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"""
Embedding generation and similarity computation for MCP integration.
Features:
- Text embedding generation
- Batch embedding
- Similarity computation
- Embedding caching
"""
from typing import List, Dict, Optional, Tuple, Any
import numpy as np
from functools import lru_cache
# Embedding cache
_embedding_cache: Dict[str, List[float]] = {}
_cache_max_size = 1000
def generate_embeddings(text: str, use_cache: bool = True) -> Dict:
"""
Generate embeddings for text.
Args:
text: Input text to embed
use_cache: Whether to use cached embeddings
Returns:
Embedding vector and metadata
"""
global _embedding_cache
if not text or not text.strip():
return {
"success": False,
"error": "Empty text provided",
"embedding": None
}
# Check cache
cache_key = text[:500] # Use first 500 chars as key
if use_cache and cache_key in _embedding_cache:
return {
"success": True,
"embedding": _embedding_cache[cache_key],
"cached": True,
"dimensions": len(_embedding_cache[cache_key])
}
try:
from core.llm import embed_text
embedding = embed_text(text)
if embedding is None:
return {
"success": False,
"error": "Embedding generation failed",
"embedding": None
}
# Convert to list if numpy array
if isinstance(embedding, np.ndarray):
embedding = embedding.tolist()
# Cache the result
if use_cache:
if len(_embedding_cache) >= _cache_max_size:
# Remove oldest entries
oldest = list(_embedding_cache.keys())[:100]
for k in oldest:
del _embedding_cache[k]
_embedding_cache[cache_key] = embedding
return {
"success": True,
"embedding": embedding,
"cached": False,
"dimensions": len(embedding)
}
except Exception as e:
return {
"success": False,
"error": str(e),
"embedding": None
}
def batch_embed(
texts: List[str],
batch_size: int = 10,
use_cache: bool = True
) -> Dict:
"""
Generate embeddings for multiple texts.
Args:
texts: List of texts to embed
batch_size: Number of texts per batch
use_cache: Whether to use cached embeddings
Returns:
List of embeddings with metadata
"""
if not texts:
return {
"success": False,
"error": "No texts provided",
"embeddings": []
}
embeddings = []
errors = []
cached_count = 0
for i, text in enumerate(texts):
result = generate_embeddings(text, use_cache=use_cache)
if result["success"]:
embeddings.append({
"index": i,
"text_preview": text[:50],
"embedding": result["embedding"],
"cached": result.get("cached", False)
})
if result.get("cached"):
cached_count += 1
else:
embeddings.append({
"index": i,
"text_preview": text[:50],
"embedding": None,
"error": result.get("error")
})
errors.append(f"Text {i}: {result.get('error')}")
return {
"success": len(errors) == 0,
"embeddings": embeddings,
"total": len(texts),
"successful": len(texts) - len(errors),
"cached_hits": cached_count,
"errors": errors if errors else None
}
def compute_similarity(
embedding1: List[float],
embedding2: List[float],
method: str = "cosine"
) -> Dict:
"""
Compute similarity between two embeddings.
Args:
embedding1: First embedding vector
embedding2: Second embedding vector
method: "cosine", "euclidean", "dot"
Returns:
Similarity score and metadata
"""
try:
vec1 = np.array(embedding1)
vec2 = np.array(embedding2)
if len(vec1) != len(vec2):
return {
"success": False,
"error": f"Dimension mismatch: {len(vec1)} vs {len(vec2)}",
"similarity": None
}
if method == "cosine":
# Cosine similarity
dot = np.dot(vec1, vec2)
norm1 = np.linalg.norm(vec1)
norm2 = np.linalg.norm(vec2)
if norm1 == 0 or norm2 == 0:
similarity = 0.0
else:
similarity = dot / (norm1 * norm2)
elif method == "euclidean":
# Euclidean distance (converted to similarity)
distance = np.linalg.norm(vec1 - vec2)
similarity = 1.0 / (1.0 + distance)
elif method == "dot":
# Dot product
similarity = float(np.dot(vec1, vec2))
else:
return {
"success": False,
"error": f"Unknown method: {method}",
"similarity": None
}
return {
"success": True,
"similarity": float(similarity),
"method": method,
"dimensions": len(vec1)
}
except Exception as e:
return {
"success": False,
"error": str(e),
"similarity": None
}
def find_most_similar(
query_embedding: List[float],
candidate_embeddings: List[Dict],
top_k: int = 5,
threshold: float = 0.0
) -> Dict:
"""
Find most similar embeddings from candidates.
Args:
query_embedding: Query vector
candidate_embeddings: List of {"id": str, "embedding": List[float]}
top_k: Number of top results
threshold: Minimum similarity threshold
Returns:
Top k most similar candidates
"""
try:
query_vec = np.array(query_embedding)
similarities = []
for candidate in candidate_embeddings:
cand_vec = np.array(candidate["embedding"])
# Cosine similarity
dot = np.dot(query_vec, cand_vec)
norm_q = np.linalg.norm(query_vec)
norm_c = np.linalg.norm(cand_vec)
if norm_q > 0 and norm_c > 0:
sim = dot / (norm_q * norm_c)
else:
sim = 0.0
if sim >= threshold:
similarities.append({
"id": candidate.get("id", "unknown"),
"similarity": float(sim),
"metadata": candidate.get("metadata", {})
})
# Sort by similarity descending
similarities.sort(key=lambda x: x["similarity"], reverse=True)
return {
"success": True,
"results": similarities[:top_k],
"total_candidates": len(candidate_embeddings),
"above_threshold": len(similarities)
}
except Exception as e:
return {
"success": False,
"error": str(e),
"results": []
}
def compute_centroid(embeddings: List[List[float]]) -> Dict:
"""
Compute centroid of multiple embeddings.
Args:
embeddings: List of embedding vectors
Returns:
Centroid vector
"""
try:
if not embeddings:
return {
"success": False,
"error": "No embeddings provided",
"centroid": None
}
vectors = np.array(embeddings)
centroid = np.mean(vectors, axis=0)
return {
"success": True,
"centroid": centroid.tolist(),
"num_vectors": len(embeddings),
"dimensions": len(centroid)
}
except Exception as e:
return {
"success": False,
"error": str(e),
"centroid": None
}
def clear_embedding_cache() -> Dict:
"""Clear the embedding cache"""
global _embedding_cache
count = len(_embedding_cache)
_embedding_cache = {}
return {
"success": True,
"cleared_entries": count
}
def get_cache_stats() -> Dict:
"""Get embedding cache statistics"""
global _embedding_cache
return {
"entries": len(_embedding_cache),
"max_size": _cache_max_size,
"utilization": len(_embedding_cache) / _cache_max_size
}
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