Interactive cli impl

bootstraprag/cli.py

@@ -3,12 +3,14 @@
 from pathlib import Path
 import os
 import zipfile
+from InquirerPy import inquirer
 
 
 @click.group()
 def cli():
     pass
 
+
 # used for downloading the project as zip.
 def create_zip(project_name):
     zip_path = shutil.make_archive(project_name, 'zip', project_name)
@@ -17,29 +19,34 @@ def create_zip(project_name):
 
 @click.command()
 @click.argument('project_name')
-@click.option('--framework', type=click.Choice(['llamaindex', 'None']),
-              prompt="Which technology would you like to use ('None' will make you to use qdrant direct search)?",
-              default='', required=False)
+@click.option('--framework', type=click.Choice([]),prompt=False)
 @click.option('--template', type=click.Choice([]), prompt=False)
 @click.option('--observability', type=click.Choice([]), prompt=False)
 def create(project_name, framework, template, observability):
     template_choices = []
     observability_choices = []
-
+    framework_choices = ['llamaindex', 'None']
+    framework = inquirer.select(
+        message="Which technology would you like to use ('None' will make you to use qdrant direct search)?",
+        choices=framework_choices
+    ).execute()
     if framework == 'llamaindex' or framework == 'langchain' or framework == 'haystack':
         template_choices = ['simple-rag', 'rag-with-react', 'rag-with-hyde', 'rag-with-flare']
     elif framework == 'None':
         framework = 'qdrant'
         template_choices = ['simple-search']
-
-    template = click.prompt("Which template would you like to use?",
-                            type=click.Choice(template_choices)
-                            )
+    # Use InquirerPy to select template with arrow keys
+    template = inquirer.select(
+        message="Which template would you like to use?",
+        choices=template_choices,
+    ).execute()
     if framework == 'llamaindex' or framework == 'langchain' or framework == 'haystack':
         observability_choices = ['Yes', 'No']
-        observability = click.prompt("Do you wish to enable observability?",
-                                     type=click.Choice(observability_choices)
-                                     )
+        # Use InquirerPy to select observability with arrow keys
+        observability = inquirer.select(
+            message="Do you wish to enable observability?",
+            choices=observability_choices,
+        ).execute()
 
     click.echo(f'You have selected framework: {framework} and template: {template} and observability: {observability}')
     download_and_extract_template(project_name, framework, template, observability)
@@ -67,4 +74,4 @@ def download_and_extract_template(project_name, framework, template, observabili
 cli.add_command(create)
 
 if __name__ == "__main__":
-    cli()
+    cli()

bootstraprag/templates/llamaindex/llama_agents_simpleq/agents_core.py

@@ -0,0 +1,44 @@
+from llama_agents import (
+    AgentService,
+    AgentOrchestrator,
+    ControlPlaneServer,
+    SimpleMessageQueue,
+)
+
+from llama_index.core.agent import ReActAgent
+from llama_index.core.tools import FunctionTool
+from llama_index.llms.openai import OpenAI
+
+
+# create an agent
+def get_the_secret_fact() -> str:
+    """Returns the secret fact."""
+    return "The secret fact is: A baby llama is called a 'Cria'."
+
+
+tool = FunctionTool.from_defaults(fn=get_the_secret_fact)
+
+agent1 = ReActAgent.from_tools([tool], llm=OpenAI())
+agent2 = ReActAgent.from_tools([], llm=OpenAI())
+
+# create our multi-agent framework components
+message_queue = SimpleMessageQueue(port=8000)
+control_plane = ControlPlaneServer(
+    message_queue=message_queue,
+    orchestrator=AgentOrchestrator(llm=OpenAI(model="gpt-4-turbo")),
+    port=8001,
+)
+agent_server_1 = AgentService(
+    agent=agent1,
+    message_queue=message_queue,
+    description="Useful for getting the secret fact.",
+    service_name="secret_fact_agent",
+    port=8002,
+)
+agent_server_2 = AgentService(
+    agent=agent2,
+    message_queue=message_queue,
+    description="Useful for getting random dumb facts.",
+    service_name="dumb_fact_agent",
+    port=8003,
+)

bootstraprag/templates/llamaindex/llama_agents_simpleq/main.py

@@ -0,0 +1,16 @@
+from llama_agents import LocalLauncher
+import nest_asyncio
+from agents_core import agent_server_1, agent_server_2, control_plane, message_queue
+
+# needed for running in a notebook
+nest_asyncio.apply()
+
+# launch it
+launcher = LocalLauncher(
+    [agent_server_1, agent_server_2],
+    control_plane,
+    message_queue,
+)
+result = launcher.launch_single("What is the secret fact?")
+
+print(f"Result: {result}")

bootstraprag/templates/llamaindex/llama_agents_simpleq/requirements.txt

@@ -0,0 +1,10 @@
+llama-agents==0.0.14
+llama-index==0.10.65
+llama-index-llms-openai==0.1.29
+llama-index-llms-ollama==0.2.2
+llama-index-embeddings-openai==0.1.11
+llama-index-embeddings-ollama==0.2.0
+llama-index-vector-stores-qdrant==0.2.16
+qdrant-client==1.11.0
+fastapi==0.112.1
+uvicorn==0.30.6

bootstraprag/templates/llamaindex/simple_rag/requirements.txt

@@ -1,10 +1,10 @@
 python-dotenv==1.0.1
-llama-index==0.10.58
-llama-index-llms-openai==0.1.27
-llama-index-llms-ollama==0.2.0
+llama-index==0.10.65
+llama-index-llms-openai==0.1.29
+llama-index-llms-ollama==0.2.2
 llama-index-embeddings-openai==0.1.11
-llama-index-embeddings-ollama==0.1.2
-llama-index-vector-stores-qdrant==0.2.14
-qdrant-client==1.10.1
-fastapi==0.112.0
-uvicorn==0.30.5
+llama-index-embeddings-ollama==0.2.0
+llama-index-vector-stores-qdrant==0.2.16
+qdrant-client==1.11.0
+fastapi==0.112.1
+uvicorn==0.30.6

requirements.txt

@@ -1,2 +1,3 @@
 click==8.1.7
-python-dotenv==1.0.1
+python-dotenv==1.0.1
+inquirerpy==0.3.4