Fix project link to include org

Description: This commit uses the new Service object in Selenium
webdriver as executable_path has been [deprecated and removed in
selenium version
4.11.2](9f5801c82f)
Issue: https://github.com/langchain-ai/langchain/issues/9808
Tag Maintainer: @eyurtsev

.github/CONTRIBUTING.md

@@ -44,7 +44,7 @@ If you are adding an issue, please try to keep it focused on a single, modular b
 If two issues are related, or blocking, please link them rather than combining them.
 
 We will try to keep these issues as up to date as possible, though
-with the rapid rate of develop in this field some may get out of date.
+with the rapid rate of development in this field some may get out of date.
 If you notice this happening, please let us know.
 
 ### 🙋Getting Help
@@ -87,7 +87,7 @@ This will install all requirements for running the package, examples, linting, f
 
 ❗Note: If during installation you receive a `WheelFileValidationError` for `debugpy`, please make sure you are running Poetry v1.5.1. This bug was present in older versions of Poetry (e.g. 1.4.1) and has been resolved in newer releases. If you are still seeing this bug on v1.5.1, you may also try disabling "modern installation" (`poetry config installer.modern-installation false`) and re-installing requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
 
-Now, you should be able to run the common tasks in the following section. To double check, run `make test`, all tests should pass. If they don't you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
+Now assuming `make` and `pytest` are installed, you should be able to run the common tasks in the following section. To double check, run `make test` under `libs/langchain`, all tests should pass. If they don't, you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
 
 ## ✅ Common Tasks
 
@@ -134,7 +134,7 @@ We recognize linting can be annoying - if you do not want to do it, please conta
 ### Spellcheck
 
 Spellchecking for this project is done via [codespell](https://github.com/codespell-project/codespell).
-Note that `codespell` finds common typos, so could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.
+Note that `codespell` finds common typos, so it could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.
 
 To check spelling for this project:
 

.github/workflows/_release.yml

@@ -31,13 +31,15 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
     steps:
       - uses: actions/checkout@v3
-      - name: Install poetry
-        run: pipx install "poetry==$POETRY_VERSION"
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v4
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: "3.10"
-          cache: "poetry"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: release
+
       - name: Build project for distribution
         run: poetry build
       - name: Check Version

docs/docs_skeleton/docs/get_started/introduction.mdx

@@ -42,9 +42,9 @@ Log and stream intermediate steps of any chain
 ## Examples, ecosystem, and resources
 ### [Use cases](/docs/use_cases/)
 Walkthroughs and best-practices for common end-to-end use cases, like:
-- [Chatbots](/docs/use_cases/chatbots/)
+- [Chatbots](/docs/use_cases/chatbots)
 - [Answering questions using sources](/docs/use_cases/question_answering/)
-- [Analyzing structured data](/docs/use_cases/tabular.html)
+- [Analyzing structured data](/docs/use_cases/sql)
 - and much more...
 
 ### [Guides](/docs/guides/)
@@ -56,9 +56,8 @@ LangChain is part of a rich ecosystem of tools that integrate with our framework
 ### [Additional resources](/docs/additional_resources/)
 Our community is full of prolific developers, creative builders, and fantastic teachers. Check out [YouTube tutorials](/docs/additional_resources/youtube.html) for great tutorials from folks in the community, and [Gallery](https://github.com/kyrolabs/awesome-langchain) for a list of awesome LangChain projects, compiled by the folks at [KyroLabs](https://kyrolabs.com).
 
-<h3><span style={{color:"#2e8555"}}> Support </span></h3>
-
-Join us on [GitHub](https://github.com/hwchase17/langchain) or [Discord](https://discord.gg/6adMQxSpJS) to ask questions, share feedback, meet other developers building with LangChain, and dream about the future of LLM’s.
+### [Community](/docs/community)
+Head to the [Community navigator](/docs/community) to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s.
 
