Add dashboard to documentation (#585)

* dashboard docs

* Update azure.mdx

* Update anyscale.mdx

* Update project.mdx

* Update prompts.mdx

---------

Co-authored-by: Dhruv Chawla <43818888+Dominastorm@users.noreply.github.com>

docs/dashboard/evaluations.mdx

@@ -0,0 +1,69 @@
+---
+title: Evaluations
+---
+
+### What are Evaluations?
+
+Using UpTrain you can run evaluations on 20+ pre-configured metrics like:
+1. [Context Relevance](/predefined-evaluations/context-awareness/context-relevance): Evaluates how relevant the retrieved context is to the question specified.
+
+2. [Factual Accuracy](/predefined-evaluations/context-awareness/factual-accuracy): Evaluates whether the response generated is factually correct and grounded by the provided context.
+
+3. [Response Completeness](/predefined-evaluations/response-quality/response-completeness): Evaluates whether the response has answered all the aspects of the question specified
+
+You can look at the complete list of UpTrain's supported metrics [here](/predefined-evaluations/overview)
+
+### How does it work?
+
+<Steps>
+  <Step title = "Create a new Project">
+    Click on `Create New Project` from Home
+    <Frame>
+      <img src="/assets/dashboard/dashboard_home.png" />
+    </Frame>
+  </Step>
+  <Step title = "Enter Project Information">
+    <Frame>
+      <img src="/assets/dashboard/dashboard_project1.png" />
+    </Frame>
+    * `Project name:` Create a name for your project
+    * `Dataset name:` Create a name for your dataset
+    * `Project Type:` Select project type: `Evaluations`
+    * `Choose File:` Upload your Dataset
+      Sample Dataset:
+      ```jsonl
+      {"question":"","response":"","context":""}
+      {"question":"","response":"","context":""}
+      ```
+    * `Evaluation LLM:` Select an LLM to run evaluations
+  </Step>
+  <Step title = "Select Evaluations to Run">
+    <Frame>
+      <img src="/assets/dashboard/eval_select_metrics.png" />
+    </Frame>
+  </Step>
+  <Step title = "View Evaluations">
+    You can see all the evaluations ran using UpTrain
+    <Frame>
+      <img src="/assets/dashboard/eval.png" />
+    </Frame>
+
+    You can also see individual logs
+    <Frame>
+      <img src="/assets/dashboard/eval_logs.png" />
+    </Frame>
+  </Step>
+</Steps>
+
+<CardGroup cols={1}>
+  <Card
+    title="Have Questions?"
+    href="https://join.slack.com/t/uptraincommunity/shared_invite/zt-1yih3aojn-CEoR_gAh6PDSknhFmuaJeg"
+    icon="slack"
+    color="#808080"
+  >
+    Join our community for any questions or requests
+  </Card>
+
+</CardGroup>
+

docs/dashboard/getting_started.mdx

@@ -0,0 +1,38 @@
+---
+title: Getting Started
+---
+
+### What is UpTrain Dashboard?
+
+The UpTrain dashboard is a web-based interface that allows you to evaluate your LLM applications. 
+
+It is a self-hosted dashboard that runs on your local machine. You don't need to write any code to use the dashboard. 
+
+You can use the dashboard to evaluate your LLM applications, view the results, manage prompts, run experiments, and perform root cause analysis.
+
+<Note>Before you start, ensure you have docker installed on your machine. If not, you can install it from [here](https://docs.docker.com/get-docker/). </Note>
+
+### How to install?
+
+The following commands will download the UpTrain dashboard and start it on your local machine:
+```bash
+# Clone the repository
+git clone https://github.com/uptrain-ai/uptrain
+cd uptrain
+
+# Run UpTrain
+bash run_uptrain.sh
+```
+
+<CardGroup cols={1}>
+  <Card
+    title="Have Questions?"
+    href="https://join.slack.com/t/uptraincommunity/shared_invite/zt-1yih3aojn-CEoR_gAh6PDSknhFmuaJeg"
+    icon="slack"
+    color="#808080"
+  >
+    Join our community for any questions or requests
+  </Card>
+
+</CardGroup>
+

docs/dashboard/project.mdx

@@ -0,0 +1,52 @@
+---
+title: Create a Project
+---
+
+### What is a Project?
+
+Using UpTrain Dashboard you can manage all your projects.
+
+There are 2 types of projects we support:
+  * **[Evaluations](/dashboard/evaluations):** Run evaluations on your queries, documents and LLM responses
+  * **[Prompts](/dashboard/prompts):** Find the best way to ask questions to your LLM using prompt iteration, experimentation and evaluations
+
+### How does it work?
+
+<Steps>
+  <Step title = "Create a new Project">
+    Click on `Create New Project` from Home
+    <Frame>
+      <img src="/assets/dashboard/dashboard_home.png" />
+    </Frame>
+  </Step>
+  <Step title = "Enter Project Information">
+    * `Project name:` Create a name for your project
+    * `Dataset name:` Create a name for your dataset
+    * `Project Type:` Select a project type between `Evaluations` and `Prompts`
+    * `Choose File:` Upload your Dataset
+      Sample Dataset:
+      ```jsonl
+      {"question":"", "response":"", "context":""}
+      {"question":"", "response":"", "context":""}
+      ```
+    * `Evaluation LLM:` Select an LLM to run evaluations
+    <Frame>
+      <img src="/assets/dashboard/dashboard_project1.png" />
+    </Frame>
+  </Step>
+</Steps>
+
+Now that you have created a project, you can run evaluations or experiment with prompts
+
+<CardGroup cols={1}>
+  <Card
+    title="Have Questions?"
+    href="https://join.slack.com/t/uptraincommunity/shared_invite/zt-1yih3aojn-CEoR_gAh6PDSknhFmuaJeg"
+    icon="slack"
+    color="#808080"
+  >
+    Join our community for any questions or requests
+  </Card>
+
+</CardGroup>
+

