Upload PDFs and chat with them using RAG with Gemini embeddings and vector search.
Tick off requirements as you build to track real-time completion.
Follow this chronological guide to build the project from scratch.
Extract raw text chunks from uploaded PDF files, generate vector embeddings, and save the coordinates locally in ChromaDB.
import os
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenAIEmbeddings
from langchain_community.vectorstores import Chroma
def ingest_pdf_to_chroma(file_path: str):
# Load and Parse PDF
loader = PyPDFLoader(file_path)
documents = loader.load()
# Split documents into small chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
chunks = text_splitter.split_documents(documents)
# Generate embeddings and store vectors
embeddings = GoogleGenAIEmbeddings(model="models/text-embedding-004")
db = Chroma.from_documents(
chunks,
embeddings,
persist_directory="./chroma_db"
)
return dbReal issues students hit during development and how to troubleshoot them fast.
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