DOCS: format notebooks (#13371)

cookbook/Multi_modal_RAG.ipynb

@@ -221,13 +221,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import base64\n",
     "import io\n",
     "import os\n",
-    "import base64\n",
+    "\n",
     "import numpy as np\n",
-    "from PIL import Image\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema.messages import HumanMessage, SystemMessage\n",
+    "from PIL import Image\n",
     "\n",
     "\n",
     "def encode_image(image_path):\n",
@@ -294,7 +295,7 @@
     }
    ],
    "source": [
-    "from IPython.display import display, HTML\n",
+    "from IPython.display import HTML, display\n",
     "\n",
     "\n",
     "def plt_img_base64(img_base64):\n",
@@ -347,11 +348,12 @@
    "outputs": [],
    "source": [
     "import uuid\n",
-    "from langchain.vectorstores import Chroma\n",
-    "from langchain.storage import InMemoryStore\n",
-    "from langchain.schema.document import Document\n",
+    "\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
+    "from langchain.schema.document import Document\n",
+    "from langchain.storage import InMemoryStore\n",
+    "from langchain.vectorstores import Chroma\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(\n",
@@ -524,7 +526,8 @@
    "outputs": [],
    "source": [
     "from operator import itemgetter\n",
-    "from langchain.schema.runnable import RunnablePassthrough, RunnableLambda\n",
+    "\n",
+    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
     "\n",
     "\n",
     "def prompt_func(dict):\n",

cookbook/Semi_Structured_RAG.ipynb

@@ -102,8 +102,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel\n",
     "from typing import Any\n",
+    "\n",
+    "from pydantic import BaseModel\n",
     "from unstructured.partition.pdf import partition_pdf\n",
     "\n",
     "# Get elements\n",
@@ -316,11 +317,12 @@
    "outputs": [],
    "source": [
     "import uuid\n",
-    "from langchain.vectorstores import Chroma\n",
-    "from langchain.storage import InMemoryStore\n",
-    "from langchain.schema.document import Document\n",
+    "\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
+    "from langchain.schema.document import Document\n",
+    "from langchain.storage import InMemoryStore\n",
+    "from langchain.vectorstores import Chroma\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",

cookbook/Semi_structured_and_multi_modal_RAG.ipynb

@@ -92,8 +92,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel\n",
     "from typing import Any\n",
+    "\n",
+    "from pydantic import BaseModel\n",
     "from unstructured.partition.pdf import partition_pdf\n",
     "\n",
     "# Get elements\n",
@@ -336,8 +337,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "import glob\n",
+    "import os\n",
     "\n",
     "# Get all .txt file summaries\n",
     "file_paths = glob.glob(os.path.expanduser(os.path.join(path, \"*.txt\")))\n",
@@ -371,11 +372,12 @@
    "outputs": [],
    "source": [
     "import uuid\n",
-    "from langchain.vectorstores import Chroma\n",
-    "from langchain.storage import InMemoryStore\n",
-    "from langchain.schema.document import Document\n",
+    "\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
+    "from langchain.schema.document import Document\n",
+    "from langchain.storage import InMemoryStore\n",
+    "from langchain.vectorstores import Chroma\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -82,8 +82,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pydantic import BaseModel\n",
     "from typing import Any\n",
+    "\n",
+    "from pydantic import BaseModel\n",
     "from unstructured.partition.pdf import partition_pdf\n",
     "\n",
     "# Path to save images\n",
@@ -320,8 +321,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "import glob\n",
+    "import os\n",
     "\n",
     "# Get all .txt files in the directory\n",
     "file_paths = glob.glob(os.path.expanduser(os.path.join(path, \"*.txt\")))\n",
@@ -374,11 +375,12 @@
    ],
    "source": [
     "import uuid\n",
-    "from langchain.vectorstores import Chroma\n",
-    "from langchain.storage import InMemoryStore\n",
-    "from langchain.schema.document import Document\n",
+    "\n",
     "from langchain.embeddings import GPT4AllEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
+    "from langchain.schema.document import Document\n",
+    "from langchain.storage import InMemoryStore\n",
+    "from langchain.vectorstores import Chroma\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(\n",

cookbook/advanced_rag_eval.ipynb

@@ -132,8 +132,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores import Chroma\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import Chroma\n",
     "\n",
     "baseline = Chroma.from_texts(\n",
     "    texts=all_splits_pypdf_texts,\n",
@@ -197,12 +197,13 @@
    "outputs": [],
    "source": [
     "# Image summary chain\n",
-    "import os\n",
     "import base64\n",
     "import io\n",
+    "import os\n",
     "from io import BytesIO\n",
-    "from PIL import Image\n",
+    "\n",
     "from langchain.schema.messages import HumanMessage\n",
+    "from PIL import Image\n",
     "\n",
     "\n",
     "def encode_image(image_path):\n",
@@ -270,9 +271,10 @@
    "source": [
     "import uuid\n",
     "from base64 import b64decode\n",
-    "from langchain.storage import InMemoryStore\n",
-    "from langchain.schema.document import Document\n",
+    "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
+    "from langchain.schema.document import Document\n",
+    "from langchain.storage import InMemoryStore\n",
     "\n",
     "\n",
     "def create_multi_vector_retriever(\n",
@@ -363,7 +365,7 @@
     }
    ],
    "source": [
-    "from IPython.display import display, HTML\n",
+    "from IPython.display import HTML, display\n",
     "\n",
     "\n",
     "def plt_img_base64(img_base64):\n",
@@ -472,6 +474,7 @@
    "outputs": [],
    "source": [
     "from operator import itemgetter\n",
+    "\n",
     "from langchain.schema.runnable import RunnablePassthrough\n",
     "\n",
     "# Prompt\n",
@@ -516,6 +519,7 @@
    "outputs": [],
    "source": [
     "import re\n",
+    "\n",
     "from langchain.schema import Document\n",
     "from langchain.schema.runnable import RunnableLambda\n",
     "\n",

cookbook/autogpt/autogpt.ipynb

@@ -27,10 +27,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.utilities import SerpAPIWrapper\n",
     "from langchain.agents import Tool\n",
-    "from langchain.tools.file_management.write import WriteFileTool\n",
     "from langchain.tools.file_management.read import ReadFileTool\n",
+    "from langchain.tools.file_management.write import WriteFileTool\n",
+    "from langchain.utilities import SerpAPIWrapper\n",
     "\n",
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
@@ -61,9 +61,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores import FAISS\n",
     "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain.embeddings import OpenAIEmbeddings"
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import FAISS"
    ]
   },
   {
@@ -100,8 +100,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_experimental.autonomous_agents import AutoGPT\n",
-    "from langchain.chat_models import ChatOpenAI"
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain_experimental.autonomous_agents import AutoGPT"
    ]
   },
   {

cookbook/autogpt/marathon_times.ipynb

@@ -34,16 +34,15 @@
    "outputs": [],
    "source": [
     "# General\n",
-    "import os\n",
-    "import pandas as pd\n",
-    "from langchain_experimental.autonomous_agents import AutoGPT\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "\n",
-    "from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent\n",
-    "from langchain.docstore.document import Document\n",
     "import asyncio\n",
-    "import nest_asyncio\n",
+    "import os\n",
     "\n",
+    "import nest_asyncio\n",
+    "import pandas as pd\n",
+    "from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.docstore.document import Document\n",
+    "from langchain_experimental.autonomous_agents import AutoGPT\n",
     "\n",
     "# Needed synce jupyter runs an async eventloop\n",
     "nest_asyncio.apply()"
@@ -92,6 +91,7 @@
     "import os\n",
     "from contextlib import contextmanager\n",
     "from typing import Optional\n",
+    "\n",
     "from langchain.agents import tool\n",
     "from langchain.tools.file_management.read import ReadFileTool\n",
     "from langchain.tools.file_management.write import WriteFileTool\n",
@@ -223,14 +223,13 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.tools import BaseTool, DuckDuckGoSearchRun\n",
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "\n",
-    "from pydantic import Field\n",
     "from langchain.chains.qa_with_sources.loading import (\n",
-    "    load_qa_with_sources_chain,\n",
     "    BaseCombineDocumentsChain,\n",
+    "    load_qa_with_sources_chain,\n",
     ")\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.tools import BaseTool, DuckDuckGoSearchRun\n",
+    "from pydantic import Field\n",
     "\n",
     "\n",
     "def _get_text_splitter():\n",
@@ -311,9 +310,9 @@
    "source": [
     "# Memory\n",
     "import faiss\n",
-    "from langchain.vectorstores import FAISS\n",
     "from langchain.docstore import InMemoryDocstore\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import FAISS\n",
     "\n",
     "embeddings_model = OpenAIEmbeddings()\n",
     "embedding_size = 1536\n",

