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from qdrant_client import QdrantClient, models
from sentence_transformers import SentenceTransformer
from transformers import AutoModel, AutoImageProcessor
import torch
import os
from datasets import load_dataset
from dotenv import load_dotenv
import numpy as np
import uuid
from PIL import Image, ImageFile
from fastembed import SparseTextEmbedding
import cohere
load_dotenv()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder = SentenceTransformer("sentence-transformers/LaBSE").to(device)
processor = AutoImageProcessor.from_pretrained('facebook/dinov2-large')
image_encoder = AutoModel.from_pretrained("facebook/dinov2-large").to(device)
qdrant_client = QdrantClient(url=os.getenv("qdrant_url"), api_key=os.getenv("qdrant_api_key"))
sparse_encoder = SparseTextEmbedding(model_name="prithivida/Splade_PP_en_v1")
co = cohere.ClientV2(os.getenv("cohere_api_key"))
dataset = load_dataset("Karbo31881/Pokemon_images")
ds = dataset["train"]
labels = ds["text"]
def get_sparse_embedding(text: str, model: SparseTextEmbedding):
embeddings = list(model.embed(text))
vector = {f"sparse-text": models.SparseVector(indices=embeddings[0].indices, values=embeddings[0].values)}
return vector
def get_query_sparse_embedding(text: str, model: SparseTextEmbedding):
embeddings = list(model.embed(text))
query_vector = models.NamedSparseVector(
name="sparse-text",
vector=models.SparseVector(
indices=embeddings[0].indices,
values=embeddings[0].values,
),
)
return query_vector
def upload_text_to_qdrant(client: QdrantClient, collection_name: str, encoder: SentenceTransformer, text: str, point_id_dense: int, point_id_sparse: int):
try:
docs = {"text": text}
client.upsert(
collection_name=collection_name,
points=[
models.PointStruct(
id=point_id_dense,
vector={f"dense-text": encoder.encode(docs["text"]).tolist()},
payload=docs,
)
],
)
client.upsert(
collection_name=collection_name,
points=[
models.PointStruct(
id=point_id_sparse,
vector=get_sparse_embedding(docs["text"], sparse_encoder),
payload=docs,
)
],
)
return True
except Exception as e:
return False
def upload_images_to_qdrant(client: QdrantClient, collection_name: str, vectorsfile: str, labelslist: list):
try:
vectors = np.load(vectorsfile)
docs = []
for label in labelslist:
docs.append({"label": label})
client.upload_points(
collection_name=collection_name,
points=[
models.PointStruct(
id=idx,
vector=vectors[idx].tolist(),
payload=doc,
)
for idx, doc in enumerate(docs)
],
)
return True
except Exception as e:
return False
class SemanticCache:
def __init__(self, client: QdrantClient, text_encoder: SentenceTransformer, collection_name: str, threshold: float = 0.75):
self.client = client
self.text_encoder = text_encoder
self.collection_name = collection_name
self.threshold = threshold
def upload_to_cache(self, question: str, answer: str):
docs = {"question": question, "answer": answer}
point_id = str(uuid.uuid4())
self.client.upsert(
collection_name=self.collection_name,
points=[
models.PointStruct(
id=point_id,
vector=self.text_encoder.encode(docs["question"]).tolist(),
payload=docs,
)
],
)
def search_cache(self, question: str, limit: int = 5):
vector = self.text_encoder.encode(question).tolist()
search_result = self.client.search(
collection_name=self.collection_name,
query_vector=vector,
query_filter=None,
limit=limit,
)
payloads = [hit.payload["answer"] for hit in search_result if hit.score > self.threshold]
if len(payloads) > 0:
return payloads[0]
else:
return ""
class NeuralSearcher:
def __init__(self, text_collection_name: str, image_collection_name: str, client: QdrantClient, text_encoder: SentenceTransformer , image_encoder: AutoModel, image_processor: AutoImageProcessor, sparse_encoder: SparseTextEmbedding):
self.text_collection_name = text_collection_name
self.image_collection_name = image_collection_name
self.text_encoder = text_encoder
self.image_encoder = image_encoder
self.image_processor = image_processor
self.qdrant_client = client
self.sparse_encoder = sparse_encoder
def search_text(self, text: str, limit: int = 5):
vector = self.text_encoder.encode(text).tolist()
search_result_dense = self.qdrant_client.search(
collection_name=self.text_collection_name,
query_vector=models.NamedVector(name="dense-text", vector=vector),
query_filter=None,
limit=limit,
)
search_result_sparse = self.qdrant_client.search(
collection_name=self.text_collection_name,
query_vector=get_query_sparse_embedding(text, self.sparse_encoder),
query_filter=None,
limit=limit,
)
payloads = [hit.payload["text"] for hit in search_result_dense]
payloads += [hit.payload["text"] for hit in search_result_sparse]
return payloads
def reranking(self, text: str, search_result: list):
results = co.rerank(model="rerank-v3.5", query=text, documents=search_result, top_n = 3)
ranked_results = [search_result[results.results[i].index] for i in range(3)]
return ranked_results
def search_image(self, image: ImageFile, limit: int = 5):
img = image
inputs = self.image_processor(images=img, return_tensors="pt").to(device)
with torch.no_grad():
outputs = self.image_encoder(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
search_result = self.qdrant_client.search(
collection_name=self.image_collection_name,
query_vector=outputs[0].tolist(),
query_filter=None,
limit=limit,
)
payloads = [f"- {hit.payload['label']} with score {hit.score}" for hit in search_result]
return payloads |