ai-batch is now batchata

This package has been renamed. Use pip install batchata instead.

New package: https://pypi.org/project/batchata/
New repository: https://github.com/agamm/batchata

API Reference

• batch() - Process message conversations or PDF files
• BatchJob - Job status and results
• BatchManager - Manage large-scale batch processing with parallel execution

Quick Start

from ai_batch import batch
from pydantic import BaseModel

class Invoice(BaseModel):
    company_name: str
    total_amount: str
    date: str

# Process PDFs with structured output + citations
job = batch(
    files=["invoice1.pdf", "invoice2.pdf", "invoice3.pdf"],
    prompt="Extract the company name, total amount, and date.",
    model="claude-3-5-sonnet-20241022",
    response_model=Invoice,
    enable_citations=True
)

# Wait for completion
while not job.is_complete():
    time.sleep(30)
    
results = job.results()
# Results now contain both data and citations together:
# [{"result": Invoice(...), "citations": {"company_name": [Citation(...)], ...}}, ...]

Installation

pip install ai-batch

Usage

Create a .env file in your project root:

ANTHROPIC_API_KEY=your-api-key

API Functions

batch()

Process multiple message conversations with optional structured output.

from ai_batch import batch
from pydantic import BaseModel

class SpamResult(BaseModel):
    is_spam: bool
    confidence: float
    reason: str

# Process messages
job = batch(
    messages=[
        [{"role": "user", "content": "Is this spam? You've won $1000!"}],
        [{"role": "user", "content": "Meeting at 3pm tomorrow"}],
        [{"role": "user", "content": "URGENT: Click here now!"}]
    ],
    model="claude-3-haiku-20240307",
    response_model=SpamResult
)

# Get results
results = job.results()

Response:

[
    SpamResult(is_spam=True, confidence=0.95, reason="Contains monetary prize claim"),
    SpamResult(is_spam=False, confidence=0.98, reason="Normal meeting reminder"),
    SpamResult(is_spam=True, confidence=0.92, reason="Urgent call-to-action pattern")
]

batch() with files

Process PDF files with optional structured output and citations.

from ai_batch import batch
from pydantic import BaseModel

class Invoice(BaseModel):
    company_name: str
    total_amount: str
    date: str

# Process PDFs with citations
job = batch(
    files=["invoice1.pdf", "invoice2.pdf"],
    prompt="Extract the company name, total amount, and date.",
    model="claude-3-5-sonnet-20241022",
    response_model=Invoice,
    enable_citations=True
)

results = job.results()
# Results now contain both data and citations together

Response:

# Results now contain both data and citations together
[
    {
        "result": Invoice(company_name="TechCorp Solutions", total_amount="$12,500.00", date="March 15, 2024"),
        "citations": {
            "company_name": [Citation(cited_text="TechCorp Solutions", start_page=1)],
            "total_amount": [Citation(cited_text="TOTAL: $12,500.00", start_page=2)],
            "date": [Citation(cited_text="Date: March 15, 2024", start_page=1)]
        }
    },
    {
        "result": Invoice(company_name="DataFlow Systems", total_amount="$8,750.00", date="March 18, 2024"),
        "citations": {
            "company_name": [Citation(cited_text="DataFlow Systems", start_page=1)],
            "total_amount": [Citation(cited_text="Total Due: $8,750.00", start_page=3)],
            "date": [Citation(cited_text="Invoice Date: March 18, 2024", start_page=1)]
        }
    }
]

BatchJob

The job object returned by batch().

# Check completion status
if job.is_complete():
    results = job.results()

# Get processing statistics with cost tracking
stats = job.stats(print_stats=True)
# Output:
# 📊 Batch Statistics
#    ID: msgbatch_01BPtdnmEwxtaDcdJ2eUsq4T
#    Status: ended
#    Complete: ✅
#    Elapsed: 41.8s
#    Mode: Text + Citations
#    Results: 0
#    Citations: 0
#    Input tokens: 2,117
#    Output tokens: 81
#    Total cost: $0.0038
#    (50% batch discount applied)

# Citations are now included in results (if enabled)
# Access via: results[0]["citations"]

# Save raw API responses
job = batch(..., raw_results_dir="./raw_responses")

BatchManager

Manage large-scale batch processing with automatic job splitting, parallel execution, state persistence, and cost management.

from ai_batch import BatchManager
from pydantic import BaseModel

class Invoice(BaseModel):
    company_name: str
    total_amount: float
    invoice_number: str

# Initialize BatchManager for large-scale processing
manager = BatchManager(
    files=["invoice1.pdf", "invoice2.pdf", ...],  # 100+ files
    prompt="Extract invoice data",
    model="claude-3-5-sonnet-20241022",
    response_model=Invoice,
    enable_citations=True,
    items_per_job=10,      # Process 10 files per job
    max_parallel_jobs=5,   # 5 jobs in parallel
    max_cost=50.0,         # Stop if cost exceeds $50
    state_path="batch_state.json",  # Auto-resume capability
    save_results_dir="results/"     # Save results to disk
)

# Run processing (auto-resumes if interrupted)
summary = manager.run(print_progress=True)

# Retry failed items
if summary['failed_items'] > 0:
    retry_summary = manager.retry_failed()

# Get statistics
stats = manager.stats
print(f"Completed: {stats['completed_items']}/{stats['total_items']}")
print(f"Total cost: ${stats['total_cost']:.2f}")

