Qdrant import / collection not working

[tkreuder]

Details

curl -L -X POST 'http://localhost:6333/collections/my_imported_collection/points/search' -H 'Content-Type: application/json' \                                                                                                                                                                                      
                                                                                        --data-raw '{
                                                                                   "vector": [0.69,0.69,0.59,0.74,0.18,0.44,0.91,0.76,0.35,0.8,0.31,0.61,0.62,0.49,0.85,0.41,0.26,0.43,0.09,0.34,0.41,0.07,0.16,0.75,0.24,0.87,0.89,0.29,0.43,0.78,0.55,0.78,0.97,0.28,0.68,0.44,0.01,0.41,0.63,0.64,0.41,0.69,0.36,0.92,0.29,0.52,0.37,0.49,0.83,0.53,0.24,0.04,0.78,0.7,0.04,0.48,0.81,
0.33,0.39,0.24,0.26,0.68,0.26,0.02,0.69,0.38,0.76,0.67,0.65,0.83,0.28,0.98,0.59,0.49,0.67,0.42,0.4,0.11,0.1,0.94,0.89,0.45,0.73,0.87,0.76,0.7,0.06,0.45,0.95,0.27,0.3,0.84,0.11,0.06,0.6,0.94,0.56,0.68,0.99,0.33,0.22,0.71,0.49,0.6,0.84,0.63,0.71,0.1,0.96,0.59,0.9,0.92,0.5,0.03,0.65,0.39,0.96,0.72,0.87,0.02,0.38,0.61,0.91,0.34,0.95,0.47,0.82,0.81,0.47,0.31,0.72,0.27,0.88,0.47,0
.51,0.04,0.5,0.99,0.09,0.77,0.13,0.5,0.78,0.33,0.26,0.28,0.1,0.27,0.27,0.92,0.15,0.49,0.88,0.55,0.4,0.2,0.61,0.6,0.88,0.29,0.48,0.1,0.41,0.21,0.41,0.27,0.73,0.25,0.52,0.89,0.94,0.28,0.16,0.17,0.03,0.34,0.85,0.52,0.83,0.38,0.17,0.57,0.28,0.29,0.94,0.89,0.46,0.81,0.93,0.88,0.07,0.62,0.6,0.75,0.63,0.63,0.37,0.2,0.41,0.15,0.34,0.39,0.96,0.63,0.02,0.53,0.23,0.2,0.11,0.09,0.4,0.42
,0.64,0.68,0.78,0.13,0.45,0.47,0.32,0.95,0.66,0.41,0.02,0.22,0.16,0.1,0.51,0.31,0.3,0.41,0.85,0.36,0.85,0.27,0.26,0.96,0.73,0.62,0.99,0.96,0.47,0.3,0.78,0.53,0.46,0.24,0.39,0.58,0.64,0.4,0.68,0.02,0.86,0.04,0.38,0.11,0.69,0.9,0.4,0.88,0.14,0.96,0.65,0.74,0.32,0.59,0.83,0.22,0.91,0.55,0.7,0.87,0.23,0.19,0.94,0.98,0.16,0.86,0.76,0.18,0.43,0.18,0.69,0.07,0.31,0.52,0.93,0.91,0.9
7,0.32,0.15,0.98,0.23,0.36,0.48,0.18,0.56,0.77,0.21,0.87,0.65,0.1,0.3,0.52,0.57,0.9,0.65,0.62,0.94,0.96,0.33,0.24,0.89,0.02,0.75,0.77,0.1,0.75,0.84,0.49,0.15,0.19,0.37,0.12,0.2,0.56,0.99,0.44,0.74,0.08,0.52,0.36,0.07,0.07,0.78,0.8,0.39,0.79,0.58,0.16,0,0.46,0.38,0.05,0.26,0.18,0.27,0.21,0.57,0.07,0.86,0.54,0.31,0.25,0.14,0.56,0.14,0.98,0.06,0.14,0.76,0.6,0.93,0.58,0.9,0.18,0
.46,0.33,0.27,0.34,0.89,0.27,0.69,0.89,0.41,0.05,0.07,0.28,0.28,0.17,0.88,0.62,0.81,0.92,0.05,0.13,0.11,0.82,0.23,0.96,0.88,0.07,0.71,0.12,0.38,0.11,0.1,0.94,0.63,0.38,0.25,0.54,0.85,0.93,0.65,0.33,0.52,0.6,0.99,0.24,0.47,0.09,0.94,0.65,0.44,0.52,0.35,0.24,0.66,0.59,0.59,0.68,0.37,0.3,0.22,0.28,0.25,0.4,0.6,0.98,0.94,0.88,0.33,0.94,0.59,0.2,0.48,0.96,0.52,0.56,0.13,0.1,0.05,
