# SAP generative AI hub SDK
With this SDK you can leverage the power of generative Models like chatGPT available in SAP's generative AI hub.
<!-- List of available models: #Todo -->
## Installation
pip install generative-ai-hub-sdk
## Configuration
The configuration from ai-core-sdk is reused:
- `AICORE_CLIENT_ID`: This represents the client ID.
- `AICORE_CLIENT_SECRET`: This stands for the client secret.
- `AICORE_AUTH_URL`: This is the URL used to retrieve a token using the client ID and secret.
- `AICORE_BASE_URL`: This is the URL of the service (with suffix /v2).
- `AICORE_RESOURCE_GROUP`: This represents the resource group that should be used.
We recommend setting these values as environment variables or via config file. The default path for this file
is `~/.aicore/config.json`
```json
{
"AICORE_AUTH_URL": "https://* * * .authentication.sap.hana.ondemand.com",
"AICORE_CLIENT_ID": "* * * ",
"AICORE_CLIENT_SECRET": "* * * ",
"AICORE_RESOURCE_GROUP": "* * * ",
"AICORE_BASE_URL": "https://api.ai.* * *.cfapps.sap.hana.ondemand.com/v2"
}
```
## Usage
### Prerequisite
*Activate* the generative AI hub for your tenant according to the [Generative AI Hub document](https://help.sap.com/doc/7ca8e589556c4596abafe95545dfc212/CLOUD/en-US/553250b6ec764a05be43a7cd8cba0526.pdf).
### OpenAI-like API
#### Completion
Below is an example usage of openai.Completions in generative-ai-hub sdk:
from gen_ai_hub.proxy.native.openai import completions
response = completions.create(
model_name="tiiuae--falcon-40b-instruct",
prompt="The Answer to the Ultimate Question of Life, the Universe, and Everything is",
max_tokens=7,
temperature=0
)
print(response)
#### ChatCompletion
Below is an example usage of openai.ChatCompletions:
from gen_ai_hub.proxy.native.openai import chat
messages = [ {"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},
{"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."},
{"role": "user", "content": "Do other Azure Cognitive Services support this too?"} ]
kwargs = dict(model_name='gpt-35-turbo', messages=messages)
response = chat.completions.create(**kwargs)
print(response)
#### Embeddings
Below is an example usage of openai.Embeddings:
from gen_ai_hub.proxy.native.openai import embeddings
response = embeddings.create(
input="Every decoding is another encoding.",
model_name="text-embedding-ada-002"
encoding_format='base64'
)
print(response.data)
### Langchain Api
#### Model Initialization
The `init_llm` and `init_embedding_model` functions allow easy initialization of langchain model interfaces
in a harmonized way by the generative AI hub SDK:
Function: `init_llm`
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from gen_ai_hub.proxy.langchain.init_models import init_llm
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=['question'])
question = 'What is a supernova?'
llm = init_llm('gpt-4', max_tokens=100)
llm_chain = LLMChain(prompt=prompt, llm=llm)
response = llm_chain.invoke(question)
print(response['text'])
Function `init_embedding_model`
from gen_ai_hub.proxy.langchain.init_models import init_embedding_model
text = 'Every decoding is another encoding.'
embeddings = init_embedding_model('text-embedding-ada-002')
response = embeddings.embed_query(text)
print(response)
#### Completion Model
from langchain import PromptTemplate, LLMChain
from gen_ai_hub.proxy.langchain.openai import OpenAI # langchain class representing the AICore OpenAI models
from gen_ai_hub.proxy.core.proxy_clients import get_proxy_client
proxy_client = get_proxy_client('aicore')
# non-chat model
model_name = "tiiuae--falcon-40b-instruct"
llm = OpenAI(proxy_model_name=model_name, proxy_client=proxy_client) # standard langchain usage
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm, verbose=True)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
print(llm_chain.predict(question=question))
#### Chat Model
from langchain.prompts.chat import (
AIMessagePromptTemplate,
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from gen_ai_hub.proxy.langchain.openai import ChatOpenAI
from gen_ai_hub.proxy.core.proxy_clients import get_proxy_client
proxy_client = get_proxy_client('aicore')
chat_llm = ChatOpenAI(proxy_model_name='gpt-35-turbo', proxy_client=proxy_client)
template = 'You are a helpful assistant that translates english to pirate.'
