mirror of
https://github.com/Sosokker/plain-rag.git
synced 2025-12-18 14:34:05 +01:00
feat(base): provide base code that work
This commit is contained in:
parent
9985ba430d
commit
cf3fe50f1a
4
.gitignore
vendored
4
.gitignore
vendored
@ -105,4 +105,6 @@ venv.bak/
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# mypy
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.mypy_cache/
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.idea/*
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.idea/*
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/base
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66
app/api/endpoints.py
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66
app/api/endpoints.py
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@ -0,0 +1,66 @@
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import shutil
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from pathlib import Path
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from typing import Annotated
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from fastapi import APIRouter, BackgroundTasks, Depends, File, HTTPException, UploadFile
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from structlog import get_logger
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from app.schemas.models import IngestResponse, QueryRequest, QueryResponse
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from app.services.rag_service import RAGService
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logger = get_logger()
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router = APIRouter()
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# Dependency function to get the RAG service instance
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def get_rag_service():
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from app.main import app_state
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return app_state["rag_service"]
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@router.post("/ingest", response_model=IngestResponse)
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async def ingest_file(
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background_tasks: BackgroundTasks,
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file: Annotated[UploadFile, File(...)],
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rag_service: Annotated[RAGService, Depends(get_rag_service)],
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):
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# Save the uploaded file temporarily
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temp_dir = Path("temp_files")
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Path.mkdir(temp_dir, exist_ok=True)
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if not file.filename:
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raise HTTPException(status_code=400, detail="File name is required")
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file_path = temp_dir / Path(file.filename)
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with file_path.open("wb") as buffer:
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shutil.copyfileobj(file.file, buffer)
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# Add the ingestion task to run in the background
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background_tasks.add_task(
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rag_service.ingest_document, file_path.as_posix(), file.filename
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)
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# Immediately return a response to the user
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return {
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"message": "File upload successful. Ingestion has started in the background.",
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"filename": file.filename,
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}
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@router.post("/query", response_model=QueryResponse)
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async def query_index(
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request: QueryRequest,
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rag_service: Annotated[RAGService, Depends(get_rag_service)],
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):
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try:
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result = rag_service.answer_query(request.question)
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answer = result.get("answer", "No answer generated")
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sources = result.get("sources", ["No sources"])
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return QueryResponse(answer=answer, sources=sources)
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except Exception:
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logger.exception("Failed to answer query")
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raise HTTPException(status_code=500, detail="Internal server error")
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@ -3,7 +3,7 @@ import secrets
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from functools import lru_cache
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from pathlib import Path
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from pydantic import AnyHttpUrl, PostgresDsn, field_validator, model_validator
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from pydantic import PostgresDsn, model_validator
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from pydantic_settings import BaseSettings, SettingsConfigDict
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ROOT = Path(__file__).resolve().parent.parent.parent
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@ -21,24 +21,20 @@ class Settings(BaseSettings):
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# Security
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ALGORITHM: str = "HS256"
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# Backend Server
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BACKEND_CORS_ORIGINS: list[str | AnyHttpUrl] = []
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@field_validator("BACKEND_CORS_ORIGINS", mode="before")
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def assemble_cors_origins(self, v: str | list[str]) -> list[str] | str:
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if isinstance(v, str) and not v.startswith("["):
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return [i.strip() for i in v.split(",")]
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if isinstance(v, (list, str)):
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return v
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raise ValueError(v)
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# Database
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POSTGRES_SERVER: str = "localhost"
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POSTGRES_USER: str = "postgres"
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POSTGRES_PASSWORD: str = ""
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POSTGRES_DB: str = "chat_hub"
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POSTGRES_PORT: str = "5432"
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DATABASE_URI: PostgresDsn | None = None
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DATABASE_URI: PostgresDsn = PostgresDsn.build(
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scheme="postgresql+asyncpg",
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username=POSTGRES_USER,
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password=POSTGRES_PASSWORD,
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host=POSTGRES_SERVER,
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port=int(POSTGRES_PORT),
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path=f"/{POSTGRES_DB or ''}",
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)
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@model_validator(mode="after")
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def assemble_db_connection(self) -> "Settings":
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@ -61,6 +57,11 @@ class Settings(BaseSettings):
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VECTOR_STORE_TYPE: str = "pgvector" # or "chroma", "faiss", etc.