0.14,0.97,0.14,0.35,0.67,0.36,0.22,0.58,0.29,0.85,0.07,0.18,0.77,0.5,0.13,0.51,0.11,0.92,0.53,0.34,0.85,0.63,0.7,0.07,0.31,0.12,0.64,0.47,0.56,0.17,0.54,0.68,0.95,0.2,0.3,0.12,0.42,0.83,0.42,0.23,0.34,0.7,0.66,0.77,0.15,0.97,0.22,0.26,0.6,0.99,0.67,0.1,0.82,0.03,0.72,0.8,0.88,0.59,0.71,0.77,0.65,0.88,0.59,0.6,0.24,0.22,0.08,0.17,0.69,0.34,0.63,0.24,0.94,0.17,0.12,0.08,0.12,0
.18,0.44,0.16,0.25,0.91,0.41,0.72,0.66,0.63,0.86,0.94,0.1,0.91,0.52,0.17,0.13,0.23,0.97,0.29,0.12,1,0.95,0.7,0.31,0.09,0.68,0.18,0.82,0.88,0.11,0.43,0.75,0.51,0.77,0.41,0.87,0.3,0.39,0.99,0.27,0.44,0.42,0.84,0.86,0.77,0.28,0.26,0.62,0.99,0.32,0.41,0.45,0.95,0.85,0.13,0.28,0.18,0.1,0.54,0.37,0.86,0.86,0.79,0.36,0.64,0.98,0.79,0.07,1,0.87,0.4,0.04,0.24,0.78,0.91,0.41,0.98,0.63
,0.73,0.49,0.35,0.55,0.87,0.24,0.26,0.14,0.67,0.1,0.97,0.09,0.44,0.04,0.87,0.24,0.4,0.49,0.32,0.5,0.44,0.91,0.57,0.67,0.39,0.22,0.81,0.28,0.14,0.73,0.93,0.27,0.63,0.67,0.06,0.89,0.8,0.82,0.09,0.81,0.32,0.16,0.19,0.71,0.03,0.84,0.19,0.89,0.83,0.52,0.61,0.79,0.2,0.54,0.64,0.13,0.46,0.24,0.15,0.84,0.31,0.43,0.52,0.4,0.49,0.32,0.63,0.05,0.69,0.67,0.48,0.94,0.89,0.02,0.28,0.4,0.3
4,0.34,0.03,0.02,0.65,0.85,0.78,0.72,0.47,0.67,0.32,0.03,0.11,0.27,0.8,0.59,0.58,0.83,0.92,0.87,0.95,0.61,0.06,0.72,0.13,0.63,0.47,0.59,0.1,0.95,0.75,0.37,0.63,0.99,0.58,0.1,0.37,0.8,0.36,0.05,0.92,0.76,0.41,0.99,0.03,0.06,0.09,0.28,0.86,0.99,0.69,0.52,0.62,0.79,0.33,0.12,0.7,0.58,0.56,0.02,0.46,0.77,0.88,0.09,0.37,0.51,0.87,0.45,0.5,0.45,0.42,0.43,0.64,0.66,0.13,0.94,0.35,0
.35,0.62,0.61,0.64,0.8,0.18,0.59,0.77,0.02,0.54,0.94,0.56,0.26,0.97,0.55,0.71,0.08,0.89,0.82,0.26,0.24,0.18,0.27,0.77,0.79,0.18,0.03,0.68,0.56,0.63,0.08,0.15,0.76,0.91,0.41,0.94,0.44,0.78,0.95,0.52,0.68,0.45,0.16,0.29,0.08,0.86,0.26,0.65,0.21,0.39,0.77,0.45,0.19,0.81,0.48,0.9,0.55,0.42,0.03,0.37,0.72,0.99,0.4,0.01,0.86,0.96,0.69,0.67,0.12,0.85,0.79,0.95,0.19,0.46,0.57,0.76,0
.82,0.63,0.51,0.86,0.29,0.05,0.24,0.42,0.99,0.13,0.55,0.73,0.63,0.24,0.94,0.67,0.34,0.79,0.47,0.89,0.98,0.98,0.99,0.32,0.93,0.42,0.95,0.74,0.64,0.33,0.98,0.64,0.35,0.91,0.4,0.9,0.97,0.22,0.82,0.65,0.47,0.61,0.94,0.24,0.42,0.64,0.24,0.63,0.17,0.39,0.42,0.31,0.84,0.34,0.41,0.78,0.13,0.02,0.63,0.87,0.58,0.37,0.62,0.31,0.9,0.02,0.47,0.12,0.39,0.84,0.96,0.55,0.24,0.21,0,0.96,0.56
,0.31,0.85,0.28,0.61,0.35,0.76,0.93,0.37,0.73,0.07,0.08,0.36,0.21,0.77,0.75,0.53,0.45,0.21,0.79,0.57,0.67,0.74,0.39,0.97,0.46,0.41,0.24,0.85,0,0.51,0.19,0.4,0.41,0.48,0.64,0.86,0.75,0.79,0.65,0.4,0.59,0.69,0.44,0.77,0.85,0.95,0.06,0.94,0.61,0.2,0.19,0.21,0.32,0.38,0.99,0.58,0.38,0.16,0.9,0.2,0.18,0.16,0.78,0.41,0.22,0.57,0.88,0.78,0.56,0.91,0.12,0.14,0.74,0.97,0.68,0.32,0.8,
