The community member is having trouble indexing a large number of documents using VectorStoreIndex, as they are encountering an error that the context window of the model is too small. A community member suggests splitting the documents into smaller chunks that fit within the model's context window before indexing them. However, the community member is still facing issues even after reducing the chunk size, and is looking for a good tutorial on this topic.
Looking to index a larger amount of Documents with VectorStoreIndex, but always get an error, that the context window of the model is too small. Any example on how to mitigate this? I would highly appreciate 🙏
Split your documents into smaller chunks that fit within the model's context window before indexing them. This will ensure that each chunk of text is small enough to be processed by the model.
Remember to handle the splitting carefully, as splitting in the middle of sentences or important context could affect the quality of the vectors generated by the model.
Hi, thank you so much for the quick answer. Could you give me an example based on my LlamaIndexTS code. I'm parsing through a few documents: const serviceContext = serviceContextFromDefaults({ chunkSize: CHUNK_SIZE, chunkOverlap: CHUNK_OVERLAP, });
