File size: 6,058 Bytes
1e81c91 faaabaa 1e81c91 7bc7ddb 1e81c91 6495d55 bfad56c 1e81c91 6495d55 1e81c91 6495d55 c6f0ab2 6495d55 1e81c91 7bc7ddb 1e81c91 152241c 1e81c91 7bc7ddb 1e81c91 152241c 179cd4f 1e81c91 179cd4f 152241c 179cd4f 69623ca 179cd4f bd4417e 179cd4f 69623ca 179cd4f c6f0ab2 152241c 858f321 152241c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
from utils.youtube_extractor import YoutubeExtractor
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
from typing import List, Dict, Optional, Union
from class_mod.rest_qdrant import RestQdrantClient
import uuid
class DataImporter:
def __init__(self, qdrant_url: str = "https://qdrant.taspolsd.dev", collection_name: str = "demo_bge_m3"):
self.model = SentenceTransformer("BAAI/bge-m3")
# self.client = QdrantClient(url=qdrant_url)
self.qdrant_url = qdrant_url
self.client = None
self.collection_name = collection_name
self.youtube_extractor = YoutubeExtractor()
self._init_qdrant()
# Create collection if it doesn't exist
self._create_collection()
def _init_qdrant(self):
"""Initialize Qdrant client with error handling"""
try:
self.client = RestQdrantClient(url=self.qdrant_url,timeout=15)
# Test connection
self.client.get_collections()
self.qdrant_available = True
print(f"Successfully connected to Qdrant at {self.qdrant_url}")
self._create_collection()
except Exception as e:
print(f"Warning: Could not connect to Qdrant: {e}")
print("Running in offline mode - vector operations will be disabled")
self.client = None
self.qdrant_available = False
def _create_collection(self):
try:
collections = self.client.get_collection(self.collection_name)
if collections:
print(f"Collection '{self.collection_name}' already exists.")
return
self.client.recreate_collection(
collection_name=self.collection_name,
vectors_config=VectorParams(size=1024, distance=Distance.COSINE)
)
print(f"Collection '{self.collection_name}' created successfully")
except Exception as e:
print(f"Error creating collection: {e}")
def encode_text(self, texts: Union[str, List[str]]) -> List[List[float]]:
if isinstance(texts, str):
texts = [texts]
embeddings = self.model.encode(texts, normalize_embeddings=True)
return embeddings.tolist()
def insert_text(self, text: str, metadata: Optional[Dict] = None, custom_id: Optional[str] = None) -> str:
point_id = custom_id or str(uuid.uuid4())
embedding = self.encode_text(text)[0]
payload = {"text": text}
if metadata:
payload.update(metadata)
self.client.upsert(
collection_name=self.collection_name,
points=[PointStruct(id=point_id, vector=embedding, payload=payload)]
)
print(f"Inserted text with ID: {point_id}")
return point_id
def insert_texts(self, texts: List[str], metadata_list: Optional[List[Dict]] = None) -> List[str]:
embeddings = self.encode_text(texts)
point_ids = [str(uuid.uuid4()) for _ in texts]
points = []
for i, (text, embedding, point_id) in enumerate(zip(texts, embeddings, point_ids)):
payload = {"text": text}
if metadata_list and i < len(metadata_list):
payload.update(metadata_list[i])
points.append(PointStruct(id=point_id, vector=embedding, payload=payload))
self.client.upsert(collection_name=self.collection_name, points=points)
print(f"Inserted {len(texts)} texts")
return point_ids
def insert_from_youtube(self, video_id: str, metadata: Optional[Dict] = None) -> Optional[str]:
try:
# Extract text from YouTube (assuming your YoutubeExtractor has this method)
text = self.youtube_extractor.get_full_text(video_id)
if text:
video_metadata = {"source": "youtube", "video_id": video_id}
if metadata:
video_metadata.update(metadata)
return self.insert_text(text, video_metadata)
return None
except Exception as e:
print(f"Error extracting from YouTube: {e}")
return None
def search_similar(self, query: str, limit: int = 1) -> List[Dict]:
"""Search with Qdrant availability check - always returns a list"""
if not self.qdrant_available or not self.client:
print("Warning: Qdrant not available, returning empty results")
return []
try:
query_embedding = self.encode_text(query)[0]
results = self.client.search(
collection_name=self.collection_name,
query_vector=query_embedding,
limit=limit,
timeout=15
)
print(f"Search results: {results}")
return [
{
"id": result['id'],
"score": float(result['score']) if result['score'] else 0.0,
"text": result['payload'].get("text", ""),
"metadata": {k: v for k, v in result['payload'].items() if k != "text"}
}
for result in results['result']
]
except Exception as e:
print(f"Error searching: {e}")
raise ValueError(f"Search failed: {str(e)}")
def coldStartDatabase(self):
coldstart_texts = "I want to go to Chiang Mai"
try:
query_embedding = self.encode_text(coldstart_texts)[0]
results = self.client.search(
collection_name=self.collection_name,
query_vector=query_embedding,
limit=1,
timeout=10
)
print(f"Cold start results: {results}")
except Exception as e:
print(f"finish cold start, with error: {e}")
|