docs: notebook linting

cookbook/multi_modal_output_agent.ipynb

@@ -31,7 +31,7 @@
    "source": [
     "import re\n",
     "\n",
-    "from IPython.display import Image\n",
+    "from IPython.display import Image, display\n",
     "from steamship import Block, Steamship"
    ]
   },
@@ -180,7 +180,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

cookbook/qianfan_baidu_elasticesearch_RAG.ipynb

@@ -37,7 +37,8 @@
    "source": [
     "#!pip install qianfan\n",
     "#!pip install bce-python-sdk\n",
-    "#!pip install elasticsearch == 7.11.0"
+    "#!pip install elasticsearch == 7.11.0\n",
+    "#!pip install sentence-transformers"
    ]
   },
   {
@@ -54,8 +55,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import sentence_transformers\n",
     "from baidubce.auth.bce_credentials import BceCredentials\n",
     "from baidubce.bce_client_configuration import BceClientConfiguration\n",
+    "from langchain.chains.retrieval_qa import RetrievalQA\n",
     "from langchain.document_loaders.baiducloud_bos_directory import BaiduBOSDirectoryLoader\n",
     "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
     "from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\n",
@@ -161,21 +164,28 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.9.17"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
   },
-  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
    }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

cookbook/retrieval_in_sql.ipynb

@@ -133,7 +133,7 @@
     "from tqdm import tqdm\n",
     "\n",
     "for i in tqdm(range(len(title_embeddings))):\n",
-    "    title = titles[i].replace(\"'\", \"''\")\n",
+    "    title = song_titles[i].replace(\"'\", \"''\")\n",
     "    embedding = title_embeddings[i]\n",
     "    sql_command = (\n",
     "        f'UPDATE \"Track\" SET \"embeddings\" = ARRAY{embedding} WHERE \"Name\" ='\n",
@@ -681,9 +681,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/expression_language/how_to/fallbacks.ipynb

@@ -291,7 +291,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/promptlayer_chatopenai.ipynb

@@ -129,12 +129,6 @@
     "**The above request should now appear on your [PromptLayer dashboard](https://www.promptlayer.com).**"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "05e9e2fe",
-   "metadata": {},
-   "source": []
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -152,6 +146,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import promptlayer\n",
+    "\n",
     "chat = PromptLayerChatOpenAI(return_pl_id=True)\n",
     "chat_results = chat.generate([[HumanMessage(content=\"I am a cat and I want\")]])\n",
     "\n",
@@ -172,7 +168,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -186,7 +182,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8 (default, Apr 13 2021, 12:59:45) \n[Clang 10.0.0 ]"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/docs/integrations/chat_loaders/imessage.ipynb

@@ -153,7 +153,7 @@
    "source": [
     "# Now all of the Tortoise's messages will take the AI message class\n",
     "# which maps to the 'assistant' role in OpenAI's training format\n",
-    "alternating_sessions[0][\"messages\"][:3]"
+    "chat_sessions[0][\"messages\"][:3]"
    ]
   },
   {
@@ -191,7 +191,7 @@
     }
    ],
    "source": [
-    "training_data = convert_messages_for_finetuning(alternating_sessions)\n",
+    "training_data = convert_messages_for_finetuning(chat_sessions)\n",
     "print(f\"Prepared {len(training_data)} dialogues for training\")"
    ]
   },
@@ -416,7 +416,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/arcgis.ipynb

@@ -23,8 +23,18 @@
    "source": [
     "from langchain.document_loaders import ArcGISLoader\n",
     "\n",
-    "url = \"https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7\"\n",
-    "loader = ArcGISLoader(url)"
+    "URL = \"https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7\"\n",
+    "loader = ArcGISLoader(URL)\n",
+    "\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e174ebd-bbbd-4a66-a644-51e0df12982d",
+   "metadata": {},
+   "source": [
+    "Let's measure loader latency."
    ]
   },
   {
@@ -261,7 +271,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader_geom = ArcGISLoader(url, return_geometry=True)"
+    "loader_geom = ArcGISLoader(URL, return_geometry=True)"
    ]
   },
   {

docs/docs/integrations/document_loaders/datadog_logs.ipynb

@@ -30,6 +30,16 @@
     "#!pip install datadog-api-client"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DD_API_KEY = \"...\"\n",
+    "DD_APP_KEY = \"...\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -73,7 +83,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -87,10 +97,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.11"
-  },
-  "orig_nbformat": 4
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/integrations/document_loaders/etherscan.ipynb

@@ -65,6 +65,16 @@
     "%pip install langchain -q"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ab73cc1-d8e0-4b6d-bb03-9522b112fce5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "etherscanAPIKey = \"...\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/docs/integrations/document_loaders/larksuite.ipynb

@@ -74,7 +74,9 @@
    "source": [
     "# see https://python.langchain.com/docs/use_cases/summarization for more details\n",
     "from langchain.chains.summarize import load_summarize_chain\n",
+    "from langchain.llms.fake import FakeListLLM\n",
     "\n",
+    "llm = FakeListLLM()\n",
     "chain = load_summarize_chain(llm, chain_type=\"map_reduce\")\n",
     "chain.run(docs)"
    ]
@@ -96,7 +98,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/tensorflow_datasets.ipynb

@@ -166,6 +166,9 @@
     }
    ],
    "source": [
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "\n",
     "def decode_to_str(item: tf.Tensor) -> str:\n",
     "    return item.numpy().decode(\"utf-8\")\n",
     "\n",

docs/docs/integrations/llms/anyscale.ipynb

@@ -12,6 +12,18 @@
     "This example goes over how to use LangChain to interact with [Anyscale Endpoint](https://app.endpoints.anyscale.com/). "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "515070aa-e241-480e-8d9a-afdf52f35322",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ANYSCALE_API_BASE = \"...\"\n",
+    "ANYSCALE_API_KEY = \"...\"\n",
+    "ANYSCALE_MODEL_NAME = \"...\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -160,7 +172,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/docs/integrations/llms/bittensor.ipynb

@@ -112,6 +112,24 @@
     "##  Using NIBittensorLLM with Conversational Agent and Google Search Tool"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools import Tool\n",
+    "from langchain.utilities import GoogleSearchAPIWrapper\n",
+    "\n",
+    "search = GoogleSearchAPIWrapper()\n",
+    "\n",
+    "tool = Tool(\n",
+    "    name=\"Google Search\",\n",
+    "    description=\"Search Google for recent results.\",\n",
+    "    func=search.run,\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -129,22 +147,22 @@
     "\n",
     "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
     "\n",
-    "\n",
+    "tools = [tool]\n",
     "prefix = \"\"\"Answer prompt based on LLM if there is need to search something then use internet and observe internet result and give accurate reply of user questions also try to use authenticated sources\"\"\"\n",
     "suffix = \"\"\"Begin!\n",
     "            {chat_history}\n",
     "            Question: {input}\n",
     "            {agent_scratchpad}\"\"\"\n",
     "\n",
     "prompt = ZeroShotAgent.create_prompt(\n",
-    "    tools,\n",
+    "    tools=tools,\n",
     "    prefix=prefix,\n",
     "    suffix=suffix,\n",
     "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"],\n",
     ")\n",
     "\n",
     "llm = NIBittensorLLM(\n",
-    "    system_prompt=\"Your task is to determine response based on user prompt\"\n",
+    "    system_prompt=\"Your task is to determine a response based on user prompt\"\n",
     ")\n",
     "\n",
     "llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
@@ -176,7 +194,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,