mirror of
https://github.com/Sosokker/plain-rag.git
synced 2025-12-18 22:44:03 +01:00
284 lines
9.3 KiB
Python
284 lines
9.3 KiB
Python
import os
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from collections.abc import Generator
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from pathlib import Path
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from typing import TypedDict
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import litellm
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import numpy as np
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import psycopg2
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from dotenv import load_dotenv
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from psycopg2 import extras
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from psycopg2.extensions import AsIs, register_adapter
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from PyPDF2 import PdfReader
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from sentence_transformers import SentenceTransformer
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from structlog import get_logger
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from app.core.config import settings
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from app.core.exception import DocumentExtractionError, DocumentInsertionError
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from app.core.utils import RecursiveCharacterTextSplitter
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register_adapter(np.ndarray, AsIs) # for psycopg2 adapt
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register_adapter(np.float32, AsIs) # for psycopg2 adapt
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logger = get_logger()
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# pyright: reportArgumentType=false
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# Load environment variables
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load_dotenv()
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# Initialize the embedding model globally to load it only once
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EMBEDDING_MODEL = SentenceTransformer("all-MiniLM-L6-v2")
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EMBEDDING_DIM = 384 # Dimension of the all-MiniLM-L6-v2 model
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os.environ["GEMINI_API_KEY"] = settings.GEMINI_API_KEY
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class AnswerResult(TypedDict):
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answer: str
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sources: list[str]
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class RAGService:
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def __init__(self):
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logger.info("Initializing RAGService...")
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# Load the embedding model ONCE
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self.embedding_model = SentenceTransformer(
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"all-MiniLM-L6-v2", device="cpu"
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) # Use 'cuda' if GPU is available
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self.db_conn = psycopg2.connect(
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host=settings.POSTGRES_SERVER,
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port=settings.POSTGRES_PORT,
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user=settings.POSTGRES_USER,
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password=settings.POSTGRES_PASSWORD,
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dbname=settings.POSTGRES_DB,
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)
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logger.info("RAGService initialized.")
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self.prompt = """Answer the question based on the following context.
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If you don't know the answer, say you don't know. Don't make up an answer.
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Context:
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{context}
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Question: {question}
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Answer:"""
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def _split_text(
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self, text: str, chunk_size: int = 500, chunk_overlap: int = 100
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) -> list[str]:
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"""
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Split text into chunks with specified size and overlap.
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Args:
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text: Input text to split
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chunk_size: Maximum size of each chunk in characters
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chunk_overlap: Number of characters to overlap between chunks
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Returns:
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List of text chunks
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"""
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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)
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return text_splitter.split_text(text)
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def _get_embedding(self, text: str, show_progress_bar: bool = False) -> np.ndarray:
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"""
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Generate embedding for a text chunk.
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Args:
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text: Input text to embed
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show_progress_bar: Whether to show a progress bar
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Returns:
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Numpy array containing the embedding vector
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"""
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return EMBEDDING_MODEL.encode(
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text, convert_to_numpy=True, show_progress_bar=show_progress_bar
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)
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def _store_document(
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self, contents: list[str], embeddings: list[np.ndarray], source: str
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) -> int:
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"""
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Store a document chunk in the database.
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Args:
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contents: List of text content of the chunk
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embeddings: List of embedding vectors of the chunk
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source: Source file path
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Returns:
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ID of the inserted document
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"""
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data_to_insert = [
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(chunk, f"[{', '.join(map(str, embedding))}]", source)
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for chunk, embedding in zip(contents, embeddings, strict=True)
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]
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query = """
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INSERT INTO documents (content, embedding, source)
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VALUES %s
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RETURNING id
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"""
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with self.db_conn.cursor() as cursor:
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extras.execute_values(
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cursor,
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query,
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data_to_insert,
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template="(%s, %s::vector, %s)",
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page_size=100,
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)
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inserted_ids = [row[0] for row in cursor.fetchall()]
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self.db_conn.commit()
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if not inserted_ids:
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raise DocumentInsertionError("No documents were inserted.")