0.56,0.93,0.15,0.4,0.93,0.33,0.28,0.67,0.5,0.93,0.89,0.54,0.79,0.87,0.74,0.25,0.66,0.42,0.03,0.24,0.06,0.36,0.54,0.41,0.05,0.05,0.39,0.24,0.15,0.89,0.67,0.84,0.47,0.3,0.9,0.79,0.58,0.1,0.95,0.49,0.78,0.75,0.93,0.42,0.53,0.17,0.12,0.01,0.34,0.65,0.46,0.32,0.04,0.15,0.03,0.6,0.29,0.07,0.87,0.29,0.6,0.6,0.15,0.14,0.29,0.52,0.34,0.58,0.44,0.85,0.23,0.08,0.26,0.21,0.02,0.4,0.89,0
.8,0.66,0.65,0.28,0.56,0.14,0.36,0.61,0.22,0.85,0.32,0.83,0.44,0.44,0.72,0.85,0.01,0.23,0.51,0.65,0.22,0.58,0.99,0.31,0.34,0.85,0.45,0.78,0.49,0.94,0.36,0.95,0.15,0.86,0.96,0.01,0.72,0.08,0.92,0.22,0.57,0.39,0.31,0.47,1,0.34,0.29,0.82,0.31,0.44,0.07,0.5,0.25,0.25,0.77,0.03,0.66,0.96,0.46,0.52,0.1,0.52,0.92,0.61,0.07,0.24,0.81,0.73,0.81,0,0.12,0.92,0.9,0.37,0.49,0.4,0.98,0.58
,0.35,0.59,0.11,0.05,0.17,0.61,0.85,0.07,0.33,0.5,0.97,0.87,0.22,0.63,1,0.1,0.28,0.15,0.49,0.5,0.25,0.68,0.8,0.24,0,0.05,0.59,0.54,0.88,0.49,0.17,0.36,0.03,0.46,0.03,0.31,0.46,0.39,0.55,0.05,0.92,0.42,0.37,0.87,0.49,0.52,0.21,0.2,0.86,0.49,0.75,0.97,0.65,0.57,0.74,0.72,0.77,0.51,0.04,0.02,0.08,0.52,0.5,0.66,0.39,0.85,0.63,0.11,0.37,0.51,0.85,0.03,0.19,0.65,0.65,0.33,0.28,0.7
1,0.68,0.88,0.31,0.99,0.52,0.08,0.83,0.59,0.22,0.31,0.86,0.44,0.59,0.4,0.61,0.33,0.3,0.23,0.83,0.02,0.8,0.98,0.11,0.35,0.48,0.45,0.58,0.23,0.18,0.23,0.36,0.16,0.35,0.88,0.25,0.39,0.9,0.03,0.81,0.69,0.2,0.36,0.61,0.85,0.17,0.43,0.5,0.57,0.95,0.94,0.67,0.27,0.42,0.76,0.63,0.41,0.46,0.99,0.37,0.56,0.21,0.32,0.98,0.7,0.7,0.32,0.93,0.34,0.84,0.34,0.94,0.18,0.31,0.07,0.59,0.98,0.9
1,0.3,0.06,0.17,0.73,0.72,0.93,0.5,0.04,0.22,0.54,0.06,0.21,0.09,0.77,0.67,0.94,0.39,0.66,0.93,0.03,0.11,0.92,0.47,0.81,0.38,0.03,0.06,0.84,0.66,0.01,0.82,0.68,0.86,0.97,0.15,0.99,0.29,0.56,0.41,0.4,0.11,0.65,0.34,0.47,0.1,0.22,0.56,0.32,0.74,0.18,0.15,0.42,0.69,0.18,0.17,0.07,0.98,0,0.86,0.92,0.56,0.53,0.12,0.01,0.58,0.72,0.28,0.59,0.17,0.95,0.21,0.29,0.78,0.45,0.19,0.26,0.
8,0.29,0.33,0.48,0.53,0.73,0.44,0.95,0.9,0.49,0.32,0,0.37,0.72,0.29,0.3,0.09,0.91,0.25,0.51,0.23,0.27,0.86,0.73,0.4,0.63,0.7,0.5,0.03,0.46,0.66,0.13,0.28,0.22,0.77,0.24,0.19,0.59,0.32,0.12,0.28,0.83,0.45,0.96,0.14,0.45,0.93,0.28,0.46,0.97,0.4,0.94,0.57,0.87,0.57,0.22,0.35,0.9,0.34,0.41,0.7,0.35,0.38,0.04,0.27,0.25,0.69,0.02,0.91,0.35,0.76,0.62,0.46,0.49,0.46,0.45,0.9,0.1,0.3
2,0.09,0.91,0.13,0.87,0.83,0.06,0.84,0.1,0.97,0.11,0.31,0.18,0.02,0.76,0.51,0.5,0.17,0.61,0.12,0.25,0.51,0.65,0.01,0.93,0.59,0.27,0.35,0.22,0.43,0.02,0.7,0.55,0.9,0.37,0.92,0.41,0.32,0.21,0.57,0.49,0.64,0.54,0.85,0.98,0.87,0.14,0.43,0.15,0.04,0.71,0.01,0.43,0.1,0.72,0.32,0.96,0.34,0.83,0.72,0.96,0.82,0.07,0.95,0,0.51,0.15,0.43,0.8,0.57,0.11,0.27,0.14,0.56,0.01,0.03,0.04,0.99
,0.92,0.49,0.39,0.64,0.13,0.82,0.66,0.1,0.94,0.47,0.61,0.3,0.3], "top": 3 }'
{"status":{"error":"Wrong input: Vector params for  are not specified in config"},"time":0.001418866}

