From 80ff9dcefae2bc46fce522e7a8a9ca7ff1282eb9 Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Wed, 6 Nov 2024 20:25:47 +0100 Subject: [PATCH 1/3] docs(app): AGE-1228 changelog 0.27.0 Closes: AGE-1228 --- docs/blog/main.mdx | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/docs/blog/main.mdx b/docs/blog/main.mdx index 3c6b24de40..d308e6724f 100644 --- a/docs/blog/main.mdx +++ b/docs/blog/main.mdx @@ -9,6 +9,40 @@ import Image from "@theme/IdealImage";
+### Observability and Prompt Management + +_6 November 2024_ + +**v0.27.0** + +This release is one of our biggest yet—one changelog hardly does it justice. + +**First up: Observability** + +We’ve had observability in beta for a while, but now it’s been completely rewritten, with a brand-new UI and fully **open-source code**. + +The new Observability SDK is compatible with OpenTelemetry (Otel) and semantic conventions (semconv). This means you get a lot of integrations right out of the box, like LangChain, OpenAI, and more. + +We’ll publish a full blog post soon, but here’s a quick look at what the new observability offers: + +A redesigned UI that lets you visualize nested traces, making it easier to understand what’s happening behind the scenes. + +The web UI lets you filter traces by name, cost, and other attributes—you can even search through them easily. + +The SDK is Otel-compatible, and we’ve already tested integrations for OpenAI, LangChain, LiteLLM, and Instructor, with guides available for each. In most cases, adding a few lines of code will have you seeing traces directly in Agenta. + +**Next: Prompt Management** + +We’ve completely rewritten the prompt management SDK, giving you full CRUD capabilities for prompts and configurations. This includes creating, updating, reading history, deploying new versions, and deleting old ones. You can find the documentation for this here: [link] + +**And finally: LLM-as-a-Judge Overhaul** + +We’ve made significant upgrades to the LLM-as-a-Judge evaluator. It now supports prompts with multiple messages and has access to all variables in a test case. You can also switch models (currently supporting OpenAI and Anthropic). These changes make the evaluator much more flexible, and we’re seeing faster evaluations as a result. + +We’re excited about these improvements and believe they’ll make a real difference for your workflows. Let us know what you think, and keep an eye out for the upcoming blog post with more details! + +--- + ### New Application Management View and Various Improvements _22 October 2024_ From e75d159ea9699057e38e54f5003dc4ba5cfc99ac Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Tue, 12 Nov 2024 16:53:41 +0100 Subject: [PATCH 2/3] docs(app): AGE-1228 updated llm as a judge docs --- .../evaluators/04-llm-as-a-judge.mdx | 47 ++++++++++++------ .../images/evaluation/llm-as-a-judge.png | Bin 946789 -> 913268 bytes 2 files changed, 33 insertions(+), 14 deletions(-) diff --git a/docs/docs/evaluation/evaluators/04-llm-as-a-judge.mdx b/docs/docs/evaluation/evaluators/04-llm-as-a-judge.mdx index b2c7331c54..3b3fbe52c7 100644 --- a/docs/docs/evaluation/evaluators/04-llm-as-a-judge.mdx +++ b/docs/docs/evaluation/evaluators/04-llm-as-a-judge.mdx @@ -6,26 +6,45 @@ LLM-as-a-Judge is an evaluator that uses an LLM to assess LLM outputs. It's part ![Configuration of LLM-as-a-judge](/images/evaluation/llm-as-a-judge.png) -The evaluator uses the `gpt-3.5` model. To use LLM-as-a-Judge, you'll need to set your OpenAI API key in the settings. The key is saved locally and only sent to our servers for evaluation—it's not stored there. +The evaluator has the following parameters: -You can configure the prompt used for evaluation. This evaluator has access to the inputs, outputs, and reference answers. +#### The Prompt -Here is an example prompt: +You can configure the prompt used for evaluation. The prompt can contain multiple messages in OpenAI format (role/content). All messages in the prompt have access to the inputs, outputs, and reference answers (any columns in the test set). To reference these in your prompts, use the following variables: + +- `correct_answer`: the column with the reference answer in the test set. You can configure the name of this column under `Advanced Setting` in the configuration modal. +- `prediction`: the output of the llm application +- `$input_column_name`: the value of any input column for the given row of your test set + +Here's the default prompt used for the country expert demo application (note that it uses the `country` input column from our test set): + +**System prompt:** ``` -We have an LLM App that we want to evaluate its outputs. -Based on the prompt and the parameters provided below evaluate the output based on the evaluation strategy below: -Evaluation strategy: 0 to 10 0 is very bad and 10 is very good. +You are an evaluator grading an LLM App. + You will be given INPUTS, the LLM APP OUTPUT, the CORRECT ANSWER, the PROMPT used in the LLM APP. + Here is the grade criteria to follow: +:- Ensure that the LLM APP OUTPUT has the same meaning as the CORRECT ANSWER -Inputs: country: {country} -Expected Answer Column: {correct_answer} -Evaluate this: {variant_output} +SCORE: +-The score should be between 0 and 10 +-A score of 10 means that the answer is perfect. This is the highest (best) score. +A score of 0 means that the answer does not any of of the criteria. This is the lowest possible score you can give. -Answer ONLY with one of the given grading or evaluation options. +ANSWER ONLY THE SCORE. DO NOT USE MARKDOWN. DO NOT PROVIDE ANYTHING OTHER THAN THE NUMBER ``` -The prompt has access to these variables: +**User prompt:** -- `correct_answer`: the column with the reference answer in the test set. You can configure the name of this column under `Advanced Setting` in the configuration modal. -- `variant_output`: the output of the llm application -- The inputs to the LLM application, named as the variable name. For the default prompt (Capital expert), the input is `country` +``` + +INPUTS: +country: {country} +CORRECT ANSWER:{correct_answer} +LLM APP OUTPUT: {prediction}. + +``` + +### The Model + +The model can be configured to select one of the supported options (`gpt-3.5-turbo`, `gpt-4`, `claude-3-5-sonnet`, `claude-3-5-haiku`, `claude-3-5-opus`). To use LLM-as-a-Judge, you'll need to set your OpenAI or Anthropic API key in the settings. 