Refreshed notebooks, particularly with new Week 1

week5/day1.ipynb

@@ -199,11 +199,7 @@
    "outputs": [],
    "source": [
     "def chat(message, history):\n",
-    "    messages = [{\"role\": \"system\", \"content\": system_message}]\n",
-    "    for user_message, assistant_message in history:\n",
-    "        messages.append({\"role\": \"user\", \"content\": user_message})\n",
-    "        messages.append({\"role\": \"assistant\", \"content\": assistant_message})\n",
-    "    \n",
+    "    messages = [{\"role\": \"system\", \"content\": system_message}] + history\n",
     "    message = add_context(message)\n",
     "    messages.append({\"role\": \"user\", \"content\": message})\n",
     "\n",
@@ -232,7 +228,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "view = gr.ChatInterface(chat).launch()"
+    "view = gr.ChatInterface(chat, type=\"messages\").launch()"
    ]
   },
   {

week5/day3.ipynb

@@ -101,6 +101,23 @@
     "        documents.append(doc)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f065d4b1-80b7-4e15-abd4-60a83e752ea8",
+   "metadata": {},
+   "source": [
+    "# Please note:\n",
+    "\n",
+    "In the next cell, we split the text into chunks.\n",
+    "\n",
+    "2 students let me know that the next cell crashed their computer.  \n",
+    "They were able to fix it by changing the chunk_size from 1,000 to 2,000 and the chunk_overlap from 200 to 400.  \n",
+    "This shouldn't be required; but if it happens to you, please make that change!  \n",
+    "(Note that LangChain may give a warning about a chunk being larger than 1,000 - this can be safely ignored).\n",
+    "\n",
+    "_With much thanks to Steven W and Nir P for this valuable contribution._"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

week5/day4.ipynb

@@ -313,7 +313,7 @@
     "# the retriever is an abstraction over the VectorStore that will be used during RAG\n",
     "retriever = vectorstore.as_retriever()\n",
     "\n",
-    "# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n",
+    "# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n",
     "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
    ]
   },
@@ -329,6 +329,20 @@
     "print(result[\"answer\"])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6eb99fb-33ec-4025-ab92-b634ede03647",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up a new conversation memory for the chat\n",
+    "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
+    "\n",
+    "# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n",
+    "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bbbcb659-13ce-47ab-8a5e-01b930494964",
@@ -346,7 +360,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Wrapping that in a function\n",
+    "# Wrapping in a function - note that history isn't used, as the memory is in the conversation_chain\n",
     "\n",
     "def chat(message, history):\n",
     "    result = conversation_chain.invoke({\"question\": message})\n",
@@ -362,7 +376,7 @@
    "source": [
     "# And in Gradio:\n",
     "\n",
-    "view = gr.ChatInterface(chat).launch()"
+    "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)"
    ]
   },
   {

week5/day5.ipynb

@@ -313,6 +313,20 @@
     "print(result[\"answer\"])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b5a9013-d5d4-4e25-9e7c-cdbb4f33e319",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up a new conversation memory for the chat\n",
+    "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
+    "\n",
+    "# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n",
+    "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bbbcb659-13ce-47ab-8a5e-01b930494964",
@@ -346,7 +360,7 @@
    "source": [
     "# And in Gradio:\n",
     "\n",
-    "view = gr.ChatInterface(chat).launch()"
+    "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)"
    ]
   },
   {