Voyage AI provides cutting-edge embedding and rerankers. Voyage AI’s generalist embedding models continually top the MTEB leaderboard, and the domain-specific embeddings enhance the retrieval quality for enterprise use cases significantly.

Setup guide

In this guide, we use the Voyage Embedding API endpoint to generate text embeddings for terms of service and consumer contract documents, and then index those embeddings in the Pinecone vector database.

This is a powerful and common combination for building retrieval-augmented generation (RAG), semantic search, question-answering, code assistants, and other applications that rely on NLP and search over a large corpus of text data.

1. Set up the environment

Start by installing the Voyage and Pinecone clients and HuggingFace Datasets for downloading the LegalBench: Consumer Contracts QA (mteb/legalbench_consumer_contracts_qa) dataset used in this guide:

pip install -U voyageai pinecone-client datasets

2. Create embeddings

Sign up for an API key at Voyage AI and then use it to initialize your connection.

import voyageai

vc = voyageai.Client(api_key="<<YOUR_VOYAGE_API_KEY>>")

Load the LegalBench: Consumer Contracts QA dataset, which contains 154 consumer contract documents and 396 labeled queries about these documents.

from datasets import load_dataset

# load the documents and queries of legalbench consumer contracts qa dataset
documents = load_dataset('mteb/legalbench_consumer_contracts_qa', 'corpus', cache_dir = './', split='corpus')
queries = load_dataset('mteb/legalbench_consumer_contracts_qa', 'queries', cache_dir = './', split='queries')

Each document in mteb/legalbench_consumer_contracts_qa contains a text field by which we will embed using the Voyage AI client.

num_documents = len(documents['text'])
voyageai_batch_size = 128  # Please check the restrictions of number of examples and number of tokens per request here
embeds = []
while len(embeds) < num_documents:
        model='voyage-law-2',  # Please check the available models here

Check the dimensionality of the returned vectors. You will need to save the embedding dimensionality from this to be used when initializing your Pinecone index later.

import numpy as np

shape = np.array(embeds).shape
(154, 1024)

In this example, you can see that for each of the 154 documents, we created a 1024-dimensional embedding with the Voyage AI voyage-law-2 model.

3. Store the Embeddings

Now that you have your embeddings, you can move on to indexing them in the Pinecone vector database. For this, you need a Pinecone API key. Sign up for one here.

You first initialize our connection to Pinecone and then create a new index called voyageai-pinecone-legalbench for storing the embeddings. When creating the index, you specify that you would like to use the cosine similarity metric to align with Voyage AI’s embeddings, and also pass the embedding dimensionality of 1024.

from pinecone import Pinecone, ServerlessSpec

# initialize connection to pinecone (get API key at
pc = Pinecone(api_key="<<YOUR_PINECONE_API_KEY>>")

index_name = 'voyageai-pinecone-legalbench'

# if the index does not exist, we create it
if index_name not in pc.list_indexes().names():

# connect to index
index = pc.Index(index_name)

Now you can begin populating the index with your embeddings. Pinecone expects you to provide a list of tuples in the format (id, vector, metadata), where the metadata field is an optional extra field where you can store anything you want in a dictionary format. For this example, you will store the original text of the embeddings.

While uploading your data, you will batch everything to avoid pushing too much data in one go.

batch_size = 128

ids = [str(i) for i in range(shape[0])]
# create list of metadata dictionaries
meta = [{'text': text} for text in documents['text']]

# create list of (id, vector, metadata) tuples to be upserted
to_upsert = list(zip(ids, embeds, meta))

for i in range(0, shape[0], batch_size):
    i_end = min(i+batch_size, shape[0])

# let's view the index statistics

{'dimension': 1024,
 'index_fullness': 0.0,
 'namespaces': {'': {'vector_count': 154}},
 'total_vector_count': 154}

You can see from index.describe_index_stats that you have a 1024-dimensionality index populated with 154 embeddings. The indexFullness metric tells you how full your index is. At the moment, it is empty. Using the default value of one p1 pod, you can fit around 750K embeddings before the indexFullness reaches capacity. The Usage Estimator can be used to identify the number of pods required for a given number of n-dimensional embeddings.

Now that you have your indexed vectors, you can perform a few search queries. When searching, you will first embed your query using voyage-law-2, and then search using the returned vector in Pinecone.

# get a sample query from the dataset, "Will Google help me if I think someone has taken and used content Ive created without my permission?" 
query = queries['text'][0]
print(f"Query: {query}")

# create the query embedding
xq = vc.embed(

# query, returning the top 3 most similar results
res = index.query(vector=xq, top_k=3, include_metadata=True)

The response from Pinecone includes your original text in the metadata field. Let’s print out the top_k most similar questions and their respective similarity scores.

for match in res['matches']:
    print(f"{match['score']:.2f}: {match['metadata']['text']}")
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The semantic search pipeline with Voyage AI and Pinecone is able to identify the relevant consumer contract documents to answer the user query.