assert-eval 0.1.3

LLM-based summary quality evaluation

assert-eval

LLM-based summary quality evaluation.

Scores a summary against source text for coverage, factual accuracy, alignment, and topic preservation. No PyTorch, no BERT, no heavy dependencies.

Installation

pip install assert-eval

Quick Start

from assert_eval import evaluate_summary, LLMConfig

config = LLMConfig(
    provider="bedrock",
    model_id="us.amazon.nova-pro-v1:0",
    region="us-east-1",
)

results = evaluate_summary(
    full_text="Original long text goes here...",
    summary="Summary to evaluate goes here...",
    metrics=["coverage", "factual_consistency", "factual_alignment", "topic_preservation"],
    llm_config=config,
)

print(results)
# {'coverage': 0.85, 'factual_consistency': 0.92, 'factual_alignment': 0.88, 'topic_preservation': 0.90}

Available Metrics

Metric	Description
coverage	What % of source document claims appear in the summary (recall/completeness)
factual_consistency	What % of summary claims are supported by the source (precision/accuracy)
factual_alignment	F1 score combining coverage and factual_consistency
topic_preservation	How well the main topics from the source are preserved in the summary

Custom Evaluation Instructions

Tailor the LLM's evaluation criteria for your domain:

results = evaluate_summary(
    full_text=text,
    summary=summary,
    metrics=["coverage", "factual_consistency"],
    llm_config=config,
    custom_prompt_instructions={
        "coverage": "Apply strict standards. Only mark a claim as covered if it is clearly and explicitly represented.",
        "factual_consistency": "Flag any claim that adds detail not present in the original text.",
    },
)

Verbose Output

Pass verbose=True to include per-claim LLM reasoning in the results:

results = evaluate_summary(
    full_text=text,
    summary=summary,
    metrics=["coverage", "factual_consistency"],
    llm_config=config,
    verbose=True,
)

LLM Configuration

from assert_eval import LLMConfig

# AWS Bedrock (uses ~/.aws credentials by default)
config = LLMConfig(
    provider="bedrock",
    model_id="us.amazon.nova-pro-v1:0",
    region="us-east-1",
)

# AWS Bedrock with explicit credentials
config = LLMConfig(
    provider="bedrock",
    model_id="us.amazon.nova-pro-v1:0",
    region="us-east-1",
    api_key="your-aws-access-key-id",
    api_secret="your-aws-secret-access-key",
    aws_session_token="your-session-token",  # optional
)

# OpenAI
config = LLMConfig(
    provider="openai",
    model_id="gpt-4o",
    api_key="your-openai-api-key",
)

Supported Bedrock Model Families

Model Family	Example Model IDs
Amazon Nova	us.amazon.nova-pro-v1:0, amazon.nova-lite-v1:0
Anthropic Claude	anthropic.claude-3-sonnet-20240229-v1:0
Meta Llama	meta.llama3-70b-instruct-v1:0
Mistral AI	mistral.mistral-large-2402-v1:0
Cohere Command	cohere.command-r-plus-v1:0
AI21 Labs	ai21.jamba-1-5-large-v1:0

Proxy Configuration

# Single proxy
config = LLMConfig(
    provider="bedrock", model_id="us.amazon.nova-pro-v1:0", region="us-east-1",
    proxy_url="http://proxy.example.com:8080",
)

# Protocol-specific proxies
config = LLMConfig(
    provider="bedrock", model_id="us.amazon.nova-pro-v1:0", region="us-east-1",
    http_proxy="http://proxy.example.com:8080",
    https_proxy="http://proxy.example.com:8443",
)

# Authenticated proxy
config = LLMConfig(
    provider="bedrock", model_id="us.amazon.nova-pro-v1:0", region="us-east-1",
    proxy_url="http://username:[e-mail]:8080",
)

Standard HTTP_PROXY / HTTPS_PROXY environment variables are also respected.

Dependencies

• assert-core — shared LLM provider layer (AWS Bedrock, OpenAI)

Migrating from assert_llm_tools

assert-eval replaces the summary evaluation functionality of assert_llm_tools, which is now deprecated. The API is largely the same — swap the import:

# Before
from assert_llm_tools import evaluate_summary, LLMConfig

# After
from assert_eval import evaluate_summary, LLMConfig

License

MIT