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from qdrant_client import QdrantClient # main component to provide the access to db
from qdrant_client.http.models import (
ScoredPoint,
Filter,
FieldCondition,
MatchText
)
from qdrant_client.models import (
VectorParams,
Distance,
PointStruct,
TextIndexParams,
TokenizerType
) # VectorParams -> config of vectors that will be used as primary keys
from app.core.models import Embedder # Distance -> defines the metric
from app.core.chunks import Chunk # PointStruct -> instance that will be stored in db
import numpy as np
from uuid import UUID
from app.settings import settings
import time
from fastapi import HTTPException
import re
class VectorDatabase:
def __init__(self, embedder: Embedder, host: str = "qdrant", port: int = 6333):
self.host: str = host
self.client: QdrantClient = self._initialize_qdrant_client()
self.embedder: Embedder = embedder # embedder is used to convert a user's query
self.already_stored: np.array[np.array] = np.array([]).reshape(
0, embedder.get_vector_dimensionality()
)
def store(
self, collection_name: str, chunks: list[Chunk], batch_size: int = 1000
) -> None:
points: list[PointStruct] = []
vectors = self.embedder.encode([chunk.get_raw_text() for chunk in chunks])
for vector, chunk in zip(vectors, chunks):
if self.accept_vector(collection_name, vector):
points.append(
PointStruct(
id=str(chunk.id),
vector=vector,
payload={
"metadata": chunk.get_metadata(),
"text": chunk.get_raw_text(),
},
)
)
if len(points):
for group in range(0, len(points), batch_size):
self.client.upsert(
collection_name=collection_name,
points=points[group : group + batch_size],
wait=False,
)
"""
Measures a cosine of angle between tow vectors
"""
def cosine_similarity(self, vec1, vec2):
vec1_np = np.array(vec1)
vec2_np = np.array(vec2)
return vec1_np @ vec2_np / (np.linalg.norm(vec1_np) * np.linalg.norm(vec2_np))
"""
Defines weather the vector should be stored in the db by searching for the most
similar one
"""
def accept_vector(self, collection_name: str, vector: np.array) -> bool:
most_similar = self.client.query_points(
collection_name=collection_name, query=vector, limit=1, with_vectors=True
).points
if not len(most_similar):
return True
else:
most_similar = most_similar[0]
if 1 - self.cosine_similarity(vector, most_similar.vector) < settings.max_delta:
return False
return True
def construct_keywords_list(self, query: str) -> list[FieldCondition]:
keywords = re.findall(r'\b[A-Z]{2,}\b', query)
filters = []
print(keywords)
for word in keywords:
if len(word) > 30 or len(word) < 2:
continue
filters.append(FieldCondition(key="text", match=MatchText(text=word)))
return filters
"""
According to tests, re-ranker needs ~7-10 chunks to generate the most accurate hit
TODO: implement hybrid search
"""
def search(self, collection_name: str, query: str, top_k: int = 5) -> list[Chunk]:
query_embedded: np.ndarray = self.embedder.encode(query)
if isinstance(query_embedded, list):
query_embedded = query_embedded[0]
keywords = self.construct_keywords_list(query)
dense_result: list[ScoredPoint] = self.client.query_points(
collection_name=collection_name, query=query_embedded, limit=int(top_k * 0.7)
).points
sparse_result: list[ScoredPoint] = self.client.query_points(
collection_name=collection_name, query=query_embedded, limit=int(top_k * 0.3),
query_filter=Filter(should=keywords)
).points
combined = [*dense_result, *sparse_result]
print(len(combined))
return [
Chunk(
id=UUID(point.payload.get("metadata", {}).get("id", "")),
filename=point.payload.get("metadata", {}).get("filename", ""),
page_number=point.payload.get("metadata", {}).get("page_number", 0),
start_index=point.payload.get("metadata", {}).get("start_index", 0),
start_line=point.payload.get("metadata", {}).get("start_line", 0),
end_line=point.payload.get("metadata", {}).get("end_line", 0),
text=point.payload.get("text", ""),
)
for point in combined
]
def _initialize_qdrant_client(self, max_retries=5, delay=2) -> QdrantClient:
for attempt in range(max_retries):
try:
client = QdrantClient(**settings.qdrant.model_dump())
client.get_collections()
return client
except Exception as e:
if attempt == max_retries - 1:
raise HTTPException(
500,
f"Failed to connect to Qdrant server after {max_retries} attempts. "
f"Last error: {str(e)}",
)
print(
f"Connection attempt {attempt + 1} out of {max_retries} failed. "
f"Retrying in {delay} seconds..."
)
time.sleep(delay)
delay *= 2
def _check_collection_exists(self, collection_name: str) -> bool:
try:
return self.client.collection_exists(collection_name)
except Exception as e:
raise HTTPException(
500,
f"Failed to check collection {collection_name} exists. Last error: {str(e)}",
)
def _create_collection(self, collection_name: str) -> None:
try:
self.client.create_collection(
collection_name=collection_name,
vectors_config=VectorParams(
size=self.embedder.get_vector_dimensionality(),
distance=Distance.COSINE,
),
)
self.client.create_payload_index(
collection_name=collection_name,
field_name="text",
field_schema=TextIndexParams(
type="text",
tokenizer=TokenizerType.WORD,
min_token_len=2,
max_token_len=30,
lowercase=True
)
)
except Exception as e:
raise HTTPException(
500, f"Failed to create collection {self.collection_name}: {str(e)}"
)
def create_collection(self, collection_name: str) -> None:
try:
if self._check_collection_exists(collection_name):
return
self._create_collection(collection_name)
except Exception as e:
print(e)
raise HTTPException(500, e)
def __del__(self):
if hasattr(self, "client"):
self.client.close()
def get_collections(self) -> list[str]:
try:
return self.client.get_collections()
except Exception as e:
print(e)
raise HTTPException(500, "Failed to get collection names")
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