Chroma vector store setup in LangChain - Mini Project: Build & Apply

Chroma Vector Store Setup

📖 Scenario: You are building a simple application that stores text data as vectors using the Chroma vector store from LangChain. This will help you quickly find similar texts later.

🎯 Goal: Create a Chroma vector store with some sample documents and set it up to be ready for similarity search.

📋 What You'll Learn

Create a list of documents with exact texts

Set a collection name for the Chroma vector store

Initialize the Chroma vector store with the documents and collection name

Add the final step to persist the vector store

💡 Why This Matters

🌍 Real World

Vector stores help applications find similar texts quickly, useful in chatbots, search engines, and recommendation systems.

💼 Career

Understanding how to set up vector stores is important for roles in AI development, data science, and software engineering working with natural language processing.

1

Create the documents list

Create a list called documents with these exact strings: 'Hello world', 'LangChain is awesome', and 'Vector stores are useful'.

LangChain

# Create the documents list with exact texts
# Your code here

Hint

Use a Python list with the exact strings inside quotes.

2

Set the collection name

Create a variable called collection_name and set it to the string 'my_collection'.

LangChain

documents = ['Hello world', 'LangChain is awesome', 'Vector stores are useful']
# Set the collection name variable
# Your code here

Hint

Assign the exact string 'my_collection' to the variable collection_name.

3

Initialize the Chroma vector store

Import Chroma from langchain.vectorstores and OpenAIEmbeddings from langchain.embeddings. Then create a variable called embedding as an instance of OpenAIEmbeddings(). Finally, create a variable called vectordb by initializing Chroma with embedding, documents, and collection_name as the collection name.

LangChain

documents = ['Hello world', 'LangChain is awesome', 'Vector stores are useful']
collection_name = 'my_collection'
# Import Chroma and OpenAIEmbeddings
# Create embedding instance
# Initialize Chroma vector store with documents and collection_name
# Your code here

Hint

Use Chroma.from_texts with the exact parameters: texts=documents, embedding=embedding, and collection_name=collection_name.

4

Persist the vector store

Call the persist method on the vectordb variable to save the vector store data.

LangChain

from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

documents = ['Hello world', 'LangChain is awesome', 'Vector stores are useful']
collection_name = 'my_collection'

embedding = OpenAIEmbeddings()
vectordb = Chroma.from_texts(texts=documents, embedding=embedding, collection_name=collection_name)
# Persist the vector store
# Your code here

Hint

Call persist() on the vectordb variable to save the data.

Practice

1. What is the main purpose of setting up a Chroma vector store in Langchain?

easy

A. To generate text summaries automatically

B. To store text as vectors for fast similarity search

C. To create a relational database for text storage

D. To visualize text data in charts

Solution

1. Step 1: Understand Chroma vector store role

   Chroma stores text as vectors to quickly find similar meanings, not for relational data or visualization.

2. Step 2: Identify main use case

   Its main use is fast similarity search using vector embeddings.

3. Final Answer:

   To store text as vectors for fast similarity search -> Option B

4. Quick Check:

   Chroma = vector storage for similarity [OK]

Hint: Chroma = vector storage for quick similarity search [OK]

Common Mistakes:

• Thinking Chroma is a database for tables
• Confusing it with text summarization tools
• Assuming it creates visual charts

2. Which of the following is the correct way to initialize a Chroma vector store with an embedding function named embed and collection name my_collection?

easy

A. store = Chroma(embedding=embed, collection='my_collection')

B. store = Chroma(collection='my_collection', embed=embedding_function)

C. store = Chroma('my_collection', embed_function=embed)

D. store = Chroma(embedding_function=embed, collection_name='my_collection')

Solution

1. Step 1: Check parameter names for Chroma initialization

   The correct parameters are embedding_function and collection_name.

2. Step 2: Match correct syntax

   store = Chroma(embedding_function=embed, collection_name='my_collection') uses embedding_function=embed and collection_name='my_collection', which is correct.

