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#!/usr/bin/env python

"""

Serverless-compatible Lambda for embedding videos → OpenSearch

• Idempotent via logical _id

• No delete_by_query

• Bulk with refresh=False, then one final refresh

"""

import os, json, base64, logging, pathlib, boto3, numpy as np

from concurrent.futures import ThreadPoolExecutor, as_completed

from typing import List

from dotenv import load_dotenv

from opensearchpy import OpenSearch, helpers, RequestsHttpConnection

from requests_aws4auth import AWS4Auth

from supabase import create_client

load_dotenv()

# ─── 1. CONFIG ─────────────────────────────────────────────────────────

S3_BUCKET_FRAMES = os.getenv("S3_BUCKET_FRAMES", "oriane-contents")

MODEL_ID = os.getenv("MODEL_ID", "amazon.titan-embed-image-v1")

AWS_REGION = os.getenv("AWS_REGION", "us-east-1")

EMB_DIM = int(os.getenv("EMB_DIM", "1024"))

MAX_BATCH = int(os.getenv("MAX_FRAMES_PER_BATCH", "20"))

CONCURRENCY_LIMIT = int(os.getenv("CONCURRENCY_LIMIT", "4"))

SUPABASE_URL = os.getenv("SUPABASE_URL")

SUPABASE_KEY = os.getenv("SUPABASE_KEY")

OS_ENDPOINT = os.getenv("OS_ENDPOINT")

LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO").upper()

for var in ("SUPABASE_URL","SUPABASE_KEY","OS_ENDPOINT"):

if not globals()[var]:

raise RuntimeError(f"{var} is required")

# ─── 2. LOGGING & CLIENTS ───────────────────────────────────────────────

logging.basicConfig(level=LOG_LEVEL, format="%(asctime)s %(levelname)s %(message)s")

def get_log(code: str):

lg = logging.getLogger(f"embed[{code}]")

if not lg.handlers:

h = logging.StreamHandler()

h.setFormatter(logging.Formatter("%(asctime)s %(levelname)s [%(name)s] %(message)s"))

lg.addHandler(h)

lg.setLevel(LOG_LEVEL)

return lg

session = boto3.Session(region_name=AWS_REGION)

s3 = session.client("s3")

bedrock = session.client("bedrock-runtime")

supabase = create_client(SUPABASE_URL, SUPABASE_KEY)

creds = session.get_credentials()

auth = AWS4Auth(creds.access_key, creds.secret_key, AWS_REGION, "aoss", session_token=creds.token)

host = OS_ENDPOINT.split("://",1)[-1].rstrip("/")

os_client = OpenSearch(

hosts=[{"host": host, "port": 443}],

http_auth=auth,

use_ssl=True,

verify_certs=True,

connection_class=RequestsHttpConnection,

)

# ─── 3. HELPERS ─────────────────────────────────────────────────────────

def list_frame_keys(platform: str, code: str) -> List[str]:

pfx = f"{platform}/{code}/frames/"

pages = s3.get_paginator("list_objects_v2").paginate(Bucket=S3_BUCKET_FRAMES, Prefix=pfx)

return [o["Key"] for pg in pages for o in pg.get("Contents",[]) if o["Key"].endswith(".jpg")]

def titan_embed(b64s: List[str]) -> List[List[float]]:

out = []

for b64 in b64s:

body = json.dumps({"inputImage": b64, "embeddingConfig": {"outputEmbeddingLength": EMB_DIM}})

rsp = bedrock.invoke_model(modelId=MODEL_ID, body=body,

accept="application/json", contentType="application/json")

emb = json.loads(rsp["body"].read())["embedding"]

out.append(emb)

return out

def bulk_gen(idx: str, docs: List[dict]):

for d in docs:

yield {"index": {"_index": idx, "_id": d["_id"]}}

yield d

def mark_video(code: str, **fields):

supabase.table("insta_content").update(fields).eq("code",code).execute()

# ─── 4. PER-VIDEO EMBED ─────────────────────────────────────────────────

def embed_video(platform: str, code: str):

log = get_log(code)

log.info("Starting embedding")

keys = list_frame_keys(platform, code)

if not keys:

raise RuntimeError(f"No frames for {code}")

video_id = f"{platform}.{code}"

frame_docs, vecs = [], []

for i in range(0, len(keys), MAX_BATCH):

chunk = keys[i:i+MAX_BATCH]

try:

b64s = [base64.b64encode(s3.get_object(Bucket=S3_BUCKET_FRAMES,Key=k)["Body"].read()).decode() for k in chunk]

embeds = titan_embed(b64s)

except Exception as e:

for k in chunk:

idx = int(pathlib.Path(k).stem)

supabase.table("embedding_errors").insert({"code":code,"frame":idx,"error":str(e)}).execute()

raise

for k, v in zip(chunk, embeds):

fno = int(pathlib.Path(k).stem)

frame_docs.append({

"_id": f"{video_id}#{fno}",

"video_id": video_id,

"vector": v,

"platform": platform,

"code": code,

"frame": fno,

})

vecs.append(v)

# upsert frames batch

helpers.bulk(os_client, bulk_gen("video_frames", frame_docs), refresh=False)

# upsert summary

summary = {

"_id": video_id,

"video_id": video_id,

"vector": np.mean(vecs, axis=0).tolist(),

"platform": platform,

"code": code,

"frames": len(vecs),

}

helpers.bulk(os_client, bulk_gen("videos", [summary]), refresh=False)

mark_video(code, is_embedded=True)

log.info("Indexed %d frames", len(vecs))

# ─── 5. RECORD HANDLER ───────────────────────────────────────────────────

def already_embedded(code: str) -> bool:

res = supabase.table("insta_content").select("is_embedded").eq("code",code).maybe_single().execute()

return bool(res.data and res.data.get("is_embedded"))

def process_record(rec: dict):

body = json.loads(rec.get("body","{}"))

platform, code = body.get("platform"), body.get("code")

if not platform or not code:

return {"code": None, "status":"invalid"}

if already_embedded(code):

return {"code":code, "status":"skipped"}

embed_video(platform, code)

return {"code":code, "status":"done"}

# ─── 6. LAMBDA HANDLER ──────────────────────────────────────────────────

def lambda_handler(event, _):

results = []

with ThreadPoolExecutor(max_workers=CONCURRENCY_LIMIT) as pool:

for fut in as_completed([pool.submit(process_record,r) for r in event.get("Records",[])]):

results.append(fut.result())

# one final refresh

os_client.indices.refresh("video_frames")

os_client.indices.refresh("videos")

return {"status":"completed","results":results}

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