Astra Assistant API Service

create_assistant

A drop-in compatible service for the latest OpenAI Assistants API v2 (with streaming) with support for persistent threads, files, vectorstores, assistants, retreival, function calling and more using AstraDB (DataStax's db as a service offering powered by [Apache Cassandra](https://cassandra.apache.org//index.html) and jvector).

Supports dozens of third party LLM providers (or even local models) for both completion and embeddings (powered by LiteLLM).

You can use our hosted Astra Assistants service, or host the open source API server yourself.

Client Getting Started

To build an app that uses the Astra Asistants service install the astra-assistants python library with your favorite package manager. The code for astra-assistants can be found under (clients/)[./clients/]:

poetry add astra_assistants

Signup for Astra and get an Admin API token:

Set your environment variables (depending on what LLMs you want to use), see the .env.bkp file for an example:

#!/bin/bash

# AstraDB -> https://astra.datastax.com/ --> tokens --> administrator user --> generate
export ASTRA_DB_APPLICATION_TOKEN=""

# OpenAI Models - https://platform.openai.com/api-keys --> create new secret key
export OPENAI_API_KEY=""

# Groq Models - https://console.groq.com/keys
export GROQ_API_KEY=""

# Anthropic claude models - https://console.anthropic.com/settings/keys
export ANTHROPIC_API_KEY=""

# Gemini models -> https://makersuite.google.com/app/apikey
export GEMINI_API_KEY=""

# Perplexity models -> https://www.perplexity.ai/settings/api  --> generate
export PERPLEXITYAI_API_KEY=""

# Cohere models -> https://dashboard.cohere.com/api-keys
export COHERE_API_KEY=""

# Bedrock models -> https://docs.aws.amazon.com/bedrock/latest/userguide/setting-up.html
export AWS_REGION_NAME=""
export AWS_ACCESS_KEY_ID=""
export AWS_SECRET_ACCESS_KEY=""

# vertexai models https://console.cloud.google.com/vertex-ai
export GOOGLE_JSON_PATH=""
export GOOGLE_PROJECT_ID=""

# ... for all models see the .env.bkp file

Then import and patch your client:

from openai import OpenAI
from astra_assistants import patch
client = patch(OpenAI())

The system will create a db on your behalf and name it assistant_api_db using your token. Note, this means that the first request will hang until your db is ready (could be a couple of minutes). This will only happen once.

Now you're ready to create an assistant

assistant = client.beta.assistants.create(
  instructions="You are a personal math tutor. When asked a math question, write and run code to answer the question.",
  model="gpt-4-1106-preview",
  tools=[{"type": "retrieval"}]
)

By default, the service uses AstraDB as the database/vector store and OpenAI for embeddings and chat completion.

Third party LLM Support

We now support many third party models for both embeddings and completion thanks to litellm. Pass the api key of your service using api-key and embedding-model headers.

You can pass different models, just make sure you have the right corresponding api key in your environment.

model="gpt-4-1106-preview"
#model="gpt-3.5-turbo"
#model="cohere_chat/command-r"
#model="perplexity/mixtral-8x7b-instruct"
#model="perplexity/llama-3-sonar-large-32k-online"
#model="anthropic.claude-v2"
#model="gemini/gemini-1.5-pro-latest"
#model = "meta.llama2-13b-chat-v1"

assistant = client.beta.assistants.create(
    name="Math Tutor",
    instructions="You are a personal math tutor. Answer questions briefly, in a sentence or less.",
    model=model,
)

for third party embedding models we support embedding_model in client.files.create:

file = client.files.create(
    file=open(
        "./test/language_models_are_unsupervised_multitask_learners.pdf",
        "rb",
    ),
    purpose="assistants",
    embedding_model="text-embedding-3-large",
)

To run the examples using poetry create a .env file in this directory with your secrets and run:

poetry install

Create your .env file and add your keys to it:

cp .env.bkp .env

and

poetry run python examples/python/chat_completion/basic.py

poetry run python examples/python/retrieval/basic.py

poetry run python examples/python/streaming_retrieval/basic.py

poetry run python examples/python/function_calling/basic.py

Running yourself

Docker

with docker, first pull the image from docker hub

docker pull datastax/astra-assistants

or a specific version if you don't want latest:

docker pull datastax/astra-assistants:v0.2.12

then run (-p to map your docker port 8080 to your host port 8080):

docker run -p 8080:8080 datastax/astra-assistants

Locally with poetry

or locally with poetry:

poetry install

poetry run python run.py

Docker-compose with ollama

or with docker-compose for integration with ollama

cd examples/ollama/gpu # or examples/ollama/cpu for cpu only for gpu you need docker-toolkit

docker-compose up -d

you need to pull the model you want to ollama before using it

curl http://localhost:11434/api/pull -d '{ "name": "deepseek-coder-v2" }'

your assistants client should route to the ollama container by passing the llm-param-base-url header:

client = patch(OpenAI(default_headers={"LLM-PARAM-base-url": "http://ollama:11434"}))

Feedback / Help

For help or feedback file an issue or reach out to us on Discord

Contributing

Check out our contributing guide

Coverage

See our coverage report here

Roadmap:

[X] Support for other embedding models and LLMs
[X] function calling
[X] Streaming support
[X] Assistants V2 with vector_store support

datastax / astra-assistants-api

readme