the config.json of the collection looks strange to me:

{
    "params": {
        "vectors": {
            "vector": {
                "size": 1536,
                "distance": "Cosine"
            }
        },
        "shard_number": 1,
        "replication_factor": 1,
        "write_consistency_factor": 1,
        "on_disk_payload": true
    },
    "hnsw_config": {
        "m": 16,
        "ef_construct": 100,
        "full_scan_threshold": 10000,
        "max_indexing_threads": 0,
        "on_disk": false
    },
    "optimizer_config": {
        "deleted_threshold": 0.2,
        "vacuum_min_vector_number": 1000,
        "default_segment_number": 0,
        "max_segment_size": null,
        "memmap_threshold": null,
        "indexing_threshold": 20000,
        "flush_interval_sec": 5,
        "max_optimization_threads": null
    },
    "wal_config": {
        "wal_capacity_mb": 32,
        "wal_segments_ahead": 0
    },
    "quantization_config": null
}

Probably the reason for this issue is: "vectors": { "vector": { ... instead of

{
    "params": {
        "vectors": {
                "size": 1536,
                "distance": "Cosine"
        },
...}

Branch

No response

Checklist

• Modify src/vdf_io/import_vdf/qdrant_import.py ✓ 01405c0 Edit

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vector-io/src/vdf_io/import_vdf/qdrant_import.py

