Create main.py

main.py

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+import io
+from pydantic import BaseModel
+from fastapi import FastAPI, HTTPException, File, UploadFile
+from pdfminer.high_level import extract_text
+from datetime import datetime
+from fastapi.middleware.cors import CORSMiddleware
+from sqlalchemy import create_engine, MetaData, Table, Column, Integer, String
+from databases import Database
+
+from langchain.chains.question_answering import load_qa_chain
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.docstore.document import Document as LangchainDocument
+
+
+import os
+
+app = FastAPI()
+
+# Set up CORS (Cross-Origin Resource Sharing) for allowing requests from all origins
+origins=["*"]
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["GET", "POST", "PUT", "DELETE"],
+    allow_headers=["*"],
+)
+
+# Define SQLAlchemy engine and metadata
+DATABASE_URL = "sqlite:///./test.db"
+engine = create_engine(DATABASE_URL)
+metadata = MetaData()
+
+# Define the document table schema
+documents = Table(
+    "documents",
+    metadata,
+    Column("id", Integer, primary_key=True),
+    Column("filename", String),
+    Column("upload_date", String),
+    Column("content", String),
+)
+
+# Create the document table in the database
+metadata.create_all(engine)
+
+# Define Pydantic model for the document
+class Document(BaseModel):
+    filename: str
+    upload_date: str
+    content: str
+
+# Function to save uploaded files
+async def save_uploaded_file(file: UploadFile, destination: str):
+    with open(destination, "wb") as buffer:
+        while chunk := await file.read(1024):
+            buffer.write(chunk)
+
+# Endpoint for uploading PDF files
+@app.post("/upload/")
+async def upload_pdf(file: UploadFile = File(...)):
+
+    # Check if the uploaded file is a PDF
+    if not file.filename.lower().endswith('.pdf'):
+        raise HTTPException(status_code=400, detail="Only PDF files are allowed.")
+    
+    # Read content of the uploaded PDF file
+    content = await file.read()
+    
+    # Extract text from the PDF
+    with io.BytesIO(content) as pdf_file:
+        text_content = extract_text(pdf_file)
+    
+    # Create a document object
+    doc = Document(filename=file.filename, upload_date=str(datetime.now()), content=text_content)
+    
+    # Insert the document data into the database
+    async with Database(DATABASE_URL) as database:
+        query = documents.insert().values(
+            filename=doc.filename,
+            upload_date=doc.upload_date,
+            content=doc.content
+        )
+        last_record_id = await database.execute(query)
+    
+    # Save the uploaded PDF file
+    destination = f"files/{file.filename}"
+    await save_uploaded_file(file, destination)
+    
+    # Return the document object
+    return doc
+
+# Pydantic model for input data
+class DataInput(BaseModel):
+    responseData: str
+    userInput: str
+
+# Endpoint for processing user data
+@app.post("/doc/")
+async def process_data(data: DataInput):
+    # Access responseData and userInput
+    response_data = data.responseData
+    user_input = data.userInput
+    
+    # Load required models and components from Langchain library 
+    dom = [LangchainDocument(page_content=response_data, metadata={"source": "local"})]
+
+    text_spliter = CharacterTextSplitter(chunk_size=3000, chunk_overlap=0)
+    docs = text_spliter.split_documents(dom)
+
+    embeddings = HuggingFaceEmbeddings()
+    db = FAISS.from_documents(docs, embeddings)
+    llm = HuggingFaceEndpoint(
+        repo_id="google/flan-t5-xxl",
+        temperature=0.8,
+    )
+
+    chain = load_qa_chain(llm, chain_type="stuff")
+
+    # Perform similarity search and question answering
+    dm = db.similarity_search(user_input)
+    result = chain.run(input_documents=dm, question=user_input)
+    
+    return result