 ## API reference
 

docs/docs_skeleton/docs/guides/safety/amazon_comprehend_chain.ipynb

@@ -30,12 +30,7 @@
    "source": [
     "import boto3\n",
     "\n",
-    "comprehend_client = boto3.client('comprehend', \n",
-    "                                 region_name='us-east-1', \n",
-    "                                 aws_access_key_id=\"ASIA6BR6ZDLNQLMEGWHM\",\n",
-    "                                 aws_secret_access_key=\"Y79nefFoOfvgrog6sojSe55xTuKqDJY53BgfrtlG\",\n",
-    "                                 aws_session_token=\"IQoJb3JpZ2luX2VjEIP//////////wEaCXVzLWVhc3QtMSJGMEQCIBvUl0Wj5Gu5GrHB+i5fHkaVc2V1381M7UNRX8EggHORAiB+dG/uKJ4loHn2oAcXIEy6+lfU7wygl4zw/vUo2VItFiqfAghMEAIaDDk2NTQyNTU2ODQ3NSIMfbh8uyoO1XONSkuEKvwBTMxeDCi//9U9LGIwZZzIiHOudQAqR2wlIGZKcw//abSeHNBE1AoDT8ibcqk7EuIt9fwnj1WYiLGmSIWd9/kSZShiKdYg0UpNWyr1/LdeutV5byFAjT21RnWTgSMr0QeSCU698PFusvO1Coph8C75pcqTVYsxi/HypJT8OfB5iCxKgfzx0qD4X6hScpIAEYZhgQXHFBAeubqMkVPYEqSob6fSm1vEI8LkU8HG1N2M2p8TzGCQWo5uBgtNkipxve++bkR+xjiNLIpAN3P1xF2/W/lYlz+4xGsi90aZqIVh/tOvAjg7Yx1Dd5Ir2C0fZc7wbtabzVFlJZ7GFcpcMOX0o6cGOp4BismuW2CJRBmFFpoparqraQaiQBY/VDbQg9KQc/Y6o0oCxkESLUdY6ino3yrheT3W832eAg0RwrmEaQqT8kKGyJFimUxrAF/otNQhySLKuSXLooguammJiQAtgK1EhmuLBUBoLcngxQ31kDqw13g7Ccwuo68fnI/QzQLj5MX+V5VLCSp9VrOzi9XSjmeF/TJQARdZeL3CSeu2pATQc80=\"\n",
-    "                                )"
+    "comprehend_client = boto3.client('comprehend', region_name='us-east-1')"
    ]
   },
   {
@@ -389,7 +384,6 @@
     "comp_moderation_with_config = AmazonComprehendModerationChain(\n",
     "        moderation_config=moderation_config, # specify the configuration\n",
     "        client=comprehend_client,            # optionally pass the Boto3 Client\n",
-    "        force_base_exception=True,           # Force BaseModerationError\n",
     "        unique_id='john.doe@email.com',      # A unique ID\n",
     "        moderation_callback=my_callback,     # BaseModerationCallbackHandler\n",
     "        verbose=True\n",

docs/docs_skeleton/docs/modules/memory/types/buffer.mdx

@@ -1,4 +1,4 @@
-# Conversation buffer memory
+# Conversation Buffer
 
 This notebook shows how to use `ConversationBufferMemory`. This memory allows for storing of messages and then extracts the messages in a variable.
 

docs/docs_skeleton/docs/modules/memory/types/buffer_window.mdx

@@ -1,4 +1,4 @@
-# Conversation buffer window memory
+# Conversation Buffer Window
 
 `ConversationBufferWindowMemory` keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large
 

docs/docs_skeleton/docs/modules/memory/types/entity_summary_memory.mdx

@@ -1,4 +1,4 @@
-# Entity memory
+# Entity
 
 Entity Memory remembers given facts about specific entities in a conversation. It extracts information on entities (using an LLM) and builds up its knowledge about that entity over time (also using an LLM).
 

docs/docs_skeleton/docs/modules/memory/types/index.mdx

@@ -4,5 +4,5 @@ sidebar_position: 2
 # Memory Types
 
 There are many different types of memory.
-Each have their own parameters, their own return types, and are useful in different scenarios.
+Each has their own parameters, their own return types, and is useful in different scenarios.
 Please see their individual page for more detail on each one.

docs/docs_skeleton/docs/modules/memory/types/summary.mdx

@@ -1,4 +1,4 @@
-# Conversation summary memory
+# Conversation Summary
 Now let's take a look at using a slightly more complex type of memory - `ConversationSummaryMemory`. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time.
 Conversation summary memory summarizes the conversation as it happens and stores the current summary in memory. This memory can then be used to inject the summary of the conversation so far into a prompt/chain. This memory is most useful for longer conversations, where keeping the past message history in the prompt verbatim would take up too many tokens.
 