docs/dashboard/prompts.mdx

@@ -0,0 +1,69 @@
+---
+title: Prompts
+---
+
+### What are Prompts?
+
+You can manage your prompt iterations and experiment with them using UpTrain on 20+ pre-configured evaluation metrics like:
+1. [Context Relevance](/predefined-evaluations/context-awareness/context-relevance): Evaluates how relevant the retrieved context is to the question specified.
+
+2. [Factual Accuracy](/predefined-evaluations/context-awareness/factual-accuracy): Evaluates whether the response generated is factually correct and grounded by the provided context.
+
+3. [Response Completeness](/predefined-evaluations/response-quality/response-completeness): Evaluates whether the response has answered all the aspects of the question specified
+
+You can look at the complete list of UpTrain's supported metrics [here](/predefined-evaluations/overview)
+
+### How does it work?
+
+<Steps>
+  <Step title = "Create a new Project">
+    Click on `Create New Project` from Home
+    <Frame>
+      <img src="/assets/dashboard/dashboard_home.png" />
+    </Frame>
+  </Step>
+  <Step title = "Enter Project Information">
+    <Frame>
+      <img src="/assets/dashboard/dashboard_project1.png" />
+    </Frame>
+    * `Project name:` Create a name for your project
+    * `Dataset name:` Create a name for your dataset
+    * `Project Type:` Select project type: `Prompts`
+    * `Choose File:` Upload your Dataset
+      Sample Dataset:
+      ```jsonl
+      {"question":"","response":"","context":""}
+      {"question":"","response":"","context":""}
+      ```
+    * `Evaluation LLM:` Select an LLM to run evaluations
+  </Step>
+  <Step title = "Enter your Prompt">
+    <Frame>
+      <img src="/assets/dashboard/prompt_select.png" />
+    </Frame>
+  </Step>
+  <Step title = "Select Evaluations to Run">
+    <Frame>
+      <img src="/assets/dashboard/eval_select_metrics.png" />
+    </Frame>
+  </Step>
+  <Step title = "View Prompts">
+    You can see all the evaluations ran on your prompts using UpTrain
+    <Frame>
+      <img src="/assets/dashboard/prompt.png" />
+    </Frame>
+  </Step>
+</Steps>
+
+<CardGroup cols={1}>
+  <Card
+    title="Have Questions?"
+    href="https://join.slack.com/t/uptraincommunity/shared_invite/zt-1yih3aojn-CEoR_gAh6PDSknhFmuaJeg"
+    icon="slack"
+    color="#808080"
+  >
+    Join our community for any questions or requests
+  </Card>
+
+</CardGroup>
+

docs/llms/anyscale.mdx

@@ -23,6 +23,11 @@ ANYSCALE_API_KEY = "esecret_***********************"
 
 settings = Settings(model='anyscale/mistralai/Mistral-7B-Instruct-v0.1', anyscale_api_key=ANYSCALE_API_KEY)
 ```
+<Note>
+    The model name should start with `anyscale/` for UpTrain to recognize you are using models hosted on Anyscale.
+
+    For example if you are using `mistralai/Mistral-7B-Instruct-v0.1` via Anyscale, the model name should be `anyscale/mistralai/Mistral-7B-Instruct-v0.1`
+</Note>
 
 We have used Mistral-7B-Instruct-v0.1 for this example. You can find a full list of available models [here](https://docs.endpoints.anyscale.com/category/supported-models).
 

docs/llms/azure.mdx

@@ -43,6 +43,11 @@ You can use your Azure API key to run LLM evaluations using UpTrain.
     settings = Settings(model = 'azure/*', azure_api_key=AZURE_API_KEY, azure_api_version=AZURE_API_VERSION, azure_api_base=AZURE_API_BASE)
     eval_llm = EvalLLM(settings)
     ```
+    <Note>
+    The model name should start with `azure/` for UpTrain to recognize you are using models hosted on Azure.
+
+    For example if you are using `gpt-35-turbo` via Azure, the model name should be `azure/gpt-35-turbo`
+    </Note>
   </Step>
 
   <Step title="Evaluate data using UpTrain">

docs/llms/mistral.mdx

@@ -41,7 +41,11 @@ You can use your Mistral API key to run LLM evaluations using UpTrain.
     settings = Settings(model = 'mistral/mistral-tiny', mistral_api_key=MISTRAL_API_KEY)
     eval_llm = EvalLLM(settings)
     ```
-    We use GPT 3.5 Turbo be default, you can use any other OpenAI models as well
+    <Note>
+    The model name should start with `mistral/` for UpTrain to recognize you are using Mistral.
+
+    For example if you are using `mistral-tiny`, the model name should be `mistral/mistral-tiny`
+    </Note>
   </Step>
 
   <Step title="Evaluate data using UpTrain">

docs/mint.json

@@ -156,6 +156,16 @@
         }
       ]
     },
+    {
+      "group": "Dashboard",
+      "version": "v1",
+      "pages": [
+          "dashboard/getting_started",
+          "dashboard/project",
+          "dashboard/evaluations",
+          "dashboard/prompts"
+      ]
+    },
     {
       "group": "Supported LLMs",
       "version": "v1",