split code in several files

.gitignore

@@ -0,0 +1 @@
+.pyc

app.py

@@ -1,5 +1,5 @@
-from langchain_community.retrievers import BM25Retriever
-from langchain.tools import Tool
+import os
+
 from typing import TypedDict, Annotated
 from langgraph.graph.message import add_messages
 from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
@@ -7,42 +7,8 @@ from langgraph.prebuilt import ToolNode
 from langgraph.graph import START, StateGraph
 from langgraph.prebuilt import tools_condition
 from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
-from datasets import load_dataset
-from langchain.docstore.document import Document
-import os
-
-# Load the dataset
-guest_dataset = 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
-]
-
-bm25_retriever = BM25Retriever.from_documents(docs)
-
-def extract_text(query: str) -> str:
-    """Retrieves detailed information about gala guests based on their name or relation."""
-    results = bm25_retriever.invoke(query)
-    if results:
-        return "\n\n".join([doc.page_content for doc in results[:3]])
-    else:
-        return "No matching guest information found."
-
-guest_info_tool = Tool(
-    name="guest_info_retriever",
-    func=extract_text,
-    description="Retrieves detailed information about gala guests based on their name or relation."
-)
+from tools import guest_info_tool
 
 # Generate the chat interface, including the tools
 llm = HuggingFaceEndpoint(

retriever.py

@@ -0,0 +1,19 @@
+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
+]

tools.py

@@ -0,0 +1,20 @@
+from langchain_community.retrievers import BM25Retriever
+from langchain.tools import Tool
+
+from retriever import docs
+
+bm25_retriever = BM25Retriever.from_documents(docs)
+
+def extract_text(query: str) -> str:
+    """Retrieves detailed information about gala guests based on their name or relation."""
+    results = bm25_retriever.invoke(query)
+    if results:
+        return "\n\n".join([doc.page_content for doc in results[:3]])
+    else:
+        return "No matching guest information found."
+
+guest_info_tool = Tool(
+    name="guest_info_retriever",
+    func=extract_text,
+    description="Retrieves detailed information about gala guests based on their name or relation."
+)