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In this short tutorial, we create a chain with Relevance AI, Redis VSS, OpenAI GPT, and Cohere Wikipedia embeddings.
Working with large language models (LLM) often requires retrieving the correct data to inject as context into a prompt. This is to give the LLM understanding of your own custom data without having to finetune the model. A popular strategy for retrieving the data is using vector search, as it’s exceptionally good at matching similar data without having an exact match. Redis natively supports vector similarity search and is built for speed.
In this example, we show how to build and deploy a chain, or a sequence of operations, in Relevance AI that allows us to ask questions of Wikipedia using Redis vector search to extract the best article based on our question. For embeddings, which are lists of numbers that can represent many types of data, we use Cohere’s multilingual model.
To follow along, you need a Redis database that supports JSON document data structures and built-in real-time Search and Query features. You can create one on Redis Enterprise Cloud or with Docker using Redis Stack.
Once you have Redis running, we import data from Cohere’s multilingual Wikipedia embeddings dataset on HuggingFace. This requires a few simple steps. You can see the full code in this jupyter notebook.
Step 1. Install the Python libraries for redis and datasets.
Become a Redis expert.
pip install redis datasets
Step 2. Create a client.
import redis
redis_client = redis.from_url('redis://connection-string')
Step 3. Download the sample dataset.
from datasets
import load_dataset
docs = load_dataset(
"Cohere/wikipedia-22-12-simple-embeddings", split="train"
)
Step 4. Ingest each document into Redis using JSON.
pipe = redis_client.pipeline()
index = 0
for doc in docs:
pipe.json().set(f"wiki:{doc['id']}", '$', doc)
if index % 500 == 0:
pipe.execute()
index += 1
pipe.execute()
Step 5. Create a vector search index.
redis_client.execute_command("FT.create wikipedia ON JSON PREFIX 1 wiki: SCHEMA $.id AS id NUMERIC $.title AS title TEXT $.text AS text TEXT $.wiki_id as wiki_id NUMERIC $.views as views NUMERIC $.emb as emb VECTOR HNSW 6 DIM 768 DISTANCE_METRIC L2 TYPE FLOAT32")
This command specifies the index, Wikipedia, which stores data in JSON, where all keys are indexed with the prefix wiki:. In the schema, we reference each field in JSON as $.field_name and give it a friendly label using as name and its data type. The vector field, emb, is of type vector. and uses HNSW as the index type with L2 as the distance metric.
Once the command runs, you’ll have a Redis index supporting vector similarity search.
Consult the documentation for more details on how to set up an index in Redis.
Now it’s time to jump into a Relevance AI notebook to start building our chain.
Step 1. If necessary, sign up for a free account with Relevance AI. Once you’re logged in, choose “Build AI chains” and click “Create new chain.” This takes you to a notebook.
Step 2. You need to configure an OpenAI API key and the Redis connection string before we can execute the chain. To do this, select “API keys” from the sidebar, then provide your Redis connection string and OpenAI API key.
Step 3. Choose “Start with prompt.” Add a new transformation for “Vector search (Redis).” Fill out the form with the following details:
Step 4. Configure the LLM prompt to inject the context from the vector search and to ask our question. You can customize the prompt to fit your needs.
The intent here is to help people query vast swathes of information. Say you’re mulling over your favorite Bob Dylan song, “Stuck Inside of Mobile,” and you wonder, “Who strummed those infectious guitar riffs?” With our LLM chain, you could effortlessly pose that question to an AI-infused Wikipedia search, which would swiftly return with your answer. (We leave the non-AI-infused search to the casual observer, which may demonstrate the usefulness of such a tool.)
This is more than just an exercise in technological wizardry; it’s about enriching your understanding and satiating your curiosity at lightning speed. Like a knowledgeable friend eager to share trivia over a cup of coffee, our LLM chain stands ready to engage in a dialogue with you, adding that personal touch to your search for knowledge.
A chain can be deployed in two ways with Relevance AI: as an embeddable application or as an API endpoint. An application can also be shared directly with a link where a user can see the form, fill it out, and run the chain. These are now ready to be used in production.
Want to experiment more or try out the SDK? You can view a technical tutorial on building a business analyst agent that can query SQL, create charts, and answer questions.
Get started today with your own vector-search powered retrieval system by signing up for Redis Enterprise Cloud and Relevance AI.
Join us on June 3, 2023, in person in San Francisco for a 12-hour LLM hackathon with the MLOps community featuring Redis and Relevance AI. Collaborate with other machine learning practitioners for a chance to win prizes.
