How do I manually index papers?

[Smogglethorp-T-Hendlesworthington]

Context: I've got several thousand papers I'd like to index and ask questions of, and would like to build some kind of indexing pipeline such that, if one paper fails, the whole indexing process does not.

This means I've got to manually index papers and add the result to the whole index. I can create and add papers to Docs, but this doesn't seem like quite the correct answer; it takes increasingly long to add a new paper to a large Docs object.

Is there a way to manually index papers in this way and save their embedding so that when I ask a question at any time, PaperQA can reference my large index?

[dosubot[bot]]

Hey @Smogglethorp-T-Hendlesworthington, I'm here to help you with any bugs, questions, or contributions you have! I'm a bot that can assist you while you're waiting for a human maintainer.

To manually index papers and save their embeddings in a way that allows PaperQA to handle failures gracefully, you can use the aadd method from the Docs class. This method is designed to add documents to the collection asynchronously and can handle errors for individual documents without affecting the entire indexing process. Here's a basic approach:

from paperqa.docs import Docs
from pathlib import Path

async def index_papers(docs: Docs, paper_paths: list[Path], embedding_model):
    for paper_path in paper_paths:
        try:
            await docs.aadd(
                path=paper_path,
                embedding_model=embedding_model
            )
            print(f"Successfully indexed: {paper_path}")
        except Exception as e:
            print(f"Failed to index {paper_path}: {e}")

# Example usage
# docs = Docs()
# paper_paths = [Path("paper1.pdf"), Path("paper2.pdf"), ...]
# embedding_model = ...  # Your embedding model instance
# await index_papers(docs, paper_paths, embedding_model)

This code snippet demonstrates how to iterate over a list of paper paths, attempting to add each one to the Docs collection. If an error occurs while processing a specific paper, it catches the exception and logs the failure, allowing the process to continue with the next paper. This ensures that a failure in processing one paper does not halt the entire indexing process ^[1].

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Bug Report | Other}

[Smogglethorp-T-Hendlesworthington]

So this is close to what I've done in the past, but I find that when it comes to a large number of papers, adding a paper to Docs seems very, very slow.

Is this just the way things will go, or is there a way of alleviating this?

[dosubot[bot]]

The performance issue you're experiencing when adding a large number of papers to Docs could be related to the underlying data structures and the VectorStore used for indexing. The Docs class uses a dictionary for storing indexed papers, which is efficient for lookups, but the VectorStore (specifically NumpyVectorStore) used for similarity searches can become a bottleneck as the dataset grows ^[1].

To alleviate this, consider the following strategies:

1. Batch Processing: If the aadd method supports batch processing, adding documents in batches can reduce overhead and improve performance. Unfortunately, I couldn't find specific information on whether aadd supports this, so you might need to explore the implementation or documentation further.

2. Parallel Processing: If possible, use parallel processing to add papers concurrently. This can significantly speed up the process, especially if your system has multiple cores.

3. Optimize VectorStore: Check if there are configuration options for NumpyVectorStore that can optimize performance for large datasets. This might involve adjusting parameters related to indexing or retrieval.

4. Incremental Indexing: Instead of adding all papers at once, consider incrementally updating the index. This can help manage memory usage and reduce the impact of large batch operations.

These strategies can help improve the performance of adding papers to Docs, especially when dealing with large datasets.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Bug Report | Other}

[Smogglethorp-T-Hendlesworthington]

Is there a means of disambiguating the larger Docs object such that it's easier to add to, query, maintain, etc.?

[dosubot[bot]]

To disambiguate and manage a large Docs object more effectively, consider the following strategies:

1. Partitioning or Sharding: Although the Docs class doesn't natively support partitioning or sharding, you can implement a custom solution by dividing your dataset into smaller, more manageable subsets. Each subset can be represented by a separate Docs instance, allowing you to add, query, and maintain them independently.

2. Query Subsets: Use the retrieve_texts method to query specific subsets of your indexed papers. This method allows you to perform searches on the internal index and retrieve relevant texts based on a query, which can help in managing and accessing specific parts of your dataset ^[1].

3. Incremental Updates: Instead of updating the entire Docs object at once, perform incremental updates. This approach can help manage memory usage and reduce the complexity of maintaining a large dataset.

4. Efficient Indexing: Explore options to optimize the NumpyVectorStore for better performance with large datasets. Although specific configuration options weren't found, reviewing the implementation or documentation might reveal ways to adjust parameters for improved efficiency.

These strategies can help make your Docs object easier to manage and maintain, especially when dealing with large datasets.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Bug Report | Other}