Lines 1 to 469 in ad971da

	import json
	from dotenv import load_dotenv
	import numpy as np
	from tqdm import tqdm
	from grpc import RpcError
	from typing import Any, Dict, List
	from PIL import Image
	from halo import Halo
	import concurrent.futures
	from qdrant_client import QdrantClient
	from qdrant_client.http.exceptions import UnexpectedResponse
	from qdrant_client.http.models import VectorParams, Distance, PointStruct
	from vdf_io.constants import INT_MAX
	from vdf_io.names import DBNames
	from vdf_io.util import (
	expand_shorthand_path,
	get_qdrant_id_from_id,
	set_arg_from_input,
	set_arg_from_password,
	)
	from vdf_io.import_vdf.vdf_import_cls import ImportVDB
	from vdf_io.meta_types import NamespaceMeta
	load_dotenv()
	class ImportQdrant(ImportVDB):
	DB_NAME_SLUG = DBNames.QDRANT
	@classmethod
	def import_vdb(cls, args):
	"""
	Import data to Qdrant
	"""
	set_arg_from_input(
	args,
	"url",
	"Enter the URL of Qdrant instance (default: 'http://localhost:6334'): ",
	str,
	"http://localhost:6334",
	)
	set_arg_from_input(
	args,
	"prefer_grpc",
	"Whether to use GRPC. Recommended. (default: True): ",
	bool,
	True,
	)
	set_arg_from_input(
	args,
	"qdrant_local_persist_path",
	"Enter the path to the local persist directory (default: None): ",
	str,
	"DO_NOT_PROMPT",
	)
	set_arg_from_input(
	args,
	"parallel",
	"Enter the number of parallel processes of upload (default: 64): ",
	int,
	1,
	)
	set_arg_from_input(
	args,
	"max_retries",
	"Enter the maximum number of retries in case of a failure (default: 3): ",
	int,
	3,
	)
	set_arg_from_password(
	args, "qdrant_api_key", "Enter your Qdrant API key: ", "QDRANT_API_KEY"
	)
	qdrant_import = ImportQdrant(args)
	try:
	qdrant_import.upsert_data()
	# keyboard interrupt
	except KeyboardInterrupt:
	tqdm.write(
	f"Data import interrupted. {qdrant_import.total_imported_count} rows imported."
	)
	return qdrant_import
	@classmethod
	def make_parser(cls, subparsers):
	parser_qdrant = subparsers.add_parser(
	cls.DB_NAME_SLUG, help="Import data to Qdrant"
	)
	parser_qdrant.add_argument(
	"-u",
	"--url",
	type=str,
	help="Qdrant instance url",
	default="http://localhost:6334",
	)
	parser_qdrant.add_argument(
	"--prefer_grpc",
	type=bool,
	help="Whether to use Qdrant's GRPC interface",
	default=True,
	)
	parser_qdrant.add_argument(
	"--qdrant_local_persist_path",
	type=str,
	help="Path to the local persist directory (default: None)",
	default=None,
	)
	parser_qdrant.add_argument(
	"--parallel",
	type=int,
	help="Number of parallel processes of upload (default: 5).",
	default=5,
	)
	parser_qdrant.add_argument(
	"--max_retries",
	type=int,
	help="Maximum number of retries in case of a failure (default: 3).",
	default=3,
	)
	parser_qdrant.add_argument(
	"--shard_key_selector",
	type=Any,
	help="Shard to be queried (default: None)",
	default=None,
	)
	def __init__(self, args):
	# call super class constructor
	super().__init__(args)
	url, api_key, prefer_grpc, path = (
	self.args.get("url", None),
	self.args.get("qdrant_api_key", None),
	self.args.get("prefer_grpc", True),
	expand_shorthand_path(self.args.get("qdrant_local_persist_path", None)),
	)
	if path:
	url = None
	if url:
	path = None
	self.client = QdrantClient(
	url=url,
	api_key=api_key,
	prefer_grpc=prefer_grpc,
	path=path,
	)
	def upsert_data(self):
	max_hit = False
	self.total_imported_count = 0
	# we know that the self.vdf_meta["indexes"] is a list
	index_meta: Dict[str, List[NamespaceMeta]] = {}
	for index_name, index_meta in tqdm(
	self.vdf_meta["indexes"].items(), desc="Importing indexes"
	):
	tqdm.write(f"Importing data for index '{index_name}'")
	for namespace_meta in tqdm(index_meta, desc="Importing namespaces"):
	self.set_dims(namespace_meta, index_name)
	data_path = namespace_meta["data_path"]
	final_data_path = self.get_final_data_path(data_path)
	# list indexes
	collections = [
	x.name for x in self.client.get_collections().collections
	]
	# check if index exists
	new_collection_name = index_name + (
	f"_{namespace_meta['namespace']}"
	if namespace_meta["namespace"]
	else ""
	)
