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# api.py
from __future__ import annotations
import os
import json
import logging
import time
import shutil
from models import OptimizeRequest, QARequest, AutotuneRequest
from fastapi import FastAPI, HTTPException, UploadFile, File, Form
from fastapi.middleware.cors import CORSMiddleware
try:
from ragmint.autotuner import AutoRAGTuner
from ragmint.qa_generator import generate_validation_qa
from ragmint.explainer import explain_results
from ragmint.leaderboard import Leaderboard
from ragmint.tuner import RAGMint
except Exception as e:
AutoRAGTuner = None
generate_validation_qa = None
explain_results = None
Leaderboard = None
RAGMint = None
_import_error = e
else:
_import_error = None
from dotenv import load_dotenv
load_dotenv()
# Logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("ragmint_mcp_server")
# FastAPI app (exported for mounting)
app = FastAPI(title="Ragmint MCP Server", version="0.1.0")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Use repo-local data folder (not parent dirs)
DEFAULT_DATA_DIR = "data/docs"
LEADERBOARD_STORAGE = "experiments/leaderboard.jsonl"
# ensure folders exist
os.makedirs(DEFAULT_DATA_DIR, exist_ok=True)
os.makedirs("experiments", exist_ok=True)
@app.get("/health")
def health():
return {
"status": "ok",
"ragmint_imported": _import_error is None,
"import_error": str(_import_error) if _import_error else None,
}
@app.post("/upload_docs")
async def upload_docs(
docs_path: str = Form(...),
files: list[UploadFile] = File(...)
):
os.makedirs(docs_path, exist_ok=True)
saved_files = []
for file in files:
file_path = os.path.join(docs_path, file.filename)
with open(file_path, "wb") as f:
shutil.copyfileobj(file.file, f)
saved_files.append(file.filename)
return {"status": "ok", "uploaded_files": saved_files, "docs_path": docs_path}
@app.post("/optimize_rag")
def optimize_rag(req: OptimizeRequest):
logger.info("Received optimize_rag request: %s", req.json())
if RAGMint is None:
raise HTTPException(status_code=500, detail=f"Ragmint imports failed: {_import_error}")
docs_path = req.docs_path or DEFAULT_DATA_DIR
if not os.path.isdir(docs_path):
raise HTTPException(status_code=400, detail=f"docs_path does not exist: {docs_path}")
try:
rag = RAGMint(
docs_path=docs_path,
retrievers=req.retriever,
embeddings=req.embedding_model,
rerankers=(req.rerankers or ["mmr"]),
chunk_sizes=req.chunk_sizes,
overlaps=req.overlaps,
strategies=req.strategy,
)
# validation set handling
validation_set = None
validation_choice = (req.validation_choice or "").strip()
default_val_path = os.path.join(docs_path, "validation_qa.json")
if not validation_choice:
if os.path.exists(default_val_path):
validation_set = default_val_path
logger.info("Using default validation set: %s", validation_set)
else:
logger.warning("No validation_choice provided and no default found.")
validation_set = None
elif "/" in validation_choice and not os.path.exists(validation_choice):
validation_set = validation_choice
logger.info("Using HF dataset as validation: %s", validation_set)
elif os.path.exists(validation_choice):
validation_set = validation_choice
logger.info("Using local validation dataset: %s", validation_set)
elif validation_choice.lower() == "generate":
gen_path = os.path.join(docs_path, "validation_qa.json")
generate_validation_qa(
docs_path=docs_path,
output_path=gen_path,
llm_model=req.llm_model if hasattr(req, "llm_model") else "gemini-2.5-flash-lite"
)
validation_set = gen_path
logger.info("Generated validation QA at: %s", validation_set)
start_time = time.time()
best, results = rag.optimize(
validation_set=validation_set,
metric=req.metric,
trials=req.trials,
search_type=req.search_type
)
elapsed = time.time() - start_time
run_id = f"opt_{int(time.time())}"
try:
corpus_stats = {
"num_docs": len(rag.documents),
"avg_len": sum(len(d.split()) for d in rag.documents) / max(1, len(rag.documents)),
"corpus_size": sum(len(d) for d in rag.documents),
}
except Exception:
corpus_stats = None
try:
if Leaderboard:
lb = Leaderboard()
lb.upload(
run_id=run_id,
best_config=best,
best_score=best.get("faithfulness", best.get("score", 0.0)),
all_results=results,
documents=os.listdir(docs_path),
model=best.get("embedding_model", req.embedding_model),