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EMBEDDING_MODEL: str = "sentence-transformers/all-mpnet-base-v2"
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# File uploads
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UPLOAD_DIR: str = "uploads" # Relative to project root
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MAX_UPLOAD_SIZE: int = 100 * 1024 * 1024 # 100MB
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ALLOWED_DOCUMENT_TYPES: list[str] = ["pdf", "txt", "md"]
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# Logging
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LOG_LEVEL: str = "INFO"
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@ -75,6 +76,8 @@ class Settings(BaseSettings):
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# Caching
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CACHE_TTL: int = 300 # 5 minutes
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GEMINI_API_KEY: str
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model_config = SettingsConfigDict(
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env_file=ENV_FILE,
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env_file_encoding="utf-8",
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6
app/core/exception.py
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6
app/core/exception.py
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class DocumentInsertionError(Exception):
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"""Exception raised when document insertion to database fails."""
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class DocumentExtractionError(Exception):
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"""Exception raised when document extraction from PDF fails."""
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63
app/core/utils.py
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63
app/core/utils.py
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class RecursiveCharacterTextSplitter:
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def __init__(self, chunk_size=500, chunk_overlap=50, separators=None):
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self.chunk_size = chunk_size
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self.chunk_overlap = chunk_overlap
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self.separators = separators or ["\n\n", "\n", ".", " ", ""]
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def split_text(self, text: str) -> list[str]:
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# Start with the full document
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return self._split_recursive(text, self.separators)
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def _split_recursive(self, text: str, separators: list[str]) -> list[str]:
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separator = separators[0] if separators else ""
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splits = (
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text.split(separator) if separator else list(text)
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) # character-level split
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chunks = []
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current_chunk = ""
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for part in splits:
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next_chunk = current_chunk + separator + part if current_chunk else part
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if len(next_chunk) <= self.chunk_size:
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current_chunk = next_chunk
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else:
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if current_chunk:
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chunks.append(current_chunk.strip())
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current_chunk = part
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if current_chunk:
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chunks.append(current_chunk.strip())
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# If any chunk is too long and we have more separators to try
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if (
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any(len(chunk) > self.chunk_size for chunk in chunks)
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and len(separators) > 1
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):
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refined_chunks = []
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for chunk in chunks:
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if len(chunk) > self.chunk_size:
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refined_chunks.extend(self._split_recursive(chunk, separators[1:]))
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else:
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refined_chunks.append(chunk)
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chunks = refined_chunks
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# Add overlap
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return self._add_overlap(chunks)
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def _add_overlap(self, chunks: list[str]) -> list[str]:
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overlapped_chunks = []
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for i, chunk in enumerate(chunks):
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start = max(0, i - 1)
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if i == 0:
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overlapped_chunks.append(chunk)
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else:
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overlap_text = (
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chunks[start][-self.chunk_overlap :]
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if self.chunk_overlap > 0
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else ""
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)
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merged = overlap_text + chunk
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overlapped_chunks.append(merged)
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return overlapped_chunks
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90
app/main.py
90
app/main.py
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import os
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from contextlib import asynccontextmanager
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from typing import Any
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from dotenv import load_dotenv
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from structlog import get_logger
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# Load environment variables
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from app.api import endpoints
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from app.services.rag_service import RAGService
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logger = get_logger()
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# Load environment variables from .env file
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load_dotenv()
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# Application metadata
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APP_NAME = "Chat Hub API"
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API_VERSION = "0.1.0"
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APP_DESCRIPTION = """
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Chat Hub API - A modern chat application with AI capabilities.
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"""
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# Dictionary to hold our application state, including the RAG service instance
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app_state = {}
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# Application lifespan
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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# Startup: Initialize resources
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print("Starting application...")
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# This code runs on startup
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logger.info("Application starting up...")
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# Initialize the RAG Service and store it in the app_state
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app_state["rag_service"] = RAGService()
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yield
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# This code runs on shutdown
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logger.info("Application shutting down...")
