Upload rag.py

rag.py

@@ -61,22 +61,20 @@ class Rag:
         self.vectorstore = vectorstore
         self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key="answer")
 
-        #
-        #Do not use only your knowledge to make the news.
-        prompt_template = """Your task is to create news for a newspaper based on pieces of text delimited by <> and a question delimited by <>.
-                    Do not use only your knowledge to make the news. Make the news based on the question, but using the pieces of text.
-                    If the pieces of text don't enough information about the question to create the news, just say that you need more sources of information, nothing more.
-                    The news should have a title.
-                    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.
-                    The news should be about the following context: <{context}>
-                    Question: <{question}>
-                    Answer here:"""
-        self.prompt = PromptTemplate(template=prompt_template,
-                                     input_variables=["context", "question"],
-                                     partial_variables={"min_words": min_words, "max_words": max_words})
-
         if model == 'openai':
+            prompt_template = """Your task is to create news for a newspaper based on pieces of text delimited by <> and a question delimited by <>.
+                        Do not use only your knowledge to make the news. Make the news based on the question, but using the pieces of text.
+                        If the pieces of text don't enough information about the question to create the news, just say that you need more sources of information, nothing more.
+                        The news should have a title.
+                        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.
+                        The news should be about the following context: <{context}>
+                        Question: <{question}>
+                        Answer here:"""
+            self.prompt = PromptTemplate(template=prompt_template,
+                                         input_variables=["context", "question"],
+                                         partial_variables={"min_words": min_words, "max_words": max_words})
+
             self.qa = ConversationalRetrievalChain.from_llm(
                         llm=ChatOpenAI(model_name="gpt-3.5-turbo-0125", #0125 #1106
                                        temperature=0,
@@ -91,6 +89,20 @@ class Rag:
                         return_source_documents=True,
                     )
         else:
+            prompt_template = """Your task is to create news for a newspaper based on pieces of text delimited by <> and a question delimited by <>.
+                        Do not use only your knowledge to make the news. Make the news based on the question, but using the pieces of text.
+                        If the pieces of text don't enough information about the question to create the news, just say that you need more sources of information, nothing more.
+                        The news should have a title.
+                        The news should be written in a formal language.
+                        The source should not be in the news.
+                        The news should have between {min_words} and {max_words} words and it should be in Portuguese language.
+                        The news should be about the following context: <{context}>
+                        Question: <{question}>
+                        Answer here:"""
+            self.prompt = PromptTemplate(template=prompt_template,
+                                         input_variables=["context", "question"],
+                                         partial_variables={"min_words": min_words, "max_words": max_words})
+
             self.qa = ConversationalRetrievalChain.from_llm(
                         llm=Together(model="mistralai/Mixtral-8x7B-Instruct-v0.1", #0125 #1106
                                        temperature=0,