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Rockset

Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data.

This notebook demonstrates how to use Rockset as a vector store in LangChain. Before getting started, make sure you have access to a Rockset account and an API key available. Start your free trial today.

You'll need to install langchain-community with pip install -qU langchain-community to use this integration

Setting Up Your Environment​

  1. Leverage the Rockset console to create a collection with the Write API as your source. In this walkthrough, we create a collection named langchain_demo.

    Configure the following ingest transformation to mark your embeddings field and take advantage of performance and storage optimizations:

(We used OpenAI text-embedding-ada-002 for this examples, where #length_of_vector_embedding = 1536)

SELECT _input.* EXCEPT(_meta), 
VECTOR_ENFORCE(_input.description_embedding, #length_of_vector_embedding, 'float') as description_embedding 
FROM _input

  1. After creating your collection, use the console to retrieve an API key. For the purpose of this notebook, we assume you are using the Oregon(us-west-2) region.

  2. Install the rockset-python-client to enable LangChain to communicate directly with Rockset.

%pip install --upgrade --quiet  rockset

LangChain Tutorial​

Follow along in your own Python notebook to generate and store vector embeddings in Rockset. Start using Rockset to search for documents similar to your search queries.

1. Define Key Variables​

import os

import rockset

ROCKSET_API_KEY = os.environ.get(
    "ROCKSET_API_KEY"
)  # Verify ROCKSET_API_KEY environment variable
ROCKSET_API_SERVER = rockset.Regions.usw2a1  # Verify Rockset region
rockset_client = rockset.RocksetClient(ROCKSET_API_SERVER, ROCKSET_API_KEY)

COLLECTION_NAME = "langchain_demo"
TEXT_KEY = "description"
EMBEDDING_KEY = "description_embedding"

2. Prepare Documents​

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Rockset
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

3. Insert Documents​

embeddings = OpenAIEmbeddings()  # Verify OPENAI_API_KEY environment variable

docsearch = Rockset(
    client=rockset_client,
    embeddings=embeddings,
    collection_name=COLLECTION_NAME,
    text_key=TEXT_KEY,
    embedding_key=EMBEDDING_KEY,
)

ids = docsearch.add_texts(
    texts=[d.page_content for d in docs],
    metadatas=[d.metadata for d in docs],
)

4. Search for Similar Documents​

query = "What did the president say about Ketanji Brown Jackson"
output = docsearch.similarity_search_with_relevance_scores(
    query, 4, Rockset.DistanceFunction.COSINE_SIM
)
print("output length:", len(output))
for d, dist in output:
    print(dist, d.metadata, d.page_content[:20] + "...")

##
# output length: 4
# 0.764990692109871 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...
# 0.7485416901622112 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...
# 0.7468678973398306 {'source': '../../../state_of_the_union.txt'} And so many families...
# 0.7436231261419488 {'source': '../../../state_of_the_union.txt'} Groups of citizens b...

5. Search for Similar Documents with Filtering​

output = docsearch.similarity_search_with_relevance_scores(
    query,
    4,
    Rockset.DistanceFunction.COSINE_SIM,
    where_str="{} NOT LIKE '%citizens%'".format(TEXT_KEY),
)
print("output length:", len(output))
for d, dist in output:
    print(dist, d.metadata, d.page_content[:20] + "...")

##
# output length: 4
# 0.7651359650263554 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...
# 0.7486265516824893 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...
# 0.7469625542348115 {'source': '../../../state_of_the_union.txt'} And so many families...
# 0.7344177777547739 {'source': '../../../state_of_the_union.txt'} We see the unity amo...

6. [Optional] Delete Inserted Documents​

You must have the unique ID associated with each document to delete them from your collection. Define IDs when inserting documents with Rockset.add_texts(). Rockset will otherwise generate a unique ID for each document. Regardless, Rockset.add_texts() returns the IDs of inserted documents.

To delete these docs, simply use the Rockset.delete_texts() function.

docsearch.delete_texts(ids)

Summary​

In this tutorial, we successfully created a Rockset collection, inserted documents with OpenAI embeddings, and searched for similar documents with and without metadata filters.

Keep an eye on https://rockset.com/ for future updates in this space.

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