{ "info": { "author": "AI21 Labs", "author_email": null, "bugtrack_url": null, "classifiers": [ "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9" ], "description": "

\n AI21 Labs Python SDK\n

\n\n[//]: # \"Add when public\"\n[//]: # '\"Test\"'\n[//]: # '\"Supported'\n\n

\n\"Test\"\n\"Integration\n\"Package\n\"Poetry\"\n\"Supported\n\"Semantic\n\"License\"\n

\n\n---\n\n## Table of Contents\n\n- [Examples](#examples-tldr) \ud83d\uddc2\ufe0f\n- [Migration from v1.3.4 and below](#migration-from-v134-and-below)\n- [AI21 Official Documentation](#Documentation)\n- [Installation](#Installation) \ud83d\udcbf\n- [Usage - Chat Completions](#Usage)\n- [Conversational RAG (Beta)](#Conversational-RAG-Beta)\n- [Older Models Support Usage](#Older-Models-Support-Usage)\n- [More Models](#More-Models)\n - [Streaming](#Streaming)\n - [TSMs](#TSMs)\n- [Token Counting](#Token-Counting)\n- [Environment Variables](#Environment-Variables)\n- [Error Handling](#Error-Handling)\n- [Cloud Providers](#Cloud-Providers) \u2601\ufe0f\n - [AWS](#AWS)\n - [Bedrock](#Bedrock)\n - [SageMaker](#SageMaker)\n - [Azure](#Azure)\n - [Vertex](#Vertex)\n\n## Examples (tl;dr)\n\nIf you want to quickly get a glance how to use the AI21 Python SDK and jump straight to business, you can check out the examples. Take a look at our models and see them in action! Several examples and demonstrations have been put together to show our models' functionality and capabilities.\n\n### [Check out the Examples](examples/)\n\nFeel free to dive in, experiment, and adapt these examples to suit your needs. We believe they'll help you get up and running quickly.\n\n## Migration from v1.3.4 and below\n\nIn `v2.0.0` we introduced a new SDK that is not backwards compatible with the previous version.\nThis version allows for non-static instances of the client, defined parameters to each resource, modelized responses and\nmore.\n\n
\nMigration Examples\n\n### Instance creation (not available in v1.3.4 and below)\n\n```python\nfrom ai21 import AI21Client\n\nclient = AI21Client(api_key='my_api_key')\n\n# or set api_key in environment variable - AI21_API_KEY and then\nclient = AI21Client()\n```\n\nWe No longer support static methods for each resource, instead we have a client instance that has a method for each\nallowing for more flexibility and better control.\n\n### Completion before/after\n\n```diff\nprompt = \"some prompt\"\n\n- import ai21\n- response = ai21.Completion.execute(model=\"j2-light\", prompt=prompt, maxTokens=2)\n\n+ from ai21 import AI21Client\n+ client = ai21.AI21Client()\n+ response = client.completion.create(model=\"j2-light\", prompt=prompt, max_tokens=2)\n```\n\nThis applies to all resources. You would now need to create a client instance and use it to call the resource method.\n\n### Tokenization and Token counting before/after\n\n```diff\n- response = ai21.Tokenization.execute(text=prompt)\n- print(len(response)) # number of tokens\n\n+ from ai21 import AI21Client\n+ client = AI21Client()\n+ token_count = client.count_tokens(text=prompt)\n```\n\n### Key Access in Response Objects before/after\n\nIt is no longer possible to access the response object as a dictionary. Instead, you can access the response object as an object with attributes.