bettershot 💡🚀

⚡️ A Python package for adding error monitoring to LLM Apps in a few minutes ⚡

BetterShot by BerriAI let’s you add error (hallucinations/refusal to answer) monitoring to your LLM App in a few minutes. Like Bugsnag/Sentry for LLM apps!

How it works

We designed Bettershot to flag errors in retrieval-augmented LLM Apps (i.e. you're trying to get GPT to answer questions over specific data).

Bettershot aims to detect 3 things:

• Was the question relevant to the data (i.e. filter out questions like "how's the weather?" if the chatbot's purpose was to answer questions on the history of jeans)

If relevant then,

• Did the model response invent new information when answering the question (i.e. information that was not in the prompt passed in)
• Did the model refuse to answer the question (e.g. "Sorry as an AI language model...")

We do this by using chatgpt (currently gpt-3.5-turbo-16k) to evaluate each prompt-response pair and determine these things.

Key Technical Challenges

LLM-based evaluations are inconsistent We solved this by doing 2 things:

• We ask the LLM to write a rationale before making it's decision. Similar to CoT, this helps the model think through the decision - making results more consistent and understandable.
• We evaluate each prompt-response pair 5 times. Each evaluation returns either True or False, along with the model's rationale for why it chose what it did. We then pick the evaluation (True/False) that occurs most, along with the model rationale to explain reasoning.

We base this on the assumption that if it made the same decision 4 times out of 5, then it's probably the right one (minimizing inconcistencies as much as possible).

Latency for Production Applications We do not want to add latency to your application. When you call our API endpoint, we pass your request in a separate thread, and immediately return a response received message - ending the call. This ensures minimal latency (~0.004s) is added to your application.

We will be sharing the prompts soon! Reach out to us @ [email protected] if that's a blocker to using this.

Getting Started

Install bettershot by running this command.:

pip install bettershot

Using bettershot

It's just 1 line of code:

log(messages=messages, completion=result, user_email="YOUR_EMAIL", query=query)

Dashboard

All your logs are available @ 'https://better-test.vercel.app/' + YOUR_EMAIL

e.g. log(messages=messages, completion=result, user_email="[email protected]", query=query)

will have it's results logged @

https://better-test.vercel.app/[email protected]

Implementation

Here are all the items you can log:

Parameter	Type	Required/Optional	Description
messages	List	Required	The list of messages sent to the OpenAI chat completions endpoint
completion	Dictionary	Required	The response received from the OpenAI chat completions endpoint
user_email	String	Required	Your user email
query	String	Required	The query being asked by your user
customer_id	String	Optional	A way to identify your customer

Here's 2 examples of using it:

Calling the 'raw' OpenAI API

from bettershot import log
import openai 

def simple_openai_call(query):
    messages = [{'role': 'user', 'content': query}]
    completion = openai.ChatCompletion.create(
                model="gpt-3.5-turbo",
                messages=messages
    
            )
    log(messages=messages, completion=completion, user_email="YOUR_EMAIL", query=query, customer_id="fake_user@fake_accounts.xyz") #JUST 1 LINE OF CODE 🤯

simple_openai_call("hey! how's it going?")

Calling the Langchain OpenAI API

import openai
import langchain 
from bettershot import log

def simple_langchain_call(query):
    chat = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)
    prompt = "You are an extremely intelligent AI assistant for BerriAI, answer all questions resepectfully and in a warm tone"
    messages = [
      SystemMessage(content=prompt),
      HumanMessage(content=query)
    ]
    result = chat(messages)
    log(messages=messages, completion=completion, user_email="YOUR_EMAIL", query=query, customer_id="fake_user@fake_accounts.xyz") #JUST 1 LINE OF CODE 🎉 

simple_langchain_call("hey! how's it going?")

bettershot automatically evaluates your OpenAI responses to determine if the model either invented new information (hallucination) or refused to answer ("Sorry, as an AI language model...") a user's question.

Contributing

We welcome contributions to bettershot! Feel free to create issues/PR's/or DM us (👋 Hi I'm Krrish - +17708783106)

License

bettershot is released under the MIT License.