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@@ -47,7 +47,7 @@ A flexible, extensible, and reproducible framework for evaluating LLM workflows,
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LLM-Eval is an open-source toolkit designed to evaluate large language model workflows, applications, retrieval-augmented generation pipelines, and standalone models. Whether you're developing a conversational agent, a summarization service, or a RAG-based search tool, LLM-Eval provides a clear, reproducible framework to test and compare performance across providers, metrics, and datasets.
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*Key benefits include:* end-to-end evaluation of real-world applications, reproducible reports, and an extensible platform for custom metrics and datasets.
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_Key benefits include:_ end-to-end evaluation of real-world applications, reproducible reports, and an extensible platform for custom metrics and datasets.
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## Getting Started
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# Edit .env to add your API keys and secrets as needed
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```
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**Required:** Generate the encryption keys set to `CHANGEME` with the respective commands commented next to them in `.env`
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**Required:**
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- Generate the encryption keys set to `CHANGEME` with the respective commands commented next to them in `.env`
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- Don't forget to set azure openai keys and the `AZURE_OPENAI_EMBEDDING_DEPLOYMENT=`
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3.**Enable host networking in docker desktop (for macos users):**
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Go to `Settings -> Resources -> Network` and check `Enable host networking`, without this step on macos, the frontend wouldn't be reachable on localhost.
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Go to `Settings -> Resources -> Network` and check `Enable host networking`, without this step on macos, the frontend wouldn't be reachable on localhost.
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4.**Start the stack:**
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- Web UI: [http://localhost:3000](http://localhost:3000) (Default login: `username`:`password`)
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