cookbook/baby_agi.ipynb

@@ -31,8 +31,8 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.llms import OpenAI\n",
     "from langchain_experimental.autonomous_agents import BabyAGI"
    ]
   },
@@ -53,8 +53,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores import FAISS\n",
-    "from langchain.docstore import InMemoryDocstore"
+    "from langchain.docstore import InMemoryDocstore\n",
+    "from langchain.vectorstores import FAISS"
    ]
   },
   {

cookbook/baby_agi_with_agent.ipynb

@@ -28,9 +28,9 @@
     "from typing import Optional\n",
     "\n",
     "from langchain.chains import LLMChain\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain_experimental.autonomous_agents import BabyAGI"
    ]
   },
@@ -62,8 +62,8 @@
    "source": [
     "%pip install faiss-cpu > /dev/null\n",
     "%pip install google-search-results > /dev/null\n",
-    "from langchain.vectorstores import FAISS\n",
-    "from langchain.docstore import InMemoryDocstore"
+    "from langchain.docstore import InMemoryDocstore\n",
+    "from langchain.vectorstores import FAISS"
    ]
   },
   {
@@ -106,10 +106,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
+    "from langchain.agents import AgentExecutor, Tool, ZeroShotAgent\n",
+    "from langchain.chains import LLMChain\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.utilities import SerpAPIWrapper\n",
-    "from langchain.chains import LLMChain\n",
     "\n",
     "todo_prompt = PromptTemplate.from_template(\n",
     "    \"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\"\n",

cookbook/camel_role_playing.ipynb

@@ -35,16 +35,17 @@
    "outputs": [],
    "source": [
     "from typing import List\n",
+    "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts.chat import (\n",
-    "    SystemMessagePromptTemplate,\n",
     "    HumanMessagePromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
     ")\n",
     "from langchain.schema import (\n",
     "    AIMessage,\n",
+    "    BaseMessage,\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    "    BaseMessage,\n",
     ")"
    ]
   },

cookbook/causal_program_aided_language_model.ipynb

@@ -47,10 +47,9 @@
    "outputs": [],
    "source": [
     "from IPython.display import SVG\n",
-    "\n",
+    "from langchain.llms import OpenAI\n",
     "from langchain_experimental.cpal.base import CPALChain\n",
     "from langchain_experimental.pal_chain import PALChain\n",
-    "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0, max_tokens=512)\n",
     "cpal_chain = CPALChain.from_univariate_prompt(llm=llm, verbose=True)\n",

cookbook/code-analysis-deeplake.ipynb

@@ -717,7 +717,6 @@
    "source": [
     "from langchain.vectorstores import DeepLake\n",
     "\n",
-    "\n",
     "username = \"<USERNAME_OR_ORG>\"\n",
     "\n",
     "\n",
@@ -834,8 +833,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.chains import ConversationalRetrievalChain\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(\n",
     "    model_name=\"gpt-3.5-turbo-0613\"\n",

cookbook/custom_agent_with_plugin_retrieval.ipynb

@@ -32,19 +32,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import re\n",
+    "from typing import Union\n",
+    "\n",
     "from langchain.agents import (\n",
     "    AgentExecutor,\n",
-    "    LLMSingleActionAgent,\n",
     "    AgentOutputParser,\n",
+    "    LLMSingleActionAgent,\n",
     ")\n",
-    "from langchain.prompts import StringPromptTemplate\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.chains import LLMChain\n",
-    "from typing import Union\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain.agents.agent_toolkits import NLAToolkit\n",
-    "from langchain.tools.plugin import AIPlugin\n",
-    "import re"
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import StringPromptTemplate\n",
+    "from langchain.schema import AgentAction, AgentFinish\n",
+    "from langchain.tools.plugin import AIPlugin"
    ]
   },
   {
@@ -113,9 +114,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores import FAISS\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.schema import Document"
+    "from langchain.schema import Document\n",
+    "from langchain.vectorstores import FAISS"
    ]
   },
   {

cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb

@@ -56,20 +56,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import re\n",
+    "from typing import Union\n",
+    "\n",
+    "import plugnplai\n",
     "from langchain.agents import (\n",
     "    AgentExecutor,\n",
-    "    LLMSingleActionAgent,\n",
     "    AgentOutputParser,\n",
+    "    LLMSingleActionAgent,\n",
     ")\n",
-    "from langchain.prompts import StringPromptTemplate\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.chains import LLMChain\n",
-    "from typing import Union\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain.agents.agent_toolkits import NLAToolkit\n",
-    "from langchain.tools.plugin import AIPlugin\n",
-    "import re\n",
-    "import plugnplai"
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import StringPromptTemplate\n",
+    "from langchain.schema import AgentAction, AgentFinish\n",
+    "from langchain.tools.plugin import AIPlugin"
    ]
   },
   {
@@ -137,9 +138,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores import FAISS\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.schema import Document"
+    "from langchain.schema import Document\n",
+    "from langchain.vectorstores import FAISS"
    ]
   },
   {

cookbook/deeplake_semantic_search_over_chat.ipynb

@@ -48,16 +48,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "import getpass\n",
+    "import os\n",
+    "\n",
+    "from langchain.chains import RetrievalQA\n",
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.llms import OpenAI\n",
     "from langchain.text_splitter import (\n",
-    "    RecursiveCharacterTextSplitter,\n",
     "    CharacterTextSplitter,\n",
+    "    RecursiveCharacterTextSplitter,\n",
     ")\n",
     "from langchain.vectorstores import DeepLake\n",
-    "from langchain.chains import RetrievalQA\n",
-    "from langchain.llms import OpenAI\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
     "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",

cookbook/elasticsearch_db_qa.ipynb

@@ -38,9 +38,8 @@
    "outputs": [],
    "source": [
     "from elasticsearch import Elasticsearch\n",
-    "\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain"
+    "from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain\n",
+    "from langchain.chat_models import ChatOpenAI"
    ]
   },
   {

cookbook/extraction_openai_tools.ipynb

@@ -19,10 +19,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from typing import List, Optional\n",
+    "\n",
+    "from langchain.chains.openai_tools import create_extraction_chain_pydantic\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
-    "from typing import Optional, List\n",
-    "from langchain.chains.openai_tools import create_extraction_chain_pydantic"
+    "from langchain.pydantic_v1 import BaseModel"
    ]
   },
   {

cookbook/fake_llm.ipynb

@@ -30,9 +30,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import load_tools\n",
-    "from langchain.agents import initialize_agent\n",
-    "from langchain.agents import AgentType"
+    "from langchain.agents import AgentType, initialize_agent, load_tools"
    ]
   },
   {