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
example_human = HumanMessagePromptTemplate.from_template('Hi')
example_ai = AIMessagePromptTemplate.from_template('Ahoy!')
human_template = '{text}'
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages(
[system_message_prompt, example_human, example_ai, human_message_prompt])
chain = LLMChain(llm=chat_llm, prompt=chat_prompt)
response = chain.invoke('I love planking.')
print(response['text'])
#### Embedding Model
from gen_ai_hub.proxy.native.openai import OpenAIEmbeddings
from gen_ai_hub.proxy.core.proxy_clients import get_proxy_client
proxy_client = get_proxy_client('aicore')
# can be called without passing proxy_client
embedding_model = OpenAIEmbeddings(proxy_model_name='text-embedding-ada-002')
response = embedding_model.embed_query('Every decoding is another encoding.')
print(response)
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"description": "# SAP generative AI hub SDK\n\nWith this SDK you can leverage the power of generative Models like chatGPT available in SAP's generative AI hub.\n\n<!-- List of available models: #Todo -->\n\n## Installation\n pip install generative-ai-hub-sdk\n\n## Configuration\nThe configuration from ai-core-sdk is reused:\n- `AICORE_CLIENT_ID`: This represents the client ID.\n- `AICORE_CLIENT_SECRET`: This stands for the client secret.\n- `AICORE_AUTH_URL`: This is the URL used to retrieve a token using the client ID and secret.\n- `AICORE_BASE_URL`: This is the URL of the service (with suffix /v2).\n- `AICORE_RESOURCE_GROUP`: This represents the resource group that should be used.\n\nWe recommend setting these values as environment variables or via config file. The default path for this file\nis `~/.aicore/config.json`\n\n```json\n{\n \"AICORE_AUTH_URL\": \"https://* * * .authentication.sap.hana.ondemand.com\",\n \"AICORE_CLIENT_ID\": \"* * * \",\n \"AICORE_CLIENT_SECRET\": \"* * * \",\n \"AICORE_RESOURCE_GROUP\": \"* * * \",\n \"AICORE_BASE_URL\": \"https://api.ai.* * *.cfapps.sap.hana.ondemand.com/v2\"\n}\n```\n\n## Usage\n\n### Prerequisite\n*Activate* the generative AI hub for your tenant according to the [Generative AI Hub document](https://help.sap.com/doc/7ca8e589556c4596abafe95545dfc212/CLOUD/en-US/553250b6ec764a05be43a7cd8cba0526.pdf).\n### OpenAI-like API\n\n#### Completion\nBelow is an example usage of openai.Completions in generative-ai-hub sdk:\n\n from gen_ai_hub.proxy.native.openai import completions\n\n response = completions.create(\n model_name=\"tiiuae--falcon-40b-instruct\",\n prompt=\"The Answer to the Ultimate Question of Life, the Universe, and Everything is\",\n max_tokens=7,\n temperature=0\n )\n print(response)\n\n#### ChatCompletion\nBelow is an example usage of openai.ChatCompletions:\n\n from gen_ai_hub.proxy.native.openai import chat\n\n messages = [ {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": \"Does Azure OpenAI support customer managed keys?\"},\n {\"role\": \"assistant\", \"content\": \"Yes, customer managed keys are supported by Azure OpenAI.\"},\n {\"role\": \"user\", \"content\": \"Do other Azure Cognitive Services support this too?\"} ]\n\n kwargs = dict(model_name='gpt-35-turbo', messages=messages)\n response = chat.completions.create(**kwargs)\n print(response)\n\n#### Embeddings\nBelow is an example usage of openai.Embeddings:\n\n from gen_ai_hub.proxy.native.openai import embeddings\n\n response = embeddings.create(\n input=\"Every decoding is another encoding.