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import sqlite3
import warnings
import zlib
import numpy as np
import pandas as pd
documents_table = """CREATE TABLE IF NOT EXISTS documents (
id INTEGER PRIMARY KEY AUTOINCREMENT,
source TEXT NOT NULL,
title TEXT NOT NULL,
url TEXT NOT NULL,
content TEXT NOT NULL,
n_tokens INTEGER,
embedding BLOB,
current INTEGER
)"""
qa_table = """CREATE TABLE IF NOT EXISTS qa (
id INTEGER PRIMARY KEY AUTOINCREMENT,
source TEXT NOT NULL,
prompt TEXT NOT NULL,
answer TEXT NOT NULL,
document_id_1 INTEGER,
document_id_2 INTEGER,
document_id_3 INTEGER,
label_question INTEGER,
label_answer INTEGER,
testset INTEGER,
FOREIGN KEY (document_id_1) REFERENCES documents (id),
FOREIGN KEY (document_id_2) REFERENCES documents (id),
FOREIGN KEY (document_id_3) REFERENCES documents (id)
)"""
class DocumentsDB:
"""Simple SQLite database for storing documents and questions/answers.
The database is just a file on disk. It can store documents from different sources, and it can store multiple versions of the same document (e.g. if the document is updated).
Questions/answers refer to the version of the document that was used at the time.
Example:
>>> db = DocumentsDB("/path/to/the/db.db")
>>> db.write_documents("source", df) # df is a DataFrame containing the documents from a given source, obtained e.g. by using buster.docparser.generate_embeddings
>>> df = db.get_documents("source")
"""
def __init__(self, db_path):
self.db_path = db_path
self.conn = sqlite3.connect(db_path)
self.cursor = self.conn.cursor()
self.__initialize()
def __del__(self):
self.conn.close()
def __initialize(self):
"""Initialize the database."""
self.cursor.execute(documents_table)
self.cursor.execute(qa_table)
self.conn.commit()
def write_documents(self, source: str, df: pd.DataFrame):
"""Write all documents from the dataframe into the db. All previous documents from that source will be set to `current = 0`."""
df = df.copy()
# Prepare the rows
df["source"] = source
df["current"] = 1
columns = ["source", "title", "url", "content", "current"]
if "embedding" in df.columns:
columns.extend(
[
"n_tokens",
"embedding",
]
)
# Check that the embeddings are float32
if not df["embedding"].iloc[0].dtype == np.float32:
warnings.warn(
f"Embeddings are not float32, converting them to float32 from {df['embedding'].iloc[0].dtype}.",
RuntimeWarning,
)
df["embedding"] = df["embedding"].apply(lambda x: x.astype(np.float32))
# ZLIB compress the embeddings
df["embedding"] = df["embedding"].apply(lambda x: sqlite3.Binary(zlib.compress(x.tobytes())))
data = df[columns].values.tolist()
# Set `current` to 0 for all previous documents from that source
self.cursor.execute("UPDATE documents SET current = 0 WHERE source = ?", (source,))
# Insert the new documents
insert_statement = f"INSERT INTO documents ({', '.join(columns)}) VALUES ({', '.join(['?']*len(columns))})"
self.cursor.executemany(insert_statement, data)
self.conn.commit()
def get_documents(self, source: str) -> pd.DataFrame:
"""Get all current documents from a given source."""
# Execute the SQL statement and fetch the results
results = self.cursor.execute("SELECT * FROM documents WHERE source = ? AND current = 1", (source,))
rows = results.fetchall()
# Convert the results to a pandas DataFrame
df = pd.DataFrame(rows, columns=[description[0] for description in results.description])
# ZLIB decompress the embeddings
df["embedding"] = df["embedding"].apply(lambda x: np.frombuffer(zlib.decompress(x), dtype=np.float32).tolist())
# Drop the `current` column
df.drop(columns=["current"], inplace=True)
return df
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