cookbook/forward_looking_retrieval_augmented_generation.ipynb

@@ -67,16 +67,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema import BaseRetriever\n",
+    "from typing import Any, List\n",
+    "\n",
     "from langchain.callbacks.manager import (\n",
     "    AsyncCallbackManagerForRetrieverRun,\n",
     "    CallbackManagerForRetrieverRun,\n",
     ")\n",
-    "from langchain.utilities import GoogleSerperAPIWrapper\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.schema import Document\n",
-    "from typing import Any, List"
+    "from langchain.schema import BaseRetriever, Document\n",
+    "from langchain.utilities import GoogleSerperAPIWrapper"
    ]
   },
   {

cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb

@@ -46,14 +46,13 @@
    "source": [
     "from datetime import datetime, timedelta\n",
     "from typing import List\n",
-    "from termcolor import colored\n",
-    "\n",
     "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.docstore import InMemoryDocstore\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers import TimeWeightedVectorStoreRetriever\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain.vectorstores import FAISS\n",
+    "from termcolor import colored"
    ]
   },
   {
@@ -153,6 +152,7 @@
    "outputs": [],
    "source": [
     "import math\n",
+    "\n",
     "import faiss\n",
     "\n",
     "\n",

cookbook/gymnasium_agent_simulation.ipynb

@@ -28,12 +28,11 @@
    "outputs": [],
    "source": [
     "import tenacity\n",
-    "\n",
+    "from langchain.output_parsers import RegexParser\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")\n",
-    "from langchain.output_parsers import RegexParser"
+    ")"
    ]
   },
   {

cookbook/human_input_chat_model.ipynb

@@ -55,9 +55,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import load_tools\n",
-    "from langchain.agents import initialize_agent\n",
-    "from langchain.agents import AgentType"
+    "from langchain.agents import AgentType, initialize_agent, load_tools"
    ]
   },
   {

cookbook/human_input_llm.ipynb

@@ -28,9 +28,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import load_tools\n",
-    "from langchain.agents import initialize_agent\n",
-    "from langchain.agents import AgentType"
+    "from langchain.agents import AgentType, initialize_agent, load_tools"
    ]
   },
   {

cookbook/hypothetical_document_embeddings.ipynb

@@ -20,9 +20,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI\n",
+    "from langchain.chains import HypotheticalDocumentEmbedder, LLMChain\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.chains import LLMChain, HypotheticalDocumentEmbedder\n",
+    "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate"
    ]
   },

cookbook/learned_prompt_optimization.ipynb

@@ -790,8 +790,8 @@
     }
    ],
    "source": [
-    "from langchain.prompts.prompt import PromptTemplate\n",
     "from langchain.globals import set_debug\n",
+    "from langchain.prompts.prompt import PromptTemplate\n",
     "\n",
     "set_debug(True)\n",
     "\n",

cookbook/llm_bash.ipynb

@@ -43,8 +43,8 @@
     }
    ],
    "source": [
-    "from langchain_experimental.llm_bash.base import LLMBashChain\n",
     "from langchain.llms import OpenAI\n",
+    "from langchain_experimental.llm_bash.base import LLMBashChain\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "\n",
@@ -69,8 +69,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts.prompt import PromptTemplate\n",
     "from langchain.chains.llm_bash.prompt import BashOutputParser\n",
+    "from langchain.prompts.prompt import PromptTemplate\n",
     "\n",
     "_PROMPT_TEMPLATE = \"\"\"If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put \"#!/bin/bash\" in your answer. Make sure to reason step by step, using this format:\n",
     "Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"\n",
@@ -185,7 +185,6 @@
    "source": [
     "from langchain_experimental.llm_bash.bash import BashProcess\n",
     "\n",
-    "\n",
     "persistent_process = BashProcess(persistent=True)\n",
     "bash_chain = LLMBashChain.from_llm(llm, bash_process=persistent_process, verbose=True)\n",
     "\n",

cookbook/llm_math.ipynb

@@ -45,7 +45,8 @@
     }
    ],
    "source": [
-    "from langchain.llms import OpenAI\nfrom langchain.chains import LLMMathChain\n",
+    "from langchain.chains import LLMMathChain\n",
+    "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "llm_math = LLMMathChain.from_llm(llm, verbose=True)\n",

cookbook/meta_prompt.ipynb

@@ -56,10 +56,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.memory import ConversationBufferWindowMemory"
+    "from langchain.llms import OpenAI\n",
+    "from langchain.memory import ConversationBufferWindowMemory\n",
+    "from langchain.prompts import PromptTemplate"
    ]
   },
   {

cookbook/multi_modal_QA.ipynb

@@ -40,12 +40,13 @@
     }
    ],
    "source": [
-    "import os\n",
-    "import io\n",
     "import base64\n",
+    "import io\n",
+    "import os\n",
+    "\n",
     "import numpy as np\n",
+    "from IPython.display import HTML, display\n",
     "from PIL import Image\n",
-    "from IPython.display import display, HTML\n",
     "\n",
     "\n",
     "def encode_image(image_path):\n",

cookbook/multi_modal_RAG_chroma.ipynb

@@ -184,11 +184,12 @@
    "source": [
     "import os\n",
     "import uuid\n",
+    "\n",
     "import chromadb\n",
     "import numpy as np\n",
-    "from PIL import Image as _PILImage\n",
     "from langchain.vectorstores import Chroma\n",
     "from langchain_experimental.open_clip import OpenCLIPEmbeddings\n",
+    "from PIL import Image as _PILImage\n",
     "\n",
     "# Create chroma\n",
     "vectorstore = Chroma(\n",
@@ -233,10 +234,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import io\n",
-    "import numpy as np\n",
     "import base64\n",
+    "import io\n",
     "from io import BytesIO\n",
+    "\n",
+    "import numpy as np\n",
     "from PIL import Image\n",
     "\n",
     "\n",
@@ -312,10 +314,11 @@
    "outputs": [],
    "source": [
     "from operator import itemgetter\n",
+    "\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough, RunnableLambda\n",
     "from langchain.schema.messages import HumanMessage, SystemMessage\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
     "\n",
     "\n",
     "def prompt_func(data_dict):\n",
@@ -421,7 +424,7 @@
     }
    ],
    "source": [
-    "from IPython.display import display, HTML\n",
+    "from IPython.display import HTML, display\n",
     "\n",
     "\n",
     "def plt_img_base64(img_base64):\n",

cookbook/multi_modal_output_agent.ipynb

@@ -29,9 +29,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from steamship import Block, Steamship\n",
     "import re\n",
-    "from IPython.display import Image"
+    "\n",
+    "from IPython.display import Image\n",
+    "from steamship import Block, Steamship"
    ]
   },
   {
@@ -41,9 +42,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.agents import initialize_agent\n",
-    "from langchain.agents import AgentType\n",
     "from langchain.tools import SteamshipImageGenerationTool"
    ]
   },

cookbook/multi_player_dnd.ipynb

@@ -26,7 +26,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import List, Callable\n",
+    "from typing import Callable, List\n",
+    "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",

cookbook/multiagent_authoritarian.ipynb

@@ -27,17 +27,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from collections import OrderedDict\n",
     "import functools\n",
     "import random\n",
-    "import tenacity\n",
-    "from typing import List, Callable\n",
+    "from collections import OrderedDict\n",
+    "from typing import Callable, List\n",
     "\n",
+    "import tenacity\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.output_parsers import RegexParser\n",
     "from langchain.prompts import (\n",
     "    PromptTemplate,\n",
     ")\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.output_parsers import RegexParser\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",

cookbook/multiagent_bidding.ipynb

@@ -24,11 +24,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
+    "from typing import Callable, List\n",
+    "\n",
     "import tenacity\n",
-    "from typing import List, Callable\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.output_parsers import RegexParser\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",

