feat: gerando documento final corretamente

_utils/gerar_relatorio_modelo_usuario/GerarDocumento.py

@@ -177,41 +177,7 @@ class GerarDocumento:
                 vector_store, bm25, chunk_ids, query
             )
 
-            # Prepare context and track sources
-            contexts = []
-            sources = []
-
-            # Get full documents for top results
-            for chunk_id, score in ranked_results[: self.config.num_chunks]:
-                results = vector_store.get(
-                    where={"chunk_id": chunk_id}, include=["documents", "metadatas"]
-                )
-
-                if results["documents"]:
-                    context = results["documents"][0]
-                    metadata = results["metadatas"][0]
-
-                    contexts.append(context)
-                    sources.append(
-                        {
-                            "content": context,
-                            "page": metadata["page"],
-                            "chunk_id": chunk_id,
-                            "relevance_score": score,
-                            "context": metadata.get("context", ""),
-                        }
-                    )
-
-            if llm_ultimas_requests == "gpt-4o-mini":
-                llm = ChatOpenAI(
-                    temperature=self.gpt_temperature,
-                    model=self.gpt_model,
-                    api_key=SecretStr(self.openai_api_key),
-                )
-            elif llm_ultimas_requests == "deepseek-chat":
-                llm_instance = LLM()
-                llm = llm_instance.deepseek()
-
+            llm = self.select_model_for_last_requests(llm_ultimas_requests)
             # prompt_auxiliar = PromptTemplate(
             #     template=self.prompt_auxiliar, input_variables=["context"]
             # )
@@ -224,10 +190,10 @@ class GerarDocumento:
 
             prompt_gerar_documento = PromptTemplate(
                 template=self.prompt_gerar_documento,
-                input_variables=["documento_gerado", "context"],
+                input_variables=["context"],
             )
 
-            documento_gerado_final = cast(
+            documento_gerado = cast(
                 str,
                 llm.invoke(
                     prompt_gerar_documento.format(
@@ -238,9 +204,7 @@ class GerarDocumento:
             )
 
             # Split the response into paragraphs
-            summaries = [
-                p.strip() for p in documento_gerado_final.split("\n\n") if p.strip()
-            ]
+            summaries = [p.strip() for p in documento_gerado.split("\n\n") if p.strip()]
 
             # Create structured output
             structured_output = []