EigenDB's Python SDK

Seamlessly integrate EigenDB into your Python applications

Installation

pip

pip install eigen-client

Supported embedding models

If you desire to use a model that is not listed here, you can simply create the index with client.create_index(...) and provide the desired dimensions and similarity metric.

Model Name	Provider	Dimensions	Similarity Metric
text-embedding-3-small	OpenAI	1536	cosine
text-embedding-3-large	OpenAI	3072	cosine
text-embedding-ada-002	OpenAI	1536	cosine
all-minilm:22m	Ollama	384	cosine
nomic-embed-text:v1.5	Ollama	768	cosine
mxbai-embed-large:335m	Ollama	1024	cosine
snowflake-arctic-embed2:568m	Ollama	1024	cosine
snowflake-arctic-embed:335m	Ollama	1024	cosine
bge-m3:567m	Ollama	1024	cosine
bge-large:335m	Ollama	1024	cosine

Usage

Creating an index

python

import os
from eigen_client.client import Client
from eigen_client.data_types import Document

client = Client(
    url="http://localhost:8080",
    api_key="eigendb-***",
)

index = client.create_index_from_model(
    index_name="food-facts",
    model_name="text-embedding-3-small",
    model_provider="openai",
    model_provider_api_key="your openai api key..."
)

documents = [
    Document(id=1, data="Fresh herbs boost flavor.", metadata={"recipe_id": "123"}),
    Document(id=2, data="Slow simmer blends soup.", metadata={"recipe_id": "456"}),
    Document(id=3, data="Homemade bread smells great.", metadata={"recipe_id": "789"}),
    Document(id=4, data="Grilled veggies taste sweeter.", metadata={"recipe_id": "987"}),
    Document(id=5, data="Cast iron sears steak well.", metadata={"recipe_id": "654"})
]

index.upsert_docs(documents)

results = index.search_docs(
    string="Baking",
    k=3
)

print(results)

Using an existing index

python

import os
from eigen_client.index import Index
from eigen_client.data_types import Document

index = Index(
    url="http://localhost:8080",
    api_key="eigendb-***",
    index_name="food-facts",
    model_name="text-embedding-3-small",
    model_provider="openai",
    model_provider_api_key="your openai api key..."
)

documents = [
    Document(id=1, data="Fresh herbs boost flavor.", metadata={"recipe_id": "123"}),
    Document(id=2, data="Slow simmer blends soup.", metadata={"recipe_id": "456"}),
    Document(id=3, data="Homemade bread smells great.", metadata={"recipe_id": "789"}),
    Document(id=4, data="Grilled veggies taste sweeter.", metadata={"recipe_id": "987"}),
    Document(id=5, data="Cast iron sears steak well.", metadata={"recipe_id": "654"})
]

index.upsert_docs(documents)

results = index.search_docs(
    string="Baking",
    k=3
)

print(results)