Warning

The domain for the Anaconda Assistant API has changed. Please upgrade to version 0.4 or greater by April 15, 2025.

conda update anaconda-assistant-sdk

anaconda-assistant#

The Anaconda Assistant Python client

Installation#

conda install -c anaconda-cloud anaconda-assistant-sdk

How to authenticate#

To use the Python client or CLI you can use anaconda login CLI, Anaconda Navigator, or

from anaconda_auth import login
login()

to launch a browser to login and save your API token to disk. For cases where you cannot utilize a browser to login you can grab your API and set the ANACONDA_AUTH_API_KEY=<api-key> env var.

The Python clients and integrations provide api_key as a keyword argument.

Terms of use and data collection#

In order to use the Anaconda Assistant SDK and derived integrations the user must first agree to

On Data Collection: If you opt-in you will enjoy personalized recommendations and contribute to smarter features.

We prioritize your privacy:

  • Your data is never sold

  • Always secured

  • This setting only affects the data Anaconda stores

  • It does not affect the data that is sent to Open AI

To agree to the terms of service and configure data collection edit the ~/.anaconda/config.toml file

[plugin.assistant]
accepted_terms = true
data_collection = true

You may set data_collection = false if you chose to opt-out.

If you set accepted_terms = false the Anaconda Assistant SDK and derived integrations will not function.

If either or both of these values are unset in the ~/.anaconda/config.toml file, an exception will be raised.

Chat session#

The ChatSession provides a multi-turn chat interface that saves input and output messages. The response can be streamed or provided in one chunk.

from anaconda_assistant import ChatSession

chat = ChatSession()

text = chat.completions("what are the the first 5 fibonacci numbers?", stream=False)
print(text)

text = chat.completions("make that the first 10 numbers", stream=True)
for chunk in text:
    print(chunk, end="")

Chat client#

The ChatClient provides a low-level completions function that accepts a list of messages in the same format as OpenAI. The completions() method returns a ChatResponse object that allows streaming of the response similar to requests Response with .iter_content(), .iter_lines(), and .message, which returns the whole message as a string. Once the who message has been access or consumed through an iterable it is retained in the .message object. Additionally, the response object stores .tokens_used and .tokens_limit integers.

from anaconda_assistant import ChatClient

client = ChatClient()

messages = [
    {"role": "user", "content": "What is pi?"}
]

response = client.completions(messages=messages)

for chunk in response.iter_content():
    print(chunk, end="")

You can only consume the message with .iter_content() once, but the result is captured to the .message attribute while streaming.

Daily quotas#

Each Anaconda subscription plan enforces a limit on the number of requests (calls to .completions()). The limits are documented on the Plans and Pricing page. Once the limit is reached the .completions() function will throw a DailyQuotaExceeded exception.

Users can upgrade their plans by visiting https://anaconda.cloud/profile/subscriptions.

Integrations#

A number of 3rd party integrations are provided. In each case you will need to have optional packages installed.

LLM CLI#

The LLM CLI can be use to send and receive messages with the Anaconda Assistant.

Required packages: llm

To direct your messages to Anaconda Assistant use the model name anaconda-assistant

> llm -m anaconda-assistant 'what is pi?'

LlamaIndex#

To use the LlamaIndex integration you will need to install at least llama-index-core

Required packages: llama-index-core

The AnacondaAssistant class supports streaming and non-streaming completions and chat methods. A system prompt can be provided to AnacondaAssistant with the system_prompt keyword argument

from anaconda_assistant.integrations.llama_index import AnacondaAssistant

llm = AnacondaAssistant()

# Completions example
for c in llm.stream_complete('who are you?'):
    print(c.delta, end='')

# Chat example
from llama_index.core.llms import ChatMessage
response = model.chat(messages=[ChatMessage(content="Who are you?")])

# custom system prompt
prompted = AnacondaAssistant(system_prompt='you are a kitty, you will response with meow!')
print(prompted.complete('what is pi?'))

LangChain#

A LangChain integration is provided that supports message streaming and non-streaming responses.

Required packages: langchain-core >=0.3 and langchain >=0.3

from anaconda_assistant.integrations.langchain import AnacondaAssistant
from langchain.prompts import ChatPromptTemplate

model = AnacondaAssistant()
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
chain = prompt | model

message = chain.invoke({'topic': 'python'})
print(message.content)

ELL#

You can use Anaconda Assistant as a model in the ell prompt engineering framework.

Required packages: ell-ai[sqlite] or ell-ai[postgres]

import ell
import anaconda_assistant.integrations.ell

ell.init(verbose=True)

@ell.simple(model="anaconda-assistant")
def who():
    return "Who are you?"

who()

PandasAI#

To use Anaconda Assistant with PandasAI configure the SmartDataFrame using the AnacondaAssistant plugin

Required packages: pandasai

from anaconda_assistant.integrations.pandasai import AnacondaAssistant
from pandasai import SmartDataframe

ai = AnacondaAssistant()
sdf = SmartDataframe(df, config={'llm': ai})
sdf.chat('what is the average of this column where some condition is true?')

Panel#

You can integrate the Anaconda Assistant in your Panel application using the chat features including the name and avatar for the signed in user.

Required packages: panel

import panel as pn

from anaconda_auth import BaseClient
from anaconda_assistant.integrations.panel import AnacondaAssistantCallbackHandler

callback = AnacondaAssistantCallbackHandler()
auth_client = BaseClient()

chat = pn.chat.ChatInterface(
    callback=callback,
    user=auth_client.name,
    avatar=auth_client.avatar,
    placeholder_threshold=0.05
)

Setup for development#

Ensure you have conda installed. Then run:

make setup

Run the unit tests#

make test

Run the unit tests across isolated environments with tox#

make tox
On this page
Show Source