Pandas Integration

DocETL provides seamless integration for a few operators (map, filter, merge, agg) with pandas through a dataframe accessor. This idea was proposed by LOTUS¹.

Installation

The pandas integration is included in the main DocETL package:

pip install docetl

Overview

The pandas integration provides a .semantic accessor that enables:

• Semantic mapping with LLMs (df.semantic.map())
• Intelligent filtering (df.semantic.filter())
• Fuzzy merging of DataFrames (df.semantic.merge())
• Semantic aggregation (df.semantic.agg())
• Cost tracking and operation history

Quick Example

import pandas as pd
from docetl import SemanticAccessor

# Create a DataFrame
df = pd.DataFrame({
    "text": [
        "Apple released the iPhone 15 with USB-C port",
        "Microsoft's new Surface laptops feature AI capabilities",
        "Google announces Pixel 8 with enhanced camera features"
    ]
})

# Configure the semantic accessor
df.semantic.set_config(default_model="gpt-4o-mini")

# Extract structured information
result = df.semantic.map(
    prompt="Extract company and product from: {{input.text}}",
    output_schema={
        "company": "str",
        "product": "str",
        "features": "list[str]"
    }
)

# Track costs
print(f"Operation cost: ${result.semantic.total_cost}")

Configuration

Configure the semantic accessor with your preferred settings:

df.semantic.set_config(
    default_model="gpt-4o-mini",  # Default LLM to use
    max_threads=64,              # Maximum concurrent threads,
    rate_limits={
        "embedding_call": [
            {"count": 1000, "per": 1, "unit": "second"}
        ],
        "llm_call": [
            {"count": 1, "per": 1, "unit": "second"},
            {"count": 10, "per": 5, "unit": "hour"}
        ]
    } 
)

Pipeline Optimization

While individual semantic operations are optimized internally, pipelines created through the pandas .semantic accessor (sequences of operations like map → filter → merge) cannot be optimized as a whole. For pipeline-level optimizations like operation rewriting and automatic resolve operation insertion, you must use either:

• The YAML configuration interface
• The Python API

For detailed configuration options and best practices, refer to:

• DocETL Best Practices
• Pipeline Configuration
• Output Schemas
• Rate Limiting

Cost Tracking

All semantic operations track their LLM usage costs:

# Get total cost of operations
total_cost = df.semantic.total_cost

# Get operation history
history = df.semantic.history
for op in history:
    print(f"Operation: {op.op_type}")
    print(f"Modified columns: {op.output_columns}")

Implementation

This implementation is inspired by LOTUS, a system introduced by Patel et al. ¹. Our implementation has a few differences:

• We use DocETL's query engine to run the LLM operations. This allows us to use retries, validation, well-defined output schemas, and other features described in our documentation.
• Our aggregation operator combines the resolve and reduce operators, so you can get a fuzzy groupby.
• Our merge operator is based on our equijoin operator implementation, which optimizes LLM call usage by generating blocking rules before running the LLM. See the Equijoin Operator for more details.
• We do not implement LOTUS's sem_extract, sem_topk, sem_sim_join, and sem_search operators. However, sem_extract can effectively be implemented by running the map operator with a prompt that describes the extraction.

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1. Patel, L., Jha, S., Asawa, P., Pan, M., Guestrin, C., & Zaharia, M. (2024). Semantic Operators: A Declarative Model for Rich, AI-based Analytics Over Text Data. arXiv preprint arXiv:2407.11418. https://arxiv.org/abs/2407.11418 ↩↩