flow-prompt
Open source library for production prompt engineering and load balancing of AI Models
Stars: 68
Flow Prompt is a dynamic library for managing and optimizing prompts for large language models. It facilitates budget-aware operations, dynamic data integration, and efficient load distribution. Features include CI/CD testing, dynamic prompt development, multi-model support, real-time insights, and prompt testing and evolution.
README:
Flow Prompt is a dynamic, all-in-one library designed for managing and optimizing prompts and making tests based on the ideal answer for large language models (LLMs) in production and R&D. It facilitates budget-aware operations, dynamic data integration, latency and cost metrics visibility, and efficient load distribution across multiple AI models.
- CI/CD testing: Generates tests based on the context and ideal answer (usually written by the human).
- Dynamic Prompt Development: Avoid budget exceptions with dynamic data.
- Multi-Model Support: Seamlessly integrate with various LLMs like OpenAI, Anthropic, and more.
- Real-Time Insights: Monitor interactions, request/response metrics in production.
- Prompt Testing and Evolution: Quickly test and iterate on prompts using historical data.
Install Flow Prompt using pip:
pip install flow-prompt# setting as os.env
os.setenv('OPENAI_API_KEY', 'your_key_here')
# or creating flow_prompt obj
FlowPrompt(openai_key="your_key", openai_org="your_org")Add Azure keys to accommodate multiple realms:
# setting as os.env
os.setenv('AZURE_KEYS', '{"name_realm":{"url": "https://baseurl.azure.com/","key": "secret"}}')
# or creating flow_prompt obj
FlowPrompt(azure_keys={"realm_name":{"url": "https://baseurl.azure.com/", "key": "your_secret"}})Mix models easily, and districute the load across models. The system will automatically distribute your load based on the weights. We support:
- Claude
- Gemini
- OpenAI (Azure OpenAI models)
def_behaviour = behaviour.AIModelsBehaviour(attempts=[
AttemptToCall(
ai_model=OpenAIModel(
model='gpt-4o',
max_tokens=128_000,
),
weight=100
),
AttemptToCall(
ai_model=AzureAIModel(
realm='useast,
deployment_id='gpt-4o',
max_tokens=128_000,
),
weight=100
),
AttemptToCall(
ai_model=ClaudeAIModel(
model = 'claude-3-5-sonnet-20240620',
max_tokens=200_000,
),
weight=100
),
AttemptToCall(
ai_model=GeminiAIModel(
model = 'gemini-1.5-pro',
max_tokens=1_000_000,
),
weight=100
)
])
response_llm = fp.call(agent.id, context, def_behaviour)
Obtain an API token from Flow Prompt and add it:
# As an environment variable:
os.setenv('FLOW_PROMPT_API_TOKEN', 'your_token_here')
# Via code:
FlowPrompt(api_token='your_api_token')- use OPENAI_BEHAVIOR
- or add your own Behaviour, you can set max count of attempts, if you have different AI Models, if the first attempt will fail because of retryable error, the second will be called, based on the weights.
from flow_prompt import OPENAI_GPT4_0125_PREVIEW_BEHAVIOUR
flow_behaviour = OPENAI_GPT4_0125_PREVIEW_BEHAVIOUR
or:
from flow_prompt import behaviour
flow_behaviour = behaviour.AIModelsBehaviour(
attempts=[
AttemptToCall(
ai_model=AzureAIModel(
realm='us-east-1',
deployment_name="gpt-4-1106-preview",
max_tokens=C_128K,
support_functions=True,
),
weight=100,
),
AttemptToCall(
ai_model=OpenAIModel(
model="gpt-4-1106-preview",
max_tokens=C_128K,
support_functions=True,
),
weight=100,
),
]
)
from flow_prompt import FlowPrompt, PipePrompt
# Initialize and configure FlowPrompt
flow = FlowPrompt(openai_key='your_api_key', openai_org='your_org')
# Create a prompt
prompt = PipePrompt('greet_user')
prompt.add("You're {name}. Say Hello and ask what's their name.", role="system")
# Call AI model with FlowPrompt
context = {"name": "John Doe"}
# test_data - optional parameter used for generating tests
response = flow.call(prompt.id, context, flow_behaviour, test_data={
'ideal_answer': "Hello, I'm John Doe. What's your name?",
'behavior_name': "gemini"
}
)
print(response.content)-
To review your created tests and score please go to https://cloud.flow-prompt.com/tests. You can update there Prompt and rerun tests for a published version, or saved version. If you will update and publish version online - library will automatically use the new updated version of the prompt. It's made for updating prompt without redeployment of the code, which is costly operation to do if it's required to update just prompt.
-
To review logs please proceed to https://cloud.flow-prompt.com/logs, there you can see metrics like latency, cost, tokens;
For production environments, it is recommended to store secrets securely and not directly in your codebase. Consider using a secret management service or encrypted environment variables.
We welcome contributions! Please see our Contribution Guidelines for more information on how to get involved.
This project is licensed under the Apache2.0 License - see the LICENSE file for details.
For support or contributions, please contact us via GitHub Issues.
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