docs/docs_skeleton/docs/modules/memory/types/vectorstore_retriever_memory.mdx

@@ -1,4 +1,4 @@
-# Vector store-backed memory
+# Backed by a Vector Store
 
 `VectorStoreRetrieverMemory` stores memories in a VectorDB and queries the top-K most "salient" docs every time it is called.
 

docs/extras/ecosystem/dependents.mdx

@@ -51,7 +51,7 @@ Dependents stats for `langchain-ai/langchain`
 |[e2b-dev/e2b](https://github.com/e2b-dev/e2b) | 5365 |
 |[mage-ai/mage-ai](https://github.com/mage-ai/mage-ai) | 5352 |
 |[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 5192 |
-|[LangChain-Chinese-Getting-Started-Guide](https://github.com/liaokongVFX/LangChain-Chinese-Getting-Started-Guide) | 5129 |
+|[liaokongVFX/LangChain-Chinese-Getting-Started-Guide](https://github.com/liaokongVFX/LangChain-Chinese-Getting-Started-Guide) | 5129 |
 |[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 4993 |
 |[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 4831 |
 |[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 4824 |

docs/extras/integrations/callbacks/infino.ipynb

@@ -1,86 +1,73 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "8d10861f-a550-4443-bc63-4ce2ae13b841",
    "metadata": {},
    "source": [
-    "# Infino - LangChain LLM Monitoring Example\n",
+    "# Infino\n",
     "\n",
-    "This example shows how one can track the following while calling OpenAI models via LangChain and [Infino](https://github.com/infinohq/infino):\n",
+    "This example shows how one can track the following while calling OpenAI models via `LangChain` and [Infino](https://github.com/infinohq/infino):\n",
     "\n",
     "* prompt input,\n",
-    "* response from chatgpt or any other LangChain model,\n",
+    "* response from `ChatGPT` or any other `LangChain` model,\n",
     "* latency,\n",
     "* errors,\n",
     "* number of tokens consumed"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "3a5a0976-9953-41d8-880c-eb3f2992e936",
+   "cell_type": "markdown",
+   "id": "64d14c88-b71c-4524-ab1b-4250a7dbb62b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: matplotlib in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (3.7.1)\n",
-      "Requirement already satisfied: contourpy>=1.0.1 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (1.0.7)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (4.39.4)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n",
-      "Requirement already satisfied: numpy>=1.20 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (1.24.3)\n",
-      "Requirement already satisfied: packaging>=20.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (23.1)\n",
-      "Requirement already satisfied: pillow>=6.2.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (9.5.0)\n",
-      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n",
-      "Requirement already satisfied: python-dateutil>=2.7 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
-      "Requirement already satisfied: six>=1.5 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
-      "Requirement already satisfied: infinopy in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (0.0.1)\n",
-      "Requirement already satisfied: docker in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from infinopy) (6.1.3)\n",
-      "Requirement already satisfied: requests in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from infinopy) (2.31.0)\n",
-      "Requirement already satisfied: packaging>=14.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from docker->infinopy) (23.1)\n",
-      "Requirement already satisfied: urllib3>=1.26.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from docker->infinopy) (2.0.2)\n",
-      "Requirement already satisfied: websocket-client>=0.32.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from docker->infinopy) (1.5.2)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from requests->infinopy) (3.1.0)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from requests->infinopy) (3.4)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from requests->infinopy) (2023.5.7)\n"
-     ]
-    }
-   ],
+   "source": [
+    "## Initializing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed46c894-caa6-49b2-85d1-f275374fa308",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Install necessary dependencies.\n",
     "!pip install infinopy\n",
-    "!pip install matplotlib\n",
-    "\n",
+    "!pip install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a5a0976-9953-41d8-880c-eb3f2992e936",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Remove the (1) import sys and sys.path.append(..) and (2) uncomment `!pip install langchain` after merging the PR for Infino/LangChain integration.\n",
     "import sys\n",
     "\n",
     "sys.path.append(\"../../../../../langchain\")\n",
-    "#!pip install langchain\n",
     "\n",
     "\n",
     "import datetime as dt\n",
-    "from infinopy import InfinoClient\n",
     "import json\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.callbacks import InfinoCallbackHandler\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.dates as md\n",
     "import os\n",
     "import time\n",
-    "import sys"
+    "import sys\n",
+    "\n",
+    "from infinopy import InfinoClient\n",
+    "from langchain.callbacks import InfinoCallbackHandler"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "9f90210d-c805-4a0c-81e4-d5298942afc4",
    "metadata": {},
    "source": [
-    "## Start Infino server, initialize the Infino client\n"
+    "## Start Infino server, initialize the Infino client"
    ]
   },
   {
@@ -106,7 +93,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "b6b81cda-b841-43ee-8c5e-b1576555765f",
    "metadata": {},
@@ -148,7 +134,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "dce1b820-3f1a-4b94-b848-4c6032cadc18",
    "metadata": {},
@@ -214,7 +199,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "b68ec697-c922-4fd9-aad1-f49c6ac24e8a",
    "metadata": {},
@@ -326,7 +310,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c3d61822-1781-4bc6-97a2-2abc5c2b2e75",
    "metadata": {},
@@ -364,12 +347,11 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "4b171074-c775-48e0-a4b3-f550e2c8eccb",
    "metadata": {},
    "source": [
-    "## Step 5: Stop infino server"
+    "## Stop infino server"
    ]
   },
   {
@@ -415,7 +397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/callbacks/llmonitor.md

