pyepsilla

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Python Client for Epsilla Vector Database

Welcome to Python SDK for Epsilla Vector Database!

Install pyepsilla

pip3 install --upgrade pyepsilla

Connect to Epsilla Vector Database

Run epsilla vectordb on localhost

docker pull epsilla/vectordb
docker run -d -p 8888:8888 epsilla/vectordb

When Port 8888 conflicted with Jupyter Notebook

If you are using Jupyter Notebook on localhost, the port 8888 maybe conflict!

So you can change the vectordb port to another number, such as 18888

docker run -d -p 18888:8888 epsilla/vectordb

Use pyepsilla to connect to and interact with local vector database

from pyepsilla import vectordb

db_name = "MyDB"
db_path = "/tmp/epsilla"
table_name = "MyTable"

## 1.Connect to vectordb
client = vectordb.Client(
  host='localhost',
  port='8888'
)

## 2.Load and use a database
client.load_db(db_name, db_path)
client.use_db(db_name)

## 3.Create a table in the current database
client.create_table(
  table_name=table_name,
  table_fields=[
    {"name": "ID", "dataType": "INT", "primaryKey": True},
    {"name": "Doc", "dataType": "STRING"},
    {"name": "Embedding", "dataType": "VECTOR_FLOAT", "dimensions": 4}
  ]
)

## 4.Insert records
client.insert(
  table_name=table_name,
  records=[
    {"ID": 1, "Doc": "Berlin", "Embedding": [0.05, 0.61, 0.76, 0.74]},
    {"ID": 2, "Doc": "London", "Embedding": [0.19, 0.81, 0.75, 0.11]},
    {"ID": 3, "Doc": "Moscow", "Embedding": [0.36, 0.55, 0.47, 0.94]},
    {"ID": 4, "Doc": "San Francisco", "Embedding": [0.18, 0.01, 0.85, 0.80]},
    {"ID": 5, "Doc": "Shanghai", "Embedding": [0.24, 0.18, 0.22, 0.44]}
  ]
)

## 5.Search with specific response field
status_code, response = client.query(
  table_name=table_name,
  query_field="Embedding",
  query_vector=[0.35, 0.55, 0.47, 0.94],
  response_fields = ["Doc"],
  limit=2
)
print(response)

## 6.Search without specific response field, then it will return all fields
status_code, response = client.query(
  table_name=table_name,
  query_field="Embedding",
  query_vector=[0.35, 0.55, 0.47, 0.94],
  limit=2
)
print(response)

## 7.Delete records by primary_keys (and filter)
status_code, response =  client.delete(table_name=table_name, primary_keys=[3, 4])
status_code, response =  client.delete(table_name=table_name, filter="Doc <> 'San Francisco'")
print(response)


## 8.Drop a table
client.drop_table(table_name)

## 9.Unload a database from memory
client.unload_db(db_name)

Connect to Epsilla Cloud

Register and create vectordb on Epsilla Cloud

https://cloud.epsilla.com

Use Epsilla Cloud module to connect with the vectordb

Please get the project_id, db_id, epsilla_api_key from Epsilla Cloud at first

from pyepsilla import cloud

epsilla_api_key = os.getenv("EPSILLA_API_KEY", "Your-Epsilla-API-Key")
project_id = os.getenv("EPSILLA_PROJECT_ID", "Your-Project-ID")
db_id = os.getenv("EPSILLA_DB_ID", "Your-DB-ID")


# 1.Connect to Epsilla Cloud
cloud_client = cloud.Client(project_id="*****-****-****-****-************", api_key="eps_**********")

# 2.Connect to Vectordb
db_client = cloud_client.vectordb(db_id)

# 3.Create a table with schema
status_code, response = db.create_table(
    table_name="MyTable",
    table_fields=[
        {"name": "ID", "dataType": "INT", "primaryKey": True},
        {"name": "Doc", "dataType": "STRING"},
        {"name": "Embedding", "dataType": "VECTOR_FLOAT", "dimensions": 4},
    ],
)
print(status_code, response)

