Overcoming Chunking Challenges for Thai Chatbots
This article discusses the challenges of building a retrieval-augmented chatbot for a Thai e-commerce company, specifically the issue of naive chunking failing for Thai text due to the lack of word boundaries.
Why it matters
This article provides valuable insights into the unique challenges of building AI-powered chatbots for languages like Thai, which have fundamentally different text structures compared to English.
Key Points
- 1Thai text has no spaces between words, making it difficult to split into coherent chunks for retrieval
- 2Naive chunking based on character count or period splitting leads to poor embeddings and retrieval performance
- 3The author describes a pipeline that uses Thai text tokenization, OpenAI embeddings, and a Qdrant vector database to address the chunking problem
Details
The article explains that most RAG (Retrieval-Augmented Generation) chatbot tutorials are written with English in mind, where chunking text based on periods or character count works well due to the clear word boundaries. However, Thai text presents a unique challenge, as it has no spaces between words. This means that a naive chunker will treat an entire Thai sentence as a single, unsplittable blob, leading to poor embeddings and retrieval performance. The author describes the pipeline they built to address this issue, which includes extracting raw text from PDF product manuals, tokenizing the Thai text using the PyThaiNLP library, embedding the tokenized text using OpenAI's text-embedding-3-small model, and storing the embeddings in a Qdrant vector database for efficient similarity-based retrieval. When a user query is received, the system tokenizes the query, embeds it, and searches the Qdrant database to retrieve the most relevant chunks, which are then used by a GPT-4-based language model to generate the final answer.
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