pip3 install --upgrade pyepsilla

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

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

from pyepsilla import vectordb

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

## 2.Load and use a database
client.load_db(db_name="MyDB", db_path="/tmp/epsilla")
client.use_db(db_name="MyDB")

## 3.Create a table in the current database
client.create_table(
  table_name="MyTable",
  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="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]}
  ]
)

## 5.Search with specific response field
status_code, response = client.query(
  table_name="MyTable",
  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="MyTable",
  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="MyTable", primary_keys=[3, 4])
status_code, response =  client.delete(table_name="MyTable", filter="Doc <> 'San Francisco'")
print(response)


## 8.Drop a table
client.drop_table("MyTable")

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

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
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)

from pyepsilla import cloud

# 1.Connect to Epsilla RAG
client = cloud.RAG(
    project_id="ce07c6fc-****-****-b7bd-b7819f22bcff",
    api_key="eps_**********",
    ragapp_id="153a5a49-****-****-b2b8-496451eda8b5",
    conversation_id="6fa22a6a-****-****-b1c3-5c795d0f45ef",
)

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

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