Using Chroma For Embeddings Search
chatgptchromaopenaigpt-4examplesvector_databasesopenai-apiopenai-cookbook
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Using Chroma for Embeddings Search
This notebook takes you through a simple flow to download some data, embed it, and then index and search it using a selection of vector databases. This is a common requirement for customers who want to store and search our embeddings with their own data in a secure environment to support production use cases such as chatbots, topic modelling and more.
What is a Vector Database
A vector database is a database made to store, manage and search embedding vectors. The use of embeddings to encode unstructured data (text, audio, video and more) as vectors for consumption by machine-learning models has exploded in recent years, due to the increasing effectiveness of AI in solving use cases involving natural language, image recognition and other unstructured forms of data. Vector databases have emerged as an effective solution for enterprises to deliver and scale these use cases.
Why use a Vector Database
Vector databases enable enterprises to take many of the embeddings use cases we've shared in this repo (question and answering, chatbot and recommendation services, for example), and make use of them in a secure, scalable environment. Many of our customers make embeddings solve their problems at small scale but performance and security hold them back from going into production - we see vector databases as a key component in solving that, and in this guide we'll walk through the basics of embedding text data, storing it in a vector database and using it for semantic search.
Demo Flow
The demo flow is:
- Setup: Import packages and set any required variables
- Load data: Load a dataset and embed it using OpenAI embeddings
- Chroma:
- Setup: Here we'll set up the Python client for Chroma. For more details go here
- Index Data: We'll create collections with vectors for titles and content
- Search Data: We'll run a few searches to confirm it works
Once you've run through this notebook you should have a basic understanding of how to setup and use vector databases, and can move on to more complex use cases making use of our embeddings.
Setup
Import the required libraries and set the embedding model that we'd like to use.
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Collecting wget Using cached wget-3.2.zip (10 kB) Preparing metadata (setup.py) ... done Building wheels for collected packages: wget Building wheel for wget (setup.py) ... done Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9657 sha256=b2d83c5fcdeab398d0a4e9808a470bbf725fffea4a6130e731c6097b9561005b Stored in directory: /Users/antontroynikov/Library/Caches/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769 Successfully built wget Installing collected packages: wget Successfully installed wget-3.2 Note: you may need to restart the kernel to use updated packages. Requirement already satisfied: numpy in /Users/antontroynikov/miniforge3/envs/chroma-openai-cookbook/lib/python3.10/site-packages (1.25.1) Note: you may need to restart the kernel to use updated packages.
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Load data
In this section we'll load embedded data that we've prepared previous to this session.
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'vector_database_wikipedia_articles_embedded.zip'
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<class 'pandas.core.frame.DataFrame'> RangeIndex: 25000 entries, 0 to 24999 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 25000 non-null int64 1 url 25000 non-null object 2 title 25000 non-null object 3 text 25000 non-null object 4 title_vector 25000 non-null object 5 content_vector 25000 non-null object 6 vector_id 25000 non-null object dtypes: int64(1), object(6) memory usage: 1.3+ MB
Chroma
We'll index these embedded documents in a vector database and search them. The first option we'll look at is Chroma, an easy to use open-source self-hosted in-memory vector database, designed for working with embeddings together with LLMs.
In this section, we will:
- Instantiate the Chroma client
- Create collections for each class of embedding
- Query each collection
Instantiate the Chroma client
Create the Chroma client. By default, Chroma is ephemeral and runs in memory. However, you can easily set up a persistent configuration which writes to disk.
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Create collections
Chroma collections allow you to store and filter with arbitrary metadata, making it easy to query subsets of the embedded data.
Chroma is already integrated with OpenAI's embedding functions. The best way to use them is on construction of a collection, as follows. Alternatively, you can 'bring your own embeddings'. More information can be found here
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OPENAI_API_KEY is ready
Populate the collections
Chroma collections allow you to populate, and filter on, whatever metadata you like. Chroma can also store the text alongside the vectors, and return everything in a single query call, when this is more convenient.
For this use-case, we'll just store the embeddings and IDs, and use these to index the original dataframe.
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Search the collections
Chroma handles embedding queries for you if an embedding function is set, like in this example.
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Now that you've got a basic embeddings search running, you can hop over to the Chroma docs to learn more about how to add filters to your query, update/delete data in your collections, and deploy Chroma.