AwaDB - AI Native Database for embedding vectors

Easily Use - No boring database schema definition. No need to pay attention to vector indexing details.

Realtime Search - Lock free realtime index keeps new data fresh with millisecond level latency. No wait no manual operation.

Stability - AwaDB builds upon over 5 years experience running production workloads at scale using a system called Vearch, combined with best-of-breed ideas and practices from the community.

Run awadb locally on Mac OSX or Linux

First install awadb:

pip3 install awadb

Then use as below:

import awadb
# 1. Initialize awadb client!
awadb_client = awadb.Client()

# 2. Create table
awadb_client.Create("test_llm1") 

# 3. Add sentences, the sentence is embedded with SentenceTransformer by default
#    You can also embed the sentences all by yourself with OpenAI or other LLMs
awadb_client.Add([{'embedding_text':'The man is happy'}, {'source' : 'pic1'}])
awadb_client.Add([{'embedding_text':'The man is very happy'}, {'source' : 'pic2'}])
awadb_client.Add([{'embedding_text':'The cat is happy'}, {'source' : 'pic3'}])
awadb_client.Add([{'embedding_text':'The man is eating'}, {'source':'pic4'}])

# 4. Search the most Top3 sentences by the specified query
query = "The man is happy"
results = awadb_client.Search(query, 3)

# Output the results
print(results)

Here the text is embedded by SentenceTransformer which is supported by Hugging Face
More detailed python local library usage you can read here

Run AwaDB as a service

If you are on the Windows platform or want a awadb service, you can download and deploy the awadb docker. The installation of awadb docker please see here

• Python Usage

First, Install gRPC and awadb service python client as below:

pip3 install grpcio
pip3 install awadb-client

A simple example as below:

# Import the package and module
from awadb_client import Awa

# Initialize awadb client
client = Awa()

# Add dict with vector to table 'example1'
client.add("example1", {'name':'david', 'feature':[1.3, 2.5, 1.9]})
client.add("example1", {'name':'jim', 'feature':[1.1, 1.4, 2.3]})

# Search
results = client.search("example1", [1.0, 2.0, 3.0])

# Output results
print(results)

# '_id' is the primary key of each document
# It can be specified clearly when adding documents
# Here no field '_id' is specified, it is generated by the awadb server 
db_name: "default"
table_name: "example1"
results {
  total: 2
  msg: "Success"
  result_items {
    score: 0.860000074
    fields {
      name: "_id" 
      value: "64ddb69d-6038-4311-9118-605686d758d9"
    }
    fields {
      name: "name"
      value: "jim"
    }
  }
  result_items {
    score: 1.55
    fields {
      name: "_id"
      value: "f9f3035b-faaf-48d4-a947-801416c005b3"
    }
    fields {
      name: "name"
      value: "david"
    }
  }
}
result_code: SUCCESS

More python sdk for service is here

• RESTful Usage

# add documents to table 'test' of db 'default', no need to create table first
curl -H "Content-Type: application/json" -X POST -d '{"db":"default", "table":"test", "docs":[{"_id":1, "name":"lj", "age":23, "f":[1,0]},{"_id":2, "name":"david", "age":32, "f":[1,2]}]}' http://localhost:8080/add

# search documents by the vector field 'f' of the value '[1, 1]'
curl -H "Content-Type: application/json" -X POST -d '{"db":"default", "table":"test", "vector_query":{"f":[1, 1]}}' http://localhost:8080/search

More detailed RESTful API is here

What are the Embeddings?

Any unstructured data(image/text/audio/video) can be transferred to vectors which are generally understanded by computers through AI(LLMs or other deep neural networks).