Row 5172

Row ID: 5172 | Dataset Entry | Axioma AXP Content Repository

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Hi all, As AI applications gain traction, the costs and latency of using large language models (LLMs) can escalate. SemanticCache addresses these issues by caching LLM responses based on semantic similarity, thereby reducing both costs and response times.

I have built a simple implementation of a caching layer for LLMs. The idea is that like normal caching we should be able to cache responses from our LLMs as well and return them incase of 'similar queries'.

Semantic Cache leverages the power of LLMs to provide two main advantages: Lower Costs: It minimizes the number of direct LLM requests, thereby saving on usage costs. Faster Responses: By caching, it significantly reduces latency, offering quicker feedback to user queries. (not a lot right now, but can improve with time).

Would love for you all to take a look and provide feedback (and stars), feel free to fork and raise PRs or Issues for feature request and bugs.

It doesn't have a pip package yet, but I will be publishing one soon.

[https://github.com/shivendrasoni/semantic-cache](https://github.com/shivendrasoni/semantic-cache)

FieldValue
text Hi all, As AI applications gain traction, the costs and latency of using large language models (LLMs) can escalate. SemanticCache addresses these issues by caching LLM responses based on semantic similarity, thereby reducing both costs and response times. I have built a simple implementation of a caching layer for LLMs. The idea is that like normal caching we should be able to cache responses from our LLMs as well and return them incase of 'similar queries'. Semantic Cache leverages the powe…
label r/opensourceai
dataType post
communityName r/OpenSourceAI
datetime 2024-04-26
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Raw Record

{
  "text": "Hi all,  \nAs AI applications gain traction, the costs and latency of using large language models (LLMs) can escalate. SemanticCache addresses these issues by caching LLM responses based on semantic similarity, thereby reducing both costs and response times.\n\nI have built a simple implementation of a caching layer for LLMs. The idea is that like normal caching we should be able to cache responses from our LLMs as well and return them incase of 'similar queries'.\n\nSemantic Cache leverages the power of LLMs to provide two main advantages:  \nLower Costs: It minimizes the number of direct LLM requests, thereby saving on usage costs.  \nFaster Responses: By caching, it significantly reduces latency, offering quicker feedback to user queries. (not a lot right now, but can improve with time).\n\nWould love for you all to take a look and provide feedback (and stars), feel free to fork and raise PRs or Issues for feature request and bugs.\n\nIt doesn't have a pip package yet, but I will be publishing one soon.\n\n[https://github.com/shivendrasoni/semantic-cache](https://github.com/shivendrasoni/semantic-cache)",
  "label": "r/opensourceai",
  "dataType": "post",
  "communityName": "r/OpenSourceAI",
  "datetime": "2024-04-26",
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