enhancement - integrate with llamaindex

[angelo-smartsheet]

https://docs.llamaindex.ai/en/stable/api/llama_index.node_parser.SentenceWindowNodeParser.html

Read that using sentence window node parser is better for performance.

[dosubot[bot]]

🤖

Hi @angelo-smartsheet! :wave:

I'm Dosu, a friendly bot here to help you while we wait for a human maintainer. I'm equipped to help you solve bugs, answer questions, and contribute to our project. Remember, no issue is too big or small for me. Let's code together! :robot::keyboard:

Thank you for your suggestion to integrate the LlamaIndex's SentenceWindowNodeParser for better performance. Currently, the gpt4-pdf-chatbot-langchain codebase does not explicitly use a specific parsing method. It processes incoming request data and prepares it for further processing, which includes sanitizing the question, creating a vector store, a retriever, and a chain, and then invoking the chain with the sanitized question and chat history.

The SentenceWindowNodeParser from LlamaIndex, as per the documentation, is a class that parses a document into a sequence of overlapping windows of sentences. This could potentially improve the performance of the chatbot by reducing the amount of data that needs to be processed at once and focusing on the most relevant parts of the document.

However, integrating this into the current codebase would require significant changes. The current system uses OpenAI embeddings and a Pinecone vector store for natural language processing, and it's not immediately clear how the SentenceWindowNodeParser would fit into this architecture.

Here's a rough idea of what the integration might look like:

import { SentenceWindowNodeParser } from 'llamaindex';

// ...

const parser = new SentenceWindowNodeParser(windowSize=5, stride=1);

// Parse the document into a sequence of overlapping windows of sentences
const windows = parser.parse(document);

// Process each window with the existing pipeline
for (const window of windows) {
  const sanitizedQuestion = window.trim().replaceAll('\n', ' ');
  // ... rest of the existing pipeline ...
}

This is a simplified example and the actual integration would likely be more complex. For example, the SentenceWindowNodeParser requires a document as input, but the current pipeline processes a question from the user. It's also unclear how the results from processing each window would be combined to form the final response.

Before proceeding with this enhancement, it would be helpful to have more information about how you envision the SentenceWindowNodeParser being integrated into the current system. For example, how should the document be obtained and how should the results from each window be combined? Also, do you have any benchmarks or other evidence to support the claim that using the SentenceWindowNodeParser would improve performance?

I look forward to your response. If you have any further questions or need clarification, please don't hesitate to ask.

Best regards, Dosu

Sources

• pages/api/chat.ts

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[angelo-smartsheet]

Thank you dosubot. Do you have any recommendations on what benchmark metrics to use?

[dosubot[bot]]

🤖

Integrating the LlamaIndex's SentenceWindowNodeParser into the gpt4-pdf-chatbot-langchain codebase could indeed potentially improve performance by reducing the amount of data that needs to be processed at once. To evaluate the performance improvement, you could use the following benchmark metrics:

1. Processing Time: Measure the time it takes to parse a large PDF document using the current method and compare it with the time it takes using the SentenceWindowNodeParser. This will give you a direct measure of the speed improvement.

const startTime = Date.now();
// Run the parsing process
const endTime = Date.now();
const processingTime = endTime - startTime;

2. Memory Usage: Monitor the memory usage during the parsing process. If the SentenceWindowNodeParser reduces the amount of data that needs to be processed at once, it should also reduce the memory usage.

const used = process.memoryUsage().heapUsed / 1024 / 1024;
console.log(`The script uses approximately ${Math.round(used * 100) / 100} MB`);

3. Accuracy: Check the accuracy of the parsed data. Even if the SentenceWindowNodeParser is faster and uses less memory, it's not useful if it doesn't parse the data correctly. You could compare a sample of the parsed data with the expected output to measure the accuracy.

const accuracy = getAccuracy(parsedData, expectedOutput);

4. Scalability: Test the parsing process with different sizes of PDF documents to see how well it scales. If the SentenceWindowNodeParser performs well with large documents but not with small ones, or vice versa, this could affect its usefulness.

const sizes = [100, 1000, 10000, 100000];
sizes.forEach((size) => {
  const doc = generateDocument(size);
  const startTime = Date.now();
  // Run the parsing process
  const endTime = Date.now();
  const processingTime = endTime - startTime;
  console.log(`Processing time for document of size ${size}: ${processingTime}`);
});

Remember to run these benchmarks in a controlled environment to ensure the accuracy of the results. Also, run each benchmark multiple times and take the average to account for any variability in the results.

For more information on how to benchmark Node.js applications, you can refer to the Node.js documentation.

I hope this helps! If you have any other questions, feel free to ask.

Source

Sources

• scripts/ingest-data.ts

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.