Pattern preview · 12 of 4,089 sample rules shown · site-specific intelligence stays private
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embedding_configuration
ai-agents-embedding-configurat-cohere-embedding-models-e-g-cohere-embed-english-v-a0f47b20
IFCohere embedding models (e.g., cohere.embed-english-v3) via AWS Bedrock throw ValidationException: maxLength=2048 exceeded when chunk_size is set above ~80.
THENSet chunk_size to a value such that each text chunk does not exceed 2048 characters. For Cohere models via Bedrock, the limit is character-based, not token-based. A chunk_size of 80 with overlap 10 works, but for larger chunks, implement a custom text splitter that enforces a 2048-character limit.
Tier 170%
embedding_configuration
ai-agents-embedding-configurat-using-cohere-embedding-model-e-g-cohere-embed-engl-a06a371b
IFUsing Cohere embedding model (e.g., cohere.embed-english-v3) through AWS Bedrock with default chunk_size in LlamaIndex causes a ValidationException due to exceeding the model's maxLength=2048 character limit.
THENImplement a character-aware chunking strategy. Reduce the chunk_size to a small token count (e.g., 200 tokens) or use a custom text splitter that limits each chunk to fewer than 2048 characters. Alternatively, switch to a different embedding model like amazon.titan-embed-text-v1 which has an 8k token limit. Ensure that the chunk_size parameter accounts for the model's character limit, not just token limit.
Tier 170%
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