Introduction to Node Postprocessors

In the world of LlamaIndex and Large Language Model (LLM) applications, Node Postprocessors play a crucial role in refining and enhancing the output of your data structures. These powerful tools allow you to manipulate, filter, and transform nodes after they've been created, giving you greater control over the final results of your LLM interactions.

Understanding the Basics

Node Postprocessors in LlamaIndex are Python classes that operate on nodes after they've been created. They can modify node content, metadata, or relationships, allowing for a wide range of customizations. Let's start with a simple example:

from llama_index.node_parser.extractors import TitleExtractor
from llama_index.schema import MetadataMode

class CustomTitleExtractor(TitleExtractor):
    def extract(self, node):
        text = node.get_content(metadata_mode=MetadataMode.NONE)

# Custom logic to extract title
        title = text.split('\n')[0]

# Assume first line is the title
        return {"title": title}

In this example, we've created a custom TitleExtractor that assumes the first line of the node's content is the title. This simple postprocessor can be applied to nodes to automatically extract and set titles.

Implementing Node Postprocessors

To use Node Postprocessors in your LlamaIndex application, you'll need to integrate them into your document loading and indexing pipeline. Here's how you might do that:

from llama_index import SimpleDirectoryReader, VectorStoreIndex
from llama_index.node_parser import SimpleNodeParser

# Load documents
documents = SimpleDirectoryReader('path/to/your/docs').load_data()

# Create a node parser with our custom postprocessor
node_parser = SimpleNodeParser.from_defaults(
    text_splitter=None,
    include_metadata=True,
    include_prev_next_rel=True
)
node_parser.postprocessors = [CustomTitleExtractor()]

# Create nodes and index
nodes = node_parser.get_nodes_from_documents(documents)
index = VectorStoreIndex(nodes)

This setup ensures that our custom title extractor is applied to each node during the indexing process.

Advanced Customization Techniques

Node Postprocessors can do much more than just extract titles. Let's explore some more advanced techniques:

Sentiment Analysis Postprocessor

from textblob import TextBlob

class SentimentPostprocessor:
    def postprocess_nodes(self, nodes):
        for node in nodes:
            text = node.get_content(metadata_mode=MetadataMode.NONE)
            sentiment = TextBlob(text).sentiment.polarity
            node.metadata['sentiment'] = sentiment
        return nodes

This postprocessor uses the TextBlob library to perform sentiment analysis on each node's content and adds the sentiment score to the node's metadata.

Keyword Extraction Postprocessor

from rake_nltk import Rake

class KeywordExtractor:
    def __init__(self, top_n=5):
        self.rake = Rake()
        self.top_n = top_n

    def postprocess_nodes(self, nodes):
        for node in nodes:
            text = node.get_content(metadata_mode=MetadataMode.NONE)
            self.rake.extract_keywords_from_text(text)
            keywords = self.rake.get_ranked_phrases()[:self.top_n]
            node.metadata['keywords'] = keywords
        return nodes

This postprocessor uses the RAKE algorithm to extract key phrases from each node's content and adds the top N keywords to the node's metadata.

Chaining Multiple Postprocessors

You can chain multiple postprocessors together to apply a series of transformations to your nodes:

node_parser.postprocessors = [
    CustomTitleExtractor(),
    SentimentPostprocessor(),
    KeywordExtractor(top_n=3)
]

This setup will extract titles, perform sentiment analysis, and extract keywords for each node in your index.

Practical Applications

Node Postprocessors can significantly enhance your LlamaIndex-based applications. Here are some practical use cases:

1. Content Summarization: Create a postprocessor that generates a brief summary of each node's content using an LLM.

2. Language Detection: Implement a postprocessor that identifies the language of each node's content and adds it to the metadata.

3. Named Entity Recognition: Develop a postprocessor that extracts and categorizes named entities (e.g., people, organizations, locations) from the node content.

4. Content Classification: Build a postprocessor that assigns categories or tags to nodes based on their content.

5. Data Cleaning: Create a postprocessor that removes unwanted characters, standardizes formatting, or applies specific text transformations.

By leveraging these customization techniques, you can tailor LlamaIndex to your specific needs and create more sophisticated and powerful LLM applications.