cookbook/myscale_vector_sql.ipynb

@@ -27,17 +27,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from os import environ\n",
     "import getpass\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.utilities import SQLDatabase\n",
+    "from os import environ\n",
+    "\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
-    "from sqlalchemy import create_engine, MetaData\n",
+    "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
-    "\n",
-    "\n",
-    "from sqlalchemy import create_engine\n",
+    "from langchain.utilities import SQLDatabase\n",
+    "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
+    "from sqlalchemy import MetaData, create_engine\n",
     "\n",
     "MYSCALE_HOST = \"msc-4a9e710a.us-east-1.aws.staging.myscale.cloud\"\n",
     "MYSCALE_PORT = 443\n",
@@ -76,9 +74,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI\n",
     "from langchain.callbacks import StdOutCallbackHandler\n",
-    "\n",
+    "from langchain.llms import OpenAI\n",
     "from langchain.utilities.sql_database import SQLDatabase\n",
     "from langchain_experimental.sql.prompt import MYSCALE_PROMPT\n",
     "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
@@ -119,15 +116,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain\n",
-    "\n",
-    "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain_experimental.retrievers.vector_sql_database import (\n",
     "    VectorSQLDatabaseChainRetriever,\n",
     ")\n",
     "from langchain_experimental.sql.prompt import MYSCALE_PROMPT\n",
-    "from langchain_experimental.sql.vector_sql import VectorSQLRetrieveAllOutputParser\n",
+    "from langchain_experimental.sql.vector_sql import (\n",
+    "    VectorSQLDatabaseChain,\n",
+    "    VectorSQLRetrieveAllOutputParser,\n",
+    ")\n",
     "\n",
     "output_parser_retrieve_all = VectorSQLRetrieveAllOutputParser.from_embeddings(\n",
     "    output_parser.model\n",

cookbook/openai_functions_retrieval_qa.ipynb

@@ -50,10 +50,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains import create_qa_with_sources_chain\n",
     "from langchain.chains.combine_documents.stuff import StuffDocumentsChain\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import create_qa_with_sources_chain"
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import PromptTemplate"
    ]
   },
   {
@@ -230,9 +230,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains import ConversationalRetrievalChain\n",
+    "from langchain.chains import ConversationalRetrievalChain, LLMChain\n",
     "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain.chains import LLMChain\n",
     "\n",
     "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
     "_template = \"\"\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\\\n",
@@ -357,12 +356,10 @@
    "source": [
     "from typing import List\n",
     "\n",
-    "from pydantic import BaseModel, Field\n",
-    "\n",
     "from langchain.chains.openai_functions import create_qa_with_structure_chain\n",
-    "\n",
     "from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate\n",
-    "from langchain.schema import SystemMessage, HumanMessage"
+    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from pydantic import BaseModel, Field"
    ]
   },
   {

cookbook/openai_v1_cookbook.ipynb

@@ -167,7 +167,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.tools import E2BDataAnalysisTool, DuckDuckGoSearchRun\n",
+    "from langchain.tools import DuckDuckGoSearchRun, E2BDataAnalysisTool\n",
     "\n",
     "tools = [E2BDataAnalysisTool(api_key=\"...\"), DuckDuckGoSearchRun()]"
    ]
@@ -456,9 +456,9 @@
     "from typing import Literal\n",
     "\n",
     "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
-    "from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.pydantic_v1 import BaseModel, Field\n",
+    "from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
     "\n",
     "\n",
     "class GetCurrentWeather(BaseModel):\n",

cookbook/petting_zoo.ipynb

@@ -45,14 +45,14 @@
    "source": [
     "import collections\n",
     "import inspect\n",
-    "import tenacity\n",
     "\n",
+    "import tenacity\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.output_parsers import RegexParser\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")\n",
-    "from langchain.output_parsers import RegexParser"
+    ")"
    ]
   },
   {

cookbook/program_aided_language_model.ipynb

@@ -17,8 +17,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_experimental.pal_chain import PALChain\n",
-    "from langchain.llms import OpenAI"
+    "from langchain.llms import OpenAI\n",
+    "from langchain_experimental.pal_chain import PALChain"
    ]
   },
   {

cookbook/qianfan_baidu_elasticesearch_RAG.ipynb

@@ -54,13 +54,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from baidubce.bce_client_configuration import BceClientConfiguration\n",
     "from baidubce.auth.bce_credentials import BceCredentials\n",
+    "from baidubce.bce_client_configuration import BceClientConfiguration\n",
     "from langchain.document_loaders.baiducloud_bos_directory import BaiduBOSDirectoryLoader\n",
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
-    "from langchain.vectorstores import BESVectorStore\n",
-    "from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint"
+    "from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.vectorstores import BESVectorStore"
    ]
   },
   {

cookbook/rag_fusion.ipynb

@@ -30,8 +30,8 @@
    "outputs": [],
    "source": [
     "import pinecone\n",
-    "from langchain.vectorstores import Pinecone\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import Pinecone\n",
     "\n",
     "pinecone.init(api_key=\"...\", environment=\"...\")"
    ]

cookbook/retrieval_in_sql.ipynb

@@ -28,8 +28,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "import getpass\n",
+    "import os\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\") or getpass.getpass(\n",
     "    \"OpenAI API Key:\"\n",
@@ -42,8 +42,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.sql_database import SQLDatabase\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.sql_database import SQLDatabase\n",
     "\n",
     "CONNECTION_STRING = \"postgresql+psycopg2://postgres:test@localhost:5432/vectordb\"  # Replace with your own\n",
     "db = SQLDatabase.from_uri(CONNECTION_STRING)"
@@ -323,6 +323,7 @@
    "outputs": [],
    "source": [
     "import re\n",
+    "\n",
     "from langchain.schema.runnable import RunnableLambda\n",
     "\n",
     "\n",

cookbook/rewrite.ipynb

@@ -31,8 +31,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
     "from langchain.schema.runnable import RunnablePassthrough\n",
     "from langchain.utilities import DuckDuckGoSearchAPIWrapper"

cookbook/sales_agent_with_context.ipynb

@@ -42,22 +42,22 @@
     "OPENAI_API_KEY = \"sk-xx\"\n",
     "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n",
     "\n",
-    "from typing import Dict, List, Any, Union, Callable\n",
-    "from pydantic import BaseModel, Field\n",
-    "from langchain.chains import LLMChain\nfrom langchain.prompts import PromptTemplate\n",
-    "from langchain.llms import BaseLLM\n",
-    "from langchain.chains.base import Chain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.agents import Tool, LLMSingleActionAgent, AgentExecutor\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.chains import RetrievalQA\n",
-    "from langchain.vectorstores import Chroma\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.prompts.base import StringPromptTemplate\n",
+    "from typing import Any, Callable, Dict, List, Union\n",
+    "\n",
+    "from langchain.agents import AgentExecutor, LLMSingleActionAgent, Tool\n",
     "from langchain.agents.agent import AgentOutputParser\n",
     "from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS\n",
-    "from langchain.schema import AgentAction, AgentFinish"
+    "from langchain.chains import LLMChain, RetrievalQA\n",
+    "from langchain.chains.base import Chain\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.llms import BaseLLM, OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.prompts.base import StringPromptTemplate\n",
+    "from langchain.schema import AgentAction, AgentFinish\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from pydantic import BaseModel, Field"
    ]
   },
   {

cookbook/selecting_llms_based_on_context_length.ipynb

@@ -17,10 +17,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.prompt import PromptValue\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.schema.prompt import PromptValue"
    ]
   },
   {

cookbook/smart_llm.ipynb

@@ -51,8 +51,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_experimental.smart_llm import SmartLLMChain"
    ]
   },

cookbook/stepback-qa.ipynb

@@ -131,7 +131,6 @@
    "source": [
     "from langchain.utilities import DuckDuckGoSearchAPIWrapper\n",
     "\n",
-    "\n",
     "search = DuckDuckGoSearchAPIWrapper(max_results=4)\n",
     "\n",
     "\n",

