"
)
return
prompt = "\n".join(diff_chunks) + "\n\nGenerate a commit message:"
try:
message = generate_commit_message(prompt, config)
except Exception:
message = fallback_commit_message(file_list)
if preview:
typer.echo(f"Suggested commit message:\n{message}")
if apply:
import subprocess
subprocess.run(["git", "commit", "-m", message])
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("./models/embeddinggemma-300m")
emb = model.encode("Test string")
print("Embedding shape:", emb.shape) # Should be (768,)
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