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app_state["rag_service"].db_conn.close() # Clean up DB connection
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app_state.clear()
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# Shutdown: Clean up resources
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print("Shutting down application...")
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app = FastAPI(lifespan=lifespan)
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# Include the API router
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app.include_router(endpoints.router)
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# Initialize FastAPI application
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app = FastAPI(
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title=APP_NAME,
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description=APP_DESCRIPTION,
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version=API_VERSION,
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docs_url="/docs",
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redoc_url="/redoc",
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openapi_url="/openapi.json",
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lifespan=lifespan,
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)
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# Configure CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # In production, replace with specific origins
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Health check endpoint
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@app.get("/health", response_model=dict[str, Any])
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async def health_check() -> dict[str, Any]:
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"""
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Health check endpoint to verify the API is running.
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"""
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return {"status": "healthy", "service": APP_NAME, "version": API_VERSION}
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# Root endpoint
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@app.get("/")
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async def root():
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"""
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Root endpoint that provides basic information about the API.
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"""
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return {
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"message": f"Welcome to {APP_NAME}",
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"version": API_VERSION,
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"docs": "/docs",
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"health_check": "/health",
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}
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# This allows running the app with: uvicorn main:app --reload
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if __name__ == "__main__":
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import uvicorn
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port = int(os.getenv("API_PORT", 8001))
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uvicorn.run(
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"main:app", host="0.0.0.0", port=port, reload=True, reload_dirs=["./app"]
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)
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def read_root():
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return {"message": "Welcome to the Custom RAG API"}
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15
app/schemas/models.py
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15
app/schemas/models.py
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from pydantic import BaseModel
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class QueryRequest(BaseModel):
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question: str
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class QueryResponse(BaseModel):
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answer: str
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sources: list[str] # We'll add source tracking later, but good to have now
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class IngestResponse(BaseModel):
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message: str
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filename: str
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228
app/services/rag_service.py
Normal file
228
app/services/rag_service.py
Normal file
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import os
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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 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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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) -> 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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Returns:
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Numpy array containing the embedding vector