\n\n```diff\n- import ai21\n- response = ai21.Summarize.execute(source=\"some text\", sourceType=\"TEXT\")\n- response[\"summary\"]\n\n+ from ai21 import AI21Client\n+ from ai21.models import DocumentType\n+ client = AI21Client()\n+ response = client.summarize.create(source=\"some text\", source_type=DocumentType.TEXT)\n+ response.summary\n```\n\n---\n\n### AWS Client Creations\n\n### Bedrock Client creation before/after\n\n```diff\n- import ai21\n- destination = ai21.BedrockDestination(model_id=ai21.BedrockModelID.J2_MID_V1)\n- response = ai21.Completion.execute(prompt=prompt, maxTokens=1000, destination=destination)\n\n+ from ai21 import AI21BedrockClient, BedrockModelID\n+ client = AI21BedrockClient()\n+ response = client.completion.create(prompt=prompt, max_tokens=1000, model_id=BedrockModelID.J2_MID_V1)\n```\n\n### SageMaker Client creation before/after\n\n```diff\n- import ai21\n- destination = ai21.SageMakerDestination(\"j2-mid-test-endpoint\")\n- response = ai21.Completion.execute(prompt=prompt, maxTokens=1000, destination=destination)\n\n+ from ai21 import AI21SageMakerClient\n+ client = AI21SageMakerClient(endpoint_name=\"j2-mid-test-endpoint\")\n+ response = client.completion.create(prompt=prompt, max_tokens=1000)\n```\n\n
\n\n## Documentation\n\n---\n\nThe full documentation for the REST API can be found on [docs.ai21.com](https://docs.ai21.com/).\n\n## Installation\n\n---\n\n```bash\npip install ai21\n```\n\n## Usage\n\n---\n\n```python\nfrom ai21 import AI21Client\nfrom ai21.models.chat import ChatMessage\n\nclient = AI21Client(\n # defaults to os.enviorn.get('AI21_API_KEY')\n api_key='my_api_key',\n)\n\nsystem = \"You're a support engineer in a SaaS company\"\nmessages = [\n ChatMessage(content=system, role=\"system\"),\n ChatMessage(content=\"Hello, I need help with a signup process.\", role=\"user\"),\n]\n\nchat_completions = client.chat.completions.create(\n messages=messages,\n model=\"jamba-1.5-mini\",\n)\n```\n\n### Async Usage\n\nYou can use the `AsyncAI21Client` to make asynchronous requests.\nThere is no difference between the sync and the async client in terms of usage.\n\n```python\nimport asyncio\n\nfrom ai21 import AsyncAI21Client\nfrom ai21.models.chat import ChatMessage\n\nsystem = \"You're a support engineer in a SaaS company\"\nmessages = [\n ChatMessage(content=system, role=\"system\"),\n ChatMessage(content=\"Hello, I need help with a signup process.\", role=\"user\"),\n]\n\nclient = AsyncAI21Client(\n # defaults to os.enviorn.get('AI21_API_KEY')\n api_key='my_api_key',\n)\n\n\nasync def main():\n response = await client.chat.completions.create(\n messages=messages,\n model=\"jamba-1.5-mini\",\n )\n\n print(response)\n\n\nasyncio.run(main())\n\n```\n\nA more detailed example can be found [here](examples/studio/chat/chat_completions.py).\n\n## Older Models Support Usage\n\n
\nExamples\n\n### Supported Models:\n\n- j2-light\n- [j2-ultra](#Chat)\n- [j2-mid](#Completion)\n- [jamba-instruct](#Chat-Completion)\n\nyou can read more about the models [here](https://docs.ai21.com/reference/j2-complete-api-ref#jurassic-2-models).\n\n### Chat\n\n```python\nfrom ai21 import AI21Client\nfrom ai21.models import RoleType\nfrom ai21.models import ChatMessage\n\nsystem = \"You're a support engineer in a SaaS company\"\nmessages = [\n ChatMessage(text=\"Hello, I need help with a signup process.\", role=RoleType.USER),\n ChatMessage(text=\"Hi Alice, I can help you with that. What seems to be the problem?\", role=RoleType.ASSISTANT),\n ChatMessage(text=\"I am having trouble signing up for your product with my Google account.\", role=RoleType.USER),\n]\n\n\nclient = AI21Client()\nchat_response = client.chat.create(\n system=system,\n messages=messages,\n model=\"j2-ultra\",\n)\n```\n\nFor a more detailed example, see the chat [examples](examples/studio/chat.py).