Update app.py

app.py

@@ -24,14 +24,12 @@ model_name = "gpt-3.5-turbo"
 # Define the template
 template = PromptTemplate(
     prompt="""
-Answer the given question using the following documents \
-Formulate your answer in the style of an academic report \
+Answer the given question using the following documents. \
+Formulate your answer in the style of an academic report. \
 Provide example quotes and citations using extracted text from the documents. \
 Use facts and numbers from the documents in your answer. \
 Reference information used from documents at the end of each applicable sentence (ex: [source: document_name]), where 'document_name' is the text provided at the start of each document (demarcated by '- &&&' and '&&&:')'. \
 If no relevant information to answer the question is present in the documents, just say you don't have enough information to answer. \
-Format your response as a JSON object with "answer" and "sources" as the keys. \
-The "answer" key is the response to the query and "sources" key is the reference information used from the documents. \
 
 Context: {' - '.join(['&&& '+d.meta['document_name']+' ref. '+str(d.meta['ref_id'])+' &&&: '+d.content for d in documents])}; Question: {query}; Answer:""",
 )
@@ -118,7 +116,7 @@ retriever = EmbeddingRetriever(
 )
 
 # Initialize the PromptNode
-pn = PromptNode(model_name_or_path=model_name, default_prompt_template=template, api_key=openai_key, max_length=700, model_kwargs={"generation_kwargs": {"do_sample": False, "temperature": 0}})
+pn = PromptNode(model_name_or_path=model_name, default_prompt_template=template, api_key=openai_key, max_length=2000, model_kwargs={"generation_kwargs": {"do_sample": False, "temperature": 0}})
 
 # Initialize the pipeline
 pipe = Pipeline()