Your first embedding query in Python

This is the lesson where you make embeddings real. By the end you have a working semantic search over your own data. Module 9 will turn this into RAG.

Today you build a tiny private Google for whatever text you give it. Cool.

Three steps:

1. Embed your documents.
2. Store the vectors with their text (and ideally metadata).
3. At query time, embed the question and find nearest vectors.

We will use OpenAI embeddings + ChromaDB. Swap providers later as needed.

import os, glob, chromadb
from openai import OpenAI
from dotenv import load_dotenv
load_dotenv()

client_oai = OpenAI()
client_chroma = chromadb.PersistentClient(path="./chroma_db")
col = client_chroma.get_or_create_collection("notes")

def embed(text):
    return client_oai.embeddings.create(
        model="text-embedding-3-small", input=text
    ).data[0].embedding

# Ingest .txt files from ./notes
for path in glob.glob("notes/*.txt"):
    text = open(path).read()
    col.add(
        ids=[path],
        documents=[text],
        embeddings=[embed(text)],
        metadatas=[{"source": path}],
    )

# Query
question = "what does GeekHub do?"
results = col.query(query_embeddings=[embed(question)], n_results=3)
for doc, meta in zip(results["documents"][0], results["metadatas"][0]):
    print(meta["source"], "->", doc[:200])

50 lines and you have a semantic search engine.