Rename to lamoom (#31)

Author: Katsiarynka

.env.example

@@ -1,5 +1,5 @@
-FLOW_PROMPT_API_TOKEN=
+LAMOOM_API_TOKEN=
 AZURE_KEYS=
 OPENAI_API_KEY=
 BEARER_TOKEN=
-FLOW_PROMPT_API_URI=
+LAMOOM_API_URI=

.github/workflows/run-unit-tests.yaml

@@ -17,9 +17,10 @@ jobs:
         echo CLAUDE_API_KEY=${{ secrets.CLAUDE_API_KEY }} >> .env
         echo GEMINI_API_KEY=${{ secrets.GEMINI_API_KEY }} >> .env
         echo OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }} >> .env
+        echo LAMOOM_API_URI=${{ secrets.LAMOOM_API_URI }} >> .env
+        echo LAMOOM_API_TOKEN=${{ secrets.LAMOOM_API_TOKEN }} >> .env
         echo FLOW_PROMPT_API_URI=${{ secrets.FLOW_PROMPT_API_URI }} >> .env
         echo FLOW_PROMPT_API_TOKEN=${{ secrets.FLOW_PROMPT_API_TOKEN }} >> .env
-        cat .env
 
     - name: Install dependencies
       run: |

.pre-commit-config.yaml

@@ -43,7 +43,7 @@ repos:
 
   # - id: flake8
   #   name: Validate with flake8
-  #   entry: poetry run flake8 flow_prompt
+  #   entry: poetry run flake8 lamoom
   #   language: system
   #   pass_filenames: false
   #   types: [python]

CONTRIBUTING.md

@@ -1,5 +1,5 @@
 ### API Url
 Add your own API url in `.env` if needed:
 ```
-FLOW_PROMPT_API_URI=your_api_uri
+LAMOOM_API_URI=your_api_uri
 ```

Makefile

@@ -1,4 +1,4 @@
-PROJECT_FOLDER = 'flow_prompt'
+PROJECT_FOLDER = 'lamoom'
 
 flake8:
 	flake8 ${PROJECT_FOLDER}
@@ -19,16 +19,16 @@ isort-check:
 	isort --settings-path pyproject.toml --check-only .
 
 autopep8:
-	for f in `find flow_prompt -name "*.py"`; do autopep8 --in-place --select=E501 $f; done
+	for f in `find lamoom -name "*.py"`; do autopep8 --in-place --select=E501 $f; done
 
 lint:
 	poetry run isort --settings-path pyproject.toml --check-only .
 
 test:
-	poetry run pytest -vv tests \
+	poetry run pytest --cache-clear -vv tests \
 		--cov=${PROJECT_FOLDER} \
 		--cov-config=.coveragerc \
-		--cov-fail-under=77 \
+		--cov-fail-under=81 \
 		--cov-report term-missing
 
 .PHONY: format
@@ -52,6 +52,7 @@ clean-pyc:
 clean-test:
 		rm -f .coverage
 		rm -fr htmlcov/
+		rm -rf .pytest_cache
 
 
 publish-test-prerelease:

README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-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.
+Lamoom 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 dynamic data integration, latency and cost metrics visibility, and efficient load distribution across multiple AI models.
 
 ## Features
 
@@ -17,7 +17,7 @@ Flow Prompt is a dynamic, all-in-one library designed for managing and optimizin
 Install Flow Prompt using pip:
 
 ```bash
-pip install flow-prompt
+pip install lamoom
 ```
 
 ## Authentication
@@ -26,128 +26,94 @@ pip install flow-prompt
 ```python
 # 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")
+# or creating lamoom obj
+Lamoom(openai_key="your_key", openai_org="your_org")
 ```
 
 ### Azure Keys
 Add Azure keys to accommodate multiple realms:
 ```python
 # 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"}})
 ```
 
 ### Model Agnostic:
 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)
+- OpenAI (w/ Azure OpenAI models)
+- Nebius with (Llama, DeepSeek, Mistral, Mixtral, dolphin, Qwen and others)
 ```
+from lamoom import LamoomModelProviders
+
 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(provider='openai', model='gpt-4o', weight=100),
+    AttemptToCall(provider='azure', realm='useast-1', deployment_id='gpt-4o' weight=100),
+    AttemptToCall(provider='azure', realm='useast-2', deployment_id='gpt-4o' weight=100),
+    AttemptToCall(provider=LamoomModelProviders.anthropic, model='claude-3-5-sonnet-20240620', weight=100
     ),
-    AttemptToCall(
-        ai_model=ClaudeAIModel(
-            model = 'claude-3-5-sonnet-20240620',
-            max_tokens=200_000,
-        ),
-        weight=100
+    AttemptToCall(provider=LamoomModelProviders.gemini, model='gemini-1.5-pro', weight=100
     ),
-    AttemptToCall(
-        ai_model=GeminiAIModel(
-            model = 'gemini-1.5-pro',
-            max_tokens=1_000_000,
-        ),
-        weight=100
+    AttemptToCall(provider=LamoomModelProviders.nebius, model='deepseek-ai/DeepSeek-R1', weight=100
     )
 ])
 
-response_llm = fp.call(agent.id, context, def_behaviour)
+response_llm = client.call(agent.id, context, def_behaviour)
 ```
 