@@ -0,0 +1,63 @@
+# LLMonitor
+
+[LLMonitor](https://llmonitor.com) is an open-source observability platform that provides cost tracking, user tracking and powerful agent tracing.
+
+<video controls width='100%' >
+  <source src='https://llmonitor.com/videos/demo-annotated.mp4'/>
+</video>
+
+## Setup
+Create an account on [llmonitor.com](https://llmonitor.com), create an `App`, and then copy the associated `tracking id`.
+Once you have it, set it as an environment variable by running:
+```bash
+export LLMONITOR_APP_ID="..."
+```
+
+If you'd prefer not to set an environment variable, you can pass the key directly when initializing the callback handler:
+```python
+from langchain.callbacks import LLMonitorCallbackHandler
+
+handler = LLMonitorCallbackHandler(app_id="...")
+```
+
+## Usage with LLM/Chat models
+```python
+from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI
+from langchain.callbacks import LLMonitorCallbackHandler
+
+handler = LLMonitorCallbackHandler(app_id="...")
+
+llm = OpenAI(
+    callbacks=[handler],
+)
+
+chat = ChatOpenAI(
+    callbacks=[handler],
+    metadata={"userId": "123"},  # you can assign user ids to models in the metadata
+)
+```
+
+
+## Usage with agents
+```python
+from langchain.agents import load_tools, initialize_agent, AgentType
+from langchain.llms import OpenAI
+from langchain.callbacks import LLMonitorCallbackHandler
+
+handler = LLMonitorCallbackHandler(app_id="...")
+
+llm = OpenAI(temperature=0)
+tools = load_tools(["serpapi", "llm-math"], llm=llm)
+agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
+agent.run(
+    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",
+    callbacks=[handler],
+    metadata={
+        "agentName": "Leo DiCaprio's girlfriend",  # you can assign a custom agent in the metadata
+    },
+)
+```
+
+## Support
+For any question or issue with integration you can reach out to the LLMonitor team on [Discord](http://discord.com/invite/8PafSG58kK) or via [email](mailto:vince@llmonitor.com).

docs/extras/integrations/chat_loaders/facebook.ipynb

@@ -571,7 +571,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/extras/integrations/chat_loaders/gmail.ipynb

