Doc Update (#129)

* init update

* Update terminology.rst

* fix the branch to create an index.html, and fix pre-commit issues

* Doc update

* made several changes to the docs after Shuguang's revision

* fixing pre-commit issues

* fixed the reference file to the final prompt config file

* added google analytics

---------

Co-authored-by: Salman Paracha <[email protected]>

docs/requirements.txt

@@ -1,2 +1,5 @@
-sphinx_book_theme==1.1.3
 sphinx_copybutton==0.5.2
+sphinxawesome-theme
+sphinx_sitemap
+sphinx_design
+sphinxawesome_theme

docs/source/_templates/analytics.html

@@ -0,0 +1,12 @@
+<!-- _templates/analytics.html -->
+{% if google_analytics_id %}
+<!-- Google tag (gtag.js) -->
+<script async src="https://www.googletagmanager.com/gtag/js?id={{ google_analytics_id }}"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', '{{ google_analytics_id }}');
+</script>
+{% endif %}

docs/source/getting_started/use_cases/function_calling.rst → docs/source/build_with_arch/agent.rst

@@ -1,6 +1,6 @@
-.. _arch_function_calling_agentic_guide:
+.. _arch_agent_guide:
 
-Agentic (Text-to-Action) Apps
+Agentic Workflow
 ==============================
 
 Arch helps you easily personalize your applications by calling application-specific (API) functions
@@ -11,11 +11,10 @@ claims to creating ad campaigns - via prompts.
 
 Arch analyzes prompts, extracts critical information from prompts, engages in lightweight conversation with
 the user to gather any missing parameters and makes API calls so that you can focus on writing business logic.
-Arch does this via its purpose-built :ref:`Arch-FC LLM <llms_in_arch>` - the fastest (200ms p90 - 10x faser than GPT-4o)
+Arch does this via its purpose-built :ref:`Arch-FC LLM <function_calling>` - the fastest (200ms p90 - 10x faser than GPT-4o)
 and cheapest (100x than GPT-40) function-calling LLM that matches performance with frontier models.
-______________________________________________________________________________________________
 
-.. image:: /_static/img/function-calling-network-flow.jpg
+.. image:: includes/agent/function-calling-flow.jpg
    :width: 100%
    :align: center
 
@@ -29,7 +28,7 @@ is how you would go about enabling this scenario with Arch:
 Step 1: Define prompt targets with functions
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. literalinclude:: /_config/function-calling-network-agent.yml
+.. literalinclude:: includes/agent/function-calling-agent.yaml
     :language: yaml
     :linenos:
     :emphasize-lines: 16-37
@@ -40,10 +39,10 @@ Step 2: Process request parameters in Flask
 
 Once the prompt targets are configured as above, handling those parameters is
 
-.. literalinclude:: /_include/parameter_handling_flask.py
+.. literalinclude:: includes/agent/parameter_handling.py
     :language: python
     :linenos:
-    :caption: Flask API example for parameter extraction via HTTP request parameters
+    :caption: Parameter handling with Flask
 
 Parallel/ Multiple Function Calling
 -----------------------------------
@@ -64,7 +63,7 @@ the user's intent.
 
 Example of Multiple Prompt Targets in YAML:
 
-.. literalinclude:: /_config/function-calling-network-agent.yml
+.. literalinclude:: includes/agent/function-calling-agent.yaml
     :language: yaml
     :linenos:
     :emphasize-lines: 16-37

docs/source/_config/function-calling-network-agent.yml → docs/source/build_with_arch/includes/agent/function-calling-agent.yaml

@@ -3,15 +3,15 @@ listen:
   address: 127.0.0.1 | 0.0.0.0
   port_value: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
 
-system_prompts:
-  - name: network_assistant
-    content: You are a network assistant that just offers facts about the operational health of the network
+system_prompt: |
+  You are a network assistant that just offers facts; not advice on manufacturers or purchasing decisions.
 
 llm_providers:
-    - name: "OpenAI"
-      access_key: $OPEN_AI_KEY
-      model: gpt-4o
-      default: true
+  - name: "OpenAI"
+    provider: "openai"
+    access_key: OPENAI_API_KEY
+    model: gpt-4o
+    stream: true
 
 prompt_targets:
   - name: reboot_devices
@@ -36,6 +36,12 @@ prompt_targets:
         description: "The name of the device group to reboot."
         required: false
 
-prompt_endpoints:
-  - "http://127.0.0.2"
-  - "http://127.0.0.1"
+# Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
+endpoints:
+  app_server:
+    # value could be ip address or a hostname with port
+    # this could also be a list of endpoints for load balancing
+    # for example endpoint: [ ip1:port, ip2:port ]
+    endpoint: "127.0.0.1:80"
+    # max time to wait for a connection to be established
+    connect_timeout: 0.005s

docs/source/build_with_arch/includes/rag/parameter_handling.py

@@ -0,0 +1,41 @@
+from flask import Flask, request, jsonify
+
+app = Flask(__name__)
+
+@app.route('/agent/device_summary', methods=['POST'])
+def get_device_summary():
+    """
+    Endpoint to retrieve device statistics based on device IDs and an optional time range.
+    """
+    data = request.get_json()
+
+    # Validate 'device_ids' parameter
+    device_ids = data.get('device_ids')
+    if not device_ids or not isinstance(device_ids, list):
+        return jsonify({'error': "'device_ids' parameter is required and must be a list"}), 400
+
+    # Validate 'time_range' parameter (optional, defaults to 7)
+    time_range = data.get('time_range', 7)
+    if not isinstance(time_range, int):
+        return jsonify({'error': "'time_range' must be an integer"}), 400
+
+    # Simulate retrieving statistics for the given device IDs and time range
+    # In a real application, you would query your database or external service here
+    statistics = []
+    for device_id in device_ids:
+        # Placeholder for actual data retrieval
+        stats = {
+            'device_id': device_id,
+            'time_range': f'Last {time_range} days',
+            'data': f'Statistics data for device {device_id} over the last {time_range} days.'
+        }
+        statistics.append(stats)
+
+    response = {
+        'statistics': statistics
+    }
+
+    return jsonify(response), 200
+
+if __name__ == '__main__':
+    app.run(debug=True)

docs/source/_config/rag-prompt-targets.yml → docs/source/build_with_arch/includes/rag/prompt_targets.yaml

@@ -1,18 +1,3 @@
-version: "0.1-beta"
-listener:
-  address: 127.0.0.1 | 0.0.0.0
-  port_value: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
-
-system_prompts:
-  - name: network_assistant
-    content: You are a network assistant that just offers facts about the operational health of the network
-
-llm_providers:
-    - name: "OpenAI"
-      access_key: $OPEN_AI_KEY
-      model: gpt-4o
-      default: true
-
 prompt_targets:
   - name: get_device_statistics
     description: >
@@ -34,7 +19,3 @@ prompt_targets:
         description: "The number of days in the past over which to retrieve device statistics. Defaults to 7 days if not specified."
         required: false
         default: 7
-
-prompt_endpoints:
-  - "http://127.0.0.2"
-  - "http://127.0.0.1"