\n\n### Completion\n\n```python\nfrom ai21 import AI21Client\n\n\nclient = AI21Client()\ncompletion_response = client.completion.create(\n prompt=\"This is a test prompt\",\n model=\"j2-mid\",\n)\n```\n\n### Chat Completion\n\n```python\nfrom ai21 import AI21Client\nfrom ai21.models.chat import ChatMessage\n\nsystem = \"You're a support engineer in a SaaS company\"\nmessages = [\n ChatMessage(content=system, role=\"system\"),\n ChatMessage(content=\"Hello, I need help with a signup process.\", role=\"user\"),\n ChatMessage(content=\"Hi Alice, I can help you with that. What seems to be the problem?\", role=\"assistant\"),\n ChatMessage(content=\"I am having trouble signing up for your product with my Google account.\", role=\"user\"),\n]\n\nclient = AI21Client()\n\nresponse = client.chat.completions.create(\n messages=messages,\n model=\"jamba-instruct\",\n max_tokens=100,\n temperature=0.7,\n top_p=1.0,\n stop=[\"\\n\"],\n)\n\nprint(response)\n```\n\nNote that jamba-instruct supports async and streaming as well.\n\n
\n\nFor a more detailed example, see the completion [examples](examples/studio/completion.py).\n\n---\n\n## Streaming\n\nWe currently support streaming for the Chat Completions API in Jamba.\n\n```python\nfrom ai21 import AI21Client\nfrom ai21.models.chat import ChatMessage\n\nmessages = [ChatMessage(content=\"What is the meaning of life?\", role=\"user\")]\n\nclient = AI21Client()\n\nresponse = client.chat.completions.create(\n messages=messages,\n model=\"jamba-instruct\",\n stream=True,\n)\nfor chunk in response:\n print(chunk.choices[0].delta.content, end=\"\")\n\n```\n\n### Async Streaming\n\n```python\nimport asyncio\n\nfrom ai21 import AsyncAI21Client\nfrom ai21.models.chat import ChatMessage\n\nmessages = [ChatMessage(content=\"What is the meaning of life?\", role=\"user\")]\n\nclient = AsyncAI21Client()\n\n\nasync def main():\n response = await client.chat.completions.create(\n messages=messages,\n model=\"jamba-1.5-mini\",\n stream=True,\n )\n async for chunk in response:\n print(chunk.choices[0].delta.content, end=\"\")\n\n\nasyncio.run(main())\n\n```\n\n---\n\n### Conversational RAG (Beta)\n\nLike chat, but with the ability to retrieve information from your Studio library.\n\n```python\nfrom ai21 import AI21Client\nfrom ai21.models.chat import ChatMessage\n\nmessages = [\n ChatMessage(content=\"Ask a question about your files\", role=\"user\"),\n]\n\nclient = AI21Client()\n\nclient.library.files.create(\n file_path=\"path/to/file\",\n path=\"path/to/file/in/library\",\n labels=[\"my_file_label\"],\n)\nchat_response = client.beta.conversational_rag.create(\n messages=messages,\n labels=[\"my_file_label\"],\n)\n```\n\nFor a more detailed example, see the chat [sync](examples/studio/conversational_rag/conversational_rag.py) and [async](examples/studio/conversational_rag/async_conversational_rag.py) examples.\n\n---\n\n## More Models\n\n## TSMs\n\nAI21 Studio's Task-Specific Models offer a range of powerful tools. These models have been specifically designed for their respective tasks and provide high-quality results while optimizing efficiency.\nThe full documentation and guides can be found [here](https://docs.ai21.com/docs/task-specific).\n\n### Contextual Answers\n\nThe `answer` API allows you to access our high-quality question answering model.