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import argparse
import json
import openai
from openai_function_utils.openai_function_interface import OPENAI_FUNCTIONS_DEFINITIONS, OPENAI_AVAILABLE_FUNCTIONS
from utils import get_embeddings, search_document_annoy, transform_user_question, debug_print
def truncate_input_text(input_text, question, max_length=7000):
# Calculate the remaining length available for the input text after accounting for the question
available_length_for_input = max_length - len(question) - len(
"Based on the input text: \n Give me answers for this question: ")
# Truncate the input text to fit the available length
truncated_input_text = input_text[:available_length_for_input]
# Construct the temporary question with the truncated input text
tmp_question = f"Based on the input text: {truncated_input_text}\nGive me answers for this question: {question}"
return tmp_question
def answer_with_gpt3_with_function_calls(input_text, question, model):
question = truncate_input_text(input_text, question)
messages = [
{
"role": "system",
"content": "".join([
"You are a professional, knowledgeable, supportive, friendly but not overly casual assistant who will help the user to answer questions about a lab. ",
"In order to do so, you may use semantic_search to find relevant documents. ",
])
},
{
"role": "user",
"content": question
}
]
response = openai.ChatCompletion.create(
model=model,
messages=messages,
functions=OPENAI_FUNCTIONS_DEFINITIONS,
max_tokens=200
)
response_message = response["choices"][0]["message"]
messages.append(
{
"role": "assistant",
"content": response_message.get("content"),
"function_call": response_message.get("function_call"),
}
)
# Check if GPT wanted to call a function
if response_message.get("function_call"):
# Call the function
# Note: the JSON response may not always be valid; be sure to handle errors
available_functions = OPENAI_AVAILABLE_FUNCTIONS # only one function in this example, but you can have multiple
function_name = response_message["function_call"]["name"]
# Step 4: send the info on the function call and function response to GPT
function_to_call = available_functions[function_name]
function_args = json.loads(response_message["function_call"]["arguments"])
function_response = function_to_call(**function_args)
messages.append(response_message) # extend conversation with assistant's reply
messages.append(
{
"role": "function",
"name": function_name,
"content": function_response,
}
) # extend conversation with function response
second_response = openai.ChatCompletion.create(
model=model,
messages=messages,
) # get a new response from GPT where it can see the function response
return second_response.choices[0].message.content
else:
return response.choices[0].message.content
# add input parameter: need api_key for demo
def get_response_from_model(user_input, top_k=3, annoy_metric='dot', model_name="gpt-3.5-turbo", user_query_preprocess=False):
assert top_k > 0, 'k must be an integer greater than 0'
if user_query_preprocess:
chatgpt_question = transform_user_question(user_input, model_name)
else:
chatgpt_question = user_input
debug_print("chatgpt_question: ", chatgpt_question)
try:
user_q_embedding = get_embeddings(chatgpt_question)
document = search_document_annoy(user_q_embedding, top_k=top_k, metric=annoy_metric)
reply = answer_with_gpt3_with_function_calls(document, user_input, model_name)
print(f"returning reply: {reply}")
return reply
except Exception as e:
print(f"returning error: {e}")
return e._message
# return "Error when trying to get embedding for the user query. Please try with a shorter question."
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