corpus_stats=corpus_stats,
)
except Exception:
logger.exception("Leaderboard persistence failed for optimize_rag")
return {
"status": "finished",
"run_id": run_id,
"elapsed_seconds": elapsed,
"best_config": best,
"results": results,
"corpus_stats": corpus_stats,
}
except Exception as exc:
logger.exception("optimize_rag failed")
raise HTTPException(status_code=500, detail=str(exc))
@app.post("/autotune_rag")
def autotune_rag(req: AutotuneRequest):
logger.info("Received autotune_rag request: %s", req.json())
if AutoRAGTuner is None or RAGMint is None:
raise HTTPException(status_code=500, detail=f"Ragmint autotuner/RAGMint imports failed: {_import_error}")
docs_path = req.docs_path or DEFAULT_DATA_DIR
if not os.path.isdir(docs_path):
raise HTTPException(status_code=400, detail=f"docs_path does not exist: {docs_path}")
try:
start_time = time.time()
tuner = AutoRAGTuner(docs_path=docs_path)
rec = tuner.recommend(embedding_model=req.embedding_model, num_chunk_pairs=req.num_chunk_pairs)
chunk_candidates = tuner.suggest_chunk_sizes(
model_name=rec.get("embedding_model"),
num_pairs=int(req.num_chunk_pairs),
step=20
)
chunk_sizes = sorted({c for c, _ in chunk_candidates})
overlaps = sorted({o for _, o in chunk_candidates})
rag = RAGMint(
docs_path=docs_path,
retrievers=[rec["retriever"]],
embeddings=[rec["embedding_model"]],
rerankers=["mmr"],
chunk_sizes=chunk_sizes,
overlaps=overlaps,
strategies=[rec["strategy"]],
)
validation_set = None
validation_choice = (req.validation_choice or "").strip()
default_val_path = os.path.join(docs_path, "validation_qa.jsonl")
if not validation_choice:
if os.path.exists(default_val_path):
validation_set = default_val_path
else:
validation_set = None
elif "/" in validation_choice and not os.path.exists(validation_choice):
validation_set = validation_choice
elif os.path.exists(validation_choice):
validation_set = validation_choice
elif validation_choice.lower() == "generate":
gen_path = os.path.join(docs_path, "validation_qa.json")
generate_validation_qa(
docs_path=docs_path,
output_path=gen_path,
llm_model=req.llm_model if hasattr(req, "llm_model") else "gemini-2.5-flash-lite",
)
validation_set = gen_path
best, results = rag.optimize(
validation_set=validation_set,
metric=req.metric,
search_type=req.search_type,
trials=req.trials,
)
elapsed = time.time() - start_time
run_id = f"autotune_{int(time.time())}"
try:
corpus_stats = {
"num_docs": len(rag.documents),
"avg_len": sum(len(d.split()) for d in rag.documents) / max(1, len(rag.documents)),
"corpus_size": sum(len(d) for d in rag.documents),
}
except Exception:
corpus_stats = None
try:
if Leaderboard:
lb = Leaderboard()
lb.upload(
run_id=run_id,
best_config=best,
best_score=best.get("faithfulness", best.get("score", 0.0)),
all_results=results,
documents=os.listdir(docs_path),
model=best.get("embedding_model", rec.get("embedding_model")),
corpus_stats=corpus_stats,
)
except Exception:
logger.exception("Leaderboard persistence failed for autotune_rag")
return {
"status": "finished",
"run_id": run_id,
"elapsed_seconds": elapsed,
"recommendation": rec,
"chunk_candidates": chunk_candidates,
"best_config": best,
"results": results,
"corpus_stats": corpus_stats,
}
except Exception as exc:
logger.exception("autotune_rag failed")
raise HTTPException(status_code=500, detail=str(exc))
@app.post("/generate_validation_qa")
def generate_qa(req: QARequest):
logger.info("Received generate_validation_qa request: %s", req.json())
if generate_validation_qa is None:
raise HTTPException(status_code=500, detail=f"Ragmint imports failed: {_import_error}")
try:
out_path = os.path.join("data", "docs", "validation_qa.json")
os.makedirs(os.path.dirname(out_path), exist_ok=True)
generate_validation_qa(
docs_path=req.docs_path,
output_path=out_path,
llm_model=req.llm_model,
batch_size=req.batch_size,
min_q=req.min_q,
max_q=req.max_q,
)
with open(out_path, "r", encoding="utf-8") as f:
data = json.load(f)
return {"status": "finished", "output_path": out_path, "preview_count": len(data), "sample": data[:5]}
except Exception as exc:
logger.exception("generate_validation_qa failed")
raise HTTPException(status_code=500, detail=str(exc))
def start_api():
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info")
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