retrieval_augmented_generation.py

import csv
import os

import azure.identity
import openai
from dotenv import load_dotenv

# Setup the OpenAI client to use either Azure, OpenAI.com, or Ollama API
load_dotenv(override=True)
API_HOST = os.getenv("API_HOST")

if API_HOST == "azure":
    token_provider = azure.identity.get_bearer_token_provider(
        azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
    )
    client = openai.AzureOpenAI(
        api_version=os.getenv("AZURE_OPENAI_VERSION"),
        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
        azure_ad_token_provider=token_provider,
    )
    MODEL_NAME = os.getenv("AZURE_OPENAI_DEPLOYMENT")

elif API_HOST == "ollama":
    client = openai.OpenAI(
        base_url=os.getenv("OLLAMA_ENDPOINT"),
        api_key="nokeyneeded",
    )
    MODEL_NAME = os.getenv("OLLAMA_MODEL")

elif API_HOST == "github":
    client = openai.OpenAI(base_url="https://models.inference.ai.azure.com", api_key=os.getenv("GITHUB_TOKEN"))
    MODEL_NAME = os.getenv("GITHUB_MODEL")

else:
    client = openai.OpenAI(api_key=os.environ["OPENAI_KEY"])
    MODEL_NAME = os.environ["OPENAI_MODEL"]


USER_MESSAGE = "how fast is the prius v?"

# Open the CSV and store in a list
with open("hybrid.csv") as file:
    reader = csv.reader(file)
    rows = list(reader)

# Normalize the user question to replace punctuation and make lowercase
normalized_message = USER_MESSAGE.lower().replace("?", "").replace("(", " ").replace(")", " ")

# Search the CSV for user question using very naive search
words = normalized_message.split()
matches = []
for row in rows[1:]:
    # if the word matches any word in row, add the row to the matches
    if any(word in row[0].lower().split() for word in words) or any(word in row[5].lower().split() for word in words):
        matches.append(row)

# Format as a markdown table, since language models understand markdown
matches_table = " | ".join(rows[0]) + "\n" + " | ".join(" --- " for _ in range(len(rows[0]))) + "\n"
matches_table += "\n".join(" | ".join(row) for row in matches)
print(f"Found {len(matches)} matches:")
print(matches_table)

# Now we can use the matches to generate a response
SYSTEM_MESSAGE = """
You are a helpful assistant that answers questions about cars based off a hybrid car data set.
You must use the data set to answer the questions, you should not provide any info that is not in the provided sources.
"""

response = client.chat.completions.create(
    model=MODEL_NAME,
    temperature=0.3,
    messages=[
        {"role": "system", "content": SYSTEM_MESSAGE},
        {"role": "user", "content": USER_MESSAGE + "\nSources: " + matches_table},
    ],
)

print("Response:")
print(response.choices[0].message.content)