Chroma vector store setup in LangChain - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of a vector store like Chroma in Langchain?

A vector store like Chroma stores document embeddings so you can quickly find similar documents by comparing vectors. It helps in fast and efficient semantic search.

beginner

Which Python package do you need to install to use Chroma vector store with Langchain?

You need to install the 'chromadb' package, which provides the Chroma vector database functionality.

intermediate

How do you initialize a Chroma vector store in Langchain with existing embeddings?

You use the Chroma class from langchain.vectorstores, passing your documents and an embeddings model instance, like: Chroma.from_documents(docs, embeddings).

intermediate

What is the role of the 'embedding' parameter when setting up Chroma vector store?

The 'embedding' parameter converts text documents into vectors that Chroma can store and search. It is usually an instance of an embedding model like OpenAIEmbeddings.

intermediate

How can you persist data in Chroma vector store for reuse across sessions?

You specify a 'persist_directory' when creating the Chroma instance. This saves the vector data to disk so you can load it later without recomputing embeddings.

What does Chroma vector store primarily store?

A Database tables

B Text documents as plain text

C Images and videos

D Vectors representing document embeddings

Chroma stores vectors that represent document embeddings for fast similarity search.

Which method initializes Chroma with documents and embeddings in Langchain?

A Chroma.load()

B Chroma.from_documents()

C Chroma.create()

D Chroma.init()

Chroma.from_documents() is the standard method to create a vector store from documents and embeddings.

Why do you need an embedding model when using Chroma?

A To convert text into vectors

B To store raw text

C To display documents

D To compress files

Embedding models convert text into vectors that Chroma can store and search.

How do you save Chroma vector data for later use?

A By exporting to CSV

B By calling save() method

C By setting persist_directory during initialization

D By using a cloud service

Setting persist_directory saves the vector store data to disk for reuse.

Which package provides Chroma vector store functionality?

A chromadb

B numpy

C pandas

D scikit-learn

The chromadb package provides the Chroma vector database used by Langchain.

Explain how to set up a Chroma vector store in Langchain starting from documents.

Think about the steps from raw text to a searchable vector store.

Describe why embeddings are important when using Chroma vector store.

Focus on the role of embeddings in vector search.