Warning: This is an experimental feature. To our knowledge, this is stable, but there are still rough edges in the experience. Contributions are welcome!
Overview
Vector database allows user to store and retrieve embeddings. Feast provides general APIs to store and retrieve embeddings.
Integration
Below are supported vector databases and implemented features:
Vector Database
Retrieval
Indexing
V2 Support*
Pgvector
[x]
[ ]
[]
Elasticsearch
[x]
[x]
[]
Milvus
[x]
[x]
[x]
Faiss
[ ]
[ ]
[]
SQLite
[x]
[ ]
[]
Qdrant
[x]
[x]
[]
*Note: V2 Support means the SDK supports retrieval of features along with vector embeddings from vector similarity search.
Note: SQLite is in limited access and only working on Python 3.10. It will be updated as sqlite_vec progresses.
We will be deprecating the retrieve_online_documents method in the SDK in the future. We recommend using the retrieve_online_documents_v2 method instead, which offers easier vector index configuration directly in the Feature View and the ability to retrieve standard features alongside your vector embeddings for richer context injection.
Long term we will collapse the two methods into one, but for now, we recommend using the retrieve_online_documents_v2 method. Beyond that, we will then have retrieve_online_documents and retrieve_online_documents_v2 simply point to get_online_features for backwards compatibility and the adopt industry standard naming conventions.
Note: Milvus implements the v2 retrieve_online_documents_v2 method in the SDK. This will be the longer-term solution so that Data Scientists can easily enable vector similarity search by just flipping a flag.
Examples
See the v0 Rag Demo for an example on how to use vector database using the retrieve_online_documents method (planning migration and deprecation (planning migration and deprecation).
See the v1 Milvus Quickstart for a quickstart guide on how to use Feast with Milvus using the retrieve_online_documents_v2 method.
Prepare offline embedding dataset
Run the following commands to prepare the embedding dataset:
The output will be stored in data/city_wikipedia_summaries.csv.
Initialize Feast feature store and materialize the data to the online store
Use the feature_store.yaml file to initialize the feature store. This will use the data as offline store, and Pgvector as online store.
project: local_rag
provider: local
registry: data/registry.db
online_store:
type: milvus
path: data/online_store.db
vector_enabled: true
embedding_dim: 384
index_type: "IVF_FLAT"
offline_store:
type: file
entity_key_serialization_version: 3
# By default, no_auth for authentication and authorization, other possible values kubernetes and oidc. Refer the documentation for more details.
auth:
type: no_auth
Run the following command in terminal to apply the feature store configuration:
feast apply
Note that when you run feast apply you are going to apply the following Feature View that we will use for retrieval later:
document_embeddings = FeatureView(
name="embedded_documents",
entities=[item, author],
schema=[
Field(
name="vector",
dtype=Array(Float32),
# Look how easy it is to enable RAG!
vector_index=True,
vector_search_metric="COSINE",
),
Field(name="item_id", dtype=Int64),
Field(name="author_id", dtype=String),
Field(name="created_timestamp", dtype=UnixTimestamp),
Field(name="sentence_chunks", dtype=String),
Field(name="event_timestamp", dtype=UnixTimestamp),
],
source=rag_documents_source,
ttl=timedelta(hours=24),
)
Let's use the SDK to write a data frame of embeddings to the online store:
During inference (e.g., during when a user submits a chat message) we need to embed the input text. This can be thought of as a feature transformation of the input data. In this example, we'll do this with a small Sentence Transformer from Hugging Face.
import torch
import torch.nn.functional as F
from feast import FeatureStore
from pymilvus import MilvusClient, DataType, FieldSchema
from transformers import AutoTokenizer, AutoModel
from example_repo import city_embeddings_feature_view, item
TOKENIZER = "sentence-transformers/all-MiniLM-L6-v2"
MODEL = "sentence-transformers/all-MiniLM-L6-v2"
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[
0
] # First element of model_output contains all token embeddings
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
def run_model(sentences, tokenizer, model):
encoded_input = tokenizer(
sentences, padding=True, truncation=True, return_tensors="pt"
)
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
return sentence_embeddings
question = "Which city has the largest population in New York?"
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER)
model = AutoModel.from_pretrained(MODEL)
query_embedding = run_model(question, tokenizer, model).detach().cpu().numpy().tolist()[0]
Retrieve the top K similar documents
First create a feature store instance, and use the retrieve_online_documents_v2 API to retrieve the top 5 similar documents to the specified query.
Let's assume we have a base prompt and a function that formats the retrieved documents called format_documents that we can then use to generate the response with OpenAI's chat completion API.
FULL_PROMPT = format_documents(rag_context_data, BASE_PROMPT)
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("OPENAI_API_KEY"),
)
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": FULL_PROMPT},
{"role": "user", "content": question}
],
)
# And this will print the content. Look at the examples/rag/milvus-quickstart.ipynb for an end-to-end example.
print('\n'.join([c.message.content for c in response.choices]))