Update notebooks to work with the latest version (#870)

notebooks/anthropic.ipynb

@@ -34,6 +34,15 @@
         "!pip install embedchain"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -54,7 +63,7 @@
       "outputs": [],
       "source": [
         "import os\n",
-        "from embedchain import App\n",
+        "from embedchain import Pipeline as App\n",
         "\n",
         "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\"\n",
         "os.environ[\"ANTHROPIC_API_KEY\"] = \"xxx\""

notebooks/azure-openai.ipynb

@@ -26,6 +26,16 @@
     "!pip install embedchain"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "692ff37b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install embedchain[dataloaders]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ac982a56",
@@ -44,7 +54,7 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "from embedchain import App\n",
+    "from embedchain import Pipeline as App\n",
     "\n",
     "os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
     "os.environ[\"OPENAI_API_BASE\"] = \"https://xxx.openai.azure.com/\"\n",

notebooks/chromadb.ipynb

@@ -1,84 +1,84 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
-      "source": [
-        "## Cookbook for using ChromaDB with Embedchain"
-      ],
       "metadata": {
         "id": "b02n_zJ_hl3d"
-      }
+      },
+      "source": [
+        "## Cookbook for using ChromaDB with Embedchain"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-1: Install embedchain package"
-      ],
       "metadata": {
         "id": "gyJ6ui2vhtMY"
-      }
+      },
+      "source": [
+        "### Step-1: Install embedchain package"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "!pip install embedchain"
-      ],
       "metadata": {
         "id": "-NbXjAdlh0vJ"
       },
+      "outputs": [],
+      "source": [
+        "!pip install embedchain"
+      ]
+    },
+    {
+      "cell_type": "code",
       "execution_count": null,
-      "outputs": []
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "nGnpSYAAh2bQ"
+      },
       "source": [
         "### Step-2: Set OpenAI environment variables\n",
         "\n",
         "You can find this env variable on your [OpenAI dashboard](https://platform.openai.com/account/api-keys)."
-      ],
+      ]
-      "metadata": {
-        "id": "nGnpSYAAh2bQ"
-      }
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "import os\n",
-        "from embedchain import App\n",
-        "\n",
-        "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\""
-      ],
       "metadata": {
         "id": "0fBdQ9GAiRvK"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "import os\n",
+        "from embedchain import Pipeline as App\n",
+        "\n",
+        "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\""
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-3: Define your Vector Database config"
-      ],
       "metadata": {
         "id": "Ns6RhPfbiitr"
-      }
+      },
+      "source": [
+        "### Step-3: Define your Vector Database config"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "S9CkxVjriotB"
+      },
+      "outputs": [],
       "source": [
         "config = \"\"\"\n",
         "vectordb:\n",
@@ -95,64 +95,64 @@
         "# Write the multi-line string to a YAML file\n",
         "with open('chromadb.yaml', 'w') as file:\n",
         "    file.write(config)"
-      ],
+      ]
-      "metadata": {
-        "id": "S9CkxVjriotB"
-      },
-      "execution_count": null,
-      "outputs": []
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-4 Create embedchain app based on the config"
-      ],
       "metadata": {
         "id": "PGt6uPLIi1CS"
-      }
+      },
+      "source": [
+        "### Step-4 Create embedchain app based on the config"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "app = App.from_config(yaml_path=\"chromadb.yaml\")"
-      ],
       "metadata": {
         "id": "Amzxk3m-i3tD"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "app = App.from_config(yaml_path=\"chromadb.yaml\")"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-5: Add data sources to your app"
-      ],
       "metadata": {
         "id": "XNXv4yZwi7ef"
-      }
+      },
+      "source": [
+        "### Step-5: Add data sources to your app"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "app.add(\"https://www.forbes.com/profile/elon-musk\")"
-      ],
       "metadata": {
         "id": "Sn_0rx9QjIY9"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "app.add(\"https://www.forbes.com/profile/elon-musk\")"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-6: All set. Now start asking questions related to your data"
-      ],
       "metadata": {
         "id": "_7W6fDeAjMAP"
-      }
+      },
+      "source": [
+        "### Step-6: All set. Now start asking questions related to your data"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cvIK7dWRjN_f"
+      },
+      "outputs": [],
       "source": [
         "while(True):\n",
         "    question = input(\"Enter question: \")\n",
@@ -160,12 +160,21 @@
         "        break\n",
         "    answer = app.query(question)\n",
         "    print(answer)"
+      ]
+    }
   ],
   "metadata": {
-        "id": "cvIK7dWRjN_f"
+    "colab": {
+      "provenance": []
     },
-      "execution_count": null,
+    "kernelspec": {
-      "outputs": []
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
     }
-  ]
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