-### FlowPrompt Keys
+### Lamoom Keys
 Obtain an API token from Flow Prompt and add it:
 
 ```python
 # As an environment variable:
-os.setenv('FLOW_PROMPT_API_TOKEN', 'your_token_here')
+os.setenv('LAMOOM_API_TOKEN', 'your_token_here')
 # Via code: 
-FlowPrompt(api_token='your_api_token')
+Lamoom(api_token='your_api_token')
 ```
 
 ### Add Behavious:
 - 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
+from lamoom import OPENAI_GPT4_0125_PREVIEW_BEHAVIOUR
+behaviour = OPENAI_GPT4_0125_PREVIEW_BEHAVIOUR
 ```
 or:
 ```
-from flow_prompt import behaviour
-flow_behaviour = behaviour.AIModelsBehaviour(
+from lamoom import behaviour
+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,
-        ),
+        AttemptToCall(provider='azure', realm='useast-1', deployment_id='gpt-4o' weight=100),
+        AttemptToCall(provider='azure', realm='useast-2', deployment_id='gpt-4o' weight=100),
     ]
 )
 ```
 
 ## Usage Examples:
 
 ```python
-from flow_prompt import FlowPrompt, PipePrompt
+from lamoom import Lamoom, Prompt
 
-# Initialize and configure FlowPrompt
-flow = FlowPrompt(openai_key='your_api_key', openai_org='your_org')
+# Initialize and configure Lamoom
+client = Lamoom(openai_key='your_api_key', openai_org='your_org')
 
 # Create a prompt
-prompt = PipePrompt('greet_user')
+prompt = Prompt('greet_user')
 prompt.add("You're {name}. Say Hello and ask what's their name.", role="system")
 
-# Call AI model with FlowPrompt
+# Call AI model with Lamoom
 context = {"name": "John Doe"}
 # test_data -  optional parameter used for generating tests
-response = flow.call(prompt.id, context, flow_behaviour, test_data={
+response = client.call(prompt.id, context, behavior, 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 your created tests and score please go to https://cloud.lamoom.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;
+- To review logs please proceed to https://cloud.lamoom.com/logs, there you can see metrics like latency, cost, tokens;
 
 ## Best Security Practices
 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.

examples/evaluate_prompts_quality/evaluate_prompt_quality.py

@@ -1,20 +1,20 @@
 import logging
 import random
-from flow_prompt import FlowPrompt, behaviour, AttemptToCall, AzureAIModel, C_128K
+from lamoom import Lamoom, behaviour, AttemptToCall, AzureAIModel, C_128K
 from prompt import prompt_to_evaluate_prompt
-from flow_prompt_service import get_all_prompts, get_logs
+from lamoom_service import get_all_prompts, get_logs
 logger = logging.getLogger(__name__)
 
 
 
-flow_prompt = FlowPrompt()
+lamoom = Lamoom()
 
 gpt4_behaviour = behaviour.AIModelsBehaviour(
     attempts=[
         AttemptToCall(
             ai_model=AzureAIModel(
-                realm='westus',
-                deployment_id="gpt-4-turbo",
+                realm='useast',
+                deployment_id="gpt-4o",
                 max_tokens=C_128K,
                 support_functions=True,
             ),
@@ -41,7 +41,7 @@ def main():
             'prompt_data': prompt_chats,
             'prompt_id': prompt_id,
         }
-        result = flow_prompt.call(prompt_to_evaluate_prompt.id, context, gpt4_behaviour)
+        result = lamoom.call(prompt_to_evaluate_prompt.id, context, gpt4_behaviour)
         print(result.content)
 
 if __name__ == '__main__':

examples/evaluate_prompts_quality/flow_prompt_service.py

@@ -3,22 +3,22 @@
 import os
 import dotenv
 import requests
-from flow_prompt.settings import FLOW_PROMPT_API_URI
+from lamoom.settings import LAMOOM_API_URI
 
 dotenv.load_dotenv(dotenv.find_dotenv())
 
 BEARER_TOKEN = os.getenv('BEARER_TOKEN')
 
 
 def get_all_prompts():
-    response = requests.get(f'{FLOW_PROMPT_API_URI}/prompts', headers={'Authorization': f'Bearer {BEARER_TOKEN}'})
+    response = requests.get(f'{LAMOOM_API_URI}/prompts', headers={'Authorization': f'Bearer {BEARER_TOKEN}'})
     prompts = response.json()
     return prompts
 
 
 def get_logs(prompt_id):
     response = requests.get(
-        f'{FLOW_PROMPT_API_URI}/logs?prompt_id={prompt_id}&fields=response,context',
+        f'{LAMOOM_API_URI}/logs?prompt_id={prompt_id}&fields=response,context',
         headers={'Authorization': f'Bearer {BEARER_TOKEN}'}
     )
     logs = response.json()

examples/evaluate_prompts_quality/prompt.py

@@ -1,7 +1,7 @@
 
-from flow_prompt import PipePrompt
+from lamoom import Prompt
 
-prompt_to_evaluate_prompt = PipePrompt(id="prompt-improver")
+prompt_to_evaluate_prompt = Prompt(id="prompt-improver")
 
 prompt_to_evaluate_prompt.add(role="system", content="You're a prompt engineer, tasked with evaluating and improving prompt quality.")