Create retriver.py

retriver.py

@@ -0,0 +1,63 @@
+import datasets
+from langchain.docstore.document import Document
+
+# Load the dataset
+guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
+
+# Convert dataset entries into Document objects
+docs = [
+    Document(
+        page_content="\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
+]
+
+from smolagents import Tool
+from langchain_community.retrievers import BM25Retriever
+
+class GuestInfoRetrieverTool(Tool):
+    name = "guest_info_retriever"
+    description = "Retrieves detailed information about gala guests based on their name or relation."
+    inputs = {
+        "query": {
+            "type": "string",
+            "description": "The name or relation of the guest you want information about."
+        }
+    }
+    output_type = "string"
+
+    def __init__(self, docs):
+        self.is_initialized = False
+        self.retriever = BM25Retriever.from_documents(docs)
+
+    def forward(self, query: str):
+        results = self.retriever.get_relevant_documents(query)
+        if results:
+            return "\n\n".join([doc.page_content for doc in results[:3]])
+        else:
+            return "No matching guest information found."
+
+# Initialize the tool
+guest_info_tool = GuestInfoRetrieverTool(docs)
+
+
+from smolagents import CodeAgent, InferenceClientModel
+
+# Initialize the Hugging Face model
+model = InferenceClientModel()
+
+# Create Alfred, our gala agent, with the guest info tool
+alfred = CodeAgent(tools=[guest_info_tool], model=model)
+
+# Example query Alfred might receive during the gala
+response = alfred.run("Tell me about our guest named 'Lady Ada Lovelace'.")
+
+print("🎩 Alfred's Response:")
+print(response)
+