Create vectorstore.py

src/vectorstore.py

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+import faiss            # 📌 Library: specialized for vector search
+import numpy as np      # 📌 Library: handles numeric arrays efficiently
+
+
+def build_faiss_index(embeddings: list):
+    """
+    📌 Build a FAISS index from embeddings.
+
+    Args:
+        embeddings (list): List of embedding vectors (lists of floats).
+
+    Returns:
+        index (faiss.IndexFlatL2): FAISS index for similarity search.
+    """
+    # Step 1: Get vector size (e.g., 384 for Hugging Face embeddings)
+    dimension = len(embeddings[0])
+
+    # Step 2: Create FAISS index that uses Euclidean (L2) distance
+    index = faiss.IndexFlatL2(dimension)
+
+    # Step 3: Add all embeddings to the index (convert to float32 array first)
+    index.add(np.array(embeddings).astype('float32'))
+
+    return index
+
+
+def search_faiss(query_embedding: list, index, chunks: list, top_k: int = 3):
+    """
+    📌 Search FAISS index to find most similar chunks.
+
+    Args:
+        query_embedding (list): Embedding for user query.
+        index: FAISS index object.
+        chunks (list): Original text chunks.
+        top_k (int): Number of results to return (default = 3).
+
+    Returns:
+        list: Top matching text chunks.
+    """
+    # Step 1: Turn query embedding into shape (1, dim) numpy array
+    query_vector = np.array([query_embedding]).astype('float32')
+
+    # Step 2: Search FAISS for nearest neighbors
+    distances, indices = index.search(query_vector, top_k)
+
+    # Step 3: Map result indices back to actual text chunks
+    return [chunks[i] for i in indices[0]]