Upload 3 files

llm.py

@@ -14,7 +14,7 @@ def invoke_llm(context, task):
                  The context is: $$${context}$$$
               """
 
-    llm=ChatOpenAI(model_name="gpt-3.5-turbo",
+    llm=ChatOpenAI(model_name="gpt-3.5-turbo-0125",
                        temperature=0,
                        openai_api_key=os.environ['OPENAI_KEY'],
                        max_tokens=1000)

main.py

@@ -39,6 +39,8 @@ def generate_news(subject, min_words, max_words, sites):
     print('    Assunto: ' + subject)
     obj_rag = rag.Rag(vectorstore, min_words, max_words)
     result_news = obj_rag.generate_text(subject)
+    if type(result_news) == str:
+        return 'Erro: ' + result_news
 
     print('\n\n' + '*' * 50 + '\n\n')
     print(result_news[0])

rag.py

@@ -59,6 +59,7 @@ class Rag:
 
         prompt_template = """Your task is to create news to a newspaper based on pieces of texts delimited by <> and a question delimited by <>.
                     Do not make up any information, create the news just based on the given information on the pieces of texts delimited by <>.
+                    If you need to make up any information, please do not answer the question. Inform that you need more information to answer the question.
                     The news should have a tittle.
                     The news should be written in a formal language.
                     The news should have between {min_words} and {max_words} words and it should be in portuguese language.
@@ -70,12 +71,14 @@ class Rag:
                                      partial_variables={"min_words": min_words, "max_words": max_words})
 
         self.qa = ConversationalRetrievalChain.from_llm(
-                    llm=ChatOpenAI(model_name="gpt-3.5-turbo",
+                    llm=ChatOpenAI(model_name="gpt-3.5-turbo-0125",
                                    temperature=0.1,
                                    openai_api_key=os.environ['OPENAI_KEY'],
                                    max_tokens=int(int(max_words) + (int(max_words) / 2))), #número máximo de tokens para a resposta
                     memory=self.memory,
-                    retriever=vectorstore.as_retriever(), #search_kwargs={'k': 3}
+                    #retriever=vectorstore.as_retriever(search_type='similarity_score_threshold',
+                    #                                   search_kwargs={'k':4, 'score_threshold':0.5}), #search_kwargs={'k': 3}
+                    retriever=vectorstore.as_retriever(),
                     combine_docs_chain_kwargs={"prompt": self.prompt},
                     chain_type="stuff",#map_reduce, refine, map_rerank
                     return_source_documents=True,
@@ -84,6 +87,7 @@ class Rag:
         try:
             query = f"Elabore uma nova notícia sobre {subject}."
             result_text = self.qa.invoke({"question": query})
+            print('##### result', result_text)
 
             list_result_sources = []
             str_result_sources = ''