@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b3d1705d",
+   "metadata": {},
+   "source": [
+    "# GMail\n",
+    "\n",
+    "This loader goes over how to load data from GMail. There are many ways you could want to load data from GMail. This loader is currently fairly opionated in how to do so. The way it does it is it first looks for all messages that you have sent. It then looks for messages where you are responding to a previous email. It then fetches that previous email, and creates a training example of that email, followed by your email.\n",
+    "\n",
+    "Note that there are clear limitations here. For example, all examples created are only looking at the previous email for context.\n",
+    "\n",
+    "To use:\n",
+    "\n",
+    "- Set up a Google Developer Account: Go to the Google Developer Console, create a project, and enable the Gmail API for that project. This will give you a credentials.json file that you'll need later.\n",
+    "\n",
+    "- Install the Google Client Library: Run the following command to install the Google Client Library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84578039",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --upgrade google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "be18f796",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os.path\n",
+    "import base64\n",
+    "import json\n",
+    "import re\n",
+    "import time\n",
+    "from google.auth.transport.requests import Request\n",
+    "from google.oauth2.credentials import Credentials\n",
+    "from google_auth_oauthlib.flow import InstalledAppFlow\n",
+    "from googleapiclient.discovery import build\n",
+    "import logging\n",
+    "import requests\n",
+    "\n",
+    "SCOPES = ['https://www.googleapis.com/auth/gmail.readonly']\n",
+    "\n",
+    "\n",
+    "creds = None\n",
+    "# The file token.json stores the user's access and refresh tokens, and is\n",
+    "# created automatically when the authorization flow completes for the first\n",
+    "# time.\n",
+    "if os.path.exists('email_token.json'):\n",
+    "    creds = Credentials.from_authorized_user_file('email_token.json', SCOPES)\n",
+    "# If there are no (valid) credentials available, let the user log in.\n",
+    "if not creds or not creds.valid:\n",
+    "    if creds and creds.expired and creds.refresh_token:\n",
+    "        creds.refresh(Request())\n",
+    "    else:\n",
+    "        flow = InstalledAppFlow.from_client_secrets_file(               \n",
+    "            # your creds file here. Please create json file as here https://cloud.google.com/docs/authentication/getting-started\n",
+    "            'creds.json', SCOPES)\n",
+    "        creds = flow.run_local_server(port=0)\n",
+    "    # Save the credentials for the next run\n",
+    "    with open('email_token.json', 'w') as token:\n",
+    "        token.write(creds.to_json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a2793ba0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_loaders.gmail import GMailLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2154597f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = GMailLoader(creds=creds, n=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0b7d11bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "74764bc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sometimes there can be errors which we silently ignore\n",
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d9360a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_loaders.utils import (\n",
+    "    map_ai_messages,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a9646f7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This makes messages sent by hchase@langchain.com the AI Messages\n",
+    "# This means you will train an LLM to predict as if it's responding as hchase\n",
+    "training_data = list(map_ai_messages(data, sender=\"Harrison Chase <hchase@langchain.com>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1a182f0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "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",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/chat_loaders/imessage.ipynb

