Document deduplication in LlamaIndex involves identifying and removing duplicate documents from your dataset to ensure that your model processes only unique content. The goal is to maintain an optimal dataset size and improve the efficiency of your indexing and retrieval processes. To achieve this, you can use various techniques including hash-based deduplication, text similarity measures, and leveraging built-in functionalities that LlamaIndex may offer.
One common method for deduplication is to compute a hash for each document. By applying a hashing algorithm, such as SHA-256, you can generate a unique code for each document based on its content. Once you have hashes for all documents, you can store them in a set or dictionary to identify duplicates. If a new document’s hash already exists in your set, it indicates a duplicate, allowing you to ignore or remove it. This method is efficient and works well when the documents are relatively static.
Another approach is to use text similarity measures, such as cosine similarity or Manhattan distance. With these techniques, you can compare the content of documents and establish a threshold for what constitutes a duplicate. For instance, if the similarity score between two documents exceeds a certain percentage, you can classify them as duplicates. Implementing this approach may require additional libraries for processing text but can be beneficial for scenarios where slight variations in wording exist among documents. LlamaIndex might also provide built-in functions for deduplication, so consulting the documentation can save time and effort.