adjustments

source/product_search.py

@@ -1,9 +1,7 @@
-# product_search.py
-
-import faiss
-import pickle
-import difflib
+import os
+import oracledb
 import numpy as np
+import difflib
 from rapidfuzz import fuzz
 from langchain_community.embeddings import OCIGenAIEmbeddings
 
@@ -11,19 +9,26 @@ from langchain_community.embeddings import OCIGenAIEmbeddings
 class BuscaProdutoSimilar:
     def __init__(
             self,
-            faiss_index_path="faiss_index.bin",
-            id_map_path="produto_id_map.pkl",
             top_k=5,
             distancia_minima=1.0,
             model_id="cohere.embed-english-light-v3.0",
             service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
-            compartment_id="ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
-            auth_profile="DEFAULT"
+            compartment_id="ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
+            auth_profile="DEFAULT",
+            wallet_path="/WALLET_PATH/Wallet_oradb23ai",
+            db_alias="oradb23ai_high",
+            username="USER",
+            password="Password"
     ):
-        print("📦 Carregando índice vetorial...")
-        self.index = faiss.read_index(faiss_index_path)
-        with open(id_map_path, "rb") as f:
-            self.id_map = pickle.load(f)
+        os.environ["TNS_ADMIN"] = wallet_path
+        self.conn = oracledb.connect(
+            user=username,
+            password=password,
+            dsn=db_alias,
+            config_dir=wallet_path,
+            wallet_location=wallet_path,
+            wallet_password=password
+        )
         self.top_k = top_k
         self.distancia_minima = distancia_minima
         self.embedding = OCIGenAIEmbeddings(
@@ -33,8 +38,27 @@ class BuscaProdutoSimilar:
             auth_profile=auth_profile
         )
 
+        print("📦 Carregando vetores do Oracle...")
+        self._carregar_embeddings()
+
+    def _carregar_embeddings(self):
+        cursor = self.conn.cursor()
+        cursor.execute("SELECT id, codigo, descricao, vetor FROM embeddings_produtos")
+        self.vetores = []
+        self.produtos = []
+        for row in cursor.fetchall():
+            id_, codigo, descricao, blob = row
+            vetor = np.frombuffer(blob.read(), dtype=np.float32)
+            self.vetores.append(vetor)
+            self.produtos.append({
+                "id": id_,
+                "codigo": codigo,
+                "descricao": descricao
+            })
+        self.vetores = np.array(self.vetores)
+
     def _corrigir_input(self, input_usuario):
-        descricoes = [p["descricao"] for p in self.id_map]
+        descricoes = [p["descricao"] for p in self.produtos]
         sugestoes = difflib.get_close_matches(input_usuario, descricoes, n=1, cutoff=0.6)
         return sugestoes[0] if sugestoes else input_usuario
 
@@ -50,12 +74,16 @@ class BuscaProdutoSimilar:
         }
 
         consulta_emb = self.embedding.embed_query(descricao_corrigida)
-        consulta_emb = np.array([consulta_emb])
-        distances, indices = self.index.search(consulta_emb, self.top_k)
+        consulta_emb = np.array(consulta_emb)
 
-        for i, dist in zip(indices[0], distances[0]):
+        # Cálculo de distância euclidiana
+        dists = np.linalg.norm(self.vetores - consulta_emb, axis=1)
+        top_indices = np.argsort(dists)[:self.top_k]
+
+        for idx in top_indices:
+            dist = dists[idx]
             if dist < self.distancia_minima:
-                match = self.id_map[i]
+                match = self.produtos[idx]
                 similaridade = 1 / (1 + dist)
                 resultados["semanticos"].append({
                     "id": match["id"],
@@ -67,7 +95,7 @@ class BuscaProdutoSimilar:
 
         if not resultados["semanticos"]:
             melhores_fuzz = []
-            for produto in self.id_map:
+            for produto in self.produtos:
                 score = fuzz.token_sort_ratio(descricao_corrigida, produto["descricao"])
                 melhores_fuzz.append((produto, score))
             melhores_fuzz.sort(key=lambda x: x[1], reverse=True)