fixing imports

rag/retriever.py

@@ -1,8 +1,42 @@
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
 from config import EMBEDDING_MODEL
 
-_embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
+
+class SimpleVectorStore:
+    """Vectorstore simples usando FAISS"""
+    
+    def __init__(self, embeddings, documents):
+        self.embeddings = embeddings
+        self.documents = documents
+        self.index = None
+        self._build_index()
+    
+    def _build_index(self):
+        """Constrói índice FAISS"""
+        texts = [doc.page_content for doc in self.documents]
+        vectors = self.embeddings.encode(texts)
+        
+        dimension = vectors.shape[1]
+        self.index = faiss.IndexFlatL2(dimension)
+        self.index.add(np.array(vectors).astype('float32'))
+    
+    def similarity_search(self, query, k=3):
+        """Busca os k documentos mais similares"""
+        query_vector = self.embeddings.encode([query])
+        distances, indices = self.index.search(
+            np.array(query_vector).astype('float32'), 
+            k
+        )
+        
+        return [self.documents[i] for i in indices[0]]
+
+
+# Carrega o modelo de embeddings uma vez
+_embeddings_model = SentenceTransformer(EMBEDDING_MODEL)
+
 
 def build_vectorstore(documents):
-    return FAISS.from_documents(documents, _embeddings)
+    """Constrói vectorstore a partir de documentos"""
+    return SimpleVectorStore(_embeddings_model, documents)