updated app

.gitignore

@@ -1,2 +1,2 @@
 .env
-./__pycache__
+.__pycache__

app.py

@@ -1,6 +1,9 @@
 import os
+from typing import Dict, Optional
 
 import chainlit as cl
+from chainlit.input_widget import Select, Slider, Switch
+# from chainlit import user_session
 from langchain.chains import RetrievalQAWithSourcesChain
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings.openai import OpenAIEmbeddings
@@ -12,15 +15,101 @@ from langchain.vectorstores import FAISS
 
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
 embeddings = OpenAIEmbeddings()
+vector_store = FAISS.load_local("docs.faiss", embeddings)
+
+
+@cl.oauth_callback
+def oauth_callback(
+    provider_id: str,
+    token: str,
+    raw_user_data: Dict[str, str],
+    default_app_user: cl.AppUser,
+) -> Optional[cl.AppUser]:
+    # set AppUser tags as regular_user
+    match default_app_user.username:
+        case "Broomva":
+            default_app_user.tags = ["admin_user"]
+            default_app_user.role = "ADMIN"
+        case _:
+            default_app_user.tags = ["regular_user"]
+            default_app_user.role = "USER"
+    print(default_app_user)
+    return default_app_user
+
+
+# @cl.set_chat_profiles
+# async def chat_profile(current_user: cl.AppUser):
+#     if "admin_user" not in current_user.tags:
+#         # Default to 3.5 when not admin
+#         return [
+#             cl.ChatProfile(
+#                 name="GPT-3.5",
+#                 markdown_description="The underlying LLM model is **GPT-3.5**.",
+#                 icon="https://picsum.photos/200",
+#             )
+#         ]
+
+#     return [
+#         cl.ChatProfile(
+#             name="GPT-3.5",
+#             markdown_description="The underlying LLM model is **GPT-3.5**.",
+#             icon="https://picsum.photos/200",
+#         ),
+#         cl.ChatProfile(
+#             name="GPT-4",
+#             markdown_description="The underlying LLM model is **GPT-4**.",
+#             icon="https://picsum.photos/250",
+#         ),
+#     ]
+
+
+@cl.on_settings_update
+async def setup_agent(settings):
+    print("on_settings_update", settings)
 
 @cl.on_chat_start
 async def init():
-    vector_store = FAISS.load_local("docs.faiss", embeddings)
+    
+    settings = await cl.ChatSettings(
+        [
+            Select(
+                id="model",
+                label="OpenAI - Model",
+                values=["gpt-3.5-turbo", "gpt-3.5-turbo-1106", "gpt-4", "gpt-4-1106-preview"],
+                initial_index=0,
+            ),
+            Switch(id="streaming", label="OpenAI - Stream Tokens", initial=True),
+            Slider(
+                id="temperature",
+                label="OpenAI - Temperature",
+                initial=1,
+                min=0,
+                max=2,
+                step=0.1,
+            ),
+            Slider(
+                id="k",
+                label="RAG - Retrieved Documents",
+                initial=3,
+                min=1,
+                max=20,
+                step=1,
+            ),
+        ]
+    ).send()
+    
+    
+    # print(settings)
+    # app_user = cl.user_session.get("user")
+    # chat_profile = cl.user_session.get("chat_profile")
+    # await cl.Message(
+    #     content=f"🪼 Starting chat with {app_user.username} using the {chat_profile} chat profile"
+    # ).send()
 
     chain = RetrievalQAWithSourcesChain.from_chain_type(
-        ChatOpenAI(temperature=0, streaming=True, model="gpt-4-1106-preview"),
+        ChatOpenAI(temperature=settings['temperature'], streaming=settings['streaming'], model=settings['model']),
         chain_type="stuff",
-        retriever=vector_store.as_retriever(search_kwargs={"k": 7}),
+        retriever=vector_store.as_retriever(search_kwargs={"k": int(settings['k'])}),
     )
 
     cl.user_session.set("chain", chain)
@@ -33,7 +122,7 @@ async def main(message):
         stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
     )
     cb.answer_reached = True
-    
+
     res = await chain.acall(message.content, callbacks=[cb])
 
     if cb.has_streamed_final_answer:
@@ -42,4 +131,4 @@ async def main(message):
         answer = res["answer"]
         await cl.Message(
             content=answer,
-        ).send()
+        ).send()