Chroma vector store setup in LangChain

Introduction

Chroma vector store helps you save and search data by meaning, not just words. It makes finding related info faster and smarter.

You want to find documents similar to a question quickly.

You need to organize text data by meaning for a chatbot.

You want to build a search tool that understands context.

You are working with AI models that use vector search.

You want to store and retrieve embeddings efficiently.

Syntax

LangChain

from langchain.vectorstores import Chroma

# Create or load a Chroma vector store
vector_store = Chroma(collection_name="my_collection", embedding_function=embedding_function)

# Add documents to the store
vector_store.add_texts(texts)

# Search for similar documents
results = vector_store.similarity_search(query, k=3)

embedding_function converts text into numbers that capture meaning.

collection_name is like a folder name to organize your data.

Examples

This example creates a Chroma store for books and searches for texts related to Python programming.

LangChain

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

embedding_function = OpenAIEmbeddings()
vector_store = Chroma(collection_name="books", embedding_function=embedding_function)
vector_store.add_texts(["Learn Python", "Learn JavaScript"])
results = vector_store.similarity_search("Python programming", k=2)

This example loads an existing Chroma collection without adding new texts and searches for related info.

LangChain

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

embedding_function = OpenAIEmbeddings()
# Load existing collection
vector_store = Chroma(collection_name="my_collection", embedding_function=embedding_function)
results = vector_store.similarity_search("Find related info", k=1)

Sample Program

This program sets up a Chroma vector store, adds three greetings, and searches for texts similar to "Hello". It prints the top 2 matches.

LangChain

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

# Create embedding function
embedding_function = OpenAIEmbeddings()

# Create a new Chroma vector store
vector_store = Chroma(collection_name="example_collection", embedding_function=embedding_function)

# Add some texts
texts = ["Hello world", "Hi there", "Greetings"]
vector_store.add_texts(texts)

# Search for similar texts
query = "Hello"
results = vector_store.similarity_search(query, k=2)

# Print results
for i, doc in enumerate(results, 1):
    print(f"Result {i}: {doc.page_content}")

Important Notes

Make sure your embedding function is compatible with Chroma.

Adding texts multiple times appends data; avoid duplicates if not needed.

Use meaningful collection names to keep your data organized.

Summary

Chroma stores text as vectors to find similar meaning quickly.

Set it up by providing an embedding function and collection name.

Add texts and use similarity_search to find related content.

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