refactor: replace old schemas with pydantic models

Author: jameszyao

examples/assistant/chat_with_assistant.ipynb

@@ -208,12 +208,12 @@
     "user_input = input(\"User Input: \")\n",
     "while user_input.strip() and user_input != \"q\":\n",
     "    # create user message\n",
-    "    taskingai.assistant.create_message(\n",
+    "    user_message = taskingai.assistant.create_message(\n",
     "        assistant_id=assistant.assistant_id,\n",
     "        chat_id=chat.chat_id,\n",
     "        text=user_input,\n",
     "    )\n",
-    "    print(f\"User: {user_input}\")\n",
+    "    print(f\"User: {user_input} ({user_message.message_id})\")\n",
     "    \n",
     "    # generate assistant response\n",
     "    assistant_message_response = taskingai.assistant.generate_message(\n",
@@ -230,8 +230,8 @@
     "        if isinstance(item, MessageChunk):\n",
     "            print(item.delta, end=\"\", flush=True)\n",
     "        elif isinstance(item, Message):\n",
-    "            print(f\"\\nmessage_id: {item.message_id}\")\n",
-    "            \n",
+    "            print(f\" ({item.message_id})\")\n",
+    "    \n",
     "    time.sleep(2)\n",
     "    # quit by input 'q\n",
     "    user_input = input(\"User: \")"
@@ -274,16 +274,6 @@
     "collapsed": false
    },
    "id": "ed39836bbfdc7a4e"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "9cfed1128acbdd30"
   }
  ],
  "metadata": {

examples/crud/assistant_crud.ipynb

@@ -166,7 +166,7 @@
     "    assistant_id=assistant.assistant_id,\n",
     "    chat_id=chat_id,\n",
     ")\n",
-    "print(f\"got chat: {chat}\\n\")"
+    "print(f\"chat: {chat}\\n\")"
    ],
    "metadata": {
     "collapsed": false

examples/crud/retrieval_crud.ipynb

@@ -31,7 +31,7 @@
     "from taskingai.retrieval import Collection, Record, TokenTextSplitter\n",
     "\n",
     "# choose an available text_embedding model from your project\n",
-    "embedding_model_id = \"YOUR_MODEL_ID\""
+    "embedding_model_id = \"YOUR_EMBEDDING_MODEL_ID\""
    ],
    "metadata": {
     "collapsed": false
@@ -93,7 +93,7 @@
     "    collection_id=collection_id\n",
     ")\n",
     "\n",
-    "print(f\"got collection: {collection}\\n\")"
+    "print(f\"collection: {collection}\\n\")"
    ],
    "metadata": {
     "collapsed": false

examples/crud/tool_crud.ipynb

@@ -197,6 +197,16 @@
     "collapsed": false
    },
    "id": "5a1a36d15055918f"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "5588d5e7457225be"
   }
  ],
  "metadata": {

examples/retrieval/semantic_search.ipynb

@@ -41,7 +41,7 @@
    "outputs": [],
    "source": [
     "# choose an available text_embedding model from your project\n",
-    "embedding_model_id = \"YOUR_MODEL_ID\""
+    "embedding_model_id = \"YOUR_EMBEDDING_MODEL_ID\""
    ],
    "metadata": {
     "collapsed": false
@@ -224,6 +224,16 @@
     "collapsed": false
    },
    "id": "fc9c1fa12d893dd1"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "e0eb39fcac309768"
   }
  ],
  "metadata": {