Struggling to find info buried deep in your company's documents? Let’s build a chatbot that can understand your questions and retrieve accurate answers from thousands of PDFs, manuals, and wikis—powered by LLMs + Vector Databases.

🔍 What We'll Build

A semantic search-based chatbot that can answer employee questions like:

  • “What’s the company’s remote work policy?”
  • “How do I request PTO?”
  • “Where’s the VPN setup guide?”

No more digging through folders. Just ask.

🧠 How It Works

We’ll use a Retrieval-Augmented Generation (RAG) architecture:

  1. 📄 Split documents into chunks
  2. 🔢 Embed them using OpenAI or Sentence-BERT
  3. 🧠 Store in a vector database (e.g., Chroma, FAISS, Pinecone)
  4. 🤖 Search similar chunks based on user queries
  5. 💬 Generate answers with GPT-4 using the retrieved context

⚙️ Tech Stack

Component Tool
Embedding Model OpenAI or Sentence-BERT
Vector Database Chroma (local & fast)
LLM OpenAI GPT-4
Loader & Splitter LangChain utilities
Optional Frontend Streamlit or FastAPI

🧪 Step-by-Step Code Example 

pip install langchain openai chromadb
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains.qa_with_sources import load_qa_with_sources_chain

# 1. Load documents
loader = DirectoryLoader('./docs')  # Folder of .txt or PDFs
documents = loader.load()

# 2. Split into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
docs = splitter.split_documents(documents)

# 3. Embed and store
embeddings = OpenAIEmbeddings()
vectordb = Chroma.from_documents(docs, embeddings)

# 4. Query from user
query = "What is the company’s policy on remote work?"
retrieved_docs = vectordb.similarity_search(query, k=3)

# 5. Generate answer using LLM
llm = ChatOpenAI(temperature=0)
qa_chain = load_qa_with_sources_chain(llm, chain_type="stuff")
result = qa_chain.run(input_documents=retrieved_docs, question=query)

print(result)

🚀 Ready to Scale?

For production, consider using:

  • ✅ Pinecone or Weaviate for scalable vector storage
  • 🔒 Secure LLMs with prompt templates
  • 🖥️ Frontend using Streamlit or chatbot widget

📚 Real-World Use Cases

  • HR Knowledgebase Assistant
  • Legal Document Search Bot
  • Customer Support Answer Bot
  • Onboarding Helpdesk

🙌 Wrapping Up

By combining LLMs + vector embeddings, you unlock next-level document search—context-aware, semantically smart, and actually useful.

Got questions or building your own chatbot? Let’s connect in the comments! 💬