Chunking Evaluation

This package, developed as part of our research detailed in the Chroma Technical Report, provides tools for text chunking and evaluation. It allows users to compare different chunking methods and includes implementations of several novel chunking strategies.

Features

• Compare Chunking Methods: Evaluate and compare various popular chunking strategies.
• Novel Chunking Methods: Implementations of new chunking methods such as ClusterSemanticChunker and LLMChunker.
• Evaluation Framework: Tools to generate domain-specific datasets and evaluate retrieval quality in the context of AI applications.

Quick Start

You can immediately test the package via Google Colab.

Installation

You can install the package directly from GitHub:

pip install git+https://github.com/brandonstarxel/chunking_evaluation.git

Evaluating Your Own Custom Chunker

This example shows how to implement your own chunking logic and evaluate its performance.

from chunking_evaluation import BaseChunker, GeneralEvaluation
from chromadb.utils import embedding_functions

# Define a custom chunking class
class CustomChunker(BaseChunker):
    def split_text(self, text):
        # Custom chunking logic
        return [text[i:i+1200] for i in range(0, len(text), 1200)]

# Instantiate the custom chunker and evaluation
chunker = CustomChunker()
evaluation = GeneralEvaluation()

# Choose embedding function
default_ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="OPENAI_API_KEY",
    model_name="text-embedding-3-large"
)

# Evaluate the chunker
results = evaluation.run(chunker, default_ef)

print(results)
# {'iou_mean': 0.17715979570301696, 'iou_std': 0.10619791407460026, 
# 'recall_mean': 0.8091207841640163, 'recall_std': 0.3792297991952294}

Evaluating a Custom Embedding Function

from chromadb import Documents, EmbeddingFunction, Embeddings

class MyEmbeddingFunction(EmbeddingFunction):
    def __call__(self, input: Documents) -> Embeddings:
        # embed the documents somehow
        return embeddings

# Instantiate instance of ef
default_ef = MyEmbeddingFunction()

# Evaluate the embedding function with a chunker
results = evaluation.run(chunker, default_ef)

Usage and Evaluation of ClusterSemanticChunker

This example demonstrates how to use our ClusterSemanticChunker and how you can evaluate it yourself.

from chunking_evaluation import BaseChunker, GeneralEvaluation
from chunking_evaluation.chunking import ClusterSemanticChunker
from chromadb.utils import embedding_functions

# Instantiate evaluation
evaluation = GeneralEvaluation()

# Choose embedding function
default_ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="OPENAI_API_KEY",
    model_name="text-embedding-3-large"
)

# Instantiate chunker and run the evaluation
chunker = ClusterSemanticChunker(default_ef, max_chunk_size=400)
results = evaluation.run(chunker, default_ef)

print(results)
# {'iou_mean': 0.18255175232840098, 'iou_std': 0.12773219595465307, 
# 'recall_mean': 0.8973469551927365, 'recall_std': 0.29042203879923994}

Synthetic Dataset Pipeline for Domain Specific Evaluation

Here are the steps you can take to develop a sythetic dataset based off your own corpora for domain specific evaluation.

1. Initialize the Environment:

   from chunking_evaluation import SyntheticEvaluation
   
   # Specify the corpora paths and output CSV file
   corpora_paths = [
       'path/to/chatlogs.txt',
       'path/to/finance.txt',
       # Add more corpora files as needed
   ]
   queries_csv_path = 'generated_queries_excerpts.csv'
   
   # Initialize the evaluation
   evaluation = SyntheticEvaluation(corpora_paths, queries_csv_path, openai_api_key="OPENAI_API_KEY")

2. Generate Queries and Excerpts:

   # Generate queries and excerpts, and save to CSV
   evaluation.generate_queries_and_excerpts()

3. Apply Filters:

   # Apply filter to remove queries with poor excerpts
   evaluation.filter_poor_excerpts(threshold=0.36)
   
   # Apply filter to remove duplicates
   evaluation.filter_duplicates(threshold=0.6)

4. Run the Evaluation:

   from chunking_evaluation import BaseChunker
   
   # Define a custom chunking class
   class CustomChunker(BaseChunker):
       def split_text(self, text):
           # Custom chunking logic
           return [text[i:i+1200] for i in range(0, len(text), 1200)]
   
   # Instantiate the custom chunker
   chunker = CustomChunker()
   
   # Run the evaluation on the filtered data
   results = evaluation.run(chunker)
   print("Evaluation Results:", results)

5. Optional: If generation is unable to generate queries try approximate excerpts

   # Generate queries and excerpts, and save to CSV
   evaluation.generate_queries_and_excerpts(approximate_excerpts=True)

Package Dependancies:

• chroma
• fuzzywuzzy
• pandas
• numpy
• tiktoken

Citation

If you use this package in your research, please cite our technical report:

@techreport{smith2024evaluating,
  title = {Evaluating Chunking Strategies for Retrieval},
  author = {Smith, Brandon and Troynikov, Anton},
  year = {2024},
  month = {July},
  institution = {Chroma},
  url = {https://research.trychroma.com/evaluating-chunking},
}