TL;DR — This guide walks you through building a full RAG pipeline: ingest documents, embed them with OpenAI, store them in a vector database, and serve the results via a FastAPI endpoint. The project demonstrates data engineering, API design, and LLM integration—skills that stand out on any systems‑oriented CV.

In a market where “AI” is a checkbox, a concrete, end‑to‑end retrieval‑augmented generation (RAG) system proves you can ship a real product, not just a demo. Below you’ll find a step‑by‑step implementation that you can run on a laptop, extend to production, and list on your résumé.

Why This Project Stands Out on a CV

  • End‑to‑end ownership – you handle ingestion, embedding, storage, retrieval, and serving, mirroring a full‑stack engineer’s responsibilities.
  • Modern stack – FastAPI, LangChain, OpenAI embeddings, and a vector DB (Chroma or Pinecone) are the same tools used in production LLM applications.
  • API‑first mindset – exposing the pipeline as a REST endpoint demonstrates experience designing stateless services, request validation, and error handling.
  • Data engineering – parsing PDFs, chunking, and managing embeddings shows you can work with unstructured data at scale.
  • Scalability awareness – the architecture is written to be extended with horizontal scaling, caching, and observability, signaling senior‑level thinking.
  • Hiring signal – recruiters recognize “RAG” as a high‑impact skill; building it yourself differentiates you from candidates who only read about it.

Architecture Overview

The system is composed of four logical layers:

  1. Ingestion Layer – A CLI script that reads source files (PDF, Markdown, plain text), splits them into chunks, and creates embeddings.
  2. Vector Store – A persistent or in‑memory vector database (Chroma, Pinecone, or pgvector) that stores chunk embeddings and metadata.
  3. Retrieval & Generation Layer – LangChain orchestrates the query flow: embed the user question, search the vector store for relevant chunks, and feed them to an LLM (OpenAI gpt‑3.5‑turbo or gpt‑4).
  4. API Layer – A FastAPI app exposing a POST /ask endpoint that accepts a JSON payload, runs the RAG pipeline, and returns the answer with source citations.
+----------------+      +----------------+      +----------------+
|   CLI / Script | ---> |  Vector Store  | ---> |  LangChain RAG |
+----------------+      +----------------+      +----------------+
                                 |                     |
                                 v                     v
                           +----------------+   +----------------+
                           |   FastAPI App  |   |  LLM (OpenAI)  |
                           +----------------+   +----------------+

Each layer is independently testable, allowing you to swap implementations (e.g., FAISS → Pinecone) without touching the others.

Building It Step by Step

Below is a minimal yet complete implementation. All code assumes Python 3.10+ and the packages listed in requirements.txt.

1. Set up the project

python -m venv rag-env
source rag-env/bin/activate
pip install fastapi uvicorn langchain openai chromadb pydantic pdfplumber

2. Create the ingestion script (ingest.py)

import os
import pdfplumber
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader, PDFLoader

def load_documents(folder: str):
    docs = []
    for file in os.listdir(folder):
        path = os.path.join(folder, file)
        if file.endswith(".pdf"):
            loader = PDFLoader(path)
        elif file.endswith(".txt"):
            loader = TextLoader(path)
        else:
            continue
        docs.extend(loader.load())
    return docs

def chunk_documents(documents, chunk_size=800, chunk_overlap=100):
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_size,
        chunk_overlap=chunk_overlap,
        separators=["\n\n", "\n", " ", ""]
    )
    return splitter.split_documents(documents)

def embed_and_store(chunks, persist_dir="vectorstore"):
    embeddings = OpenAIEmbeddings()
    db = Chroma.from_documents(chunks, embeddings, persist_directory=persist_dir)
    db.persist()
    return db

if __name__ == "__main__":
    docs = load_documents("data")
    chunks = chunk_documents(docs)
    embed_and_store(chunks)
    print(f"Ingested {len(chunks)} chunks into Chroma.")

3. Build the RAG chain (rag_chain.py)

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

def build_chain(persist_dir="vectorstore"):
    embeddings = OpenAIEmbeddings()
    db = Chroma(persist_directory=persist_dir, embedding_function=embeddings)
    retriever = db.as_retriever(search_kwargs={"k": 4})
    llm = OpenAI(temperature=0.0)
    chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True,
    )
    return chain

4. Expose via FastAPI (api.py)

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from rag_chain import build_chain

app = FastAPI(title="RAG Service")
chain = build_chain()

class Query(BaseModel):
    question: str

@app.post("/ask")
async def ask(query: Question):
    try:
        result = chain({"query": query.question})
        return {
            "answer": result["result"],
            "sources": [doc.metadata for doc in result["source_documents"]],
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

5. Run the API

uvicorn api:app --reload

The server will be available at http://localhost:8000/ask.

Running and Testing It

  1. Prepare sample data – create a data/ folder with a few .txt or .pdf files.
  2. Ingest – run python ingest.py. You should see a message confirming the number of chunks.
  3. Start the API – uvicorn api:app --reload.
  4. Send a test request using curl:
curl -X POST "http://localhost:8000/ask" \
     -H "Content-Type: application/json" \
     -d '{"question":"What is the main topic of the document?"}'
  1. Verify – the response should contain an answer field with a natural‑language summary and a sources array listing the originating files.

For automated testing, add a pytest suite that mocks the LLM and checks that the endpoint returns a 200 status and a non‑empty answer.

Extending It: Your Roadmap to Senior-Level

  1. Persistence & Multi‑tenant Storage – replace Chroma with a managed vector DB like Pinecone or a self‑hosted pgvector instance. Why: isolates data per user and enables horizontal scaling.
  2. Caching Layer – add Redis to cache frequent queries and embeddings. Why: reduces latency and OpenAI API costs under load.
  3. Observability – instrument the pipeline with OpenTelemetry, exporting traces and metrics to Prometheus/Grafana. Why: makes production incidents diagnosable.
  4. Fault Tolerance & Retries – wrap LLM calls with exponential backoff and fallback to a secondary model. Why: improves reliability when the primary LLM is unavailable.
  5. Benchmarking & Evaluation – build a small evaluation harness using ragas to measure answer faithfulness and retrieval precision on a labeled set. Why: quantifies system quality and guides improvements.
  6. Async Processing – convert ingestion and query paths to asyncio and use a task queue (e.g., Celery) for long‑running jobs. Why: enables the service to handle many concurrent users without blocking.

Key Takeaways

  • You now have a complete, runnable RAG system that ingests documents, stores embeddings, and answers questions via a clean API.
  • The code demonstrates data engineering, LLM integration, and API design—competencies that hiring managers value for senior roles.
  • Each component is modular, so you can swap in production‑grade services (Pinecone, Redis, OpenTelemetry) as you scale.
  • The architecture is ready for observability, caching, and fault tolerance, showing foresight beyond a prototype.
  • By adding benchmarks and async processing, you transform the project into a senior‑level showcase of distributed systems thinking.

Further Reading