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import faiss
import numpy as np
# -----------------------------
# BUILD FAISS INDEX (Optimized for Cosine Similarity)
# -----------------------------
def build_faiss_index(embeddings: list):
"""
📘 Builds a FAISS index optimized for cosine similarity (fast + accurate).
Args:
embeddings (list): List of embedding vectors (lists of floats).
Returns:
faiss.IndexFlatIP: FAISS index for cosine similarity search.
"""
if not embeddings or not isinstance(embeddings, list):
raise ValueError("❌ Invalid input: embeddings must be a non-empty list.")
# Convert to numpy float32 array
vectors = np.array(embeddings).astype("float32")
dim = vectors.shape[1]
# ✅ Normalize embeddings (turns dot product into cosine similarity)
faiss.normalize_L2(vectors)
# ✅ Use Inner Product index (fast cosine similarity)
index = faiss.IndexFlatIP(dim)
index.add(vectors)
print(f"✅ FAISS index built with {index.ntotal} vectors of dimension {dim} (cosine similarity)")
return index
# -----------------------------
# SEARCH FAISS INDEX
# -----------------------------
def search_faiss(query_embedding: list, index, chunks: list, top_k: int = 3):
"""
🔍 Searches FAISS index for most similar chunks to query.
Args:
query_embedding (list): Embedding for user query.
index (faiss.IndexFlatL2): Pre-built FAISS index.
chunks (list[str]): Original text chunks.
top_k (int): Number of most similar results to return.
Returns:
list[str]: Top-matching text chunks.
"""
if index is None or index.ntotal == 0:
raise ValueError("❌ FAISS index is empty or not initialized.")
# Convert query embedding to correct format
query_vector = np.array([query_embedding]).astype("float32")
# Search the index (returns distances + indices)
distances, indices = index.search(query_vector, top_k)
# Extract matched chunks with their distances (sorted)
results = []
for i, idx in enumerate(indices[0]):
if idx < len(chunks):
results.append(chunks[idx])
return results
# -----------------------------
# OPTIONAL: DEBUG / DEMO
# -----------------------------
if __name__ == "__main__":
# Example usage test
sample_embeddings = [
[0.1, 0.2, 0.3],
[0.2, 0.1, 0.4],
[0.9, 0.8, 0.7]
]
query_vec = [0.15, 0.18, 0.35]
idx = build_faiss_index(sample_embeddings)
results = search_faiss(query_vec, idx, ["Chunk A", "Chunk B", "Chunk C"], top_k=2)
print("🔎 Top Results:")
for r in results:
print("-", r)
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