Integrating Semantic Search with GPT

Combining semantic search with Generative AI (GPT) enables natural question-answering on top of your private documents. This is called Retrieval-Augmented Generation (RAG).

💡 Why Combine Semantic Search with GPT?

• Semantic search finds the most relevant chunks of text.
• GPT then reads those chunks and generates a coherent answer.
• This reduces hallucination and makes answers context-aware.

⚙️ Architecture Flow

1. Load documents → split into smaller chunks.
2. Convert chunks into embeddings and store in a vector DB.
3. User query → convert to embedding → retrieve top matches.
4. Send matches + query into GPT → generate response.

🐍 Python Example with LangChain

# pip install langchain openai faiss-cpu tiktoken

from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

# 1) Load and split documents
docs = ["AI is transforming industries by automating workflows and creating insights.",
        "RPA focuses on repetitive tasks, freeing humans for creative work."]
text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=0)
chunks = text_splitter.create_documents(docs)

# 2) Create embeddings and store in FAISS
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(chunks, embeddings)

# 3) Create GPT model with retrieval
qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(temperature=0),
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

# 4) Ask a question
query = "How do AI and RPA complement each other?"
print(qa.run(query))

Example Output: “AI handles cognitive tasks like analysis, while RPA handles repetitive tasks — together they deliver end-to-end automation.”

🔑 Tools for RAG Systems

• LangChain – orchestrates retrieval + GPT.
• Vector DBs – FAISS, Pinecone, Weaviate, Milvus.
• LLMs – OpenAI GPT, Anthropic Claude, LLaMA 2.