notebooks/cohere.ipynb

@@ -33,6 +33,15 @@
         "!pip install embedchain"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -69,7 +78,7 @@
       "outputs": [],
       "source": [
         "import os\n",
-        "from embedchain import App\n",
+        "from embedchain import Pipeline as App\n",
         "\n",
         "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\"\n",
         "os.environ[\"COHERE_API_KEY\"] = \"xxx\""

notebooks/elasticsearch.ipynb

@@ -1,95 +1,95 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
-      "source": [
-        "## Cookbook for using ElasticSearchDB with Embedchain"
-      ],
       "metadata": {
         "id": "b02n_zJ_hl3d"
-      }
+      },
+      "source": [
+        "## Cookbook for using ElasticSearchDB with Embedchain"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-1: Install embedchain package"
-      ],
       "metadata": {
         "id": "gyJ6ui2vhtMY"
-      }
+      },
+      "source": [
+        "### Step-1: Install embedchain package"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "!pip install embedchain"
-      ],
       "metadata": {
         "id": "-NbXjAdlh0vJ"
       },
+      "outputs": [],
+      "source": [
+        "!pip install embedchain"
+      ]
+    },
+    {
+      "cell_type": "code",
       "execution_count": null,
-      "outputs": []
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "nGnpSYAAh2bQ"
+      },
       "source": [
         "### Step-2: Set OpenAI environment variables and install the dependencies.\n",
         "\n",
         "You can find this env variable on your [OpenAI dashboard](https://platform.openai.com/account/api-keys). Now lets install the dependencies needed for Elasticsearch."
-      ],
+      ]
-      "metadata": {
-        "id": "nGnpSYAAh2bQ"
-      }
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "!pip install --upgrade 'embedchain[elasticsearch]'"
-      ],
       "metadata": {
         "id": "-MUFRfxV7Jk7"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "!pip install --upgrade 'embedchain[elasticsearch]'"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "import os\n",
-        "from embedchain import App\n",
-        "\n",
-        "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\""
-      ],
       "metadata": {
         "id": "0fBdQ9GAiRvK"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "import os\n",
+        "from embedchain import Pipeline as App\n",
+        "\n",
+        "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\""
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-3: Define your Vector Database config"
-      ],
       "metadata": {
         "id": "Ns6RhPfbiitr"
-      }
+      },
+      "source": [
+        "### Step-3: Define your Vector Database config"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "S9CkxVjriotB"
+      },
+      "outputs": [],
       "source": [
         "config = \"\"\"\n",
         "vectordb:\n",
@@ -104,64 +104,64 @@
         "# Write the multi-line string to a YAML file\n",
         "with open('elasticsearch.yaml', 'w') as file:\n",
         "    file.write(config)"
-      ],
+      ]
-      "metadata": {
-        "id": "S9CkxVjriotB"
-      },
-      "execution_count": null,
-      "outputs": []
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-4 Create embedchain app based on the config"
-      ],
       "metadata": {
         "id": "PGt6uPLIi1CS"
-      }
+      },
+      "source": [
+        "### Step-4 Create embedchain app based on the config"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "app = App.from_config(yaml_path=\"elasticsearch.yaml\")"
-      ],
       "metadata": {
         "id": "Amzxk3m-i3tD"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "app = App.from_config(yaml_path=\"elasticsearch.yaml\")"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-5: Add data sources to your app"
-      ],
       "metadata": {
         "id": "XNXv4yZwi7ef"
-      }
+      },
+      "source": [
+        "### Step-5: Add data sources to your app"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "app.add(\"https://www.forbes.com/profile/elon-musk\")"
-      ],
       "metadata": {
         "id": "Sn_0rx9QjIY9"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "app.add(\"https://www.forbes.com/profile/elon-musk\")"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-6: All set. Now start asking questions related to your data"
-      ],
       "metadata": {
         "id": "_7W6fDeAjMAP"
-      }
+      },
+      "source": [
+        "### Step-6: All set. Now start asking questions related to your data"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cvIK7dWRjN_f"
+      },
+      "outputs": [],
       "source": [
         "while(True):\n",
         "    question = input(\"Enter question: \")\n",
@@ -169,12 +169,21 @@
         "        break\n",
         "    answer = app.query(question)\n",
         "    print(answer)"
+      ]
+    }
   ],
   "metadata": {
-        "id": "cvIK7dWRjN_f"
+    "colab": {
+      "provenance": []
     },
-      "execution_count": null,
+    "kernelspec": {
-      "outputs": []
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
     }
-  ]
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

