Improving Results - 3 strategies w/ Berri

import requests
import json

# Approach 1: Top K = 5 (picks and feeds chatGPT/LLM the 5 most relevant chunks of data, when user asks a question)

tmp_list = ["hello world", "https://stripe.com/docs/india-accept-international-payments#TransactionPurposeCode"]

url = "https://api.berri.ai/create_app"
data = {"user_email": "krrish@berri.ai", "data_source": json.dumps(tmp_list)}
response = requests.post(url, data=data)
instance_id = response.json()["instance_id"]

print("instance created!")

url = "https://api.berri.ai/query"

querystring = {
"user_email": "ishaan@berri.ai",
"instance_id": instance_id,
"model": "gpt-3.5-turbo",
"top_k": 5
}

questions = ["what do you know?", "hi"]

for question in questions:
  querystring["query"] = question
  response = requests.get(url, params=querystring)
  print(response.json()["response"])
  print("====")



# # # Approach 2: QA Gen - improve search by generating potential questions for each chunk of data + Top K = 5 (picks and feeds chatGPT/LLM the 5 most relevant chunks of data, when user asks a question)

url = "https://api.berri.ai/create_template"

data = {"advanced": {"intent": "qa_doc", "search": "qa_gen"}, "prompt": "Be legal and ethical"}

response = requests.post(url, data={"app_config": json.dumps(data)})

template_id = response.json()["template_id"]

tmp_list = ["hello world", "hi, how's it going?"]

url = "https://api.berri.ai/create_app"
data = {"user_email": "krrish@berri.ai", "data_source": json.dumps(tmp_list), "template_id":template_id}
response = requests.post(url, data=data)
print(response.text)
instance_id = response.json()["instance_id"]

print("instance created!")

url = "https://api.berri.ai/query"

querystring = {
"user_email": "ishaan@berri.ai",
"instance_id": instance_id,
"model": "gpt-3.5-turbo",
"top_k": "5"
}

questions = ["what do you know?", "hi"]

for question in questions:
  querystring["query"] = question
  response = requests.get(url, params=querystring)
  print(response.json()["response"])
  print("====")


# # Approach 3: Complex QA [Warning: Far slower!] - we first pass the user question to chatGPT/LLM to break it down into more specific sub-questions -> we then query our knowledge base with this information -> the combined chunks of data (answers from all the sub-questions) are then fed into chatGPT/LLM to come up with a final answer

url = "https://api.berri.ai/create_template"

data = {"advanced": {"intent": "qa_doc", "app_type": "complex"}, "prompt": "Be legal and ethical"}

response = requests.post(url, data={"app_config": json.dumps(data)})
template_id = response.json()["template_id"]
print(response.text)

tmp_list = ["https://stripe.com/docs/india-accept-international-payments#TransactionPurposeCode"]

url = "https://api.berri.ai/create_app"
data = {"user_email": "krrish@berri.ai", "data_source": json.dumps(tmp_list), "template_id":template_id}
response = requests.post(url, data=data)
instance_id = response.json()["instance_id"]

print("instance created!")

url = "https://api.berri.ai/query"

querystring = {
"user_email": "ishaan@berri.ai",
"instance_id": instance_id,
"model": "gpt-3.5-turbo",
"top_k": "5"
}

questions = ["what do you know?", "hi"]

for question in questions:
  querystring["query"] = question
  response = requests.get(url, params=querystring)
  print(response.json()["response"])
  print("====")