\",\n model_name=\"text-embedding-ada-002\"\n encoding_format='base64'\n )\n print(response.data)\n\n### Langchain Api\n\n#### Model Initialization\n\nThe `init_llm` and `init_embedding_model` functions allow easy initialization of langchain model interfaces\nin a harmonized way by the generative AI hub SDK:\n\nFunction: `init_llm`\n\n from langchain.chains import LLMChain\n from langchain.prompts import PromptTemplate\n\n from gen_ai_hub.proxy.langchain.init_models import init_llm\n\n template = \"\"\"Question: {question}\n Answer: Let's think step by step.\"\"\"\n prompt = PromptTemplate(template=template, input_variables=['question'])\n question = 'What is a supernova?'\n\n llm = init_llm('gpt-4', max_tokens=100)\n llm_chain = LLMChain(prompt=prompt, llm=llm)\n response = llm_chain.invoke(question)\n print(response['text'])\n\nFunction `init_embedding_model`\n\n from gen_ai_hub.proxy.langchain.init_models import init_embedding_model\n\n text = 'Every decoding is another encoding.'\n embeddings = init_embedding_model('text-embedding-ada-002')\n response = embeddings.embed_query(text)\n print(response)\n\n#### Completion Model\n\n from langchain import PromptTemplate, LLMChain\n\n from gen_ai_hub.proxy.langchain.openai import OpenAI # langchain class representing the AICore OpenAI models\n from gen_ai_hub.proxy.core.proxy_clients import get_proxy_client\n\n proxy_client = get_proxy_client('aicore')\n # non-chat model\n model_name = \"tiiuae--falcon-40b-instruct\"\n\n llm = OpenAI(proxy_model_name=model_name, proxy_client=proxy_client) # standard langchain usage\n\n template = \"\"\"Question: {question}\n\n Answer: Let's think step by step.\"\"\"\n\n prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n llm_chain = LLMChain(prompt=prompt, llm=llm, verbose=True)\n\n question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n\n print(llm_chain.predict(question=question))\n\n#### Chat Model\n\n from langchain.prompts.chat import (\n AIMessagePromptTemplate,\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n SystemMessagePromptTemplate,\n )\n\n from gen_ai_hub.proxy.langchain.openai import ChatOpenAI\n from gen_ai_hub.proxy.core.proxy_clients import get_proxy_client\n\n proxy_client = get_proxy_client('aicore')\n\n chat_llm = ChatOpenAI(proxy_model_name='gpt-35-turbo', proxy_client=proxy_client)\n template = 'You are a helpful assistant that translates english to pirate.'\n\n system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n\n example_human = HumanMessagePromptTemplate.from_template('Hi')\n example_ai = AIMessagePromptTemplate.from_template('Ahoy!')\n human_template = '{text}'\n\n human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)\n chat_prompt = ChatPromptTemplate.from_messages(\n [system_message_prompt, example_human, example_ai, human_message_prompt])\n\n chain = LLMChain(llm=chat_llm, prompt=chat_prompt)\n\n response = chain.invoke('I love planking.')\n print(response['text'])\n\n#### Embedding Model\n\n from gen_ai_hub.proxy.native.openai import OpenAIEmbeddings\n from gen_ai_hub.proxy.core.proxy_clients import get_proxy_client\n\n proxy_client = get_proxy_client('aicore')\n\n # can be called without passing proxy_client\n embedding_model = OpenAIEmbeddings(proxy_model_name='text-embedding-ada-002')\n\n response = embedding_model.embed_query('Every decoding is another encoding.')\n print(response)\n\n",
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