supporting-blog-content/ElasticDocs_GPT/elasticdocs_gpt.py

import os
import streamlit as st
import openai
from elasticsearch import Elasticsearch

# This code is part of an Elastic Blog showing how to combine
# Elasticsearch's search relevancy power with
# OpenAI's GPT's Question Answering power
# https://www.elastic.co/blog/chatgpt-elasticsearch-openai-meets-private-data

# Code is presented for demo purposes but should not be used in production
# You may encounter exceptions which are not handled in the code


# Required Environment Variables
# openai_api - OpenAI API Key
# cloud_id - Elastic Cloud Deployment ID
# cloud_user - Elasticsearch Cluster User
# cloud_pass - Elasticsearch User Password

openai.api_key = os.environ["openai_api"]
model = "gpt-3.5-turbo-0301"


# Connect to Elastic Cloud cluster
def es_connect(cid, user, passwd):
    es = Elasticsearch(cloud_id=cid, http_auth=(user, passwd))
    return es


# Search ElasticSearch index and return body and URL of the result
def search(query_text):
    cid = os.environ["cloud_id"]
    cp = os.environ["cloud_pass"]
    cu = os.environ["cloud_user"]
    es = es_connect(cid, cu, cp)

    # Elasticsearch query (BM25) and kNN configuration for hybrid search
    query = {
        "bool": {
            "must": [{"match": {"title": {"query": query_text, "boost": 1}}}],
            "filter": [{"exists": {"field": "title-vector"}}],
        }
    }

    knn = {
        "field": "title-vector",
        "k": 1,
        "num_candidates": 20,
        "query_vector_builder": {
            "text_embedding": {
                "model_id": "sentence-transformers__all-distilroberta-v1",
                "model_text": query_text,
            }
        },
        "boost": 24,
    }

    fields = ["title", "body_content", "url"]
    index = "search-elastic-docs"
    resp = es.search(
        index=index, query=query, knn=knn, fields=fields, size=1, source=False
    )

    body = resp["hits"]["hits"][0]["fields"]["body_content"][0]
    url = resp["hits"]["hits"][0]["fields"]["url"][0]

    return body, url


def truncate_text(text, max_tokens):
    tokens = text.split()
    if len(tokens) <= max_tokens:
        return text

    return " ".join(tokens[:max_tokens])


# Generate a response from ChatGPT based on the given prompt
def chat_gpt(
    prompt,
    model="gpt-3.5-turbo",
    max_tokens=1024,
    max_context_tokens=4000,
    safety_margin=5,
):
    # Truncate the prompt content to fit within the model's context length
    truncated_prompt = truncate_text(
        prompt, max_context_tokens - max_tokens - safety_margin
    )

    response = openai.ChatCompletion.create(
        model=model,
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": truncated_prompt},
        ],
    )

    return response["choices"][0]["message"]["content"]


st.title("ElasticDocs GPT")

# Main chat form
with st.form("chat_form"):
    query = st.text_input("You: ")
    submit_button = st.form_submit_button("Send")

# Generate and display response on form submission
negResponse = "I'm unable to answer the question based on the information I have from Elastic Docs."
if submit_button:
    resp, url = search(query)
    prompt = f"Answer this question: {query}\nUsing only the information from this Elastic Doc: {resp}\nIf the answer is not contained in the supplied doc reply '{negResponse}' and nothing else"
    answer = chat_gpt(prompt)

    if negResponse in answer:
        st.write(f"ChatGPT: {answer.strip()}")
    else:
        st.write(f"ChatGPT: {answer.strip()}\n\nDocs: {url}")