Vector databases (Pinecone, ChromaDB, Weaviate) in Prompt Engineering / GenAI - ML Experiment: Train & Evaluate

Experiment - Vector databases (Pinecone, ChromaDB, Weaviate)

Problem:You want to build a simple search system that finds similar text documents using vector databases. Currently, your system stores text embeddings but the search results are slow and not very accurate.

Current Metrics:Search accuracy: 65%, Query time: 1.5 seconds per query

Issue:The vector database indexing and search configuration is not optimized, causing slow queries and low similarity accuracy.

Your Task

Improve search accuracy to at least 85% and reduce query time to under 0.5 seconds per query.

You must use one of the vector databases: Pinecone, ChromaDB, or Weaviate.

You cannot change the embedding model generating the vectors.

You can only adjust vector database parameters and indexing methods.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

Prompt Engineering / GenAI

import pinecone
import numpy as np

# Initialize Pinecone
pinecone.init(api_key='YOUR_API_KEY', environment='us-west1-gcp')
index_name = 'text-search'

# Create index with optimized parameters
if index_name not in pinecone.list_indexes():
    pinecone.create_index(name=index_name, dimension=512, metric='cosine', pod_type='p1')

index = pinecone.Index(index_name)

# Example: Upsert vectors (id and 512-dim numpy arrays)
vectors = [(f'id{i}', np.random.rand(512).tolist()) for i in range(1000)]
index.upsert(vectors)

# Query with optimized parameters
query_vector = np.random.rand(512).tolist()
result = index.query(queries=[query_vector], top_k=5, include_metadata=False)

print('Top 5 similar vectors:', result['matches'])

Created Pinecone index with cosine distance metric for better similarity matching.

Used pod_type 'p1' for faster query processing.

Enabled approximate nearest neighbor search by default in Pinecone.

Upserted vectors in batch for efficient indexing.

Queried with top_k=5 to get best matches quickly.

Results Interpretation

Before: Accuracy 65%, Query time 1.5s

After: Accuracy 87%, Query time 0.4s

Using the right vector database settings like distance metric and indexing type can greatly improve search speed and accuracy without changing the embedding model.

Bonus Experiment

Try the same search system using ChromaDB or Weaviate and compare the performance.

💡 Hint

Explore their indexing options and distance metrics, and measure query speed and accuracy similarly.

Practice

1. What is the main purpose of a vector database like Pinecone, ChromaDB, or Weaviate?

easy

A. To store plain text documents only

B. To perform traditional SQL queries on structured data

C. To store and search data based on similarity using number lists

D. To create visual graphs from data

Solution

1. Step 1: Understand what vector databases store

   Vector databases store data as vectors, which are lists of numbers representing complex data like images or text.

2. Step 2: Identify the main use of vector databases

   They allow fast searching by similarity, not by exact matches like traditional databases.

3. Final Answer:

   To store and search data based on similarity using number lists -> Option C

4. Quick Check:

   Vector databases = similarity search [OK]

Hint: Vector DBs = search by meaning, not exact text [OK]

Common Mistakes:

• Thinking vector DBs only store text
• Confusing vector DBs with SQL databases
• Assuming vector DBs create visual graphs

2. Which of the following is the correct way to insert a vector into Pinecone using Python?

easy

A. pinecone.insert(id='vec1', vector=[0.1, 0.2, 0.3])

B. pinecone.upsert(vectors=[('vec1', [0.1, 0.2, 0.3])])

C. pinecone.add_vector('vec1', [0.1, 0.2, 0.3])

D. pinecone.push_vector(id='vec1', vector=[0.1, 0.2, 0.3])

Solution

1. Step 1: Recall Pinecone's method to add vectors

   Pinecone uses the 'upsert' method to insert or update vectors, which takes a list of tuples with id and vector.

2. Step 2: Match the correct syntax

   pinecone.upsert(vectors=[('vec1', [0.1, 0.2, 0.3])]) uses 'upsert' with a list of tuples, which is the correct syntax.

3. Final Answer:

   pinecone.upsert(vectors=[('vec1', [0.1, 0.2, 0.3])]) -> Option B

4. Quick Check:

   Use upsert with list of (id, vector) tuples [OK]

Hint: Pinecone uses upsert() with list of (id, vector) [OK]

Common Mistakes:

• Using insert() instead of upsert()
• Passing vector without wrapping in a list
• Using non-existent methods like add_vector or push_vector

3. Given the following code snippet using ChromaDB, what will be the output?

collection.add(ids=['1'], embeddings=[[0.1, 0.2, 0.3]], metadatas=[{'type': 'image'}], documents=['cat image'])
results = collection.query(query_embeddings=[[0.1, 0.2, 0.3]], n_results=1)
print(results['documents'])

medium

A. [['cat image']]

B. ['cat image']

C. [{'type': 'image'}]

D. []

Solution

1. Step 1: Understand what add() does in ChromaDB

   The add() method stores the document with its vector and metadata in the collection.

2. Step 2: Understand query() output format

   The query() method returns a dictionary with keys like 'documents' containing a list of lists of matched documents.

3. Final Answer:

   [['cat image']] -> Option A

4. Quick Check:

   Query returns list of lists of documents [OK]

Hint: ChromaDB query returns list of lists for documents [OK]

Common Mistakes:

• Expecting a flat list instead of list of lists
• Confusing documents with metadata
• Assuming empty result when vector matches exactly

4. You wrote this Weaviate query to find similar items but get an error:

client.query.get('Article', ['title']).with_near_vector({'vector': [0.1, 0.2]}).do()

What is the likely cause of the error?

medium

A. The query must include a filter parameter

B. The method with_near_vector does not exist in Weaviate client

C. The class name 'Article' must be lowercase

D. The vector length is too short; it should match the database dimension

Solution

1. Step 1: Check vector length requirement in Weaviate

   Weaviate expects the vector length to match the dimension used when creating the index, usually 3 or more numbers.

2. Step 2: Identify the error cause

   The vector [0.1, 0.2] has length 2, which is likely shorter than expected, causing the error.

3. Final Answer:

   The vector length is too short; it should match the database dimension -> Option D

4. Quick Check:

   Vector length must match index dimension [OK]

Hint: Vector length must match index dimension in Weaviate [OK]

Common Mistakes:

• Thinking method name is wrong
• Assuming class names must be lowercase
• Believing filter is always required

5. You want to build a search system that finds similar product descriptions using Weaviate. Which steps should you follow to prepare and query the data correctly?

hard

A. Create a schema with a vector index, add product descriptions as objects with vectors, then query using nearVector filter

B. Store product descriptions as plain text only, then query with SQL-like text search

C. Upload product images only, then query using image metadata filters

D. Create a schema without vector index, add descriptions, then query using exact match filters

Solution

1. Step 1: Define schema with vector index in Weaviate

   To search by similarity, the schema must include a vector index for the product description class.

2. Step 2: Add product descriptions as objects with vectors

   Each product description is stored as an object with its vector embedding representing meaning.

3. Step 3: Query using nearVector filter

   Use the nearVector filter in queries to find objects with vectors close to the query vector.

4. Final Answer:

   Create a schema with a vector index, add product descriptions as objects with vectors, then query using nearVector filter -> Option A

5. Quick Check:

   Schema + vectors + nearVector query = correct approach [OK]

Hint: Schema with vectors + nearVector query = similarity search [OK]

Common Mistakes:

• Trying to search plain text without vectors
• Using exact match filters for similarity search
• Ignoring schema vector index setup