mirror of
https://github.com/open-webui/open-webui
synced 2024-11-09 02:00:03 +00:00
287 lines
9.8 KiB
Python
287 lines
9.8 KiB
Python
from pymilvus import MilvusClient as Client
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from pymilvus import FieldSchema, DataType
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import json
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from typing import Optional
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from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
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from open_webui.config import (
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MILVUS_URI,
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)
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class MilvusClient:
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def __init__(self):
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self.collection_prefix = "open_webui"
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self.client = Client(uri=MILVUS_URI)
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def _result_to_get_result(self, result) -> GetResult:
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ids = []
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documents = []
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metadatas = []
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for match in result:
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_ids = []
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_documents = []
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_metadatas = []
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for item in match:
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_ids.append(item.get("id"))
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_documents.append(item.get("data", {}).get("text"))
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_metadatas.append(item.get("metadata"))
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ids.append(_ids)
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documents.append(_documents)
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metadatas.append(_metadatas)
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return GetResult(
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**{
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"ids": ids,
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"documents": documents,
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"metadatas": metadatas,
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}
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)
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def _result_to_search_result(self, result) -> SearchResult:
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ids = []
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distances = []
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documents = []
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metadatas = []
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for match in result:
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_ids = []
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_distances = []
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_documents = []
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_metadatas = []
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for item in match:
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_ids.append(item.get("id"))
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_distances.append(item.get("distance"))
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_documents.append(item.get("entity", {}).get("data", {}).get("text"))
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_metadatas.append(item.get("entity", {}).get("metadata"))
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ids.append(_ids)
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distances.append(_distances)
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documents.append(_documents)
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metadatas.append(_metadatas)
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return SearchResult(
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**{
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"ids": ids,
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"distances": distances,
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"documents": documents,
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"metadatas": metadatas,
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}
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)
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def _create_collection(self, collection_name: str, dimension: int):
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schema = self.client.create_schema(
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auto_id=False,
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enable_dynamic_field=True,
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)
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schema.add_field(
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field_name="id",
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datatype=DataType.VARCHAR,
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is_primary=True,
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max_length=65535,
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)
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schema.add_field(
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field_name="vector",
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datatype=DataType.FLOAT_VECTOR,
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dim=dimension,
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description="vector",
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)
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schema.add_field(field_name="data", datatype=DataType.JSON, description="data")
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schema.add_field(
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field_name="metadata", datatype=DataType.JSON, description="metadata"
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)
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index_params = self.client.prepare_index_params()
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index_params.add_index(
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field_name="vector",
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index_type="HNSW",
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metric_type="COSINE",
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params={"M": 16, "efConstruction": 100},
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)
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self.client.create_collection(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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schema=schema,
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index_params=index_params,
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)
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def has_collection(self, collection_name: str) -> bool:
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# Check if the collection exists based on the collection name.
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collection_name = collection_name.replace("-", "_")
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return self.client.has_collection(
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collection_name=f"{self.collection_prefix}_{collection_name}"
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)
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def delete_collection(self, collection_name: str):
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# Delete the collection based on the collection name.
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collection_name = collection_name.replace("-", "_")
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return self.client.drop_collection(
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collection_name=f"{self.collection_prefix}_{collection_name}"
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)
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def search(
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self, collection_name: str, vectors: list[list[float | int]], limit: int
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) -> Optional[SearchResult]:
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# Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
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collection_name = collection_name.replace("-", "_")
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result = self.client.search(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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data=vectors,
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limit=limit,
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output_fields=["data", "metadata"],
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)
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return self._result_to_search_result(result)
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def query(self, collection_name: str, filter: dict, limit: Optional[int] = None):
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# Construct the filter string for querying
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collection_name = collection_name.replace("-", "_")
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if not self.has_collection(collection_name):
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return None
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filter_string = " && ".join(
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[
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f'metadata["{key}"] == {json.dumps(value)}'
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for key, value in filter.items()
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]
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)
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max_limit = 16383 # The maximum number of records per request
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all_results = []
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if limit is None:
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limit = float("inf") # Use infinity as a placeholder for no limit
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# Initialize offset and remaining to handle pagination
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offset = 0
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remaining = limit
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try:
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# Loop until there are no more items to fetch or the desired limit is reached
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while remaining > 0:
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print("remaining", remaining)
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current_fetch = min(
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max_limit, remaining
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) # Determine how many items to fetch in this iteration
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results = self.client.query(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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filter=filter_string,
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output_fields=["*"],
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limit=current_fetch,
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offset=offset,
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)
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if not results:
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break
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all_results.extend(results)
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results_count = len(results)
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remaining -= (
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results_count # Decrease remaining by the number of items fetched
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)
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offset += results_count
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# Break the loop if the results returned are less than the requested fetch count
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if results_count < current_fetch:
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break
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print(all_results)
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return self._result_to_get_result([all_results])
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except Exception as e:
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print(e)
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return None
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def get(self, collection_name: str) -> Optional[GetResult]:
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# Get all the items in the collection.
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collection_name = collection_name.replace("-", "_")
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result = self.client.query(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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filter='id != ""',
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)
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return self._result_to_get_result([result])
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def insert(self, collection_name: str, items: list[VectorItem]):
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# Insert the items into the collection, if the collection does not exist, it will be created.
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collection_name = collection_name.replace("-", "_")
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if not self.client.has_collection(
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collection_name=f"{self.collection_prefix}_{collection_name}"
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):
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self._create_collection(
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collection_name=collection_name, dimension=len(items[0]["vector"])
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)
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return self.client.insert(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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data=[
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{
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"id": item["id"],
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"vector": item["vector"],
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"data": {"text": item["text"]},
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"metadata": item["metadata"],
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}
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for item in items
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],
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)
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def upsert(self, collection_name: str, items: list[VectorItem]):
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# Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
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collection_name = collection_name.replace("-", "_")
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if not self.client.has_collection(
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collection_name=f"{self.collection_prefix}_{collection_name}"
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):
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self._create_collection(
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collection_name=collection_name, dimension=len(items[0]["vector"])
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)
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return self.client.upsert(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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data=[
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{
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"id": item["id"],
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"vector": item["vector"],
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"data": {"text": item["text"]},
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"metadata": item["metadata"],
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}
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for item in items
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],
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)
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def delete(
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self,
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collection_name: str,
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ids: Optional[list[str]] = None,
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filter: Optional[dict] = None,
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):
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# Delete the items from the collection based on the ids.
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collection_name = collection_name.replace("-", "_")
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if ids:
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return self.client.delete(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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ids=ids,
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)
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elif filter:
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# Convert the filter dictionary to a string using JSON_CONTAINS.
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filter_string = " && ".join(
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[
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f'metadata["{key}"] == {json.dumps(value)}'
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for key, value in filter.items()
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]
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)
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return self.client.delete(
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collection_name=f"{self.collection_prefix}_{collection_name}",
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filter=filter_string,
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)
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def reset(self):
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# Resets the database. This will delete all collections and item entries.
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collection_names = self.client.list_collections()
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for collection_name in collection_names:
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if collection_name.startswith(self.collection_prefix):
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self.client.drop_collection(collection_name=collection_name)
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