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from qdrant_client import QdrantClient
from qdrant_client.http.models import ScoredPoint
from embedding import Embedding
from model.document import Document
from model.record import Record
from model.user import User
from qdrant_client.http import models
import uuid
import tqdm
class Index:
type: str
def load_or_update_document(self, user: User, document: Document, progress: tqdm.tqdm = None):
pass
def remove_document(self, user: User, document: Document):
pass
def query_index(self, user: User, query: str, top_k: int = 10, threshold: float = 0.5) -> list[Record]:
pass
def query_document(self, user: User, document: Document, query: str, top_k: int = 10, threshold: float = 0.5) -> list[Record]:
pass
def contains(self, user: User, document: Document) -> bool:
pass
class QDrantVectorStore(Index):
_client: QdrantClient
_embedding: Embedding
collection_name: str
batch_size: int = 10
type: str = 'qdrant'
def __init__(
self,
client: QdrantClient,
embedding: Embedding,
collection_name: str):
self._embedding = embedding
self.collection_name = collection_name
self._client = client
def _response_to_records(self, response: list[ScoredPoint]) -> list[Record]:
for point in response:
meta_data = point.payload['meta_data']
yield Record(
embedding=point.vector,
meta_data= meta_data,
content=point.payload['content'],
document_id=point.payload['document_id'],
timestamp=point.payload['timestamp'],
)
def create_collection(self):
self._client.recreate_collection(
collection_name=self.collection_name,
vectors_config=models.VectorParams(
size=self._embedding.vector_size,
distance=models.Distance.COSINE),
)
def if_collection_exists(self) -> bool:
try:
self._client.get_collection(self.collection_name)
return True
except Exception:
return False
def create_collection_if_not_exists(self):
if not self.if_collection_exists():
self.create_collection()
def load_or_update_document(self, user: User, document: Document, progress: tqdm.tqdm = None):
self.create_collection_if_not_exists()
if self.contains(user, document):
self.remove_document(user, document)
group_id = user.user_name
# upsert records in batch
records = document.load_records()
records = list(records)
batch_range = range(0, len(records), self.batch_size)
if progress is not None:
batch_range = progress(batch_range)
for i in batch_range:
batch = records[i:i+self.batch_size]
uuids = [str(uuid.uuid4()) for _ in batch]
payloads = [{
'content': record.content,
'meta_data': record.meta_data,
'document_id': record.document_id,
'group_id': group_id,
'timestamp': record.timestamp,
} for record in batch]
vectors = [record.embedding for record in batch]
self._client.upsert(
collection_name=self.collection_name,
points=models.Batch(
payloads=payloads,
ids=uuids,
vectors=vectors,
),
)
def remove_document(self, user: User, document: Document):
if not self.if_collection_exists():
return
document_id = document.name
self._client.delete(
collection_name=self.collection_name,
points_selector=models.FilterSelector(
filter=models.Filter(
must=[
models.FieldCondition(
key="document_id",
match=models.MatchValue(value=document_id)
),
models.FieldCondition(
key="group_id",
match=models.MatchValue(
value=user.user_name,
),
)
]
)
)
)
def contains(self, user: User, document: Document) -> bool:
document_id = document.name
group_id = user.user_name
count = self._client.count(
collection_name=self.collection_name,
count_filter=models.Filter(
must=[
models.FieldCondition(
key="document_id",
match=models.MatchValue(value=document_id)
),
models.FieldCondition(
key="group_id",
match=models.MatchValue(
value=group_id,
),
)
]
),
exact=True,
)
return count.count > 0
def query_index(self, user: User, query: str, top_k: int = 10, threshold: float = 0.5) -> list[Record]:
if not self.if_collection_exists():
return []
response = self._client.search(
collection_name=self.collection_name,
query_vector=self._embedding.generate_embedding(query),
limit=top_k,
query_filter= models.Filter(
must=[
models.FieldCondition(
key="group_id",
match=models.MatchValue(
value=user.user_name,
),
)
]
),
score_threshold=threshold,
)
return self._response_to_records(response)
def query_document(self, user: User, document: Document, query: str, top_k: int = 10, threshold: float = 0.5) -> list[Record]:
if not self.if_collection_exists():
return []
response = self._client.search(
collection_name=self.collection_name,
query_vector=self._embedding.generate_embedding(query),
limit=top_k,
query_filter= models.Filter(
must=[
models.FieldCondition(
key="document_id",
match=models.MatchValue(value=document.name)
),
models.FieldCondition(
key="group_id",
match=models.MatchValue(value=user.user_name),
)
]
),
score_threshold=threshold,
)
return self._response_to_records(response)
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