cookbook/tree_of_thought.ipynb

@@ -84,10 +84,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import re\n",
     "from typing import Tuple\n",
+    "\n",
     "from langchain_experimental.tot.checker import ToTChecker\n",
     "from langchain_experimental.tot.thought import ThoughtValidity\n",
-    "import re\n",
     "\n",
     "\n",
     "class MyChecker(ToTChecker):\n",

cookbook/twitter-the-algorithm-analysis-deeplake.ipynb

@@ -34,8 +34,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "import getpass\n",
+    "import os\n",
     "\n",
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
     "from langchain.vectorstores import DeepLake\n",
@@ -109,6 +109,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "from langchain.document_loaders import TextLoader\n",
     "\n",
     "root_dir = \"./the-algorithm\"\n",
@@ -3807,8 +3808,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.chains import ConversationalRetrievalChain\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(model_name=\"gpt-3.5-turbo-0613\")  # switch to 'gpt-4'\n",
     "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"

cookbook/two_agent_debate_tools.ipynb

@@ -22,7 +22,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import List, Callable\n",
+    "from typing import Callable, List\n",
+    "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.memory import ConversationBufferMemory\n",
     "from langchain.schema import (\n",
@@ -45,9 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import initialize_agent\n",
-    "from langchain.agents import AgentType\n",
-    "from langchain.agents import load_tools"
+    "from langchain.agents import AgentType, initialize_agent, load_tools"
    ]
   },
   {

cookbook/two_player_dnd.ipynb

@@ -22,7 +22,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import List, Callable\n",
+    "from typing import Callable, List\n",
+    "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",

cookbook/wikibase_agent.ipynb

@@ -192,10 +192,10 @@
     "    return current\n",
     "\n",
     "\n",
-    "import requests\n",
-    "\n",
     "from typing import Optional\n",
     "\n",
+    "import requests\n",
+    "\n",
     "\n",
     "def vocab_lookup(\n",
     "    search: str,\n",
@@ -319,9 +319,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import requests\n",
-    "from typing import List, Dict, Any\n",
     "import json\n",
+    "from typing import Any, Dict, List\n",
+    "\n",
+    "import requests\n",
     "\n",
     "\n",
     "def run_sparql(\n",
@@ -389,17 +390,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import (\n",
-    "    Tool,\n",
-    "    AgentExecutor,\n",
-    "    LLMSingleActionAgent,\n",
-    "    AgentOutputParser,\n",
-    ")\n",
-    "from langchain.prompts import StringPromptTemplate\n",
-    "from langchain.chains import LLMChain\n",
+    "import re\n",
     "from typing import List, Union\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
-    "import re"
+    "\n",
+    "from langchain.agents import (\n",
+    "    AgentExecutor,\n",
+    "    AgentOutputParser,\n",
+    "    LLMSingleActionAgent,\n",
+    "    Tool,\n",
+    ")\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain.prompts import StringPromptTemplate\n",
+    "from langchain.schema import AgentAction, AgentFinish"
    ]
   },
   {

docs/docs/expression_language/cookbook/agent.ipynb

@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import XMLAgent, tool, AgentExecutor\n",
+    "from langchain.agents import AgentExecutor, XMLAgent, tool\n",
     "from langchain.chat_models import ChatAnthropic"
    ]
   },

docs/docs/expression_language/cookbook/embedding_router.ipynb

@@ -26,7 +26,6 @@
     "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
     "from langchain.utils.math import cosine_similarity\n",
     "\n",
-    "\n",
     "physics_template = \"\"\"You are a very smart physics professor. \\\n",
     "You are great at answering questions about physics in a concise and easy to understand manner. \\\n",
     "When you don't know the answer to a question you admit that you don't know.\n",

docs/docs/expression_language/cookbook/memory.ipynb

@@ -18,10 +18,11 @@
    "outputs": [],
    "source": [
     "from operator import itemgetter\n",
+    "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain.schema.runnable import RunnablePassthrough, RunnableLambda\n",
     "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
     "\n",
     "model = ChatOpenAI()\n",
     "prompt = ChatPromptTemplate.from_messages(\n",

docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb

@@ -42,8 +42,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
     "\n",
     "prompt = ChatPromptTemplate.from_template(\"tell me a joke about {foo}\")\n",
     "model = ChatOpenAI()\n",

docs/docs/expression_language/cookbook/retrieval.ipynb

@@ -38,11 +38,11 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough, RunnableLambda\n",
+    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
     "from langchain.vectorstores import FAISS"
    ]
   },
@@ -170,8 +170,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnableMap\n",
-    "from langchain.schema import format_document"
+    "from langchain.schema import format_document\n",
+    "from langchain.schema.runnable import RunnableMap"
    ]
   },
   {
@@ -231,7 +231,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Tuple, List\n",
+    "from typing import List, Tuple\n",
     "\n",
     "\n",
     "def _format_chat_history(chat_history: List[Tuple]) -> str:\n",
@@ -335,6 +335,7 @@
    "outputs": [],
    "source": [
     "from operator import itemgetter\n",
+    "\n",
     "from langchain.memory import ConversationBufferMemory"
    ]
   },

docs/docs/expression_language/how_to/configure.ipynb

@@ -262,9 +262,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI, ChatAnthropic\n",
-    "from langchain.schema.runnable import ConfigurableField\n",
-    "from langchain.prompts import PromptTemplate"
+    "from langchain.chat_models import ChatAnthropic, ChatOpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.schema.runnable import ConfigurableField"
    ]
   },
   {

docs/docs/expression_language/how_to/fallbacks.ipynb

@@ -31,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI, ChatAnthropic"
+    "from langchain.chat_models import ChatAnthropic, ChatOpenAI"
    ]
   },
   {
@@ -50,6 +50,7 @@
    "outputs": [],
    "source": [
     "from unittest.mock import patch\n",
+    "\n",
     "from openai.error import RateLimitError"
    ]
   },

docs/docs/expression_language/how_to/functions.ipynb

@@ -19,11 +19,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnableLambda\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from operator import itemgetter\n",
     "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema.runnable import RunnableLambda\n",
+    "\n",
     "\n",
     "def length_function(text):\n",
     "    return len(text)\n",
@@ -91,8 +92,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnableConfig\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.schema.runnable import RunnableConfig"
    ]
   },
   {

docs/docs/expression_language/how_to/generators.ipynb

@@ -29,7 +29,6 @@
     "from langchain.prompts.chat import ChatPromptTemplate\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
     "\n",
-    "\n",
     "prompt = ChatPromptTemplate.from_template(\n",
     "    \"Write a comma-separated list of 5 animals similar to: {animal}\"\n",
     ")\n",

docs/docs/expression_language/how_to/map.ipynb

@@ -33,7 +33,6 @@
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema.runnable import RunnableParallel\n",
     "\n",
-    "\n",
     "model = ChatOpenAI()\n",
     "joke_chain = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",
     "poem_chain = (\n",

docs/docs/expression_language/how_to/routing.ipynb

@@ -40,8 +40,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.chat_models import ChatAnthropic\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain.schema.output_parser import StrOutputParser"
    ]
   },

docs/docs/expression_language/interface.ipynb

@@ -57,8 +57,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
     "\n",
     "model = ChatOpenAI()\n",
     "prompt = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\")\n",

docs/docs/guides/evaluation/comparison/custom.ipynb

@@ -34,7 +34,8 @@
    },
    "outputs": [],
    "source": [
-    "from typing import Optional, Any\n",
+    "from typing import Any, Optional\n",
+    "\n",
     "from langchain.evaluation import PairwiseStringEvaluator\n",
     "\n",
     "\n",
@@ -116,10 +117,11 @@
    },
    "outputs": [],
    "source": [
-    "from typing import Optional, Any\n",
-    "from langchain.evaluation import PairwiseStringEvaluator\n",
-    "from langchain.chat_models import ChatAnthropic\n",
+    "from typing import Any, Optional\n",
+    "\n",
     "from langchain.chains import LLMChain\n",
+    "from langchain.chat_models import ChatAnthropic\n",
+    "from langchain.evaluation import PairwiseStringEvaluator\n",
     "\n",
     "\n",
     "class CustomPreferenceEvaluator(PairwiseStringEvaluator):\n",