# 4.Insert new vector records into table
status_code, response = db.insert(
    table_name="MyTable",
    records=[
        {"ID": 1, "Doc": "Berlin", "Embedding": [0.05, 0.61, 0.76, 0.74]},
        {"ID": 2, "Doc": "London", "Embedding": [0.19, 0.81, 0.75, 0.11]},
        {"ID": 3, "Doc": "Moscow", "Embedding": [0.36, 0.55, 0.47, 0.94]},
        {"ID": 4, "Doc": "San Francisco", "Embedding": [0.18, 0.01, 0.85, 0.80]},
        {"ID": 5, "Doc": "Shanghai", "Embedding": [0.24, 0.18, 0.22, 0.44]},
    ],
)
print(status_code, response)


# 5.Query Vectors with specific response field, otherwise it will return all fields
status_code, response = db.query(
    table_name="MyTable",
    query_field="Embedding",
    query_vector=[0.35, 0.55, 0.47, 0.94],
    response_fields=["Doc"],
    limit=2,
)
print(status_code, response)


# 6.Delete specific records from table
status_code, response = db.delete(table_name="MyTable", primary_keys=[4, 5])
status_code, response = db.delete(table_name="MyTable", filter="Doc <> 'San Francisco'")
print(status_code, response)

# 7.Drop table
status_code, response = db.drop_table(table_name="MyTable")
print(status_code, response)

Connect to Epsilla RAG

Please get the project_id, epsilla_api_key, ragapp_id, converstation_id(optional) from Epsilla Cloud at first The resp will contains answer as well as contexts, like {"answer": "****", "contexts": ['context1','context2', ...]}

from pyepsilla import cloud

epsilla_api_key = os.getenv("EPSILLA_API_KEY", "Your-Epsilla-API-Key")
project_id = os.getenv("EPSILLA_PROJECT_ID", "Your-Project-ID")
ragapp_id = os.getenv("EPSILLA_RAGAPP_ID", "Your-RAGAPP-ID")
conversation_id = os.getenv("EPSILLA_CONVERSATION_ID", "Your-CONVERSATION-ID")

# 1.Connect to Epsilla RAG
client = cloud.RAG(
    project_id=project_id,
    api_key=epsilla_api_key,
    ragapp_id=ragapp_id,
    conversation_id=conversation_id,
)

# 2.Start a new conversation with RAG
client.start_new_conversation()
resp = client.query("What's RAG?")

print("[INFO] response is", resp)

Contributing

Bug reports and pull requests are welcome on GitHub at here

If you have any question or problem, please join our discord

We love your Feedback!

Release files for pyepsilla 0.3.15

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pyepsilla 0.3.15
File	Size	Uploaded
pyepsilla-0.3.15.tar.gz	22.0 kB	Oct 19, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for pyepsilla 0.3.15
File	Interpreter	ABI	Platform	Reset
pyepsilla-0.3.15-py3-none-any.whl 29.6 kB Oct 19, 2025	Python 3	none	any	Details

Total release size: 51.7 kB

Release files / pyepsilla-0.3.15.tar.gz

Download URL	pyepsilla-0.3.15.tar.gz
Size	22.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`c2e12d8cb65a6ae282c919132271fdf14d73b83aa35f0cda544f88e804801e22`
BLAKE2b-256 checksum How to use checksums	`1b48cf000696a4a373d85c1fede16b434fd4d84d5ca8834b334ffc610f42d351`
Upload date	Oct 19, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Release files / pyepsilla-0.3.15-py3-none-any.whl

Download URL	pyepsilla-0.3.15-py3-none-any.whl
Size	29.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`80cd7f4f942c99fc6890062ca0e77485b9fe6e90e138163aba23f4eaf7fe7bdb`
BLAKE2b-256 checksum How to use checksums	`9439be8851fb0abf5c67e0bf9dc93851f689bf63401020e391cae8e80d2fdb49`
Upload date	Oct 19, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.13.7`