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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
"""
importos, json, base64, logging, pathlib, boto3, numpyasnp
fromconcurrent.futuresimportThreadPoolExecutor, as_completed
fromtypingimportList
fromdotenvimportload_dotenv
fromopensearchpyimportOpenSearch, helpers, RequestsHttpConnection
fromrequests_aws4authimportAWS4Auth
fromsupabaseimportcreate_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()
forvarin ("SUPABASE_URL","SUPABASE_KEY","OS_ENDPOINT"):
ifnotglobals()[var]:
raiseRuntimeError(f"{var} is required")
# ─── 2. LOGGING & CLIENTS ───────────────────────────────────────────────
logging.basicConfig(level=LOG_LEVEL, format="%(asctime)s %(levelname)s %(message)s")
defget_log(code: str):
lg=logging.getLogger(f"embed[{code}]")
ifnotlg.handlers:
h=logging.StreamHandler()
h.setFormatter(logging.Formatter("%(asctime)s %(levelname)s [%(name)s] %(message)s"))
lg.addHandler(h)
lg.setLevel(LOG_LEVEL)
returnlg
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 ─────────────────────────────────────────────────────────
deflist_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"] forpginpagesforoinpg.get("Contents",[]) ifo["Key"].endswith(".jpg")]
deftitan_embed(b64s: List[str]) ->List[List[float]]:
out= []
forb64inb64s:
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)
returnout
defbulk_gen(idx: str, docs: List[dict]):
fordindocs:
yield {"index": {"_index": idx, "_id": d["_id"]}}
yieldd
defmark_video(code: str, **fields):
supabase.table("insta_content").update(fields).eq("code",code).execute()
# ─── 4. PER-VIDEO EMBED ─────────────────────────────────────────────────
defembed_video(platform: str, code: str):
log=get_log(code)
log.info("Starting embedding")
keys=list_frame_keys(platform, code)
ifnotkeys:
raiseRuntimeError(f"No frames for {code}")
video_id=f"{platform}.{code}"
frame_docs, vecs= [], []
foriinrange(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() forkinchunk]
embeds=titan_embed(b64s)
exceptExceptionase:
forkinchunk:
idx=int(pathlib.Path(k).stem)
supabase.table("embedding_errors").insert({"code":code,"frame":idx,"error":str(e)}).execute()
raise
fork, vinzip(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 ───────────────────────────────────────────────────
defalready_embedded(code: str) ->bool:
res=supabase.table("insta_content").select("is_embedded").eq("code",code).maybe_single().execute()
returnbool(res.dataandres.data.get("is_embedded"))
defprocess_record(rec: dict):
body=json.loads(rec.get("body","{}"))
platform, code=body.get("platform"), body.get("code")
ifnotplatformornotcode:
return {"code": None, "status":"invalid"}
ifalready_embedded(code):
return {"code":code, "status":"skipped"}
embed_video(platform, code)
return {"code":code, "status":"done"}
# ─── 6. LAMBDA HANDLER ──────────────────────────────────────────────────
deflambda_handler(event, _):
results= []
withThreadPoolExecutor(max_workers=CONCURRENCY_LIMIT) aspool:
forfutinas_completed([pool.submit(process_record,r) forrinevent.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}