Complete Workflow Example
This example demonstrates a complete workflow using logs, datasets, prompts, and evaluations:from keywordsai import KeywordsAI
import asyncio
# Initialize the client
client = KeywordsAI(api_key="your-api-key-here")
async def complete_workflow():
# 1. Create some logs
log1 = await client.logs.acreate(
messages=[
{"role": "user", "content": "What is machine learning?"},
{"role": "assistant", "content": "Machine learning is a subset of AI..."}
],
model="gpt-4",
prompt_tokens=20,
completion_tokens=50,
total_tokens=70
)
log2 = await client.logs.acreate(
messages=[
{"role": "user", "content": "Explain neural networks"},
{"role": "assistant", "content": "Neural networks are computing systems..."}
],
model="gpt-4",
prompt_tokens=15,
completion_tokens=45,
total_tokens=60
)
# 2. Create a dataset and add logs
dataset = await client.datasets.acreate(
name="ML Education Dataset",
description="Dataset for machine learning educational content"
)
await client.datasets.aadd_logs_to_dataset(
dataset_id=dataset.id,
log_ids=[log1.id, log2.id]
)
# 3. Create a prompt template
prompt = await client.prompts.acreate(
name="Educational Assistant",
messages=[
{"role": "system", "content": "You are an educational assistant specializing in {{subject}}."},
{"role": "user", "content": "{{question}}"}
]
)
# 4. Create an evaluator
evaluator = await client.evaluators.acreate(
name="Accuracy Evaluator",
description="Evaluates the accuracy of educational responses"
)
# 5. Run evaluation on the dataset
evaluation = await client.datasets.arun_dataset_evaluation(
dataset_id=dataset.id,
evaluator_ids=[evaluator.id]
)
print(f"Evaluation completed with ID: {evaluation.id}")
# 6. Get evaluation report
report = await client.datasets.aget_evaluation_report(
dataset_id=dataset.id,
evaluation_id=evaluation.id
)
print(f"Evaluation score: {report.score}")
# Run the async workflow
asyncio.run(complete_workflow())
Synchronous vs Asynchronous Usage
Synchronous Example
from keywordsai import KeywordsAI
client = KeywordsAI(api_key="your-api-key-here")
# Synchronous operations
log = client.logs.create(
messages=[{"role": "user", "content": "Hello"}],
model="gpt-3.5-turbo"
)
logs = client.logs.list(limit=10)
specific_log = client.logs.get(log_id=log.id)
Asynchronous Example
from keywordsai import KeywordsAI
import asyncio
client = KeywordsAI(api_key="your-api-key-here")
async def async_operations():
# Asynchronous operations
log = await client.logs.acreate(
messages=[{"role": "user", "content": "Hello"}],
model="gpt-3.5-turbo"
)
logs = await client.logs.alist(limit=10)
specific_log = await client.logs.aget(log_id=log.id)
return log, logs, specific_log
# Run async operations
results = asyncio.run(async_operations())
Dataset Management Example
from keywordsai import KeywordsAI
client = KeywordsAI(api_key="your-api-key-here")
# Create a dataset
dataset = client.datasets.create(
name="Customer Support Dataset",
description="Collection of customer support conversations"
)
# Create some logs
logs = []
for i in range(5):
log = client.logs.create(
messages=[
{"role": "user", "content": f"Customer question {i+1}"},
{"role": "assistant", "content": f"Support response {i+1}"}
],
model="gpt-3.5-turbo"
)
logs.append(log)
# Add logs to dataset
client.datasets.add_logs_to_dataset(
dataset_id=dataset.id,
log_ids=[log.id for log in logs]
)
# List dataset logs
dataset_logs = client.datasets.list_dataset_logs(
dataset_id=dataset.id,
limit=10
)
print(f"Dataset contains {len(dataset_logs)} logs")
# Update dataset
updated_dataset = client.datasets.update(
dataset_id=dataset.id,
name="Updated Customer Support Dataset",
description="Updated description"
)
Prompt Version Management
from keywordsai import KeywordsAI
client = KeywordsAI(api_key="your-api-key-here")
# Create a base prompt
prompt = client.prompts.create(
name="Code Review Assistant",
messages=[
{"role": "system", "content": "You are a code review assistant."},
{"role": "user", "content": "Please review this code: {{code}}"}
]
)
# Create multiple versions
version_1 = client.prompts.create_version(
prompt_id=prompt.id,
messages=[
{"role": "system", "content": "You are an expert code review assistant with focus on security."},
{"role": "user", "content": "Please review this code for security issues: {{code}}"}
],
version_name="security-focused"
)
version_2 = client.prompts.create_version(
prompt_id=prompt.id,
messages=[
{"role": "system", "content": "You are an expert code review assistant with focus on performance."},
{"role": "user", "content": "Please review this code for performance optimizations: {{code}}"}
],
version_name="performance-focused"
)
# List all versions
versions = client.prompts.list_versions(prompt_id=prompt.id)
print(f"Prompt has {len(versions)} versions")
# Get specific version
specific_version = client.prompts.get_version(
prompt_id=prompt.id,
version_id=version_1.id
)
Experiment Management
from keywordsai import KeywordsAI
client = KeywordsAI(api_key="your-api-key-here")
# Create an experiment
experiment = client.experiments.create(
name="Model Comparison Experiment",
description="Comparing different models on the same dataset"
)
# Add rows to experiment
client.experiments.add_rows(
experiment_id=experiment.id,
rows=[
{"input": "What is AI?", "expected_output": "Artificial Intelligence explanation"},
{"input": "Explain ML", "expected_output": "Machine Learning explanation"}
]
)
# Add columns for different models
client.experiments.add_columns(
experiment_id=experiment.id,
columns=[
{"name": "gpt-3.5-turbo", "type": "model_output"},
{"name": "gpt-4", "type": "model_output"}
]
)
# Run the experiment
result = client.experiments.run_experiment(
experiment_id=experiment.id,
model_configs=[
{"model": "gpt-3.5-turbo", "temperature": 0.7},
{"model": "gpt-4", "temperature": 0.7}
]
)
print(f"Experiment completed with result ID: {result.id}")
Error Handling
from keywordsai import KeywordsAI
from keywordsai.exceptions import KeywordsAIError, AuthenticationError, RateLimitError
client = KeywordsAI(api_key="your-api-key-here")
try:
log = client.logs.create(
messages=[{"role": "user", "content": "Hello"}],
model="gpt-3.5-turbo"
)
except AuthenticationError:
print("Invalid API key")
except RateLimitError:
print("Rate limit exceeded")
except KeywordsAIError as e:
print(f"API error: {e}")
except Exception as e:
print(f"Unexpected error: {e}")