Pattern preview · 12 of 4,089 sample rules shown · site-specific intelligence stays private
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llm_integration
ai-agents-llm-integration-when-using-ollama-or-bedrock-with-llama3-models-th-3fe78ea1
IFWhen using Ollama or Bedrock with Llama3 models, the stop token `<|eot_id|>` is not included by default, causing the model to generate endlessly.
THENExplicitly pass the stop token `stop=["<|eot_id|>"]` when initializing ChatOllama or ChatBedrock. Also ensure `langchain-community` is updated to at least version 0.0.33 to include the latest fixes.
Tier 170%
llm_integration
ai-agents-llm-integration-llama-3-via-langchain-llamacpp-produces-nonsensica-c7bfac12
IFLlama 3 via LangChain LlamaCpp produces nonsensical output when context length exceeds approximately 4k tokens.
THENSet the `rope_freq_base` parameter to 500000 (or recalculate for extended context) and ensure `rope_freq_scale` is not overridden by LangChain's hardcoded defaults. Pass these as `model_kwargs` to `LlamaCpp` or set them directly in the constructor if supported. Verify the model metadata shows the correct value.
Tier 170%
llm_integration
ai-agents-llm-integration-you-want-to-add-observability-to-existing-llm-fram-aff4bfc3
IFYou want to add observability to existing LLM frameworks with minimal code changes.
THENUse Langfuse's drop-in replacements or callback handlers for OpenAI, LangChain, LlamaIndex, Haystack, etc.
Tier 170%
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