AwaDB - the AI Native database for embedding vectors.
Store and search embedding vectors for LLM Applications!
# Install awadb
pip3 install awadb -r requirements.txt If not available, please try pip3 install --index-url https://pypi.org/simple/ --no-deps awadb
Now support Linux(python>=3.6), MacOS(python>=3.9)
The core API is only 4 steps:
import awadb
# 1. Initialize awadb client!
awadb_client = awadb.Client()
# 2. Create table!
awadb_client.Create("testdb")
# 3. Add docs to the table. Can also update and delete the doc!
awadb_client.Add([{'name':'jim'}, {'age':39}, 'hello', [1, 3.5, 3]])
awadb_client.Add([{'name':'vincent'}, {'age':28}, 'world', [1, 3.4, 2]])
awadb_client.Add([{'name':'david'}, {'age':45}, 'hi', [1, 2.4, 4]])
awadb_client.Add([{'name':'tom'}, {'age':25}, 'dolly', [1.3, 2.9, 8.9]])
# 4. Search by specified vector query and the most TopK similar results
results = awadb_client.Search([3.0, 3.1, 4.2], 3)
# Output the results
print(results)You can also directly use awadb to do the text semantic retrieval Here the text is embedded by SentenceTransformer which is supported by Hugging Face
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(['The man is eating', '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)
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).
For example, "The man is happy"-this sentence can be transferred to a 384-dimension vector(a list of numbers [0.23, 1.98, ....]) by SentenceTransformer language model. This process is called embedding.
More detailed information about embeddings can be read from OpenAI
Awadb uses Sentence Transformers to embed the sentence by default, while you can also use OpenAI or other LLMs to do the embeddings according to your needs.
Welcome any PR contributors or ideas to improve the project.