docs/docs/guides/evaluation/examples/comparisons.ipynb

@@ -98,11 +98,9 @@
    },
    "outputs": [],
    "source": [
-                "from langchain.utilities import SerpAPIWrapper\n",
-                "from langchain.agents import initialize_agent, Tool\n",
-                "from langchain.agents import AgentType\n",
+    "from langchain.agents import AgentType, Tool, initialize_agent\n",
     "from langchain.chat_models import ChatOpenAI\n",
-                "\n",
+    "from langchain.utilities import SerpAPIWrapper\n",
     "\n",
     "# Initialize the language model\n",
     "# You can add your own OpenAI API key by adding openai_api_key=\"<your_api_key>\"\n",
@@ -171,9 +169,10 @@
     }
    ],
    "source": [
-                "from tqdm.notebook import tqdm\n",
     "import asyncio\n",
     "\n",
+    "from tqdm.notebook import tqdm\n",
+    "\n",
     "results = []\n",
     "agents = [functions_agent, conversations_agent]\n",
     "concurrency_level = 6  # How many concurrent agents to run. May need to decrease if OpenAI is rate limiting.\n",

docs/docs/guides/evaluation/string/custom.ipynb

@@ -37,8 +37,8 @@
    "source": [
     "from typing import Any, Optional\n",
     "\n",
-                "from langchain.evaluation import StringEvaluator\n",
     "from evaluate import load\n",
+    "from langchain.evaluation import StringEvaluator\n",
     "\n",
     "\n",
     "class PerplexityEvaluator(StringEvaluator):\n",

docs/docs/guides/evaluation/string/scoring_eval_chain.ipynb

@@ -24,8 +24,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.evaluation import load_evaluator\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.evaluation import load_evaluator\n",
     "\n",
     "evaluator = load_evaluator(\"labeled_score_string\", llm=ChatOpenAI(model=\"gpt-4\"))"
    ]

docs/docs/guides/evaluation/trajectory/custom.ipynb

@@ -22,10 +22,11 @@
    "outputs": [],
    "source": [
     "from typing import Any, Optional, Sequence, Tuple\n",
-                "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
     "from langchain.chains import LLMChain\n",
-                "from langchain.schema import AgentAction\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.evaluation import AgentTrajectoryEvaluator\n",
+    "from langchain.schema import AgentAction\n",
     "\n",
     "\n",
     "class StepNecessityEvaluator(AgentTrajectoryEvaluator):\n",

docs/docs/guides/evaluation/trajectory/trajectory_eval.ipynb

@@ -72,13 +72,12 @@
    "outputs": [],
    "source": [
     "import subprocess\n",
+    "from urllib.parse import urlparse\n",
     "\n",
+    "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.tools import tool\n",
-    "from langchain.agents import AgentType, initialize_agent\n",
-    "\n",
     "from pydantic import HttpUrl\n",
-    "from urllib.parse import urlparse\n",
     "\n",
     "\n",
     "@tool\n",

docs/docs/guides/fallbacks.ipynb

@@ -33,7 +33,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI, ChatAnthropic"
+    "from langchain.chat_models import ChatAnthropic, ChatOpenAI"
    ]
   },
   {
@@ -52,6 +52,7 @@
    "outputs": [],
    "source": [
     "from unittest.mock import patch\n",
+    "\n",
     "from openai.error import RateLimitError"
    ]
   },

docs/docs/guides/local_llms.ipynb

@@ -482,9 +482,9 @@
     }
    ],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.chains.prompt_selector import ConditionalPromptSelector\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "DEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(\n",
     "    input_variables=[\"question\"],\n",

docs/docs/guides/model_laboratory.ipynb

@@ -19,9 +19,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI, Cohere, HuggingFaceHub\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.model_laboratory import ModelLaboratory"
+    "from langchain.llms import Cohere, HuggingFaceHub, OpenAI\n",
+    "from langchain.model_laboratory import ModelLaboratory\n",
+    "from langchain.prompts import PromptTemplate"
    ]
   },
   {

docs/docs/guides/privacy/presidio_data_anonymization/index.ipynb

@@ -127,8 +127,8 @@
     }
    ],
    "source": [
-    "from langchain.prompts.prompt import PromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts.prompt import PromptTemplate\n",
     "\n",
     "anonymizer = PresidioAnonymizer()\n",
     "\n",
@@ -286,7 +286,6 @@
     "# Define the regex pattern in a Presidio `Pattern` object:\n",
     "from presidio_analyzer import Pattern, PatternRecognizer\n",
     "\n",
-    "\n",
     "polish_phone_numbers_pattern = Pattern(\n",
     "    name=\"polish_phone_numbers_pattern\",\n",
     "    regex=\"(?<!\\w)(\\(?(\\+|00)?48\\)?)?[ -]?\\d{3}[ -]?\\d{3}[ -]?\\d{3}(?!\\w)\",\n",

docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb

@@ -233,7 +233,6 @@
     "# Define the regex pattern in a Presidio `Pattern` object:\n",
     "from presidio_analyzer import Pattern, PatternRecognizer\n",
     "\n",
-    "\n",
     "polish_id_pattern = Pattern(\n",
     "    name=\"polish_id_pattern\",\n",
     "    regex=\"[A-Z]{3}\\d{6}\",\n",
@@ -638,8 +637,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "from langchain.vectorstores import FAISS\n",
     "\n",
     "# 2. Load the data: In our case data's already loaded\n",
@@ -664,12 +663,11 @@
    "outputs": [],
    "source": [
     "from operator import itemgetter\n",
+    "\n",
     "from langchain.chat_models.openai import ChatOpenAI\n",
-    "from langchain.schema.runnable import RunnableMap\n",
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
+    "from langchain.schema.runnable import RunnableLambda, RunnableMap, RunnablePassthrough\n",
     "\n",
     "# 6. Create anonymizer chain\n",
     "template = \"\"\"Answer the question based only on the following context:\n",

docs/docs/guides/privacy/presidio_data_anonymization/reversible.ipynb

@@ -207,8 +207,8 @@
     }
    ],
    "source": [
-    "from langchain.prompts.prompt import PromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts.prompt import PromptTemplate\n",
     "\n",
     "anonymizer = PresidioReversibleAnonymizer()\n",
     "\n",

docs/docs/guides/safety/amazon_comprehend_chain.ipynb

@@ -60,9 +60,10 @@
    },
    "outputs": [],
    "source": [
-    "import boto3\n",
     "import os\n",
     "\n",
+    "import boto3\n",
+    "\n",
     "comprehend_client = boto3.client(\"comprehend\", region_name=\"us-east-1\")"
    ]
   },
@@ -102,8 +103,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.llms.fake import FakeListLLM\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (\n",
     "    ModerationPiiError,\n",
     ")\n",
@@ -174,8 +175,8 @@
    "source": [
     "from langchain_experimental.comprehend_moderation import (\n",
     "    BaseModerationConfig,\n",
-    "    ModerationPromptSafetyConfig,\n",
     "    ModerationPiiConfig,\n",
+    "    ModerationPromptSafetyConfig,\n",
     "    ModerationToxicityConfig,\n",
     ")\n",
     "\n",
@@ -239,8 +240,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.llms.fake import FakeListLLM\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"Question: {question}\n",
     "\n",
@@ -402,8 +403,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.prompts import PromptTemplate\n",
     "from langchain.llms.fake import FakeListLLM\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"Question: {question}\n",
     "\n",
@@ -691,10 +692,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import json\n",
+    "\n",
     "from langchain.llms import SagemakerEndpoint\n",
     "from langchain.llms.sagemaker_endpoint import LLMContentHandler\n",
     "from langchain.prompts import PromptTemplate\n",
-    "import json\n",
     "\n",
     "\n",
     "class ContentHandler(LLMContentHandler):\n",

