Complete up to external env config

.gitignore

@@ -0,0 +1,3 @@
+.env
+__pycache__
+*.pyc

app.py

@@ -1,9 +1,52 @@
-import gradio as gr
+# Integrates all components into an agent
+# import gradio as gr
+import asyncio
+from llama_index.core.workflow import Context
+from llama_index.core.agent.workflow import AgentWorkflow, ToolCallResult, AgentStream
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+from retriever import guest_info_retriever
+from tools import get_weather_info, get_hub_stats, google_search
+from dotenv import load_dotenv
 
+llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")
 
-def greet(name):
-    return "Hello " + name + "!!"
+alfred = AgentWorkflow.from_tools_or_functions(
+    [guest_info_retriever, get_weather_info, get_hub_stats, google_search], llm=llm
+)
 
+ctx = Context(alfred)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+
+async def main():
+    handler = alfred.run(
+        "Tell me about Lady Ada Lovelace.",
+        ctx=ctx,
+    )
+    async for ev in handler.stream_events():
+        if isinstance(ev, ToolCallResult):
+            print("")
+            print("Called tool: ", ev.tool_name, ev.tool_kwargs, "=>", ev.tool_output)
+        elif isinstance(ev, AgentStream):  # showing the thought process
+            print(ev.delta, end="", flush=True)
+    print("🎩 Alfred's Response:")
+    response = await handler
+    print(response)
+
+    handler2 = alfred.run("What projects is she currently working on?", ctx=ctx)
+    async for ev in handler2.stream_events():
+        if isinstance(ev, ToolCallResult):
+            print("")
+            print("Called tool: ", ev.tool_name, ev.tool_kwargs, "=>", ev.tool_output)
+        elif isinstance(ev, AgentStream):  # showing the thought process
+            print(ev.delta, end="", flush=True)
+    print("🎩 Alfred's Second Response:")
+    response2 = await handler2
+    print(response2)
+
+
+# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# demo.launch()
+
+if __name__ == "__main__":
+    load_dotenv()
+    asyncio.run(main())

retriever.py

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+# Implements retrieval functions to support knowledge access.
+
+import datasets
+from llama_index.core.schema import Document
+from llama_index.retrievers.bm25 import BM25Retriever
+
+# Load dataset
+guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
+
+# Convert dataset entries into Document objects
+docs = [
+    Document(
+        text="\n".join(
+            [
+                f"Name: {guest['name']}",
+                f"Relation: {guest['relation']}",
+                f"Description: {guest['description']}",
+                f"Email: {guest['email']}",
+            ]
+        ),
+        metadata={"name": guest["name"]},
+    )
+    for guest in guest_dataset
+]
+
+bm25_retriever = BM25Retriever.from_defaults(nodes=docs)
+
+
+def guest_info_retriever(query: str) -> str:
+    """Retrieves detailed info about gala guests based on their name or relation"""
+    results = bm25_retriever.retrieve(query)
+    if results:
+        return "\n\n".join([doc.text for doc in results[:3]])
+    else:
+        return "No matching guest information found."

tools.py

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+# Provides auxiliary tools for agent
+from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec
+import random
+from huggingface_hub import list_models
+import aiohttp
+import os
+import json
+from dotenv import load_dotenv
+import asyncio
+
+tool_spec = DuckDuckGoSearchToolSpec()
+
+search_tool = tool_spec.duckduckgo_full_search
+
+
+async def google_search(query: str, num_results: int = 5) -> str:
+    """
+    Args:
+        query: Search query
+        num_results: Max results to return
+    Returns:
+        dict: JSON response from Google API.
+    """
+    api_key = os.getenv("GOOGLE_API_KEY")
+    cse_id = os.getenv("GOOGLE_CSE_ID")
+
+    if not api_key or not cse_id:
+        raise ValueError(
+            "GOOGLE_API_KEY and GOOGLE_CSE_ID must be set in environment variables."
+        )
+
+    url = "https://www.googleapis.com/customsearch/v1"
+    params = {"key": api_key, "cx": cse_id, "q": query}
+
+    async with aiohttp.ClientSession() as session:
+        async with session.get(url, params=params) as response:
+            response.raise_for_status()
+            data = await response.json()
+            results = "Web Search results:\n\n" + "\n\n".join(
+                [
+                    f"Link:{result['link']}\nTitle:{result['title']}\nSnippet:{result['snippet']}"
+                    for result in data["items"][:num_results]
+                ]
+            )
+            print(results)
+            return results
+
+
+def get_weather_info(location: str) -> str:
+    """Fetches dummy weather information for a given location"""
+    # Dummy weather data
+    weather_conditions = [
+        {"condition": "Rainy", "temp_c": 15},
+        {"condition": "Clear", "temp_c": 25},
+        {"condition": "Windy", "temp_c": 20},
+    ]
+    # Randomly select a weather condition
+    data = random.choice(weather_conditions)
+    return f"Weather in {location}: {data['condition']}, {data['temp_c']}°C"
+
+
+def get_hub_stats(author: str) -> str:
+    """Fetches the most downloaded model from a specific author on the Hugging Face Hub."""
+    try:
+        # List models from the specified author, sorted by downloads
+        models = list(
+            list_models(author=author, sort="downloads", direction=-1, limit=1)
+        )
+
+        if models:
+            model = models[0]
+            return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads."
+        else:
+            return f"No models found for author {author}."
+    except Exception as e:
+        return f"Error fetching models for {author}: {str(e)}"
+
+
+async def main():
+    response = await google_search("Who's the current President of France 2025?")
+    print(response)
+
+
+if __name__ == "__main__":
+    load_dotenv()
+    asyncio.run(main())
+    # print(get_hub_stats("facebook"))