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@@ -74,3 +74,16 @@ The operator split incoming the text and return chunks.
 
 A list of the chunked document.
 
+
+
+# More Resources
+
+- [Experiment with 5 Chunking Strategies via LangChain for LLM  - Zilliz blog](https://zilliz.com/blog/experimenting-with-different-chunking-strategies-via-langchain): Explore the complexities of text chunking in retrieval augmented generation applications and learn how different chunking strategies impact the same piece of data.
+- [A Guide to Chunking Strategies for Retrieval Augmented Generation (RAG) - Zilliz blog](https://zilliz.com/learn/guide-to-chunking-strategies-for-rag): We explored various facets of chunking strategies within Retrieval-Augmented Generation (RAG) systems in this guide.
+- [Sentence Transformers for Long-Form Text - Zilliz blog](https://zilliz.com/learn/Sentence-Transformers-for-Long-Form-Text): Deep diving into modern transformer-based embeddings for long-form text.
+- [Key Strategies for Smart Retrieval Augmented Generation (RAG) - Zilliz blog](https://zilliz.com/blog/exploring-rag-chunking-llms-and-evaluations): Three key strategies to get the most out of RAG: smart text chunking, iterating on different embedding models, and experimenting with different LLMs
+- [The guide to jina-embeddings-v2-small-en | Jina AI](https://zilliz.com/ai-models/jina-embeddings-v2-small-en): jina-embeddings-v2-small-en: specialized text embedding model for long English documents; up to 8192 tokens.
+- [Massive Text Embedding Benchmark (MTEB)](https://zilliz.com/glossary/massive-text-embedding-benchmark-(mteb)): A standardized way to evaluate text embedding models across a range of tasks and languages, leading to better text embedding models for your app
+- [OpenAI text-embedding-3-large | Zilliz](https://zilliz.com/ai-models/text-embedding-3-large): Building GenAI applications with text-embedding-3-large model and Zilliz Cloud / Milvus
+- [The guide to jina-embeddings-v2-base-en | Jina AI](https://zilliz.com/ai-models/jina-embeddings-v2-base-en): jina-embeddings-v2-base-en: specialized embedding model for English text and long documents; support sequences of up to 8192 tokens
+- [Text as Data, From Anywhere to Anywhere - Zilliz blog](https://zilliz.com/blog/text-as-data-from-anywhere-to-anywhere): Whether you prefer a no-code or minimal-code approach, Airbyte and PyAirbyte offer robust solutions for integrating both structured and unstructured data. AJ Steers' painted a good picture of the potential of these tools in revolutionizing data workflows.
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