notebooks/embedchain-chromadb-server.ipynb

@@ -33,7 +33,7 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "from embedchain import App\n",
+    "from embedchain import Pipeline as App\n",
     "from embedchain.config import AppConfig\n",
     "\n",
     "\n",

notebooks/embedchain-docs-site-example.ipynb

@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from embedchain import App\n",
+    "from embedchain import Pipeline as App\n",
     "\n",
     "embedchain_docs_bot = App()"
    ]

notebooks/gpt4all.ipynb

@@ -33,6 +33,15 @@
         "!pip install embedchain"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -67,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "from embedchain import App"
+        "from embedchain import Pipeline as App"
       ]
     },
     {

notebooks/hugging_face_hub.ipynb

@@ -34,6 +34,15 @@
         "!pip install embedchain"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -84,7 +93,7 @@
       "outputs": [],
       "source": [
         "import os\n",
-        "from embedchain import App\n",
+        "from embedchain import Pipeline as App\n",
         "\n",
         "os.environ[\"HUGGINGFACE_ACCESS_TOKEN\"] = \"hf_xxx\""
       ]

notebooks/jina.ipynb

@@ -34,6 +34,15 @@
         "!pip install embedchain"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -54,7 +63,7 @@
       "outputs": [],
       "source": [
         "import os\n",
-        "from embedchain import App\n",
+        "from embedchain import Pipeline as App\n",
         "\n",
         "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\"\n",
         "os.environ[\"JINACHAT_API_KEY\"] = \"xxx\""

notebooks/llama2.ipynb

@@ -33,6 +33,15 @@
         "!pip install embedchain"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -64,7 +73,7 @@
       "outputs": [],
       "source": [
         "import os\n",
-        "from embedchain import App\n",
+        "from embedchain import Pipeline as App\n",
         "\n",
         "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\"\n",
         "os.environ[\"REPLICATE_API_TOKEN\"] = \"xxx\""

notebooks/openai.ipynb

@@ -34,6 +34,15 @@
         "!pip install embedchain"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -54,7 +63,7 @@
       "outputs": [],
       "source": [
         "import os\n",
-        "from embedchain import App\n",
+        "from embedchain import Pipeline as App\n",
         "\n",
         "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\""
       ]

