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Openai Wikipedia Semantic Search

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Openai Wikipedia Semantic Search

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Summary

Intro This notebook is an example on how you can use SingleStoreDB vector storage and functions to build an interactive Q&A application with ChatGPT. If you start a Trial in SingleStoreDB, you can find the same notebook in our sample notebooks with native connection. First let’s talk directly to ChatGPT and try and get back a response Let’s connect to OpenAI…

What This Teaches

  • How to connect OpenAI models with retrieval, embeddings, or external knowledge stores.

Implementation Use Cases

  • Use as a concrete implementation reference when building OpenAI API systems in this category.
  • Compare against current official API docs before copying model names, SDK calls, or parameters into production code.
  • Preserve this page as a mirrored source; prefer synthesis pages for personal recommendations or project-specific decisions.

Mirrored Content

Intro

This notebook is an example on how you can use SingleStoreDB vector storage and functions to build an interactive Q&A application with ChatGPT. If you start a Trial in SingleStoreDB, you can find the same notebook in our sample notebooks with native connection.

First let’s talk directly to ChatGPT and try and get back a response

!pip install openai --quiet
import openai
 
EMBEDDING_MODEL = "text-embedding-3-small"
GPT_MODEL = "gpt-3.5-turbo"

Let’s connect to OpenAI and see the result we get when asking for a date beyond 2021

openai.api_key = 'OPENAI API KEY'
 
response = openai.ChatCompletion.create(
  model=GPT_MODEL,
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who won the gold medal for curling in Olymics 2022?"},
    ]
)
 
print(response['choices'][0]['message']['content'])

Get the data about Winter Olympics and provide the information to ChatGPT as context

1. Setup

!pip install matplotlib plotly.express scikit-learn tabulate tiktoken wget --quiet
import pandas as pd
import os
import wget
import ast

Step 1 - Grab the data from CSV and prepare it

# download pre-chunked text and pre-computed embeddings
# this file is ~200 MB, so may take a minute depending on your connection speed
embeddings_path = "https://cdn.openai.com/API/examples/data/winter_olympics_2022.csv"
file_path = "winter_olympics_2022.csv"
 
if not os.path.exists(file_path):
    wget.download(embeddings_path, file_path)
    print("File downloaded successfully.")
else:
    print("File already exists in the local file system.")
df = pd.read_csv(
    "winter_olympics_2022.csv"
)
 
# convert embeddings from CSV str type back to list type
df['embedding'] = df['embedding'].apply(ast.literal_eval)
df
df.info(show_counts=True)

2. Set up SingleStore DB

import singlestoredb as s2
 
conn = s2.connect("<user>:<Password>@<host>:3306/")
 
cur = conn.cursor()
# Create database
stmt = """
    CREATE DATABASE IF NOT EXISTS winter_wikipedia2;
"""
 
cur.execute(stmt)
#create table
stmt = """
CREATE TABLE IF NOT EXISTS winter_wikipedia2.winter_olympics_2022 (
    id INT PRIMARY KEY,
    text TEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,
    embedding BLOB
);"""
 
cur.execute(stmt)

3. Populate the Table with our dataframe df and use JSON_ARRAY_PACK to compact it

%%time
 
# Prepare the statement
stmt = """
    INSERT INTO winter_wikipedia2.winter_olympics_2022 (
        id,
        text,
        embedding
    )
    VALUES (
        %s,
        %s,
        JSON_ARRAY_PACK_F64(%s)
    )
"""
 
# Convert the DataFrame to a NumPy record array
record_arr = df.to_records(index=True)
 
# Set the batch size
batch_size = 1000
 
# Iterate over the rows of the record array in batches
for i in range(0, len(record_arr), batch_size):
    batch = record_arr[i:i+batch_size]
    values = [(row[0], row[1], str(row[2])) for row in batch]
    cur.executemany(stmt, values)

4. Do a semantic search with the same question from above and use the response to send to OpenAI again

from utils.embeddings_utils import get_embedding
 
def strings_ranked_by_relatedness(
    query: str,
    df: pd.DataFrame,
    relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y),
    top_n: int = 100
) -> tuple:
    """Returns a list of strings and relatednesses, sorted from most related to least."""
 
    # Get the embedding of the query.
    query_embedding_response = get_embedding(query, EMBEDDING_MODEL)
 
    # Create the SQL statement.
    stmt = """
        SELECT
            text,
            DOT_PRODUCT_F64(JSON_ARRAY_PACK_F64(%s), embedding) AS score
        FROM winter_wikipedia2.winter_olympics_2022
        ORDER BY score DESC
        LIMIT %s
    """
 
    # Execute the SQL statement.
    results = cur.execute(stmt, [str(query_embedding_response), top_n])
 
    # Fetch the results
    results = cur.fetchall()
 
    strings = []
    relatednesses = []
 
    for row in results:
        strings.append(row[0])
        relatednesses.append(row[1])
 
    # Return the results.
    return strings[:top_n], relatednesses[:top_n]
from tabulate import tabulate
 
strings, relatednesses = strings_ranked_by_relatedness(
    "curling gold medal",
    df,
    top_n=5
)
 
for string, relatedness in zip(strings, relatednesses):
    print(f"{relatedness=:.3f}")
    print(tabulate(&#91;&#91;string&#93;&#93;, headers=['Result'], tablefmt='fancy_grid'))

5. Send the right context to ChatGPT for a more accurate answer

import tiktoken
 
def num_tokens(text: str, model: str = GPT_MODEL) -> int:
    """Return the number of tokens in a string."""
    encoding = tiktoken.encoding_for_model(model)
    return len(encoding.encode(text))
 
 
def query_message(
    query: str,
    df: pd.DataFrame,
    model: str,
    token_budget: int
) -> str:
    """Return a message for GPT, with relevant source texts pulled from SingleStoreDB."""
    strings, relatednesses = strings_ranked_by_relatedness(query, df, "winter_olympics_2022")
    introduction = 'Use the below articles on the 2022 Winter Olympics to answer the subsequent question. If the answer cannot be found in the articles, write "I could not find an answer."'
    question = f"\n\nQuestion: {query}"
    message = introduction
    for string in strings:
        next_article = f'\n\nWikipedia article section:\n"""\n{string}\n"""'
        if (
            num_tokens(message + next_article + question, model=model)
            > token_budget
        ):
            break
        else:
            message += next_article
    return message + question
 
 
def ask(
    query: str,
    df: pd.DataFrame = df,
    model: str = GPT_MODEL,
    token_budget: int = 4096 - 500,
    print_message: bool = False,
) -> str:
    """Answers a query using GPT and a table of relevant texts and embeddings in SingleStoreDB."""
    message = query_message(query, df, model=model, token_budget=token_budget)
    if print_message:
        print(message)
    messages = [
        {"role": "system", "content": "You answer questions about the 2022 Winter Olympics."},
        {"role": "user", "content": message},
    ]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0
    )
    response_message = response["choices"][0]["message"]["content"]
    return response_message

6. Get an answer from Chat GPT

from pprint import pprint
 
answer = ask('Who won the gold medal for curling in Olymics 2022?')
 
pprint(answer)

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