Add use-cases and components sections

spiceaidocs/docs/api/arrow-flight-sql/index.md

@@ -1,5 +1,5 @@
 ---
-title: 'Arrow Flight SQL'
+title: 'Arrow Flight SQL API'
 sidebar_label: 'Arrow Flight SQL'
 sidebar_position: 2
 description: 'Query Spice using JDBC/ODBC/ADBC'

spiceaidocs/docs/api/http/chat-completions.md

@@ -0,0 +1,28 @@
+---
+title: 'POST /v1/chat/completions'
+sidebar_label: 'POST /v1/chat/completions'
+description: ''
+sidebar_position: 6
+---
+
+Chat completions is an OpenAI compatible endpoint.
+
+Specify the model by providing the component name in the `model` key. For example:
+
+```shell
+curl http://localhost:3000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "my_language_model",
+    "messages": [
+      {
+        "role": "system",
+        "content": "I am just like any other OpenAI server!"
+      },
+      {
+        "role": "user",
+        "content": "That\'s cool!"
+      }
+    ]
+  }'
+```

spiceaidocs/docs/api/http/datasets.md

@@ -0,0 +1,40 @@
+---
+title: 'GET /v1/datasets'
+sidebar_label: 'GET /v1/datasets'
+description: 'Fetch datasets'
+sidebar_position: 2
+---
+
+Returns a the list of configured datasets.
+
+Example:
+
+```bash
+curl --request GET \
+  --url http://localhost:3000/v1/datasets
+```
+
+Response:
+
+```json
+[
+  {
+    "from": "postgres:syncs",
+    "name": "daily_journal_accelerated",
+    "replication_enabled": false,
+    "acceleration_enabled": true
+  },
+  {
+    "from": "databricks:hive_metastore.default.messages",
+    "name": "messages_accelerated",
+    "replication_enabled": false,
+    "acceleration_enabled": true
+  },
+  {
+    "from": "postgres:aidemo_messages",
+    "name": "general",
+    "replication_enabled": false,
+    "acceleration_enabled": false
+  }
+]
+```

spiceaidocs/docs/api/http/embeddings.md

@@ -0,0 +1,20 @@
+---
+title: 'POST /v1/embeddings'
+sidebar_label: 'POST /v1/embeddings'
+description: ''
+sidebar_position: 7
+---
+
+Chat completions is an OpenAI compatible endpoint.
+
+Specify the embedding model by providing the component name in the `model` key. For example:
+
+```shell
+curl http://localhost:3000/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -d '{
+    "input": "The food was delicious and the waiter...",
+    "model": "text-embedding-ada-002",
+    "encoding_format": "float"
+  }'
+```

spiceaidocs/docs/api/http/index.md

@@ -1,90 +1,11 @@
 ---
 title: 'HTTP API'
 sidebar_label: 'HTTP'
-sidebar_position: 1
-description: 'Spice runtime HTTP API'
+description: ''
+pagination_prev: null
+pagination_next: null
 ---
 
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
+import DocCardList from '@theme/DocCardList';
 
