Chroma vector store setup in LangChain - Step-by-Step Execution

Concept Flow - Chroma vector store setup

Import Chroma and dependencies

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Create embeddings model

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Initialize Chroma vector store

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Add documents to vector store

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Query vector store for similar docs

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Use results

This flow shows how to set up Chroma vector store: import, create embeddings, initialize store, add docs, then query.

Execution Sample

LangChain

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

embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings)
vectorstore.add_texts(["Hello world", "Langchain rocks"])

This code imports Chroma and embeddings, creates embeddings, initializes Chroma vector store, and adds two texts.

Execution Table

Step	Action	Evaluation	Result
1	Import Chroma and OpenAIEmbeddings	Modules loaded	Ready to use classes
2	Create embeddings instance	embeddings = OpenAIEmbeddings()	embeddings object created
3	Initialize Chroma vector store	vectorstore = Chroma(embedding_function=embeddings)	Empty vector store ready
4	Add texts to vector store	vectorstore.add_texts(["Hello world", "Langchain rocks"])	Texts embedded and stored
5	Query vector store (not shown here)	vectorstore.similarity_search("Hello")	Returns similar documents

💡 Setup complete, vector store ready for queries

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
embeddings	None	OpenAIEmbeddings instance	OpenAIEmbeddings instance	OpenAIEmbeddings instance	OpenAIEmbeddings instance
vectorstore	None	None	Empty Chroma vector store	Chroma vector store with 2 texts	Chroma vector store with 2 texts

Key Moments - 2 Insights

Why do we need to create an embeddings instance before initializing Chroma?

Chroma needs an embedding function to convert texts into vectors. The embeddings instance provides this function, as shown in step 2 and used in step 3.

What happens when we call add_texts on the vector store?

The texts are converted into vectors using the embeddings function and stored inside Chroma. This is shown in step 4 where texts become embedded and stored.

Visual Quiz - 3 Questions

Look at the execution table, what is the state of 'vectorstore' after step 3?

A Chroma vector store with 2 texts stored

B Empty Chroma vector store ready to add texts

C None, vectorstore not created yet

D An embeddings instance

💡 Hint

Check the 'Result' column for step 3 in the execution table

At which step are the texts converted into vectors and stored?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look for 'Texts embedded and stored' in the execution table

If we skip creating the embeddings instance, what will happen when initializing Chroma?

A Error because embedding_function is missing

B Chroma initializes with default embeddings

C Vector store initializes empty anyway

D Texts are automatically embedded

💡 Hint

Refer to step 3 where embeddings instance is passed to Chroma

Concept Snapshot

Chroma vector store setup:
1. Import Chroma and embeddings.
2. Create embeddings instance.
3. Initialize Chroma with embeddings.
4. Add texts to store (embedding happens here).
5. Query vector store for similar texts.

Full Transcript

To set up a Chroma vector store in Langchain, first import the Chroma class and an embeddings model like OpenAIEmbeddings. Then create an embeddings instance. Next, initialize the Chroma vector store by passing the embeddings instance as the embedding_function parameter. After that, add your texts to the vector store using add_texts, which converts texts into vectors and stores them. Finally, you can query the vector store to find similar documents. This process ensures your texts are searchable by their meaning, not just words.