\n\n```python\nfrom ai21 import AI21Client\n\nclient = AI21Client()\nresponse = client.answer.create(\n context=\"This is a text is for testing purposes\",\n question=\"Question about context\",\n)\n```\n\nA detailed explanation on Contextual Answers, can be found [here](https://docs.ai21.com/docs/contextual-answers-api)\n\n### File Upload\n\n---\n\n```python\nfrom ai21 import AI21Client\n\nclient = AI21Client()\n\nfile_id = client.library.files.create(\n file_path=\"path/to/file\",\n path=\"path/to/file/in/library\",\n labels=[\"label1\", \"label2\"],\n public_url=\"www.example.com\",\n)\n\nuploaded_file = client.library.files.get(file_id)\n```\n\nFor more information on more Task Specific Models, see the [documentation](https://docs.ai21.com/reference/paraphrase-api-ref).\n\n## Token Counting\n\n---\n\nBy using the `count_tokens` method, you can estimate the billing for a given request.\n\n```python\nfrom ai21.tokenizers import get_tokenizer\n\ntokenizer = get_tokenizer(name=\"jamba-tokenizer\")\ntotal_tokens = tokenizer.count_tokens(text=\"some text\") # returns int\nprint(total_tokens)\n```\n\n### Async Usage\n\n```python\nfrom ai21.tokenizers import get_async_tokenizer\n\n## Your async function code\n#...\ntokenizer = await get_async_tokenizer(name=\"jamba-tokenizer\")\ntotal_tokens = await tokenizer.count_tokens(text=\"some text\") # returns int\nprint(total_tokens)\n```\n\nAvailable tokenizers are:\n\n- `jamba-tokenizer`\n- `j2-tokenizer`\n\nFor more information on AI21 Tokenizers, see the [documentation](https://github.com/AI21Labs/ai21-tokenizer).\n\n## Environment Variables\n\n---\n\nYou can set several environment variables to configure the client.\n\n### Logging\n\nWe use the standard library [`logging`](https://docs.python.org/3/library/logging.html) module.\n\nTo enable logging, set the `AI21_LOG_LEVEL` environment variable.\n\n```bash\n$ export AI21_LOG_LEVEL=debug\n```\n\n### Other Important Environment Variables\n\n- `AI21_API_KEY` - Your API key. If not set, you must pass it to the client constructor.\n- `AI21_API_VERSION` - The API version. Defaults to `v1`.\n- `AI21_API_HOST` - The API host. Defaults to `https://api.ai21.com/v1/`.\n- `AI21_TIMEOUT_SEC` - The timeout for API requests.\n- `AI21_NUM_RETRIES` - The maximum number of retries for API requests. Defaults to `3` retries.\n- `AI21_AWS_REGION` - The AWS region to use for AWS clients. Defaults to `us-east-1`.\n\n## Error Handling\n\n---\n\n```python\nfrom ai21 import errors as ai21_errors\nfrom ai21 import AI21Client, AI21APIError\nfrom ai21.models import ChatMessage\n\nclient = AI21Client()\n\nsystem = \"You're a support engineer in a SaaS company\"\nmessages = [\n # Notice the given role does not exist and will be the reason for the raised error\n ChatMessage(text=\"Hello, I need help with a signup process.\", role=\"Non-Existent-Role\"),\n ]\n\ntry:\n chat_completion = client.chat.create(\n messages=messages,\n model=\"j2-ultra\",\n system=system\n )\nexcept ai21_errors.AI21ServerError as e:\n print(\"Server error and could not be reached\")\n print(e.details)\nexcept ai21_errors.TooManyRequestsError as e:\n print(\"A 429 status code was returned. Slow down on the requests\")\nexcept AI21APIError as e:\n print(\"A non 200 status code error. For more error types see ai21.errors\")\n\n```\n\n## Cloud Providers\n\n---\n\n### AWS\n\nAI21 Library provides convenient ways to interact with two AWS clients for use with [AWS Bedrock](https://aws.amazon.com/bedrock/ai21/) and AWS SageMaker.