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Aerospike

Aerospike Vector Search (AVS) is an extension to the Aerospike Database that enables searches across very large datasets stored in Aerospike. This new service lives outside of Aerospike and builds an index to perform those searches.

This notebook showcases the functionality of the LangChain Aerospike VectorStore integration.

Install AVS​

Before using this notebook, we need to have a running AVS instance. Use one of the available installation methods.

When finished, store your AVS instance's IP address and port to use later in this demo:

PROXIMUS_HOST = "<avs-ip>"
PROXIMUS_PORT = 5000

Install Dependencies​

The sentence-transformers dependency is large. This step could take several minutes to complete.

!pip install --upgrade --quiet aerospike-vector-search==0.6.1 langchain-community sentence-transformers langchain

Download Quotes Dataset​

We will download a dataset of approximately 100,000 quotes and use a subset of those quotes for semantic search.

!wget https://github.com/aerospike/aerospike-vector-search-examples/raw/7dfab0fccca0852a511c6803aba46578729694b5/quote-semantic-search/container-volumes/quote-search/data/quotes.csv.tgz
--2024-05-10 17:28:17--  https://github.com/aerospike/aerospike-vector-search-examples/raw/7dfab0fccca0852a511c6803aba46578729694b5/quote-semantic-search/container-volumes/quote-search/data/quotes.csv.tgz
Resolving github.com (github.com)... 140.82.116.4
Connecting to github.com (github.com)|140.82.116.4|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/aerospike/aerospike-vector-search-examples/7dfab0fccca0852a511c6803aba46578729694b5/quote-semantic-search/container-volumes/quote-search/data/quotes.csv.tgz [following]
--2024-05-10 17:28:17--  https://raw.githubusercontent.com/aerospike/aerospike-vector-search-examples/7dfab0fccca0852a511c6803aba46578729694b5/quote-semantic-search/container-volumes/quote-search/data/quotes.csv.tgz
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 11597643 (11M) [application/octet-stream]
Saving to: ‘quotes.csv.tgz’

quotes.csv.tgz      100%[===================>]  11.06M  1.94MB/s    in 6.1s    

2024-05-10 17:28:23 (1.81 MB/s) - ‘quotes.csv.tgz’ saved [11597643/11597643]

Load the Quotes Into Documents​

We will load our quotes dataset using the CSVLoader document loader. In this case, lazy_load returns an iterator to ingest our quotes more efficiently. In this example, we only load 5,000 quotes.

import itertools
import os
import tarfile

from langchain_community.document_loaders.csv_loader import CSVLoader

filename = "./quotes.csv"

if not os.path.exists(filename) and os.path.exists(filename + ".tgz"):
    # Untar the file
    with tarfile.open(filename + ".tgz", "r:gz") as tar:
        tar.extractall(path=os.path.dirname(filename))

NUM_QUOTES = 5000
documents = CSVLoader(filename, metadata_columns=["author", "category"]).lazy_load()
documents = list(
    itertools.islice(documents, NUM_QUOTES)
)  # Allows us to slice an iterator
API Reference:CSVLoader
print(documents[0])
page_content="quote: I'm selfish, impatient and a little insecure. I make mistakes, I am out of control and at times hard to handle. But if you can't handle me at my worst, then you sure as hell don't deserve me at my best." metadata={'source': './quotes.csv', 'row': 0, 'author': 'Marilyn Monroe', 'category': 'attributed-no-source, best, life, love, mistakes, out-of-control, truth, worst'}

Create your Embedder​

In this step, we use HuggingFaceEmbeddings and the "all-MiniLM-L6-v2" sentence transformer model to embed our documents so we can perform a vector search.

from aerospike_vector_search.types import VectorDistanceMetric
from langchain_community.embeddings import HuggingFaceEmbeddings

MODEL_DIM = 384
MODEL_DISTANCE_CALC = VectorDistanceMetric.COSINE
embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
API Reference:HuggingFaceEmbeddings
modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]
config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]
README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]
sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]
/opt/conda/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]
/opt/conda/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]
tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]
vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]
tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]
special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]
1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]

Create an Aerospike Index and Embed Documents​

Before we add documents, we need to create an index in the Aerospike Database. In the example below, we use some convenience code that checks to see if the expected index already exists.

from aerospike_vector_search import AdminClient, Client, HostPort
from aerospike_vector_search.types import VectorDistanceMetric
from langchain_community.vectorstores import Aerospike

# Here we are using the AVS host and port you configured earlier
seed = HostPort(host=PROXIMUS_HOST, port=PROXIMUS_PORT)

# The namespace of where to place our vectors. This should match the vector configured in your docstore.conf file.
NAMESPACE = "test"