-The Spice runtime supports SQL queries directly from HTTP requests.
-
-An example CuRL
-
-```shell
-curl -XPOST "127.0.0.1:3000/v1/sql" \
-     --data "SELECT avg(total_amount), \
-                    avg(tip_amount), \
-                    count(1), \
-                    passenger_count \
-              FROM my_table \
-              GROUP BY passenger_count \
-              ORDER BY passenger_count ASC \
-              LIMIT 3"
-```
-
-And response
-
-```json
-[
-  {
-    "AVG(my_table.tip_amount)": 3.072259971396793,
-    "AVG(my_table.total_amount)": 25.327816939456525,
-    "COUNT(Int64(1))": 31465,
-    "passenger_count": 0
-  },
-  {
-    "AVG(my_table.tip_amount)": 3.3712622884680057,
-    "AVG(my_table.total_amount)": 26.205230445474996,
-    "COUNT(Int64(1))": 2188739,
-    "passenger_count": 1
-  },
-  {
-    "AVG(my_table.tip_amount)": 3.7171302113290854,
-    "AVG(my_table.total_amount)": 29.520659930930304,
-    "COUNT(Int64(1))": 405103,
-    "passenger_count": 2
-  }
-]
-```
-
-This allows for simple integration into any language or framework
-<Tabs>
-<TabItem value="python" label="Python" default>
-
-```python
-from typing import Optional
-import requests
-import pandas as pd
-
-    def query_runtime(query: str, url: str =  "http://127.0.0.1:3000") -> Optional[pd.DataFrame]:
-        response = requests.post(url, data=query)
-
-        if response.status_code != 200:
-            print(f"Error: Received status code {response.status_code}")
-            return None
-
-        return pd.DataFrame(response.json())
-    ```
-
-  </TabItem>
-  <TabItem value="javascript" label="Javascript">
-    ```javascript
-    async function queryRuntime(query, url = "http://127.0.0.1:3000") {
-        try {
-            const response = await fetch(url, {method: 'POST', body: query});
-            if (!response.ok) {
-            console.error(`Error: Received status code ${response.status}`);
-            return null;
-            }
-
-            return await response.json();
-        } catch (error) {
-            return error;
-        }
-    }
-    ```
-
-  </TabItem>
-  </Tabs>
+<DocCardList />

spiceaidocs/docs/machine-learning/inference/index.md → spiceaidocs/docs/api/http/ml-predict.md

@@ -1,101 +1,13 @@
 ---
-title: 'Model HTTP APIs'
-sidebar_label: 'Model HTTP APIs'
+title: POST /v1/predict'
+sidebar_label: 'POST /v1/predict'
 description: ''
-sidebar_position: 2
-pagination_prev: null
-pagination_next: null
+sidebar_position: 10
 ---
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-### GET `/v1/models`
-
-Returns all currently available models 
-
-Parameters:
- - `format`: The return payload format `'json'` or `'csv'`. Default `'json'`. 
-
-
-Response:
-For each model, returns the spicepod component's `name`, `from` and `datasets`. For example
-```json
-[
-  {
-    "name": "rf_forecast",
-    "from": "file://usr/me/model.onnx",
-    "datasets": [
-      "input_times"
-    ]
-  },
-  {
-    "name": "my_language_model",
-    "from": "openai:gpt-3.5-turbo",
-    "datasets": []
-  }
-]
-
-```
-
-## Machine Learning
-Spice includes dedicated predictions APIs.
-
-### GET `/v1/models/:name/predict`
-
-Make a prediction using a specific [deployed ML model](../ml-models/index.md).
-
-Example:
-
-```shell
-curl "http://localhost:3000/v1/models/my_model_name/predict"
-```
-
-Parameters:
-
-- `name`: References the model name defined in the `spicepod.yaml`.
-
-#### Response
-
-<Tabs>
-  <TabItem value="Success" label="Success" default>
-    ```json
-    {
-        "status": "Success",
-        "model_name": "my_model_name",
-        "model_version": "1.0",
-        "lookback": 30,
-        "prediction": [0.45, 0.50, 0.55],
-        "duration_ms": 123
-    }
-    ```
-  </TabItem>
-  <TabItem value="Bad Request" label="Bad Request">
-    ```json
-    {
-        "status": "BadRequest",
-        "error_message": "You have me a bad request :(",
-        "model_name": "my_model_name",
-        "lookback": 30,
-        "duration_ms": 12
-    }
-    ```
-  </TabItem>
-  <TabItem value="Internal Error" label="Internal Error">
-    ```json
-    {
-        "status": "InternalError",
-        "error_message": "Oops, the server couldn't predict",
-        "model_name": "my_model_name",
-        "lookback": 30,
-        "duration_ms": 12
-    }
-    ```
-  </TabItem>
-</Tabs>
-
-### POST `/v1/predict`
-
 Make predictions using all loaded forecasting models in parallel, useful for ensembling or A/B testing.
 
 Example:
@@ -152,37 +64,55 @@ Parameters:
 
 :::
 
-## Language Models
-Spice supports OpenAI compatible endpoints
+## GET `/v1/models/:name/predict`
+
+Make a prediction using a specific [model](/components/models/index.md).
+
+Example:
 
-### POST `/v1/chat/completions`
-To specify the model, provide the component name in the `model` key. For example
 ```shell
-curl http://localhost:3000/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "my_language_model",
-    "messages": [
-      {
-        "role": "system",
-        "content": "I am just like any other OpenAI server!"
-      },
-      {
-        "role": "user",
-        "content": "That\'s cool!"
-      }
-    ]
-  }'
+curl "http://localhost:3000/v1/models/my_model_name/predict"
 ```
 
-### POST `/v1/embeddings`
-To specify the embedding model, provide the component name in the `model` key. For example
-```shell
-curl http://localhost:3000/v1/embeddings \
-  -H "Content-Type: application/json" \
-  -d '{
-    "input": "The food was delicious and the waiter...",
-    "model": "text-embedding-ada-002",
-    "encoding_format": "float"
-  }'
-```
+Parameters:
+
+- `name`: References the model name defined in the `spicepod.yaml`.
+
+### Response
+
+<Tabs>
+  <TabItem value="Success" label="Success" default>
+    ```json
+    {
+        "status": "Success",
+        "model_name": "my_model_name",
+        "model_version": "1.0",
+        "lookback": 30,
+        "prediction": [0.45, 0.50, 0.55],
+        "duration_ms": 123
+    }
+    ```
+  </TabItem>
+  <TabItem value="Bad Request" label="Bad Request">
+    ```json
+    {
+        "status": "BadRequest",
+        "error_message": "You have me a bad request :(",
+        "model_name": "my_model_name",
+        "lookback": 30,
+        "duration_ms": 12
+    }
+    ```
+  </TabItem>
+  <TabItem value="Internal Error" label="Internal Error">
+    ```json
+    {
+        "status": "InternalError",
+        "error_message": "Oops, the server couldn't predict",
+        "model_name": "my_model_name",
+        "lookback": 30,
+        "duration_ms": 12
+    }
+    ```
+  </TabItem>
+</Tabs>