# Load results from disk
results = manager.get_results_from_disk()

Key Features:

• Automatic job splitting: Breaks large batches into smaller chunks
• Parallel processing: Multiple jobs run concurrently with ThreadPoolExecutor
• State persistence: Resume from interruptions with JSON state files
• Cost management: Stop processing when budget limit is reached
• Progress monitoring: Real-time progress updates with statistics
• Retry mechanism: Easily retry failed items
• Result saving: Organized directory structure for results

Citations

Citations work in two modes depending on whether you use structured output:

1. Text + Citations (Flat List)

When enable_citations=True without a response model, citations are returned as a flat list:

job = batch(
    files=["document.pdf"],
    prompt="Summarize the key findings",
    enable_citations=True
)

results = job.results()   # List of {"result": str, "citations": List[Citation]}

# Example result structure:
[
    {
        "result": "Summary text...",
        "citations": [
            Citation(cited_text="AI reduces errors by 30%", start_page=2),
            Citation(cited_text="Implementation cost: $50,000", start_page=5)
        ]
    }
]

2. Structured + Field Citations (Mapping)

When using both response_model and enable_citations=True, citations are mapped to specific fields:

job = batch(
    files=["document.pdf"],
    prompt="Extract the data",
    response_model=MyModel,
    enable_citations=True
)

results = job.results()   # List of {"result": Model, "citations": Dict[str, List[Citation]]}

# Example result structure:
[
    {
        "result": MyModel(title="Annual Report 2024", revenue="$1.2M"),
        "citations": {
            "title": [Citation(cited_text="Annual Report 2024", start_page=1)],
            "revenue": [Citation(cited_text="Revenue: $1.2M", start_page=3)],
            "growth": [Citation(cited_text="YoY Growth: 25%", start_page=3)]
        }
    }
]

The field mapping allows you to trace exactly which part of the source document was used to populate each field in your structured output.

Robust Citation Parsing

AI Batch uses proper JSON parsing for citation field mapping, ensuring reliability with complex JSON structures:

Handles Complex Scenarios:

• ✅ Escaped quotes in JSON values: "name": "John \"The Great\" Doe"
• ✅ URLs with colons: "website": "http://example.com:8080"
• ✅ Nested objects and arrays: "metadata": {"nested": {"deep": "value"}}
• ✅ Multi-line strings and special characters
• ✅ Fields with numbers/underscores: user_name, age_2

Previous Limitations (Fixed): The old regex-based approach would fail on complex JSON patterns. The new JSON parser reliably handles any valid JSON structure that Claude produces, making citation mapping robust for production use.

Cost Tracking

AI Batch automatically tracks token usage and costs for all batch operations:

from ai_batch import batch

job = batch(
    messages=[...],
    model="claude-3-5-sonnet-20241022"
)

# Get cost information
stats = job.stats()
print(f"Total cost: ${stats['total_cost']:.4f}")
print(f"Input tokens: {stats['total_input_tokens']:,}")
print(f"Output tokens: {stats['total_output_tokens']:,}")

# Or print formatted statistics
job.stats(print_stats=True)

Example Scripts

• examples/spam_detection.py - Email classification
• examples/pdf_extraction.py - PDF data extraction
• examples/citation_example.py - Basic citation usage
• examples/citation_with_pydantic.py - Structured output with citations
• examples/batch_manager_example.py - Large-scale batch processing with BatchManager

Limitations

• Citationm mapping only work with flat Pydantic models (no nested models)
• No support for OpenAI.
• PDFs require Opus/Sonnet models for best results
• Batch jobs can take up to 24 hours to process
• Use job.is_complete() to check status before getting results
• Citations may not be available in all batch API responses

Comparison with Alternatives

Feature	ai-batch	LangChain	Instructor	PydanticAI
Batch Requests	✅ Native (50% cost savings)	❌ No native batch API	✅ Via OpenAI Batch API (#1092)	⚠️ Planned (#1771)
Structured Output	✅ Full support	✅ Via parsers	✅ Core feature	✅ Native
PDF File Input	✅ Native support	✅ Via document loaders	✅ Via multimodal models	✅ Via file handling
Citation Mapping	✅ Field-level citations	❌ Manual implementation	❌ Manual implementation	❌ Manual implementation
Cost Tracking	✅ Automatic with tokencost	❌ Manual implementation	❌ Manual implementation	❌ Manual implementation
Cost Limits	✅ max_cost parameter	❌ Manual implementation	❌ Manual implementation	❌ Manual implementation
Batch Providers	2/2 (Anthropic, OpenAI planned)	0/2	1/2 (OpenAI only)	0/2
Focus	Streamlined batch requests	General LLM orchestration	Structured outputs CLI	Agent framework

License

MIT

Todos

• Add pricing metadata and max_spend controls (Cost tracking implemented)
• Auto batch manager (parallel batches, retry, spend control) (BatchManager implemented)
• Test mode to run on 1% sample before full batch
• Quick batch - split into smaller chunks for faster results
• Support text/other file types (not just PDFs)
• Support for OpenAI