\n\n### Installation\n\n---\n\n```bash\npip install -U \"ai21[AWS]\"\n```\n\nThis will make sure you have the required dependencies installed, including `boto3 >= 1.28.82`.\n\n### Usage\n\n---\n\n### Bedrock\n\n```python\nfrom ai21 import AI21BedrockClient, BedrockModelID\nfrom ai21.models.chat import ChatMessage\n\nclient = AI21BedrockClient(region='us-east-1') # region is optional, as you can use the env variable instead\n\nmessages = [\n ChatMessage(content=\"You are a helpful assistant\", role=\"system\"),\n ChatMessage(content=\"What is the meaning of life?\", role=\"user\")\n]\n\nresponse = client.chat.completions.create(\n messages=messages,\n model_id=BedrockModelID.JAMBA_INSTRUCT_V1,\n)\n```\n\n#### Stream\n\n```python\nfrom ai21 import AI21BedrockClient, BedrockModelID\nfrom ai21.models.chat import ChatMessage\n\nsystem = \"You're a support engineer in a SaaS company\"\nmessages = [\n ChatMessage(content=system, role=\"system\"),\n ChatMessage(content=\"Hello, I need help with a signup process.\", role=\"user\"),\n ChatMessage(content=\"Hi Alice, I can help you with that. What seems to be the problem?\", role=\"assistant\"),\n ChatMessage(content=\"I am having trouble signing up for your product with my Google account.\", role=\"user\"),\n]\n\nclient = AI21BedrockClient()\n\nresponse = client.chat.completions.create(\n messages=messages,\n model=BedrockModelID.JAMBA_INSTRUCT_V1,\n stream=True,\n)\n\nfor chunk in response:\n print(chunk.choices[0].message.content, end=\"\")\n```\n\n#### Async\n\n```python\nimport asyncio\nfrom ai21 import AsyncAI21BedrockClient, BedrockModelID\nfrom ai21.models.chat import ChatMessage\n\nclient = AsyncAI21BedrockClient(region='us-east-1') # region is optional, as you can use the env variable instead\n\nmessages = [\n ChatMessage(content=\"You are a helpful assistant\", role=\"system\"),\n ChatMessage(content=\"What is the meaning of life?\", role=\"user\")\n]\n\nasync def main():\n response = await client.chat.completions.create(\n messages=messages,\n model_id=BedrockModelID.JAMBA_INSTRUCT_V1,\n )\n\n\nasyncio.run(main())\n```\n\n### With Boto3 Session\n\n```python\nimport boto3\n\nfrom ai21 import AI21BedrockClient, BedrockModelID\nfrom ai21.models.chat import ChatMessage\n\nboto_session = boto3.Session(region_name=\"us-east-1\")\n\nclient = AI21BedrockClient(session=boto_session)\n\nmessages = [\n ChatMessage(content=\"You are a helpful assistant\", role=\"system\"),\n ChatMessage(content=\"What is the meaning of life?\", role=\"user\")\n]\n\nresponse = client.chat.completions.create(\n messages=messages,\n model_id=BedrockModelID.JAMBA_INSTRUCT_V1,\n)\n```\n\n### Async\n\n```python\nimport boto3\nimport asyncio\n\nfrom ai21 import AsyncAI21BedrockClient, BedrockModelID\nfrom ai21.models.chat import ChatMessage\n\nboto_session = boto3.Session(region_name=\"us-east-1\")\n\nclient = AsyncAI21BedrockClient(session=boto_session)\n\nmessages = [\n ChatMessage(content=\"You are a helpful assistant\", role=\"system\"),\n ChatMessage(content=\"What is the meaning of life?