@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "01fcfa2f-33a9-48f3-835a-b1956c394d6b",
+   "metadata": {},
+   "source": [
+    "# iMessage\n",
+    "\n",
+    "This notebook shows how to use the iMessage chat loader. This class helps convert iMessage conversations to LangChain chat messages.\n",
+    "\n",
+    "On MacOS, iMessage stores conversations in a sqlite database at `~/Library/Messages/chat.db` (at least for macOS Ventura 13.4). \n",
+    "The `IMessageChatLoader` loads from this database file. \n",
+    "\n",
+    "1. Create the `IMessageChatLoader` with the file path pointed to `chat.db` database you'd like to process.\n",
+    "2. Call `loader.load()` (or `loader.lazy_load()`) to perform the conversion. Optionally use `merge_chat_runs` to combine message from the same sender in sequence, and/or `map_ai_messages` to convert messages from the specified sender to the \"AIMessage\" class.\n",
+    "\n",
+    "## 1. Access Chat DB\n",
+    "\n",
+    "It's likely that your terminal is denied access to `~/Library/Messages`. To use this class, you can copy the DB to an accessible directory (e.g., Documents) and load from there. Alternatively (and not recommended), you can grant full disk access for your terminal emulator in System Settings > Securityand Privacy > Full Disk Access.\n",
+    "\n",
+    "We have created an example database you can use at [this linked drive file](https://drive.google.com/file/d/1NebNKqTA2NXApCmeH6mu0unJD2tANZzo/view?usp=sharing)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "036ce7e0-a38f-4cbe-89a6-a205ae7c23be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File chat.db downloaded.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This uses some example data\n",
+    "import requests\n",
+    "\n",
+    "def download_drive_file(url: str, output_path: str = 'chat.db') -> None:\n",
+    "    file_id = url.split('/')[-2]\n",
+    "    download_url = f'https://drive.google.com/uc?export=download&id={file_id}'\n",
+    "\n",
+    "    response = requests.get(download_url)\n",
+    "    if response.status_code != 200:\n",
+    "        print('Failed to download the file.')\n",
+    "        return\n",
+    "\n",
+    "    with open(output_path, 'wb') as file:\n",
+    "        file.write(response.content)\n",
+    "        print(f'File {output_path} downloaded.')\n",
+    "\n",
+    "url = 'https://drive.google.com/file/d/1NebNKqTA2NXApCmeH6mu0unJD2tANZzo/view?usp=sharing'\n",
+    "\n",
+    "# Download file to chat.db\n",
+    "download_drive_file(url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf60f703-76f1-4602-a723-02c59535c1af",
+   "metadata": {},
+   "source": [
+    "## 2. Create the Chat Loader\n",
+    "\n",
+    "Provide the loader with the file path to the zip directory. You can optionally specify the user id that maps to an ai message as well an configure whether to merge message runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4b8b432a-d2bc-49e1-b35f-761730a8fd6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_loaders.imessage import IMessageChatLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8ec6661b-0aca-48ae-9e2b-6412856c287b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = IMessageChatLoader(\n",
+    "    path=\"./chat.db\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8805a7c5-84b4-49f5-8989-0022f2054ace",
+   "metadata": {},
+   "source": [
+    "## 3. Load messages\n",
+    "\n",
+    "The `load()` (or `lazy_load`) methods return a list of \"ChatSessions\" that currently just contain a list of messages per loaded conversation. All messages are mapped to \"HumanMessage\" objects to start. \n",
+    "\n",
+    "You can optionally choose to merge message \"runs\" (consecutive messages from the same sender) and select a sender to represent the \"AI\". The fine-tuned LLM will learn to generate these AI messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fcd69b3e-020d-4a15-8a0d-61c2d34e1ee1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "from langchain.chat_loaders.base import ChatSession\n",
+    "from langchain.chat_loaders.utils import (\n",
+    "    map_ai_messages,\n",
+    "    merge_chat_runs,\n",
+    ")\n",
+    "\n",
+    "raw_messages = loader.lazy_load()\n",
+    "# Merge consecutive messages from the same sender into a single message\n",
+    "merged_messages = merge_chat_runs(raw_messages)\n",
+    "# Convert messages from \"Tortoise\" to AI messages. Do you have a guess who these conversations are between?\n",
+    "chat_sessions: List[ChatSession] = list(map_ai_messages(merged_messages, sender=\"Tortoise\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "370b8c26-c7a8-434c-a225-45c20ff14a03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content=\"Slow and steady, that's my motto.\", additional_kwargs={'message_time': 1693182723, 'sender': 'Tortoise'}, example=False),\n",
+       " HumanMessage(content='Speed is key!', additional_kwargs={'message_time': 1693182753, 'sender': 'Hare'}, example=False),\n",
+       " AIMessage(content='A balanced approach is more reliable.', additional_kwargs={'message_time': 1693182783, 'sender': 'Tortoise'}, example=False)]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05208f9d-3193-4a8d-86a5-13df2c8197e5",