	new_collection_name = self.create_new_name(
	new_collection_name, collections
	)
	vector_column_names, _ = self.get_vector_column_name(
	new_collection_name, namespace_meta, multi_vector_supported=True
	)
	if new_collection_name not in collections:
	# create index
	try:
	def get_nested_config(config, keys, default=None):
	"""Helper function to get nested dictionary values."""
	if not config:
	return default
	for key in keys:
	if not config:
	return default
	config = config.get(key, {}) or {}
	if not config:
	return default
	return config or default
	index_config = namespace_meta.get("index_config", {})
	dims = (
	namespace_meta["dimensions"]
	if "dimensions" in namespace_meta
	else get_nested_config(
	namespace_meta,
	["index_config", "params", "vectors"],
	{},
	).get("size")
	)
	on_disk = get_nested_config(
	namespace_meta,
	["index_config", "params", "vectors", "on_disk"],
	None,
	)
	configs = [
	"hnsw_config",
	"optimizers_config",
	"wal_config",
	"quantization_config",
	"on_disk_payload",
	"sparse_vectors_config",
	]
	(
	hnsw_config,
	optimizers_config,
	wal_config,
	quantization_config,
	on_disk_payload,
	sparse_vectors_config,
	) = [
	get_nested_config(index_config, [config], None)
	for config in configs
	]
	distance = (
	namespace_meta.get("metric", Distance.COSINE)
	or Distance.COSINE
	)
	vectors_config = {
	vector_column_name: VectorParams(
	size=dims,
	distance=distance,
	on_disk=on_disk,
	)
	for vector_column_name in vector_column_names
	}
	self.client.create_collection(
	collection_name=new_collection_name,
	vectors_config=vectors_config,
	sparse_vectors_config=sparse_vectors_config,
	hnsw_config=hnsw_config,
	optimizers_config=optimizers_config,
	wal_config=wal_config,
	quantization_config=quantization_config,
	on_disk_payload=on_disk_payload,
	)
	except Exception as e:
	tqdm.write(
	f"Failed to create index '{new_collection_name}' {e}"
	)
	return
	prev_vector_count = self.client.get_collection(
	collection_name=new_collection_name
	).vectors_count
	if prev_vector_count > 0:
	tqdm.write(
	f"Index '{new_collection_name}' has {prev_vector_count} vectors before import"
	)
	# Load the data from the parquet files
	parquet_files = self.get_parquet_files(final_data_path)
	vectors_all = {}
	for vec_col in namespace_meta.get("vector_columns", []):
	vectors_all[vec_col] = {}
	metadata = {}
	for file in tqdm(parquet_files, desc="Iterating parquet files"):
	file_path = self.get_file_path(final_data_path, file)
	df = self.read_parquet_progress(
	file_path,
	max_num_rows=(
	(self.args.get("max_num_rows") or INT_MAX)
	- self.total_imported_count
	),
	)
	with Halo(text="Processing vectors", spinner="dots"):
	for vec_col in namespace_meta.get("vector_columns", []):
	self.update_vectors(vectors_all[vec_col], vec_col, df)
	with Halo(text="Processing metadata", spinner="dots"):
	self.update_metadata(metadata, vector_column_names, df)
	self.make_metadata_qdrant_compliant(metadata)
	# union of all keys in vectors_all
	keys = set().union(
	*[vectors_all[vec_col].keys() for vec_col in vectors_all.keys()]
	)
	points = [
	PointStruct(
	id=get_qdrant_id_from_id(idx),
	vector={
	vec_col: vectors_all[vec_col].get(idx, [])
	for vec_col in vectors_all.keys()
	},
	payload=metadata.get(idx, {}),
	)
	for idx in keys
	]
	if self.total_imported_count + len(points) >= (
	self.args.get("max_num_rows") or INT_MAX
	):
	max_hit = True
	points = points[
	: (self.args.get("max_num_rows") or INT_MAX)
	- self.total_imported_count
	]
	tqdm.write("Truncating data to limit to max rows")
	try:
	BATCH_SIZE = self.args.get("batch_size", 64) or 64
	batches = list(divide_into_batches(points, BATCH_SIZE))
	total_points = len(points)
	num_parallel_threads = self.args.get("parallel", 5) or 5
	with concurrent.futures.ThreadPoolExecutor(
	max_workers=num_parallel_threads
	) as executor, tqdm(
	total=total_points,
	desc=f"Uploading points in batches of {BATCH_SIZE} in {num_parallel_threads} threads",
	) as pbar:
	# Create a future to batch mapping to update progress bar correctly after each batch completion
	future_to_batch = {
	executor.submit(
