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## API Call
# main.py
## FASTAPI Main py file to access the POST body
from fastapi import FastAPI, Header, HTTPException
from pydantic import BaseModel
from typing import List, Optional
from utils.s3_utils import read_s3_file
from utils.embedding_utils import read_document, cumulative_semantic_chunking, embed_chunks
from utils.qdrant_utils import store_embeddings
import logging
import time
import os
from dotenv import load_dotenv
#load_dotenv()
# Retrieve the API key from the environment
API_KEY = os.getenv('X_API_KEY')
bucket_name = os.getenv('bucket_name')
# Initialize logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
app = FastAPI()
class Metadata(BaseModel):
mime_type: str
file_size_bytes: str
file_format: str
class DocumentIndexRequest(BaseModel):
metadata: Metadata
bucket_key: str
user_id: str
org_id: str
file_id: int
data_source_id: int
@app.get("/")
async def root():
return {"message": "Welcome to the Document Indexing API!"}
@app.post("/api/document-index")
async def document_index(request: DocumentIndexRequest, x_api_key: str = Header(...)):
logging.info(f"Received request: {request}")
start_time = time.time()
# Check if the API key provided in the header matches the one in the environment
if x_api_key != API_KEY:
logging.warning("Unauthorized access attempt with invalid API key.")
raise HTTPException(status_code=401, detail="Unauthorized")
try:
#bucket_name = "document-ingestion-drive-dev"
# Read file from S3 using the presigned URL
content, metadata, file_format = read_s3_file(bucket_name, request.bucket_key)
logging.info(f"File {request.bucket_key} retrieved from S3 with format {file_format}.")
# Reading content using Simple Directory Reader
text_content = read_document(content, file_id=request.file_id, file_format=file_format)
logging.info(f"Text content extracted from file {request.bucket_key}.")
#print('text_content',text_content)
# Chunking text using semantic chunking
chunks = cumulative_semantic_chunking(text_content, max_chunk_size=512, similarity_threshold=0.8)
logging.info(f"Text content chunked into {len(chunks)} chunks.")
# Embed chunks
embeddings, total_tokens = embed_chunks(chunks)
logging.info(f"Text content embedded into vectors. Total tokens used: {total_tokens}.")
# Store embeddings in Qdrant
store_embeddings(
chunks=chunks,
embeddings=embeddings,
user_id=request.user_id,
data_source_id=request.data_source_id,
file_id=request.file_id,
organization_id=request.org_id,
s3_bucket_key=request.bucket_key,
total_tokens=total_tokens
)
logging.info(f"Embeddings for {request.bucket_key} stored successfully with metadata: {metadata}")
logging.info(f"Embeddings for stored successfully with {total_tokens} tokens")
time_taken = time.time() - start_time
logging.info(f"Time taken to process and embed the document: {time_taken} seconds")
return {"message": "Embeddings stored successfully"}
except FileNotFoundError as e:
logging.error(f"File not found: {str(e)}")
raise HTTPException(status_code=404, detail=str(e))
except PermissionError as e:
logging.error(f"Permission error: {str(e)}")
raise HTTPException(status_code=403, detail=str(e))
except HTTPException as e:
logging.error(f"HTTP error: {str(e.detail)}")
raise
except Exception as e:
logging.error(f"Error processing file {request.bucket_key}: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
# Run the FastAPI app
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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