docs/docs/guides/safety/hugging_face_prompt_injection.ipynb

@@ -144,8 +144,8 @@
     }
    ],
    "source": [
+    "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.agents import initialize_agent, AgentType\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "agent = initialize_agent(\n",

docs/docs/integrations/callbacks/argilla.ipynb

@@ -281,8 +281,8 @@
    ],
    "source": [
     "from langchain.callbacks import ArgillaCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
     "\n",
     "argilla_callback = ArgillaCallbackHandler(\n",

docs/docs/integrations/callbacks/context.ipynb

@@ -104,12 +104,12 @@
    "source": [
     "import os\n",
     "\n",
+    "from langchain.callbacks import ContextCallbackHandler\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema import (\n",
-    "    SystemMessage,\n",
     "    HumanMessage,\n",
+    "    SystemMessage,\n",
     ")\n",
-    "from langchain.callbacks import ContextCallbackHandler\n",
     "\n",
     "token = os.environ[\"CONTEXT_API_TOKEN\"]\n",
     "\n",
@@ -162,14 +162,14 @@
    "source": [
     "import os\n",
     "\n",
-    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.callbacks import ContextCallbackHandler\n",
     "from langchain.chains import LLMChain\n",
+    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.prompts.chat import (\n",
     "    ChatPromptTemplate,\n",
     "    HumanMessagePromptTemplate,\n",
     ")\n",
-    "from langchain.callbacks import ContextCallbackHandler\n",
     "\n",
     "token = os.environ[\"CONTEXT_API_TOKEN\"]\n",
     "\n",

docs/docs/integrations/callbacks/infino.ipynb

@@ -46,13 +46,13 @@
    "source": [
     "import datetime as dt\n",
     "import json\n",
-    "from langchain.llms import OpenAI\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.dates as md\n",
     "import time\n",
     "\n",
+    "import matplotlib.dates as md\n",
+    "import matplotlib.pyplot as plt\n",
     "from infinopy import InfinoClient\n",
-    "from langchain.callbacks import InfinoCallbackHandler"
+    "from langchain.callbacks import InfinoCallbackHandler\n",
+    "from langchain.llms import OpenAI"
    ]
   },
   {
@@ -313,9 +313,9 @@
     "# Set your key here.\n",
     "# os.environ[\"OPENAI_API_KEY\"] = \"YOUR_API_KEY\"\n",
     "\n",
+    "from langchain.chains.summarize import load_summarize_chain\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.document_loaders import WebBaseLoader\n",
-    "from langchain.chains.summarize import load_summarize_chain\n",
     "\n",
     "# Create callback handler. This logs latency, errors, token usage, prompts, as well as prompt responses to Infino.\n",
     "handler = InfinoCallbackHandler(\n",

docs/docs/integrations/callbacks/labelstudio.ipynb

@@ -170,8 +170,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI\n",
     "from langchain.callbacks import LabelStudioCallbackHandler\n",
+    "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(\n",
     "    temperature=0, callbacks=[LabelStudioCallbackHandler(project_name=\"My Project\")]\n",
@@ -241,9 +241,9 @@
    },
    "outputs": [],
    "source": [
+    "from langchain.callbacks import LabelStudioCallbackHandler\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema import HumanMessage, SystemMessage\n",
-    "from langchain.callbacks import LabelStudioCallbackHandler\n",
     "\n",
     "chat_llm = ChatOpenAI(\n",
     "    callbacks=[\n",

docs/docs/integrations/callbacks/promptlayer.ipynb

@@ -110,7 +110,6 @@
    "source": [
     "import promptlayer  # Don't forget this 🍰\n",
     "from langchain.callbacks import PromptLayerCallbackHandler\n",
-    "\n",
     "from langchain.llms import GPT4All\n",
     "\n",
     "model = GPT4All(model=\"./models/gpt4all-model.bin\", n_ctx=512, n_threads=8)\n",

docs/docs/integrations/callbacks/sagemaker_tracking.ipynb

@@ -76,15 +76,14 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain, SimpleSequentialChain\n",
     "from langchain.agents import initialize_agent, load_tools\n",
     "from langchain.callbacks import SageMakerCallbackHandler\n",
-    "\n",
+    "from langchain.chains import LLMChain, SimpleSequentialChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from sagemaker.analytics import ExperimentAnalytics\n",
-    "from sagemaker.session import Session\n",
-    "from sagemaker.experiments.run import Run"
+    "from sagemaker.experiments.run import Run\n",
+    "from sagemaker.session import Session"
    ]
   },
   {

docs/docs/integrations/callbacks/trubrics.ipynb

@@ -147,8 +147,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.callbacks import TrubricsCallbackHandler"
+    "from langchain.callbacks import TrubricsCallbackHandler\n",
+    "from langchain.llms import OpenAI"
    ]
   },
   {
@@ -265,9 +265,9 @@
    },
    "outputs": [],
    "source": [
+    "from langchain.callbacks import TrubricsCallbackHandler\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema import HumanMessage, SystemMessage\n",
-    "from langchain.callbacks import TrubricsCallbackHandler"
+    "from langchain.schema import HumanMessage, SystemMessage"
    ]
   },
   {

docs/docs/integrations/chat/anyscale.ipynb

@@ -17,14 +17,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# !pip install openai"
-   ],
+   "id": "d00d850917865298",
    "metadata": {
     "collapsed": false
    },
-   "id": "d00d850917865298"
+   "outputs": [],
+   "source": [
+    "# !pip install openai"
+   ]
   },
   {
    "cell_type": "code",
@@ -99,7 +99,7 @@
    "source": [
     "import asyncio\n",
     "\n",
-    "from langchain.schema import SystemMessage, HumanMessage\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(content=\"You are a helpful AI that shares everything you know.\"),\n",

docs/docs/integrations/chat/baidu_qianfan_endpoint.ipynb

@@ -59,9 +59,10 @@
    ],
    "source": [
     "\"\"\"For basic init and call\"\"\"\n",
+    "import os\n",
+    "\n",
     "from langchain.chat_models import QianfanChatEndpoint\n",
     "from langchain.chat_models.base import HumanMessage\n",
-    "import os\n",
     "\n",
     "os.environ[\"QIANFAN_AK\"] = \"your_ak\"\n",
     "os.environ[\"QIANFAN_SK\"] = \"your_sk\"\n",

docs/docs/integrations/chat/everlyai.ipynb

@@ -64,7 +64,7 @@
    ],
    "source": [
     "from langchain.chat_models import ChatEverlyAI\n",
-    "from langchain.schema import SystemMessage, HumanMessage\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(content=\"You are a helpful AI that shares everything you know.\"),\n",
@@ -116,9 +116,9 @@
     }
    ],
    "source": [
-    "from langchain.chat_models import ChatEverlyAI\n",
-    "from langchain.schema import SystemMessage, HumanMessage\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.chat_models import ChatEverlyAI\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(content=\"You are a humorous AI that delights people.\"),\n",
@@ -174,9 +174,9 @@
     }
    ],
    "source": [
-    "from langchain.chat_models import ChatEverlyAI\n",
-    "from langchain.schema import SystemMessage, HumanMessage\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.chat_models import ChatEverlyAI\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(content=\"You are a humorous AI that delights people.\"),\n",

