Namitg02 commited on
Commit
1ccd79e
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verified ·
1 Parent(s): 940f5a3

Update app.py

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Files changed (1) hide show
  1. app.py +4 -7
app.py CHANGED
@@ -40,7 +40,6 @@ index = faiss.IndexFlatL2(embedding_dim)
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  data.add_faiss_index("embeddings", custom_index=index)
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  # adds an index column for the embeddings
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- print("check1")
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  #question = "How can I reverse Diabetes?"
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  SYS_PROMPT = """You are an assistant for answering questions.
@@ -95,8 +94,6 @@ def search(query: str, k: int = 2 ):
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  # returns scores (List[float]): the retrieval scores from either FAISS (IndexFlatL2 by default) and examples (dict) format
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  # called by talk function that passes prompt
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- #print(scores, retrieved_examples)
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-
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  def format_prompt(prompt,retrieved_documents,k):
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  """using the retrieved documents we will prompt the model to generate our responses"""
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  PROMPT = f"Question:{prompt}\nContext:"
@@ -129,22 +126,22 @@ def talk(prompt, history):
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  k = 2 # number of retrieved documents
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  scores , retrieved_documents = search(prompt, k) # get retrival scores and examples in dictionary format based on the prompt passed
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  print(retrieved_documents.keys())
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- print("check4")
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  formatted_prompt = format_prompt(prompt,retrieved_documents,k) # create a new prompt using the retrieved documents
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  print("check5")
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- print(retrieved_documents['0'])
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- print(formatted_prompt)
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  # formatted_prompt_with_history = add_history(formatted_prompt, history)
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  # formatted_prompt_with_history = formatted_prompt_with_history[:600] # to avoid memory issue
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  # print(formatted_prompt_with_history)
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  messages = [{"role":"system","content":SYS_PROMPT},{"role":"user","content":formatted_prompt}]
 
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  # binding the system context and new prompt for LLM
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  # the chat template structure should be based on text generation model format
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  print("check6")
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  # indicates the end of a sequence
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- import pprint
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  stream = model.create_chat_completion(messages = messages, max_tokens=1000, stop=["</s>"], stream=False)
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  print(f"{stream}")
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  print("check 7")
 
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  data.add_faiss_index("embeddings", custom_index=index)
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  # adds an index column for the embeddings
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  #question = "How can I reverse Diabetes?"
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  SYS_PROMPT = """You are an assistant for answering questions.
 
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  # returns scores (List[float]): the retrieval scores from either FAISS (IndexFlatL2 by default) and examples (dict) format
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  # called by talk function that passes prompt
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  def format_prompt(prompt,retrieved_documents,k):
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  """using the retrieved documents we will prompt the model to generate our responses"""
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  PROMPT = f"Question:{prompt}\nContext:"
 
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  k = 2 # number of retrieved documents
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  scores , retrieved_documents = search(prompt, k) # get retrival scores and examples in dictionary format based on the prompt passed
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  print(retrieved_documents.keys())
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+ # print("check4")
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  formatted_prompt = format_prompt(prompt,retrieved_documents,k) # create a new prompt using the retrieved documents
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  print("check5")
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+ # print(retrieved_documents['0'])
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+ # print(formatted_prompt)
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  # formatted_prompt_with_history = add_history(formatted_prompt, history)
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  # formatted_prompt_with_history = formatted_prompt_with_history[:600] # to avoid memory issue
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  # print(formatted_prompt_with_history)
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  messages = [{"role":"system","content":SYS_PROMPT},{"role":"user","content":formatted_prompt}]
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+ print(messages)
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  # binding the system context and new prompt for LLM
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  # the chat template structure should be based on text generation model format
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  print("check6")
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  # indicates the end of a sequence
 
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  stream = model.create_chat_completion(messages = messages, max_tokens=1000, stop=["</s>"], stream=False)
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  print(f"{stream}")
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  print("check 7")