3. Final Answer:

   store = Chroma(embedding_function=embed, collection_name='my_collection') -> Option D

4. Quick Check:

   embedding_function + collection_name = store = Chroma(embedding_function=embed, collection_name='my_collection') [OK]

Hint: Use embedding_function and collection_name keys exactly [OK]

Common Mistakes:

• Using wrong parameter names like embed or collection
• Passing parameters in wrong order without keywords
• Confusing embedding_function with embed_function

3. Given the following code snippet, what will results contain after running store.similarity_search('Hello world')?

embed = SomeEmbeddingFunction()
store = Chroma(embedding_function=embed, collection_name='texts')
store.add_texts(['Hello world', 'Goodbye world'])
results = store.similarity_search('Hello world')

medium

A. A list containing 'Hello world' as the most similar text

B. An empty list because no texts were added

C. A list containing 'Goodbye world' only

D. A syntax error due to missing parameters

Solution

1. Step 1: Analyze texts added to the store

   The store has two texts: 'Hello world' and 'Goodbye world'.

2. Step 2: Understand similarity_search behavior

   Searching 'Hello world' will return texts most similar, so 'Hello world' itself is the closest match.

3. Final Answer:

   A list containing 'Hello world' as the most similar text -> Option A

4. Quick Check:

   Added texts include 'Hello world' = A list containing 'Hello world' as the most similar text [OK]

Hint: Similarity search returns closest added text matches [OK]

Common Mistakes:

• Assuming no texts were added so result is empty
• Thinking it returns unrelated texts
• Confusing syntax error with runtime behavior

4. Identify the error in this Chroma vector store setup code:

embed = SomeEmbeddingFunction()
store = Chroma(embedding_function=embed)
store.add_texts(['Sample text'])

medium

A. Missing collection_name parameter in Chroma initialization

B. Embedding function is not assigned correctly

C. add_texts method does not exist

D. Chroma requires a database URL parameter

Solution

1. Step 1: Check required parameters for Chroma

   Chroma requires both embedding_function and collection_name to be set.

2. Step 2: Identify missing parameter

   The code misses collection_name, which is mandatory.

3. Final Answer:

   Missing collection_name parameter in Chroma initialization -> Option A

4. Quick Check:

   collection_name missing = Missing collection_name parameter in Chroma initialization [OK]

Hint: Always include collection_name when creating Chroma store [OK]

Common Mistakes:

• Assuming embedding_function alone is enough
• Thinking add_texts method is invalid
• Believing database URL is mandatory

5. You want to store multiple documents in a Chroma vector store and then find the top 2 most similar documents to a query. Which code snippet correctly sets this up and performs the search?

hard

A. embed = Embedding()
store = Chroma(collection_name='docs')
docs = ['Doc1', 'Doc2', 'Doc3']
store.add_texts(docs)
results = store.similarity_search('query text', top=2)

B. embed = Embedding()
store = Chroma(embedding_function=embed, collection='docs')
docs = ['Doc1', 'Doc2', 'Doc3']
store.add_texts(docs)
results = store.similarity_search('query text', k=2)

C. embed = Embedding()
store = Chroma(embedding_function=embed, collection_name='docs')
docs = ['Doc1', 'Doc2', 'Doc3']
store.add_texts(docs)
results = store.similarity_search('query text', k=2)

D. embed = Embedding()
store = Chroma(embedding_function=embed, collection_name='docs')
docs = ['Doc1', 'Doc2', 'Doc3']
store.add_texts(docs)
results = store.similarity_search('query text')

Solution

1. Step 1: Verify Chroma initialization parameters

   embedding_function and collection_name must be set correctly; embed = Embedding() store = Chroma(embedding_function=embed, collection_name='docs') docs = ['Doc1', 'Doc2', 'Doc3'] store.add_texts(docs) results = store.similarity_search('query text', k=2) does this properly.

2. Step 2: Check similarity_search usage for top results

   embed = Embedding() store = Chroma(embedding_function=embed, collection_name='docs') docs = ['Doc1', 'Doc2', 'Doc3'] store.add_texts(docs) results = store.similarity_search('query text', k=2) uses k=2 to get top 2 results, which is the correct parameter.

3. Final Answer:

   Correct setup with embedding_function, collection_name, and k=2 in similarity_search -> Option C

4. Quick Check:

   embedding_function + collection_name + k=2 = embed = Embedding() store = Chroma(embedding_function=embed, collection_name='docs') docs = ['Doc1', 'Doc2', 'Doc3'] store.add_texts(docs) results = store.similarity_search('query text', k=2) [OK]

Hint: Use k=2 in similarity_search to get top 2 results [OK]

Common Mistakes:

• Using wrong parameter name like top instead of k
• Missing embedding_function or collection_name
• Not specifying number of results to return