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8a682b5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 | """
Centralized embedding manager for the AI Agent system.
"""
import os
import logging
from typing import List, Optional, Dict, Any
import numpy as np
from functools import lru_cache
# Try to import embedding libraries
try:
from sentence_transformers import SentenceTransformer
SENTENCE_TRANSFORMERS_AVAILABLE = True
except ImportError:
SENTENCE_TRANSFORMERS_AVAILABLE = False
try:
import openai
OPENAI_AVAILABLE = True
except ImportError:
OPENAI_AVAILABLE = False
logger = logging.getLogger(__name__)
# Global embedding manager instance
_embedding_manager = None
class EmbeddingManager:
"""Centralized embedding manager with multiple backends"""
def __init__(self, model_name: str = "all-MiniLM-L6-v2", backend: str = "sentence_transformers"):
self.model_name = model_name
self.backend = backend
self.model = None
self.dimension = 384 # Default for all-MiniLM-L6-v2
# Initialize the appropriate backend
self._initialize_backend()
logger.info(f"Embedding manager initialized with {backend} backend using {model_name}")
def _initialize_backend(self):
"""Initialize the embedding backend"""
if self.backend == "sentence_transformers" and SENTENCE_TRANSFORMERS_AVAILABLE:
try:
self.model = SentenceTransformer(self.model_name)
self.dimension = self.model.get_sentence_embedding_dimension()
logger.info(f"Initialized SentenceTransformer with dimension {self.dimension}")
except Exception as e:
logger.error(f"Failed to initialize SentenceTransformer: {e}")
self._fallback_initialization()
elif self.backend == "openai" and OPENAI_AVAILABLE:
try:
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError("OPENAI_API_KEY not found in environment")
self.model = "text-embedding-ada-002"
self.dimension = 1536 # OpenAI ada-002 dimension
logger.info("Initialized OpenAI embedding backend")
except Exception as e:
logger.error(f"Failed to initialize OpenAI backend: {e}")
self._fallback_initialization()
else:
self._fallback_initialization()
def _fallback_initialization(self):
"""Fallback to simple hash-based embeddings"""
logger.warning("Using fallback hash-based embeddings")
self.backend = "fallback"
self.dimension = 128
@lru_cache(maxsize=1000)
def embed(self, text: str) -> List[float]:
"""Generate embedding for a text string"""
if not text or not text.strip():
return [0.0] * self.dimension
try:
if self.backend == "sentence_transformers" and self.model:
embedding = self.model.encode(text)
return embedding.tolist()
elif self.backend == "openai":
return self._openai_embed(text)
else:
return self._fallback_embed(text)
except Exception as e:
logger.error(f"Embedding failed for text: {text[:100]}... Error: {e}")
return [0.0] * self.dimension
def _openai_embed(self, text: str) -> List[float]:
"""Generate embedding using OpenAI API"""
try:
response = openai.Embedding.create(
input=text,
model=self.model
)
return response['data'][0]['embedding']
except Exception as e:
logger.error(f"OpenAI embedding failed: {e}")
return [0.0] * self.dimension
def _fallback_embed(self, text: str) -> List[float]:
"""Generate simple hash-based embedding"""
import hashlib
# Create a simple hash-based embedding
hash_obj = hashlib.sha256(text.encode())
hash_bytes = hash_obj.digest()
# Convert to float values
embedding = []
for i in range(0, min(len(hash_bytes), self.dimension * 4), 4):
if i + 3 < len(hash_bytes):
value = int.from_bytes(hash_bytes[i:i+4], byteorder='big')
embedding.append((value % 10000) / 10000.0) # Normalize to [0, 1]
# Pad or truncate to required dimension
while len(embedding) < self.dimension:
embedding.append(0.0)
return embedding[:self.dimension]
def embed_batch(self, texts: List[str]) -> List[List[float]]:
"""Generate embeddings for a batch of texts"""
if not texts:
return []
try:
if self.backend == "sentence_transformers" and self.model:
embeddings = self.model.encode(texts)
return embeddings.tolist()
elif self.backend == "openai":
return self._openai_embed_batch(texts)
else:
return [self._fallback_embed(text) for text in texts]
except Exception as e:
logger.error(f"Batch embedding failed: {e}")
return [[0.0] * self.dimension for _ in texts]
def _openai_embed_batch(self, texts: List[str]) -> List[List[float]]:
"""Generate batch embeddings using OpenAI API"""
try:
response = openai.Embedding.create(
input=texts,
model=self.model
)
return [item['embedding'] for item in response['data']]
except Exception as e:
logger.error(f"OpenAI batch embedding failed: {e}")
return [[0.0] * self.dimension for _ in texts]
def similarity(self, embedding1: List[float], embedding2: List[float]) -> float:
"""Calculate cosine similarity between two embeddings"""
try:
vec1 = np.array(embedding1)
vec2 = np.array(embedding2)
# Normalize vectors
norm1 = np.linalg.norm(vec1)
norm2 = np.linalg.norm(vec2)
if norm1 == 0 or norm2 == 0:
return 0.0
# Calculate cosine similarity
similarity = np.dot(vec1, vec2) / (norm1 * norm2)
return float(similarity)
except Exception as e:
logger.error(f"Similarity calculation failed: {e}")
return 0.0
def get_dimension(self) -> int:
"""Get the embedding dimension"""
return self.dimension
def get_backend_info(self) -> Dict[str, Any]:
"""Get information about the current backend"""
return {
"backend": self.backend,
"model_name": self.model_name,
"dimension": self.dimension,
"sentence_transformers_available": SENTENCE_TRANSFORMERS_AVAILABLE,
"openai_available": OPENAI_AVAILABLE
}
def get_embedding_manager(model_name: Optional[str] = None, backend: Optional[str] = None) -> EmbeddingManager:
"""Get or create the global embedding manager instance"""
global _embedding_manager
if _embedding_manager is None:
# Use environment variables or defaults
model_name = model_name or os.getenv("EMBEDDING_MODEL", "all-MiniLM-L6-v2")
backend = backend or os.getenv("EMBEDDING_BACKEND", "sentence_transformers")
_embedding_manager = EmbeddingManager(model_name=model_name, backend=backend)
return _embedding_manager
def reset_embedding_manager():
"""Reset the global embedding manager (useful for testing)"""
global _embedding_manager
_embedding_manager = None
# Convenience functions
def embed_text(text: str) -> List[float]:
"""Quick function to embed a single text"""
manager = get_embedding_manager()
return manager.embed(text)
def embed_batch(texts: List[str]) -> List[List[float]]:
"""Quick function to embed a batch of texts"""
manager = get_embedding_manager()
return manager.embed_batch(texts)
def calculate_similarity(embedding1: List[float], embedding2: List[float]) -> float:
"""Quick function to calculate similarity between embeddings"""
manager = get_embedding_manager()
return manager.similarity(embedding1, embedding2) |