	self.upsert_batch, batch, new_collection_name
	): batch
	for batch in batches
	}
	for future in concurrent.futures.as_completed(
	future_to_batch
	):
	batch = future_to_batch[future]
	try:
	# Attempt to get the result, which will re-raise any exceptions
	future.result()
	# Update the progress bar by the size of the successfully processed batch
	pbar.update(len(batch))
	except Exception as e:
	tqdm.write(
	f"Batch upsert failed with error: {e} " # {batch}
	)
	# Optionally, you might want to handle failed batches differently
	self.total_imported_count += len(points)
	if self.total_imported_count >= (
	self.args.get("max_num_rows") or INT_MAX
	):
	max_hit = True
	except (UnexpectedResponse, RpcError, ValueError) as e:
	tqdm.write(
	f"Failed to upsert data for collection '{new_collection_name}', {e}"
	)
	continue
	vector_count = self.client.get_collection(
	collection_name=new_collection_name
	).vectors_count
	if max_hit:
	break
	# END parquet file loop
	tqdm.write(
	f"Index '{new_collection_name}' has {vector_count} vectors after import"
	)
	tqdm.write(f"{vector_count - prev_vector_count} vectors were imported")
	if max_hit:
	break
	# END namespace loop
	if max_hit:
	tqdm.write(
	f"Max rows to be imported {self.args['max_num_rows']} hit. Exiting"
	)
	break
	# END index loop
	tqdm.write("Data import completed successfully.")
	self.args["imported_count"] = self.total_imported_count
	def make_metadata_qdrant_compliant(self, metadata):
	deleted_images = False
	parsed_json = False
	for k, v in metadata.items():
	deleted_images, parsed_json, zeroed_nan = self.normalize_dict(
	metadata, k, v
	)
	if deleted_images:
	tqdm.write("Images were deleted from metadata")
	if parsed_json:
	tqdm.write("Metadata was parsed to JSON")
	if zeroed_nan:
	tqdm.write("NaN values were replaced with 0 in metadata")
	def replace_nan_with_zero(self, data, zeroed_nan=False):
	if isinstance(data, dict):
	ret_val = {k: self.replace_nan_with_zero(v) for k, v in data.items()}
	for _, v in ret_val.items():
	if v[1]:
	zeroed_nan = True
	return {k: v[0] for k, v in ret_val.items()}, zeroed_nan
	elif isinstance(data, list):
	ret_val = [self.replace_nan_with_zero(item) for item in data]
	return [x[0] for x in ret_val], any(x[1] for x in ret_val)
	elif isinstance(data, float) and np.isnan(data):
	return 0, True
	else:
	return data, False
	def normalize_dict(self, metadata, k, v):
	deleted_images = False
	parsed_json = False
	zeroed_nan = False
	# Check for np.nan and convert to 0 for scalar values
	if np.isscalar(v) and (
	(isinstance(v, (float, int)) and np.isnan(v))
	or (isinstance(v, str) and v.lower() == "nan")
	):
	metadata[k] = 0
	zeroed_nan = True
	elif isinstance(v, np.ndarray):
	metadata[k] = v.tolist()
	elif isinstance(v, Image.Image):
	del metadata[k]
	deleted_images = True
	elif isinstance(v, bytes) or isinstance(v, str):
	if isinstance(v, bytes):
	metadata[k] = v.decode("utf-8")
	try:
	parsed_value = json.loads(metadata[k])
	# Replace nan with 0 in the parsed JSON object
	metadata[k], zeroed_nan_rec = self.replace_nan_with_zero(parsed_value)
	if zeroed_nan_rec:
	zeroed_nan = True
	parsed_json = True
	except json.JSONDecodeError:
	pass
	elif isinstance(v, dict):
	for k2, v2 in v.items():
	(
	deleted_images_rec,
	parsed_json_rec,
	zeroed_nan_rec,
	) = self.normalize_dict(v, k2, v2)
	if zeroed_nan_rec:
	zeroed_nan = True
	if deleted_images_rec:
	deleted_images = True
	if parsed_json_rec:
	parsed_json = True
	return deleted_images, parsed_json, zeroed_nan
	def upsert_batch(self, batch, new_collection_name):
	RETRIES = self.args.get("max_retries", 3)
	for attempt in range(RETRIES):
	try:
	self.client.upsert(
	collection_name=new_collection_name,
	points=batch,
	shard_key_selector=self.args.get("shard_key_selector", None),
	wait=True,
	)
	break # Break the loop on success
	except Exception:
	if attempt == RETRIES - 1:
	raise # Re-raise the last exception if all retries fail
	else:
	continue
	return len(batch)
	# Function to divide your points into batches
	def divide_into_batches(points, batch_size):
	for i in range(0, len(points), batch_size):