notebooks/opensearch.ipynb

@@ -1,95 +1,95 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
-      "source": [
-        "## Cookbook for using OpenSearchDB with Embedchain"
-      ],
       "metadata": {
         "id": "b02n_zJ_hl3d"
-      }
+      },
+      "source": [
+        "## Cookbook for using OpenSearchDB with Embedchain"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-1: Install embedchain package"
-      ],
       "metadata": {
         "id": "gyJ6ui2vhtMY"
-      }
+      },
+      "source": [
+        "### Step-1: Install embedchain package"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "!pip install embedchain"
-      ],
       "metadata": {
         "id": "-NbXjAdlh0vJ"
       },
+      "outputs": [],
+      "source": [
+        "!pip install embedchain"
+      ]
+    },
+    {
+      "cell_type": "code",
       "execution_count": null,
-      "outputs": []
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "nGnpSYAAh2bQ"
+      },
       "source": [
         "### Step-2: Set OpenAI environment variables and install the dependencies.\n",
         "\n",
         "You can find this env variable on your [OpenAI dashboard](https://platform.openai.com/account/api-keys). Now lets install the dependencies needed for Opensearch."
-      ],
+      ]
-      "metadata": {
-        "id": "nGnpSYAAh2bQ"
-      }
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "!pip install --upgrade 'embedchain[opensearch]'"
-      ],
       "metadata": {
         "id": "-MUFRfxV7Jk7"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "!pip install --upgrade 'embedchain[opensearch]'"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "import os\n",
-        "from embedchain import App\n",
-        "\n",
-        "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\""
-      ],
       "metadata": {
         "id": "0fBdQ9GAiRvK"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "import os\n",
+        "from embedchain import Pipeline as App\n",
+        "\n",
+        "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\""
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-3: Define your Vector Database config"
-      ],
       "metadata": {
         "id": "Ns6RhPfbiitr"
-      }
+      },
+      "source": [
+        "### Step-3: Define your Vector Database config"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "S9CkxVjriotB"
+      },
+      "outputs": [],
       "source": [
         "config = \"\"\"\n",
         "vectordb:\n",
@@ -108,64 +108,64 @@
         "# Write the multi-line string to a YAML file\n",
         "with open('opensearch.yaml', 'w') as file:\n",
         "    file.write(config)"
-      ],
+      ]
-      "metadata": {
-        "id": "S9CkxVjriotB"
-      },
-      "execution_count": null,
-      "outputs": []
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-4 Create embedchain app based on the config"
-      ],
       "metadata": {
         "id": "PGt6uPLIi1CS"
-      }
+      },
+      "source": [
+        "### Step-4 Create embedchain app based on the config"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "app = App.from_config(yaml_path=\"opensearch.yaml\")"
-      ],
       "metadata": {
         "id": "Amzxk3m-i3tD"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "app = App.from_config(yaml_path=\"opensearch.yaml\")"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-5: Add data sources to your app"
-      ],
       "metadata": {
         "id": "XNXv4yZwi7ef"
-      }
+      },
+      "source": [
+        "### Step-5: Add data sources to your app"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
+      "execution_count": null,
-        "app.add(\"https://www.forbes.com/profile/elon-musk\")"
-      ],
       "metadata": {
         "id": "Sn_0rx9QjIY9"
       },
-      "execution_count": null,
+      "outputs": [],
-      "outputs": []
+      "source": [
+        "app.add(\"https://www.forbes.com/profile/elon-musk\")"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Step-6: All set. Now start asking questions related to your data"
-      ],
       "metadata": {
         "id": "_7W6fDeAjMAP"
-      }
+      },
+      "source": [
+        "### Step-6: All set. Now start asking questions related to your data"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cvIK7dWRjN_f"
+      },
+      "outputs": [],
       "source": [
         "while(True):\n",
         "    question = input(\"Enter question: \")\n",
@@ -173,12 +173,21 @@
         "        break\n",
         "    answer = app.query(question)\n",
         "    print(answer)"
+      ]
+    }
   ],
   "metadata": {
-        "id": "cvIK7dWRjN_f"
+    "colab": {
+      "provenance": []
     },
-      "execution_count": null,
+    "kernelspec": {
-      "outputs": []
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
     }
-  ]
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

notebooks/pinecone.ipynb

@@ -29,6 +29,15 @@
         "!pip install embedchain"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -60,7 +69,7 @@
       "outputs": [],
       "source": [
         "import os\n",
-        "from embedchain import App\n",
+        "from embedchain import Pipeline as App\n",
         "\n",
         "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\"\n",
         "os.environ[\"PINECONE_API_KEY\"] = \"xxx\"\n",

notebooks/vertex_ai.ipynb

@@ -33,6 +33,15 @@
         "!pip install embedchain"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install embedchain[dataloaders]"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -64,7 +73,7 @@
       "outputs": [],
       "source": [
         "import os\n",
-        "from embedchain import App\n",
+        "from embedchain import Pipeline as App\n",
         "\n",
         "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\""
       ]