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.../images/evaluation/llm-as-a-judge.gif | Bin 0 -> 1697691 bytes 2 files changed, 36 insertions(+), 9 deletions(-) create mode 100644 docs/static/images/evaluation/llm-as-a-judge.gif diff --git a/docs/blog/main.mdx b/docs/blog/main.mdx index a89a1cbc0f..49c9ac55a5 100644 --- a/docs/blog/main.mdx +++ b/docs/blog/main.mdx @@ -15,31 +15,53 @@ _6 November 2024_ **v0.27.0** +Observability view showing an open trace for an OpenAI application This release is one of our biggest yet—one changelog hardly does it justice. **First up: Observability** -We’ve had observability in beta for a while, but now it’s been completely rewritten, with a brand-new UI and fully **open-source code**. +We’ve had observability in beta for a while, but now it’s been completely rewritten, +with a brand-new UI and fully **open-source code**. -The new Observability SDK is compatible with OpenTelemetry (Otel) and semantic conventions (semconv). This means you get a lot of integrations right out of the box, like LangChain, OpenAI, and more. +The new [Observability SDK](/observability/overview) is compatible with [OpenTelemetry (Otel)](https://opentelemetry.io/) and [gen-ai semantic conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/). This means you get a lot of integrations right out of the box, like [LangChain](/observability/integrations/langchain), [OpenAI](/observability/integrations/openai), and more. We’ll publish a full blog post soon, but here’s a quick look at what the new observability offers: -A redesigned UI that lets you visualize nested traces, making it easier to understand what’s happening behind the scenes. +- A redesigned UI that lets you visualize nested traces, making it easier to understand what’s happening behind the scenes. -The web UI lets you filter traces by name, cost, and other attributes—you can even search through them easily. +- The web UI lets you filter traces by name, cost, and other attributes—you can even search through them easily. -The SDK is Otel-compatible, and we’ve already tested integrations for OpenAI, LangChain, LiteLLM, and Instructor, with guides available for each. In most cases, adding a few lines of code will have you seeing traces directly in Agenta. +- The SDK is Otel-compatible, and we’ve already tested integrations for [OpenAI](/observability/integrations/openai), [LangChain](/observability/integrations/langchain), [LiteLLM](/observability/integrations/litellm), and [Instructor](/observability/integrations/instructor), with guides available for each. In most cases, adding a few lines of code will have you seeing traces directly in Agenta. **Next: Prompt Management** -We’ve completely rewritten the prompt management SDK, giving you full CRUD capabilities for prompts and configurations. This includes creating, updating, reading history, deploying new versions, and deleting old ones. You can find the documentation for this here: [link] +We’ve completely rewritten the [prompt management SDK](/prompt-management/overview), giving you full CRUD capabilities for prompts and configurations. This includes creating, updating, reading history, deploying new versions, and deleting old ones. You can find a first tutorial for this [here](/tutorials/sdk/manage-prompts-with-SDK). **And finally: LLM-as-a-Judge Overhaul** -We’ve made significant upgrades to the LLM-as-a-Judge evaluator. It now supports prompts with multiple messages and has access to all variables in a test case. You can also switch models (currently supporting OpenAI and Anthropic). These changes make the evaluator much more flexible, and we’re seeing faster evaluations as a result. +We’ve made significant upgrades to the [LLM-as-a-Judge evaluator](/evaluation/evaluators/llm-as-a-judge). It now supports prompts with multiple messages and has access to all variables in a test case. You can also switch models (currently supporting OpenAI and Anthropic). These changes make the evaluator much more flexible, and we’re seeing better results with it. -We’re excited about these improvements and believe they’ll make a real difference for your workflows. Let us know what you think, and keep an eye out for the upcoming blog post with more details! +Configuring the LLM-as-a-Judge evaluator --- @@ -90,7 +112,12 @@ _22 August 2024_ Button for exporting evaluation results diff --git a/docs/static/images/evaluation/llm-as-a-judge.gif b/docs/static/images/evaluation/llm-as-a-judge.gif new file mode 100644 index 0000000000000000000000000000000000000000..292fbbbcea414de3dbdcb1d6f9598bcbced1d5c6 GIT binary patch literal 1697691 zcmWifWmw$Y7DjO!+}+*1IK$xXFt}6P-QC@a3@*isL$Ttn#kIIYaY~`zT<*z_Kz(=etlEcvzS}iy!w4NH9eh`l@%BiNJmFE zG&bEgGPSt6DJCWcfk33CrLA28c8@L|A0H)wl5OAnT>K+u7FTt2bi|c(6SE-A9X(D? zj(q%lJNrj583q0Q{b~kZBNH-C&aaj?wl{Y7eZmu!m6iQMV6$wj+j+FMwOLtN{l0&&v3E?#E3K_>P}VVC z-r9#kp+~3Za06L((cPKDC zIx0R%Kt?MmJ)2)j)ymPUptLflv{qV0x4fn?GAc?|%lP;0Z!sB_qUuI|F&S+mo4vhX z4Xxd-ZmvewPV}tY#^zRhLZZULpI-hvJGgmsa&rd9q?3`6sTx>2`hzmuHoV?iB z*tCqS8``?f&CN44BNMna)NM&lmthyDMlR|EuP7$^+;$1%+RF^T=yBcKN%;nJd!vH?B@ 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