+   "metadata": {},
+   "source": [
+    "## 3. Prepare for fine-tuning\n",
+    "\n",
+    "Now it's time to convert our chat  messages to OpenAI dictionaries. We can use the `convert_messages_for_finetuning` utility to do so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8834861f-f37f-4c08-96c6-917269bf09b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.adapters.openai import convert_messages_for_finetuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ce7ab0f9-6e6a-4a1c-8b86-c635251d437e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prepared 10 dialogues for training\n"
+     ]
+    }
+   ],
+   "source": [
+    "training_data = convert_messages_for_finetuning(alternating_sessions)\n",
+    "print(f\"Prepared {len(training_data)} dialogues for training\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b494d64c-8056-42ae-b4c1-a9cfabc002ea",
+   "metadata": {},
+   "source": [
+    "## 4. Fine-tune the model\n",
+    "\n",
+    "It's time to fine-tune the model. Make sure you have `openai` installed\n",
+    "and have set your `OPENAI_API_KEY` appropriately"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b4b60daa-b899-4291-a09a-412ce9c218fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U openai --quiet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2cca6c95-c0d6-4826-b4fa-1c403f217f93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File file-zHIgf4r8LltZG3RFpkGd4Sjf ready after 10.19 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "from io import BytesIO\n",
+    "import time\n",
+    "\n",
+    "import openai\n",
+    "\n",
+    "# We will write the jsonl file in memory\n",
+    "my_file = BytesIO()\n",
+    "for m in training_data:\n",
+    "    my_file.write((json.dumps({\"messages\": m}) + \"\\n\").encode('utf-8'))\n",
+    "\n",
+    "my_file.seek(0)\n",
+    "training_file = openai.File.create(\n",
+    "  file=my_file,\n",
+    "  purpose='fine-tune'\n",
+    ")\n",
+    "\n",
+    "# OpenAI audits each training file for compliance reasons.\n",
+    "# This make take a few minutes\n",
+    "status = openai.File.retrieve(training_file.id).status\n",
+    "start_time = time.time()\n",
+    "while status != \"processed\":\n",
+    "    print(f\"Status=[{status}]... {time.time() - start_time:.2f}s\", end=\"\\r\", flush=True)\n",
+    "    time.sleep(5)\n",
+    "    status = openai.File.retrieve(training_file.id).status\n",
+    "print(f\"File {training_file.id} ready after {time.time() - start_time:.2f} seconds.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60ee0476-3113-4dc8-a886-bce878c60b07",
+   "metadata": {},
+   "source": [
+    "With the file ready, it's time to kick off a training job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c376ddca-5b4f-4e5a-bf4e-6beeb467eacc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job = openai.FineTuningJob.create(\n",
+    "    training_file=training_file.id,\n",
+    "    model=\"gpt-3.5-turbo\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09344c60-0bee-4989-b8d1-4a8821553cc3",
+   "metadata": {},
+   "source": [
+    "Grab a cup of tea while your model is being prepared. This may take some time!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "22eae900-04ca-456b-ba51-1dfff1f8e0e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status=[running]... 524.95s\r"
+     ]
+    }
+   ],
+   "source": [
+    "status = openai.FineTuningJob.retrieve(job.id).status\n",
+    "start_time = time.time()\n",
+    "while status != \"succeeded\":\n",
+    "    print(f\"Status=[{status}]... {time.time() - start_time:.2f}s\", end=\"\\r\", flush=True)\n",
+    "    time.sleep(5)\n",
+    "    job = openai.FineTuningJob.retrieve(job.id)\n",
+    "    status = job.status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "39e72616-a7d9-44b8-a4eb-506611d119f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ft:gpt-3.5-turbo-0613:personal::7sKoRdlz\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job.fine_tuned_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d717749-b1b6-451f-b3c5-3286b82d45b9",
+   "metadata": {},
+   "source": [
+    "## 5. Use in LangChain\n",
+    "\n",
+    "You can use the resulting model ID directly the `ChatOpenAI` model class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1579dfca-95c6-47b7-8549-1195b9dce5b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(\n",
+    "    model=job.fine_tuned_model,\n",
+    "    temperature=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "6f53d1b1-dcbf-4976-a61a-17f74c6f1b0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You are speaking to hare.\"),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "6619c9bc-54ea-4136-bd9a-44557f7da724",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A symbol of interconnectedness."
+     ]
+    }
+   ],
+   "source": [
+    "for tok in chain.stream({\"input\": \"What's the golden thread?\"}):\n",
+    "    print(tok, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88e0d1a1-48a9-4d9d-9f4e-010cbbb65af8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "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",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/Etherscan.ipynb