\", role=\"user\")\n]\n\nasync def main():\n response = await client.chat.completions.create(\n messages=messages,\n model_id=BedrockModelID.JAMBA_INSTRUCT_V1,\n )\n\nasyncio.run(main())\n```\n\n### SageMaker\n\n```python\nfrom ai21 import AI21SageMakerClient\n\nclient = AI21SageMakerClient(endpoint_name=\"j2-endpoint-name\")\nresponse = client.summarize.create(\n source=\"Text to summarize\",\n source_type=\"TEXT\",\n)\nprint(response.summary)\n```\n\n#### Async\n\n```python\nimport asyncio\nfrom ai21 import AsyncAI21SageMakerClient\n\nclient = AsyncAI21SageMakerClient(endpoint_name=\"j2-endpoint-name\")\n\nasync def main():\n response = await client.summarize.create(\n source=\"Text to summarize\",\n source_type=\"TEXT\",\n )\n print(response.summary)\n\nasyncio.run(main())\n```\n\n### With Boto3 Session\n\n```python\nfrom ai21 import AI21SageMakerClient\nimport boto3\nboto_session = boto3.Session(region_name=\"us-east-1\")\n\nclient = AI21SageMakerClient(\n session=boto_session,\n endpoint_name=\"j2-endpoint-name\",\n)\n```\n\n### Azure\n\nIf you wish to interact with your Azure endpoint on Azure AI Studio, use the `AI21AzureClient`\nand `AsyncAI21AzureClient` clients.\n\nThe following models are supported on Azure:\n\n- `jamba-instruct`\n\n```python\nfrom ai21 import AI21AzureClient\nfrom ai21.models.chat import ChatMessage\n\nclient = AI21AzureClient(\n base_url=\"https://.inference.ai.azure.com\",\n api_key=\"\",\n)\n\nmessages = [\n ChatMessage(content=\"You are a helpful assistant\", role=\"system\"),\n ChatMessage(content=\"What is the meaning of life?\", role=\"user\")\n]\n\nresponse = client.chat.completions.create(\n model=\"jamba-1.5-mini\",\n messages=messages,\n)\n```\n\n#### Async\n\n```python\nimport asyncio\nfrom ai21 import AsyncAI21AzureClient\nfrom ai21.models.chat import ChatMessage\n\nclient = AsyncAI21AzureClient(\n base_url=\"https://.inference.ai.azure.com/v1/chat/completions\",\n api_key=\"\",\n)\n\nmessages = [\n ChatMessage(content=\"You are a helpful assistant\", role=\"system\"),\n ChatMessage(content=\"What is the meaning of life?\", role=\"user\")\n]\n\nasync def main():\n response = await client.chat.completions.create(\n model=\"jamba-instruct\",\n messages=messages,\n )\n\nasyncio.run(main())\n```\n\n### Vertex\n\nIf you wish to interact with your Vertex AI endpoint on GCP, use the `AI21VertexClient`\nand `AsyncAI21VertexClient` clients.\n\nThe following models are supported on Vertex:\n\n- `jamba-1.5-mini`\n- `jamba-1.5-large`\n\n```python\nfrom ai21 import AI21VertexClient\n\nfrom ai21.models.chat import ChatMessage\n\n# You can also set the project_id, region, access_token and Google credentials in the constructor\nclient = AI21VertexClient()\n\nmessages = ChatMessage(content=\"What is the meaning of life?\", role=\"user\")\n\nresponse = client.chat.completions.create(\n model=\"jamba-1.5-mini\",\n messages=[messages],\n)\n```\n\n#### Async\n\n```python\nimport asyncio\n\nfrom ai21 import AsyncAI21VertexClient\nfrom ai21.models.chat import ChatMessage\n\n# You can also set the project_id, region, access_token and Google credentials in the constructor\nclient = AsyncAI21VertexClient()\n\n\nasync def main():\n messages = ChatMessage(content=\"What is the meaning of life?\", role=\"user\")\n\n response = await client.chat.completions.create(\n model=\"jamba-1.5-mini\",\n messages=[messages],\n )\n\nasyncio.run(main())\n```\n\nHappy prompting! \ud83d\ude80\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": null, "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "dynamic": null, "home_page": null, "keywords": null, "license": null, "maintainer": null, "maintainer_email": null, "name": "ai21", "package_url": "https://pypi.org/project/ai21/", "platform": null, "project_url": "https://pypi.org/project/ai21/", "project_urls": null, "provides_extra": [ "aws", "vertex" ], "release_url": 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