# The name of our new index.
INDEX_NAME = "quote-miniLM-L6-v2"

# AVS needs to know which metadata key contains our vector when creating the index and inserting documents.
VECTOR_KEY = "vector"

client = Client(seeds=seed)
admin_client = AdminClient(
    seeds=seed,
)
index_exists = False

# Check if the index already exists. If not, create it
for index in admin_client.index_list():
    if index["id"]["namespace"] == NAMESPACE and index["id"]["name"] == INDEX_NAME:
        index_exists = True
        print(f"{INDEX_NAME} already exists. Skipping creation")
        break

if not index_exists:
    print(f"{INDEX_NAME} does not exist. Creating index")
    admin_client.index_create(
        namespace=NAMESPACE,
        name=INDEX_NAME,
        vector_field=VECTOR_KEY,
        vector_distance_metric=MODEL_DISTANCE_CALC,
        dimensions=MODEL_DIM,
        index_meta_data={
            "model": "miniLM-L6-v2",
            "date": "05/04/2024",
            "dim": str(MODEL_DIM),
            "distance": "cosine",
        },
    )

admin_client.close()

docstore = Aerospike.from_documents(
    documents,
    embedder,
    client=client,
    namespace=NAMESPACE,
    vector_key=VECTOR_KEY,
    index_name=INDEX_NAME,
    distance_strategy=MODEL_DISTANCE_CALC,
)
API Reference:Aerospike
quote-miniLM-L6-v2 does not exist. Creating index

Search the Documents​

Now that we have embedded our vectors, we can use vector search on our quotes.

query = "A quote about the beauty of the cosmos"
docs = docstore.similarity_search(
    query, k=5, index_name=INDEX_NAME, metadata_keys=["_id", "author"]
)


def print_documents(docs):
    for i, doc in enumerate(docs):
        print("~~~~ Document", i, "~~~~")
        print("auto-generated id:", doc.metadata["_id"])
        print("author: ", doc.metadata["author"])
        print(doc.page_content)
        print("~~~~~~~~~~~~~~~~~~~~\n")


print_documents(docs)
~~~~ Document 0 ~~~~
auto-generated id: f53589dd-e3e0-4f55-8214-766ca8dc082f
author:  Carl Sagan, Cosmos
quote: The Cosmos is all that is or was or ever will be. Our feeblest contemplations of the Cosmos stir us -- there is a tingling in the spine, a catch in the voice, a faint sensation, as if a distant memory, of falling from a height. We know we are approaching the greatest of mysteries.
~~~~~~~~~~~~~~~~~~~~

~~~~ Document 1 ~~~~
auto-generated id: dde3e5d1-30b7-47b4-aab7-e319d14e1810
author:  Elizabeth Gilbert
quote: The love that moves the sun and the other stars.
~~~~~~~~~~~~~~~~~~~~

~~~~ Document 2 ~~~~
auto-generated id: fd56575b-2091-45e7-91c1-9efff2fe5359
author:  Renee Ahdieh, The Rose & the Dagger
quote: From the stars, to the stars.
~~~~~~~~~~~~~~~~~~~~

~~~~ Document 3 ~~~~
auto-generated id: 8567ed4e-885b-44a7-b993-e0caf422b3c9
author:  Dante Alighieri, Paradiso
quote: Love, that moves the sun and the other stars
~~~~~~~~~~~~~~~~~~~~

~~~~ Document 4 ~~~~
auto-generated id: f868c25e-c54d-48cd-a5a8-14bf402f9ea8
author:  Thich Nhat Hanh, Teachings on Love
quote: Through my love for you, I want to express my love for the whole cosmos, the whole of humanity, and all beings. By living with you, I want to learn to love everyone and all species. If I succeed in loving you, I will be able to love everyone and all species on Earth... This is the real message of love.
~~~~~~~~~~~~~~~~~~~~

Embedding Additional Quotes as Text​

We can use add_texts to add additional quotes.

docstore = Aerospike(
    client,
    embedder,
    NAMESPACE,
    index_name=INDEX_NAME,
    vector_key=VECTOR_KEY,
    distance_strategy=MODEL_DISTANCE_CALC,
)

ids = docstore.add_texts(
    [
        "quote: Rebellions are built on hope.",
        "quote: Logic is the beginning of wisdom, not the end.",
        "quote: If wishes were fishes, we’d all cast nets.",
    ],
    metadatas=[
        {"author": "Jyn Erso, Rogue One"},
        {"author": "Spock, Star Trek"},
        {"author": "Frank Herbert, Dune"},
    ],
)

print("New IDs")
print(ids)
New IDs
['972846bd-87ae-493b-8ba3-a3d023c03948', '8171122e-cbda-4eb7-a711-6625b120893b', '53b54409-ac19-4d90-b518-d7c40bf5ee5d']

