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6e212c2 50dcddc 762a6f5 6e212c2 4982575 6e212c2 4982575 6e212c2 50dcddc 4982575 50dcddc 762a6f5 6e212c2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | import os
from contextlib import asynccontextmanager
from fastapi import FastAPI, UploadFile, File, HTTPException, BackgroundTasks
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from dotenv import load_dotenv
from backend.jobs import create_job, get_job, update_job
from backend.parser import parse_file, row_to_text, SUPPORTED_EXTENSIONS
from backend.embedder import embed_texts, BATCH_SIZE
from backend.vectordb import ensure_collection, upsert_points, search, list_source_files, get_all_vectors
from backend.intent_classifier import classify
load_dotenv()
@asynccontextmanager
async def lifespan(app: FastAPI):
ensure_collection() # Default collection
ensure_collection("intents")
yield
app = FastAPI(title="SemanticSearch API", lifespan=lifespan)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
# ---------------------------------------------------------------------------
# Background task
# ---------------------------------------------------------------------------
def _ingest(job_id: str, filename: str, content: bytes) -> None:
try:
update_job(job_id, status="running", message="Parsing file...")
rows = parse_file(filename, content)
total = len(rows)
update_job(job_id, total_rows=total, message=f"Parsed {total} rows. Embedding...")
texts = [row_to_text(r) for r in rows]
all_vectors: list[list[float]] = []
for i in range(0, total, BATCH_SIZE):
batch_texts = texts[i : i + BATCH_SIZE]
batch_vectors = embed_texts(batch_texts)
all_vectors.extend(batch_vectors)
update_job(
job_id,
processed_rows=min(i + BATCH_SIZE, total),
message=f"Embedded {min(i + BATCH_SIZE, total)}/{total} rows...",
)
update_job(job_id, message="Storing vectors...")
upsert_points(all_vectors, rows, source_file=filename)
update_job(job_id, status="done", progress=100, message="Done!")
except Exception as exc:
update_job(job_id, status="error", message="Failed.", error=str(exc))
# ---------------------------------------------------------------------------
# Routes
# ---------------------------------------------------------------------------
@app.post("/upload")
async def upload_file(background_tasks: BackgroundTasks, file: UploadFile = File(...)):
ext = "." + file.filename.rsplit(".", 1)[-1].lower()
if ext not in SUPPORTED_EXTENSIONS:
raise HTTPException(
status_code=400,
detail=f"Unsupported file type. Allowed: {', '.join(SUPPORTED_EXTENSIONS)}",
)
content = await file.read()
job = create_job()
background_tasks.add_task(_ingest, job.id, file.filename, content)
return {"job_id": job.id}
@app.get("/status/{job_id}")
def job_status(job_id: str):
job = get_job(job_id)
if not job:
raise HTTPException(status_code=404, detail="Job not found")
return {
"job_id": job.id,
"status": job.status,
"progress": job.progress,
"message": job.message,
"error": job.error,
"total_rows": job.total_rows,
"processed_rows": job.processed_rows,
}
class SearchRequest(BaseModel):
query: str
top_k: int = 10
@app.post("/search")
def semantic_search(req: SearchRequest):
if not req.query.strip():
raise HTTPException(status_code=400, detail="Query cannot be empty")
vectors = embed_texts([req.query])
results = search(vectors[0], top_k=req.top_k)
return {"results": results}
@app.get("/collections")
def get_collections(collection: str | None = None):
return {"files": list_source_files(collection=collection)}
@app.get("/vectors")
def all_vectors(collection: str | None = None):
points = get_all_vectors(collection=collection)
return {"points": points, "count": len(points)}
class EmbedRequest(BaseModel):
query: str
@app.post("/embed")
def embed_query(req: EmbedRequest):
if not req.query.strip():
raise HTTPException(status_code=400, detail="Query cannot be empty")
vectors = embed_texts([req.query])
return {"vector": vectors[0]}
class ClassifyRequest(BaseModel):
utterance: str
@app.post("/classify")
def classify_intent(req: ClassifyRequest):
if not req.utterance.strip():
raise HTTPException(status_code=400, detail="Utterance cannot be empty")
return classify(req.utterance)
@app.get("/health")
def health():
return {"status": "ok"}
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