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"""
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return EMBEDDING_MODEL.encode(text, convert_to_numpy=True)
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def _store_document(self, content: str, embedding: np.ndarray, source: str) -> 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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content: Text content of the chunk
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embedding: Embedding vector 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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with self.db_conn.cursor() as cursor:
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cursor.execute(
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"""
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INSERT INTO documents (content, embedding, source)
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VALUES (%s, %s, %s)
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RETURNING id
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""",
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(content, embedding.tolist(), source),
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)
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doc_id = cursor.fetchone()
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if doc_id is None:
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err = "Failed to insert document into database"
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raise DocumentInsertionError(err)
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self.db_conn.commit()
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return doc_id[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():
|
||||
err = f"File not found: {filename}"
|
||||
raise FileNotFoundError(err)
|
||||
|
||||
logger.info("Processing PDF: %s : %s", filename, file_path)
|
||||
|
||||
text = self._extract_text_from_pdf(file_path)
|
||||
if not text.strip():
|
||||
err = "No text could be extracted from the PDF"
|
||||
raise ValueError(err)
|
||||
|
||||
chunks = self._split_text(text)
|
||||
logger.info("Split PDF into %d chunks", len(chunks))
|
||||
|
||||
for i, chunk in enumerate(chunks, 1):
|
||||
logger.info("Processing chunk %d/%d", i, len(chunks))
|
||||
embedding = self._get_embedding(chunk)
|
||||
self._store_document(chunk, embedding, filename)
|
||||
|
||||
logger.info("Successfully processed %d chunks from %s", len(chunks), filename)
|
||||
|
||||
def answer_query(self, question: str) -> AnswerResult:
|
||||
relevant_context = self._get_relevant_context(question, 5)
|
||||
context_str = "\n\n".join([chunk[0] for chunk in relevant_context])
|
||||
sources = list({chunk[1] for chunk in relevant_context if chunk[1]})
|
||||
|
||||
try:
|
||||
response = litellm.completion(
|
||||
model="gemini/gemini-2.0-flash",
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant that answers questions based on the provided context.",
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": self.prompt.format(
|
||||
context=context_str, question=question
|
||||
),
|
||||
},
|
||||
],
|
||||
temperature=0.1,
|
||||
max_tokens=500,
|
||||
)
|
||||
|
||||
answer_text = response.choices[0].message.content.strip()
|
||||
|
||||
if not answer_text:
|
||||
answer_text = "No answer generated"
|
||||
sources = ["No sources"]
|
||||
|
||||
return AnswerResult(answer=answer_text, sources=sources)
|
||||
|
||||
except Exception:
|
||||
logger.exception("Error generating response")
|
||||
return AnswerResult(
|
||||
answer="Error generating response", sources=["No sources"]
|
||||
)
|
||||
@ -5,9 +5,17 @@ description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = [
|
||||
"aiofiles>=24.1.0",
|
||||
"asyncpg>=0.30.0",
|
||||
"fastapi[standard]>=0.115.13",
|
||||
"litellm>=1.73.0",
|
||||
"pgvector>=0.4.1",
|
||||
"psycopg2-binary>=2.9.10",
|
||||
"pydantic-settings>=2.10.0",
|
||||
"pypdf2>=3.0.1",
|
||||
"python-jose[cryptography]>=3.5.0",
|
||||
"python-multipart>=0.0.20",
|
||||
"sentence-transformers>=4.1.0",
|
||||
"structlog>=25.4.0",
|
||||
"transformers>=4.52.4",
|
||||
"uvicorn>=0.34.3",
|
||||
|
||||
611
uv.lock
611
uv.lock
@ -2,6 +2,15 @@ version = 1
|
||||
revision = 2
|
||||
requires-python = ">=3.12"
|
||||
|
||||
[[package]]
|
||||
name = "aiofiles"
|
||||
version = "24.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "aiohappyeyeballs"
|
||||
version = "2.6.1"
|
||||
@ -97,6 +106,30 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916, upload-time = "2025-03-17T00:02:52.713Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "asyncpg"
|
||||