docs/docs/integrations/chat/fireworks.ipynb

@@ -25,9 +25,10 @@
    },
    "outputs": [],
    "source": [
+    "import os\n",
+    "\n",
     "from langchain.chat_models.fireworks import ChatFireworks\n",
-    "from langchain.schema import SystemMessage, HumanMessage\n",
-    "import os"
+    "from langchain.schema import HumanMessage, SystemMessage"
    ]
   },
   {
@@ -49,8 +50,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "import getpass\n",
+    "import os\n",
     "\n",
     "if \"FIREWORKS_API_KEY\" not in os.environ:\n",
     "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Fireworks API Key:\")\n",
@@ -147,8 +148,8 @@
    "source": [
     "from langchain.chat_models import ChatFireworks\n",
     "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
     "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain.schema.runnable import RunnablePassthrough\n",
     "\n",
     "llm = ChatFireworks(\n",
     "    model=\"accounts/fireworks/models/llama-v2-13b-chat\",\n",

docs/docs/integrations/chat/gigachat.ipynb

@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "# GigaChat\n",
     "This notebook shows how to use LangChain with [GigaChat](https://developers.sber.ru/portal/products/gigachat).\n",
     "To use you need to install ```gigachat``` python package."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -24,44 +24,47 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/api/integration)\n",
     "## Example"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import os\n",
     "from getpass import getpass\n",
     "\n",
     "os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "from langchain.chat_models import GigaChat\n",
     "\n",
     "chat = GigaChat(verify_ssl_certs=False)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -72,7 +75,7 @@
     }
    ],
    "source": [
-    "from langchain.schema import SystemMessage, HumanMessage\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(\n",
@@ -82,10 +85,7 @@
     "]\n",
     "\n",
     "print(chat(messages).content)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   }
  ],
  "metadata": {

docs/docs/integrations/chat/jinachat.ipynb

@@ -22,8 +22,8 @@
     "from langchain.chat_models import JinaChat\n",
     "from langchain.prompts.chat import (\n",
     "    ChatPromptTemplate,\n",
-    "    SystemMessagePromptTemplate,\n",
     "    HumanMessagePromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
     ")\n",
     "from langchain.schema import HumanMessage, SystemMessage"
    ]

docs/docs/integrations/chat/ollama.ipynb

@@ -59,9 +59,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOllama\n",
     "from langchain.callbacks.manager import CallbackManager\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.chat_models import ChatOllama\n",
     "\n",
     "chat_model = ChatOllama(\n",
     "    model=\"llama2:7b-chat\",\n",
@@ -168,8 +168,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores import Chroma\n",
     "from langchain.embeddings import OllamaEmbeddings\n",
+    "from langchain.vectorstores import Chroma\n",
     "\n",
     "vectorstore = Chroma.from_documents(documents=all_splits, embedding=OllamaEmbeddings())"
    ]
@@ -224,9 +224,9 @@
    "outputs": [],
    "source": [
     "# Chat model\n",
-    "from langchain.chat_models import ChatOllama\n",
     "from langchain.callbacks.manager import CallbackManager\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.chat_models import ChatOllama\n",
     "\n",
     "chat_model = ChatOllama(\n",
     "    model=\"llama2:13b\",\n",
@@ -300,8 +300,8 @@
     }
    ],
    "source": [
-    "from langchain.schema import LLMResult\n",
     "from langchain.callbacks.base import BaseCallbackHandler\n",
+    "from langchain.schema import LLMResult\n",
     "\n",
     "\n",
     "class GenerationStatisticsCallback(BaseCallbackHandler):\n",

docs/docs/integrations/chat/openai.ipynb

@@ -22,8 +22,8 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts.chat import (\n",
     "    ChatPromptTemplate,\n",
-    "    SystemMessagePromptTemplate,\n",
     "    HumanMessagePromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
     ")\n",
     "from langchain.schema import HumanMessage, SystemMessage"
    ]

docs/docs/integrations/chat/pai_eas_chat_endpoint.ipynb

@@ -30,8 +30,9 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "from langchain.chat_models.base import HumanMessage\n",
+    "\n",
     "from langchain.chat_models import PaiEasChatEndpoint\n",
+    "from langchain.chat_models.base import HumanMessage\n",
     "\n",
     "os.environ[\"EAS_SERVICE_URL\"] = \"Your_EAS_Service_URL\"\n",
     "os.environ[\"EAS_SERVICE_TOKEN\"] = \"Your_EAS_Service_Token\"\n",

docs/docs/integrations/chat/promptlayer_chatopenai.ipynb

@@ -51,6 +51,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "from langchain.chat_models import PromptLayerChatOpenAI\n",
     "from langchain.schema import HumanMessage"
    ]

docs/docs/integrations/chat/vllm.ipynb

@@ -24,8 +24,8 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts.chat import (\n",
     "    ChatPromptTemplate,\n",
-    "    SystemMessagePromptTemplate,\n",
     "    HumanMessagePromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
     ")\n",
     "from langchain.schema import HumanMessage, SystemMessage"
    ]

docs/docs/integrations/chat_loaders/discord.ipynb

@@ -79,8 +79,8 @@
     "import re\n",
     "from typing import Iterator, List\n",
     "\n",
-    "from langchain.schema import BaseMessage, HumanMessage\n",
     "from langchain.chat_loaders import base as chat_loaders\n",
+    "from langchain.schema import BaseMessage, HumanMessage\n",
     "\n",
     "logger = logging.getLogger()\n",
     "\n",
@@ -215,6 +215,7 @@
    "outputs": [],
    "source": [
     "from typing import List\n",
+    "\n",
     "from langchain.chat_loaders.base import ChatSession\n",
     "from langchain.chat_loaders.utils import (\n",
     "    map_ai_messages,\n",

docs/docs/integrations/chat_loaders/facebook.ipynb

@@ -47,9 +47,10 @@
    ],
    "source": [
     "# This uses some example data\n",
-    "import requests\n",
     "import zipfile\n",
     "\n",
+    "import requests\n",
+    "\n",
     "\n",
     "def download_and_unzip(url: str, output_path: str = \"file.zip\") -> None:\n",
     "    file_id = url.split(\"/\")[-2]\n",
@@ -106,8 +107,8 @@
    "outputs": [],
    "source": [
     "from langchain.chat_loaders.facebook_messenger import (\n",
-    "    SingleFileFacebookMessengerChatLoader,\n",
     "    FolderFacebookMessengerChatLoader,\n",
+    "    SingleFileFacebookMessengerChatLoader,\n",
     ")"
    ]
   },
@@ -201,8 +202,8 @@
    "outputs": [],
    "source": [
     "from langchain.chat_loaders.utils import (\n",
-    "    merge_chat_runs,\n",
     "    map_ai_messages,\n",
+    "    merge_chat_runs,\n",
     ")"
    ]
   },
@@ -387,8 +388,8 @@
    ],
    "source": [
     "import json\n",
-    "from io import BytesIO\n",
     "import time\n",
+    "from io import BytesIO\n",
     "\n",
     "import openai\n",
     "\n",

docs/docs/integrations/chat_loaders/gmail.ipynb

@@ -36,6 +36,7 @@
    "outputs": [],
    "source": [
     "import os.path\n",
+    "\n",
     "from google.auth.transport.requests import Request\n",
     "from google.oauth2.credentials import Credentials\n",
     "from google_auth_oauthlib.flow import InstalledAppFlow\n",

docs/docs/integrations/chat_loaders/imessage.ipynb

@@ -115,6 +115,7 @@
    "outputs": [],
    "source": [
     "from typing import List\n",
+    "\n",
     "from langchain.chat_loaders.base import ChatSession\n",
     "from langchain.chat_loaders.utils import (\n",
     "    map_ai_messages,\n",
@@ -231,8 +232,8 @@
    ],
    "source": [
     "import json\n",
-    "from io import BytesIO\n",
     "import time\n",
+    "from io import BytesIO\n",
     "\n",
     "import openai\n",
     "\n",