MetaPrompt Optimization

1
from opik_optimizer import MetaPromptOptimizer
2
3
prompter = MetaPromptOptimizer(
4
    model="openai/gpt-4",  # or "azure/gpt-4"
5
    project_name="my-project",
6
    temperature=0.1,
7
    max_tokens=5000,
8
    num_threads=8,
9
    seed=42
10
)

1
prompter = MetaPromptOptimizer(
2
    model="openai/gpt-4",
3
    project_name="my-project",
4
    temperature=0.1,
5
    max_tokens=5000,
6
    num_threads=8,
7
    seed=42,
8
    template_depth=3,           # Depth of template analysis
9
    max_variations=5,           # Maximum number of variations to test
10
    instruction_weight=0.6,     # Weight of instruction optimization
11
    example_weight=0.4,         # Weight of example optimization
12
    readability_threshold=0.8   # Minimum readability score
13
)

1
from opik_optimizer import MetaPromptOptimizer
2
from opik.evaluation.metrics import LevenshteinRatio
3
from opik_optimizer import (
4
    MetricConfig,
5
    TaskConfig,
6
    from_llm_response_text,
7
    from_dataset_field,
8
)
9
from opik_optimizer.demo import get_or_create_dataset
10
11
# Initialize optimizer
12
optimizer = MetaPromptOptimizer(
13
    model="openai/gpt-4",  # or "azure/gpt-4"
14
    temperature=0.1,
15
    max_tokens=5000,
16
    num_threads=8,
17
    seed=42
18
)
19
20
# Prepare dataset
21
dataset = get_or_create_dataset("hotpot-300")
22
23
# Define metric and task configuration (see docs for more options)
24
metric_config = MetricConfig(
25
    metric=LevenshteinRatio(),
26
    inputs={
27
        "output": from_llm_response_text(),  # Model's output
28
        "reference": from_dataset_field(name="answer"),  # Ground truth
29
    }
30
)
31
task_config = TaskConfig(
32
    type="text_generation",
33
    instruction_prompt="Provide an answer to the question.",
34
    input_dataset_fields=["question"],
35
    output_dataset_field="answer",
36
    use_chat_prompt=True
37
)
38
39
# Run optimization
40
results = optimizer.optimize_prompt(
41
    dataset=dataset,
42
    metric_config=metric_config,
43
    task_config=task_config
44
)
45
46
# Access results
47
results.display()

1
optimizer = MetaPromptOptimizer(
2
    model="google/gemini-pro",  # or any LiteLLM supported model
3
    project_name="my-project",
4
    temperature=0.1,
5
    max_tokens=5000
6
)