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sonam-pankaj95 merged 2 commits into from
Sep 16, 2025
Merged

rectified #167

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rectified
sonam-pankaj95 Sep 16, 2025
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sonam-pankaj95 Sep 16, 2025
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4 changes: 3 additions & 1 deletion README.md
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Expand Up @@ -20,7 +20,9 @@
<div align="center">

<p align="center">
<b> Highly Performant and Modular - Inference, Ingestion, and Indexing in Rust 🦀</b>
<b> Highly Performant, Modular and Memory Safe</b>
<br />
<b> Ingestion, Inference and Indexing in Rust 🦀</b>
<br />
<a href="https://embed-anything.com/references/">Python docs »</a>
<br />
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7 changes: 5 additions & 2 deletions docs/blog/posts/config.md
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Expand Up @@ -10,6 +10,9 @@ title: How to write textembedconfig for chunking

After presenting at Google, PyCon DE, Berlin Buzzwords, and GDG Berlin, I was surprised by how many people approached me with questions about writing configurations, chunk sizes, and batch sizes for EmbedAnything. Since I had never specifically covered this topic in my talks or blog posts, I decided to create this comprehensive guide to clarify these concepts and explain how we handle your chunking strategy with vector streaming.

<!-- more -->


## Understanding TextEmbedConfig

TextEmbedConfig consists of three essential components that work together to optimize your text embedding process:
Expand Down Expand Up @@ -84,8 +87,8 @@ Key benefits of late chunking:
### Chunk Size
The `chunk_size` parameter defines the maximum number of characters (or tokens, depending on the model) allowed in each chunk. Consider these factors when setting chunk size:

- **Smaller chunks (256-512)**: Better for precise retrieval, more granular search results
- **Larger chunks (1000-2000)**: Better for maintaining context, fewer total chunks to process
- **Smaller chunks**: Better for precise retrieval, more granular search results
- **Larger chunks**: Better for maintaining context, fewer total chunks to process
- **Model limitations**: Ensure chunk size doesn't exceed your embedding model's maximum input length

### Batch Size for Vector Streaming
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