Vector Databases & Retrieval

After documents are chunked, they must be converted into numerical vectors using an Embedding Model and stored in a Vector Database for similarity retrieval. By the end of this page, you will have a working semantic search engine project.

1. Vector Spaces & Distance Metrics

An embedding model maps text chunks to coordinate vectors in a high-dimensional space. To retrieve the best matches, the vector database calculates distance metrics between the user’s query vector (

q

) and the stored document vectors (

d

Cosine Similarity: Measures the cosine of the angle between two vectors. It ranges from -1 to 1 (where 1 means identical direction). Ideal for text retrieval because it is independent of document length.
L2 Distance (Euclidean): Measures the straight-line distance between two points. Closer to 0 means higher similarity.
Dot Product: Multiplies corresponding coordinates. If vectors are normalized, dot product equals cosine similarity.

2. Ingestion + Vector Search Working Project

Let’s build a working database search project that loads a text document, chunks it, generates embeddings using Gemini, stores them in ChromaDB, and runs semantic query searches.

2.1 Install Dependencies

Run in your terminal:

uv add chromadb langchain-community langchain-google-genai

Ensure your API key is in your environment:

export GOOGLE_API_KEY="your_actual_api_key_here"

2.2 Complete Code Implementation

Save and run this code:

import os
from dotenv import load_dotenv
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenAIEmbeddings
from langchain_community.vectorstores import Chroma

# 1. Load API keys
load_dotenv()

# 2. Write and Load Document Data
with open("office_policy.txt", "w") as f:
    f.write("""
Reimbursements: Submit all travel receipts by the 25th of each month.
Office Rules: Do not write passwords on post-it notes. Lock your screen when leaving your desk.
Health Policy: Sick leave of more than 2 consecutive days requires a medical certificate.
""")

loader = TextLoader("office_policy.txt")
documents = loader.load()

# 3. Chunk the documents
splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=15)
chunks = splitter.split_documents(documents)

# 4. Generate Embeddings
# text-embedding-004 maps chunks to 768-dimension vectors
embeddings = GoogleGenAIEmbeddings(model="models/text-embedding-004")

# 5. Load into ChromaDB (In-Memory Vector DB)
print("Generating embeddings and indexing into ChromaDB...")
vector_db = Chroma.from_documents(
    documents=chunks,
    embedding=embeddings
)

# 6. Perform a Semantic Similarity Search
query = "What happens if I am sick for three days?"
print(f"\nQuery: '{query}'")

# Retrieve the top semantic match (k=1)
results = vector_db.similarity_search(query, k=1)

# 7. Print Output
print("\n--- Semantic Retrieval Match ---")
for doc in results:
    print(f"Content: {doc.page_content}")
    print(f"Source: {doc.metadata['source']}")

Output:

Generating embeddings and indexing into ChromaDB...

Query: 'What happens if I am sick for three days?'

--- Semantic Retrieval Match ---
Content: Health Policy: Sick leave of more than 2 consecutive days requires a medical certificate.
Source: office_policy.txt

3. Practice Exercises

Practice 1: Search Scope (Top-K)

Modify the search query step in the working project to retrieve the top 2 matches (k=2). Run a query searching for "travel refunds and screen security" and print both returned chunks. Instructions:

Call .similarity_search(query, k=2) on the vector_db object.
Iterate through the returned list and print each chunk’s content.

Solution

query = "travel refunds and screen security"
results = vector_db.similarity_search(query, k=2)

for i, doc in enumerate(results):
    print(f"Match {i+1}: {doc.page_content}")

Foundations

RAG Implementation

1. Vector Spaces & Distance Metrics

2. Ingestion + Vector Search Working Project

2.1 Install Dependencies

2.2 Complete Code Implementation

3. Practice Exercises

Practice 1: Search Scope (Top-K)

​1. Vector Spaces & Distance Metrics

​2. Ingestion + Vector Search Working Project

​2.1 Install Dependencies

​2.2 Complete Code Implementation

​3. Practice Exercises

​Practice 1: Search Scope (Top-K)

1. Vector Spaces & Distance Metrics

2. Ingestion + Vector Search Working Project

2.1 Install Dependencies

2.2 Complete Code Implementation

3. Practice Exercises

Practice 1: Search Scope (Top-K)