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sweep-ai avatar sweep-ai commented on June 26, 2024

🚀 Here's the PR! #94

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Step 1: 🔎 Searching

I found the following snippets in your repository. I will now analyze these snippets and come up with a plan.

Some code snippets I think are relevant in decreasing order of relevance (click to expand). If some file is missing from here, you can mention the path in the ticket description.

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.py01405c0 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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from vector-io.

greptile-apps avatar greptile-apps commented on June 26, 2024

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

from vector-io.

dhruv-anand-aintech avatar dhruv-anand-aintech commented on June 26, 2024

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?

from vector-io.

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