[Note] Understand how does Dify handle token cost and latency of LLM API

Dify employs several strategies to effectively manage token costs and latency in conversational AI applications:

Annotation Reply feature allows persisting custom responses for semantically identical queries instead of querying the language model (LLM) each time. This saves tokens and reduces latency by avoiding redundant LLM requests for repeat questions[2].

Dify uses a semantic caching mechanism separate from the knowledge base to store annotated responses. This is more reliable than automatic semantic caching approaches like GPTCache, which rely on the LLM for caching[2].

Dify's RAG (Retrieval-Augmented Generation) technology uses a combination of vector search, full-text search, and a semantic rerank model to efficiently retrieve the most relevant information from knowledge bases. This hybrid approach boosts QA accuracy and reduces the need for costly LLM lookups[3].

For knowledge base Q&A using multiple datasets, Dify's multi-path retrieval feature concurrently considers all relevant datasets to extract the most pertinent information. This improves QA performance and reduces token consumption[3].

Latest release introduced Conversation Variables and Variable Assigner nodes. These enable storing specific user inputs and conversation text as variables, reducing the need to rely on full chat histories. This improves memory management and flow in conversational AI apps, optimizing token usage[4].

By combining these techniques, Dify aims to build cost-effective and low-latency conversational AI applications that can be easily deployed and optimized over time.

Sources [1] I built a voice agent that can hold a natural conversation with low ... https://www.reddit.com/r/SideProject/comments/1bwwh4u/i_built_a_voice_agent_that_can_hold_a_natural/

[2] Boosting Chatbot Quality & Cutting Costs with Dify.AI's Annotation Reply https://dify.ai/blog/boosting-chatbot-quality-cutting-costs-with-dify-annotation-replies

[3] Surpassing the Assistants API – Dify's RAG Demonstrates an Impressive ... https://dify.ai/blog/dify-ai-rag-technology-upgrade-performance-improvement-qa-accuracy

[4] Dify v0.7.0: Enhancing LLM Memory with Conversation Variables and ... https://dify.ai/blog/enhancing-llm-memory-with-conversation-variables-and-variable-assigners

[5] Hello team, I have a delay of 2 seconds when sending a streaming ... https://github.com/langgenius/dify/issues/2916

[6] Dify: Build Chatbots in Minutes using Open Source LLMOps Platform https://www.youtube.com/watch?v=D2xJaLuJ_Vo

[7] Welcome to Dify | English https://docs.dify.ai

[8] Build AI Apps in 5 Minutes: Dify AI + Docker Setup https://www.youtube.com/watch?v=jwNxfRgSr-0