---

Step 2: ⌨️ Coding

• Modify src/vdf_io/import_vdf/qdrant_import.py ✓ 01405c0 Edit

Modify src/vdf_io/import_vdf/qdrant_import.py with contents: Update the `vectors_config` dictionary in the `upsert_data` method to correctly structure the vector configuration.

<original_code>
vectors_config = {
vector_column_name: VectorParams(
size=dims,
distance=distance,
on_disk=on_disk,
)
for vector_column_name in vector_column_names
}
</original_code>

<new_code>
vectors_config = {
vector_column_name: VectorParams(
size=dims,
distance=distance,
)
for vector_column_name in vector_column_names
}
</new_code>

Remove the nested "vector" key and specify the vector configuration directly under the "vectors" key, with the vector column name as the key and the VectorParams object as the value. Also remove the on_disk parameter as it is not part of the VectorParams configuration.

---

Step 3: 🔁 Code Review

I have finished reviewing the code for completeness. I did not find errors for sweep/qdrant_import_collection_not_working.

---

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💡 To recreate the pull request edit the issue title or description.
^{Something wrong? Let us know.}

This is an automated message generated by Sweep AI.

[greptile-apps]

The issue seems to stem from the collection configuration format used during the import process. Specifically, the vectors_config setup in the upsert_data method of qdrant_import.py expects a dictionary with keys corresponding to vector column names and their configurations. However, your collection's config.json indicates a mismatch in expected structure, particularly under the params -> vectors section. To resolve this, ensure the collection configuration passed to self.client.create_collection within upsert_data matches Qdrant's expected format. This involves adjusting the vectors_config dictionary construction to align with your collection's actual vector dimension and distance metric, ensuring it accurately reflects the structure shown in your issue description.

References

/src/vdf_io/import_vdf/qdrant_import.py

Ask Greptile

[dhruv-anand-aintech]

The

"params": {
        "vectors": {
            "vector": {
                "size": 1536,
                "distance": "Cosine"
            }
        },
		...

is matching the format for named vectors.

I want to better understand the sequence of your operations. Did you first import a vdf dataset into your qdrant instance, and then try to do a search via the REST API?