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logger.info("Successfully bulk-ingested %d documents", len(inserted_ids))
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logger.info("Inserted document IDs: %s", inserted_ids)
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return inserted_ids[0]
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def _extract_text_from_pdf(self, pdf_path: str) -> str:
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"""
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Extract text from a PDF file.
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Args:
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pdf_path: Path to the PDF file
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Returns:
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Extracted text as a single string
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"""
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try:
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reader = PdfReader(pdf_path)
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text = ""
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for page in reader.pages:
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text += page.extract_text() + "\n"
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return text.strip()
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except Exception as e:
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raise DocumentExtractionError(
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"Error extracting text from PDF: " + str(e)
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) from e
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def _get_relevant_context(self, question: str, top_k: int) -> list[tuple[str, str]]:
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"""Get the most relevant document chunks for a given question"""
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question_embedding = self.embedding_model.encode(
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question, convert_to_numpy=True
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)
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try:
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with self.db_conn.cursor() as cursor:
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cursor.execute(
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"""
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SELECT content, source
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FROM documents
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ORDER BY embedding <-> %s::vector
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LIMIT %s
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""",
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(question_embedding.tolist(), top_k),
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)
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results = cursor.fetchall()
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return results
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except Exception as e:
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logger.exception("Error retrieving context: %s", e)
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return []
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def ingest_document(self, file_path: str, filename: str):
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logger.info("Ingesting %s...", filename)
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if not Path(file_path).exists():
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err = f"File not found: {filename}"
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raise FileNotFoundError(err)
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logger.info("Processing PDF: %s : %s", filename, file_path)
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text = self._extract_text_from_pdf(file_path)
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if not text.strip():
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err = "No text could be extracted from the PDF"
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raise ValueError(err)
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chunks = self._split_text(text)
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logger.info("Split PDF into %d chunks", len(chunks))
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embeddings = self._get_embedding(chunks, show_progress_bar=True)
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self._store_document(chunks, embeddings, filename)
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logger.info("Successfully processed %d chunks from %s", len(chunks), filename)
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def answer_query(self, question: str) -> AnswerResult:
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relevant_context = self._get_relevant_context(question, 5)
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context_str = "\n\n".join([chunk[0] for chunk in relevant_context])
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sources = list({chunk[1] for chunk in relevant_context if chunk[1]})
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try:
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response = litellm.completion(
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model="gemini/gemini-2.0-flash",
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messages=[
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{
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"role": "system",
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"content": "You are a helpful assistant that answers questions based on the provided context.",
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},
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{
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"role": "user",
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"content": self.prompt.format(
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context=context_str, question=question
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),
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},
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],
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temperature=0.1,
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max_tokens=500,
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)
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answer_text = response.choices[0].message.content.strip()
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if not answer_text:
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answer_text = "No answer generated"
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sources = ["No sources"]
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return AnswerResult(answer=answer_text, sources=sources)
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except Exception:
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logger.exception("Error generating response")
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return AnswerResult(
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answer="Error generating response", sources=["No sources"]
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)
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def answer_query_stream(self, question: str) -> Generator[str, None, None]:
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"""Answer a query using streaming."""
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relevant_context = self._get_relevant_context(question, 5)
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context_str = "\n\n".join([chunk[0] for chunk in relevant_context])
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sources = list({chunk[1] for chunk in relevant_context if chunk[1]})
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prompt = self.prompt.format(context=context_str, question=question)
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try:
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response = litellm.completion(
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model="gemini/gemini-2.0-flash",
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messages=[{"role": "user", "content": prompt}],
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stream=True,
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)
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# First, yield the sources so the UI can display them immediately
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import json
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sources_json = json.dumps(sources)
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yield f'data: {{"sources": {sources_json}}}\n\n'
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# Then, stream the answer tokens
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for chunk in response:
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token = chunk.choices[0].delta.content
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if token: # Ensure there's content to send
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# SSE format: data: {"token": "..."}\n\n
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yield f'data: {{"token": "{json.dumps(token)}"}}\n\n'
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# Signal the end of the stream with a special message
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yield 'data: {"end_of_stream": true}\n\n'
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except Exception:
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logger.exception("Error generating response")
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yield 'data: {"error": "Error generating response"}\n\n'
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