We can use max marginal relevance search to find vectors that are similar to our query but dissimilar to each other. In this example, we create a retriever object using as_retriever, but this could be done just as easily by calling docstore.max_marginal_relevance_search directly. The lambda_mult search argument determines the diversity of our query response. 0 corresponds to maximum diversity and 1 to minimum diversity.

query = "A quote about our favorite four-legged pets"
retriever = docstore.as_retriever(
    search_type="mmr", search_kwargs={"fetch_k": 20, "lambda_mult": 0.7}
)
matched_docs = retriever.invoke(query)

print_documents(matched_docs)
~~~~ Document 0 ~~~~
auto-generated id: 67d5b23f-b2d2-4872-80ad-5834ea08aa64
author:  John Grogan, Marley and Me: Life and Love With the World's Worst Dog
quote: Such short little lives our pets have to spend with us, and they spend most of it waiting for us to come home each day. It is amazing how much love and laughter they bring into our lives and even how much closer we become with each other because of them.
~~~~~~~~~~~~~~~~~~~~

~~~~ Document 1 ~~~~
auto-generated id: a9b28eb0-a21c-45bf-9e60-ab2b80e988d8
author:  John Grogan, Marley and Me: Life and Love With the World's Worst Dog
quote: Dogs are great. Bad dogs, if you can really call them that, are perhaps the greatest of them all.
~~~~~~~~~~~~~~~~~~~~

~~~~ Document 2 ~~~~
auto-generated id: ee7434c8-2551-4651-8a22-58514980fb4a
author:  Colleen Houck, Tiger's Curse
quote: He then put both hands on the door on either side of my head and leaned in close, pinning me against it. I trembled like a downy rabbit caught in the clutches of a wolf. The wolf came closer. He bent his head and began nuzzling my cheek. The problem was…I wanted the wolf to devour me.
~~~~~~~~~~~~~~~~~~~~

~~~~ Document 3 ~~~~
auto-generated id: 9170804c-a155-473b-ab93-8a561dd48f91
author:  Ray Bradbury
quote: Stuff your eyes with wonder," he said, "live as if you'd drop dead in ten seconds. See the world. It's more fantastic than any dream made or paid for in factories. Ask no guarantees, ask for no security, there never was such an animal. And if there were, it would be related to the great sloth which hangs upside down in a tree all day every day, sleeping its life away. To hell with that," he said, "shake the tree and knock the great sloth down on his ass.
~~~~~~~~~~~~~~~~~~~~

Search Documents with a Relevance Threshold​

Another useful feature is a similarity search with a relevance threshold. Generally, we only want results that are most similar to our query but also within some range of proximity. A relevance of 1 is most similar and a relevance of 0 is most dissimilar.

query = "A quote about stormy weather"
retriever = docstore.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={
        "score_threshold": 0.4
    },  # A greater value returns items with more relevance
)
matched_docs = retriever.invoke(query)

print_documents(matched_docs)
~~~~ Document 0 ~~~~
auto-generated id: 2c1d6ee1-b742-45ea-bed6-24a1f655c849
author:  Roy T. Bennett, The Light in the Heart
quote: Never lose hope. Storms make people stronger and never last forever.
~~~~~~~~~~~~~~~~~~~~

~~~~ Document 1 ~~~~
auto-generated id: 5962c2cf-ffb5-4e03-9257-bdd630b5c7e9
author:  Roy T. Bennett, The Light in the Heart
quote: Difficulties and adversities viciously force all their might on us and cause us to fall apart, but they are necessary elements of individual growth and reveal our true potential. We have got to endure and overcome them, and move forward. Never lose hope. Storms make people stronger and never last forever.
~~~~~~~~~~~~~~~~~~~~

~~~~ Document 2 ~~~~
auto-generated id: 3bbcc4ca-de89-4196-9a46-190a50bf6c47
author:  Vincent van Gogh, The Letters of Vincent van Gogh
quote: There is peace even in the storm
~~~~~~~~~~~~~~~~~~~~

~~~~ Document 3 ~~~~
auto-generated id: 37d8cf02-fc2f-429d-b2b6-260a05286108
author:  Edwin Morgan, A Book of Lives
quote: Valentine WeatherKiss me with rain on your eyelashes,come on, let us sway together,under the trees, and to hell with thunder.
~~~~~~~~~~~~~~~~~~~~

Clean up​

We need to make sure we close our client to release resources and clean up threads.

client.close()

Now that you are up to speed with Aerospike Vector Search's LangChain integration, you have the power of the Aerospike Database and the LangChain ecosystem at your finger tips. Happy building!

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