version = "0.30.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/2f/4c/7c991e080e106d854809030d8584e15b2e996e26f16aee6d757e387bc17d/asyncpg-0.30.0.tar.gz", hash = "sha256:c551e9928ab6707602f44811817f82ba3c446e018bfe1d3abecc8ba5f3eac851", size = 957746, upload-time = "2024-10-20T00:30:41.127Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/4b/64/9d3e887bb7b01535fdbc45fbd5f0a8447539833b97ee69ecdbb7a79d0cb4/asyncpg-0.30.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c902a60b52e506d38d7e80e0dd5399f657220f24635fee368117b8b5fce1142e", size = 673162, upload-time = "2024-10-20T00:29:41.88Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/6e/eb/8b236663f06984f212a087b3e849731f917ab80f84450e943900e8ca4052/asyncpg-0.30.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:aca1548e43bbb9f0f627a04666fedaca23db0a31a84136ad1f868cb15deb6e3a", size = 637025, upload-time = "2024-10-20T00:29:43.352Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/cc/57/2dc240bb263d58786cfaa60920779af6e8d32da63ab9ffc09f8312bd7a14/asyncpg-0.30.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6c2a2ef565400234a633da0eafdce27e843836256d40705d83ab7ec42074efb3", size = 3496243, upload-time = "2024-10-20T00:29:44.922Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f4/40/0ae9d061d278b10713ea9021ef6b703ec44698fe32178715a501ac696c6b/asyncpg-0.30.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1292b84ee06ac8a2ad8e51c7475aa309245874b61333d97411aab835c4a2f737", size = 3575059, upload-time = "2024-10-20T00:29:46.891Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c3/75/d6b895a35a2c6506952247640178e5f768eeb28b2e20299b6a6f1d743ba0/asyncpg-0.30.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0f5712350388d0cd0615caec629ad53c81e506b1abaaf8d14c93f54b35e3595a", size = 3473596, upload-time = "2024-10-20T00:29:49.201Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c8/e7/3693392d3e168ab0aebb2d361431375bd22ffc7b4a586a0fc060d519fae7/asyncpg-0.30.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:db9891e2d76e6f425746c5d2da01921e9a16b5a71a1c905b13f30e12a257c4af", size = 3641632, upload-time = "2024-10-20T00:29:50.768Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/32/ea/15670cea95745bba3f0352341db55f506a820b21c619ee66b7d12ea7867d/asyncpg-0.30.0-cp312-cp312-win32.whl", hash = "sha256:68d71a1be3d83d0570049cd1654a9bdfe506e794ecc98ad0873304a9f35e411e", size = 560186, upload-time = "2024-10-20T00:29:52.394Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/7e/6b/fe1fad5cee79ca5f5c27aed7bd95baee529c1bf8a387435c8ba4fe53d5c1/asyncpg-0.30.0-cp312-cp312-win_amd64.whl", hash = "sha256:9a0292c6af5c500523949155ec17b7fe01a00ace33b68a476d6b5059f9630305", size = 621064, upload-time = "2024-10-20T00:29:53.757Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/3a/22/e20602e1218dc07692acf70d5b902be820168d6282e69ef0d3cb920dc36f/asyncpg-0.30.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:05b185ebb8083c8568ea8a40e896d5f7af4b8554b64d7719c0eaa1eb5a5c3a70", size = 670373, upload-time = "2024-10-20T00:29:55.165Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/3d/b3/0cf269a9d647852a95c06eb00b815d0b95a4eb4b55aa2d6ba680971733b9/asyncpg-0.30.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:c47806b1a8cbb0a0db896f4cd34d89942effe353a5035c62734ab13b9f938da3", size = 634745, upload-time = "2024-10-20T00:29:57.14Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/8e/6d/a4f31bf358ce8491d2a31bfe0d7bcf25269e80481e49de4d8616c4295a34/asyncpg-0.30.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9b6fde867a74e8c76c71e2f64f80c64c0f3163e687f1763cfaf21633ec24ec33", size = 3512103, upload-time = "2024-10-20T00:29:58.499Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/96/19/139227a6e67f407b9c386cb594d9628c6c78c9024f26df87c912fabd4368/asyncpg-0.30.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:46973045b567972128a27d40001124fbc821c87a6cade040cfcd4fa8a30bcdc4", size = 3592471, upload-time = "2024-10-20T00:30:00.354Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/67/e4/ab3ca38f628f53f0fd28d3ff20edff1c975dd1cb22482e0061916b4b9a74/asyncpg-0.30.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:9110df111cabc2ed81aad2f35394a00cadf4f2e0635603db6ebbd0fc896f46a4", size = 3496253, upload-time = "2024-10-20T00:30:02.794Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ef/5f/0bf65511d4eeac3a1f41c54034a492515a707c6edbc642174ae79034d3ba/asyncpg-0.30.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:04ff0785ae7eed6cc138e73fc67b8e51d54ee7a3ce9b63666ce55a0bf095f7ba", size = 3662720, upload-time = "2024-10-20T00:30:04.501Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/e7/31/1513d5a6412b98052c3ed9158d783b1e09d0910f51fbe0e05f56cc370bc4/asyncpg-0.30.0-cp313-cp313-win32.whl", hash = "sha256:ae374585f51c2b444510cdf3595b97ece4f233fde739aa14b50e0d64e8a7a590", size = 560404, upload-time = "2024-10-20T00:30:06.537Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c8/a4/cec76b3389c4c5ff66301cd100fe88c318563ec8a520e0b2e792b5b84972/asyncpg-0.30.0-cp313-cp313-win_amd64.whl", hash = "sha256:f59b430b8e27557c3fb9869222559f7417ced18688375825f8f12302c34e915e", size = 621623, upload-time = "2024-10-20T00:30:09.024Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
version = "25.3.0"
|
||||
@ -115,6 +148,39 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl", hash = "sha256:2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057", size = 157650, upload-time = "2025-06-15T02:45:49.977Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cffi"
|
||||
version = "1.17.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "pycparser" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/fc/97/c783634659c2920c3fc70419e3af40972dbaf758daa229a7d6ea6135c90d/cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824", size = 516621, upload-time = "2024-09-04T20:45:21.852Z" }
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||||
wheels = [
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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Loading…
Reference in New Issue
Block a user