@@ -8,9 +8,9 @@
     "# Etherscan Loader\n",
     "## Overview\n",
     "\n",
-    "The Etherscan loader use etherscan api to load transacactions histories under specific account on Ethereum Mainnet.\n",
+    "The Etherscan loader use etherscan api to load transaction histories under specific account on Ethereum Mainnet.\n",
     "\n",
-    "You will need a Etherscan api key to proceed. The free api key has 5 calls per seconds quota.\n",
+    "You will need a Etherscan api key to proceed. The free api key has 5 calls per second quota.\n",
     "\n",
     "The loader supports the following six functinalities:\n",
     "* Retrieve normal transactions under specific account on Ethereum Mainet\n",

docs/extras/integrations/document_loaders/cube_semantic.ipynb

@@ -106,15 +106,39 @@
     "  - `column_data_type`\n",
     "  - `column_title`\n",
     "  - `column_description`\n",
-    "  - `column_values`"
+    "  - `column_values`\n",
+    "  - `cube_data_obj_type`"
    ]
   },
   {
-   "attachments": {},
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "> page_content='Users View City, None' metadata={'table_name': 'users_view', 'column_name': 'users_view.city', 'column_data_type': 'string', 'column_title': 'Users View City', 'column_description': 'None', 'column_member_type': 'dimension', 'column_values': ['Austin', 'Chicago', 'Los Angeles', 'Mountain View', 'New York', 'Palo Alto', 'San Francisco', 'Seattle']}"
+    "# Given string containing page content\n",
+    "page_content = 'Users View City, None'\n",
+    "\n",
+    "# Given dictionary containing metadata\n",
+    "metadata = {\n",
+    "    'table_name': 'users_view',\n",
+    "    'column_name': 'users_view.city',\n",
+    "    'column_data_type': 'string',\n",
+    "    'column_title': 'Users View City',\n",
+    "    'column_description': 'None',\n",
+    "    'column_member_type': 'dimension',\n",
+    "    'column_values': [\n",
+    "        'Austin',\n",
+    "        'Chicago',\n",
+    "        'Los Angeles',\n",
+    "        'Mountain View',\n",
+    "        'New York',\n",
+    "        'Palo Alto',\n",
+    "        'San Francisco',\n",
+    "        'Seattle'\n",
+    "    ],\n",
+    "    'cube_data_obj_type': 'view'\n",
+    "}"
    ]
   }
  ],

docs/extras/integrations/document_loaders/google_drive.ipynb

@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "878928a6-a5ae-4f74-b351-64e3b01733fe",
    "metadata": {
     "tags": []
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "2216c83f-68e4-4d2f-8ea2-5878fb18bbe7",
    "metadata": {
     "tags": []
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "8f3b6aa0-b45d-4e37-8c50-5bebe70fdb9d",
    "metadata": {
     "tags": []
@@ -93,7 +93,7 @@
    "source": [
     "loader = GoogleDriveLoader(\n",
     "    folder_id=\"1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5\",\n",
-    "    file_types=[\"document\", \"sheet\"]\n",
+    "    file_types=[\"document\", \"sheet\"],\n",
     "    recursive=False\n",
     ")"
    ]
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "94207e39",
    "metadata": {},
    "outputs": [],
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "a15fbee0",
    "metadata": {},
    "outputs": [],
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "98410bda",
    "metadata": {},
    "outputs": [],
@@ -146,21 +146,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "e3e72221",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(page_content='\\n  \\n    \\n      Team\\n      Location\\n      Stanley Cups\\n    \\n    \\n      Blues\\n      STL\\n      1\\n    \\n    \\n      Flyers\\n      PHI\\n      2\\n    \\n    \\n      Maple Leafs\\n      TOR\\n      13\\n    \\n  \\n', metadata={'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'page_number': 1, 'page_name': 'Stanley Cups', 'text_as_html': '<table border=\"1\" class=\"dataframe\">\\n  <tbody>\\n    <tr>\\n      <td>Team</td>\\n      <td>Location</td>\\n      <td>Stanley Cups</td>\\n    </tr>\\n    <tr>\\n      <td>Blues</td>\\n      <td>STL</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <td>Flyers</td>\\n      <td>PHI</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <td>Maple Leafs</td>\\n      <td>TOR</td>\\n      <td>13</td>\\n    </tr>\\n  </tbody>\\n</table>', 'category': 'Table', 'source': 'https://drive.google.com/file/d/1aA6L2AR3g0CR-PW03HEZZo4NaVlKpaP7/view'})"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "docs[0]"
    ]
@@ -175,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "0e2d093f",
    "metadata": {},
    "outputs": [],
@@ -190,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "b35ddcc6",
    "metadata": {},
    "outputs": [],
@@ -200,21 +189,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "3cc141e0",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(page_content='\\n  \\n    \\n      Team\\n      Location\\n      Stanley Cups\\n    \\n    \\n      Blues\\n      STL\\n      1\\n    \\n    \\n      Flyers\\n      PHI\\n      2\\n    \\n    \\n      Maple Leafs\\n      TOR\\n      13\\n    \\n  \\n', metadata={'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'page_number': 1, 'page_name': 'Stanley Cups', 'text_as_html': '<table border=\"1\" class=\"dataframe\">\\n  <tbody>\\n    <tr>\\n      <td>Team</td>\\n      <td>Location</td>\\n      <td>Stanley Cups</td>\\n    </tr>\\n    <tr>\\n      <td>Blues</td>\\n      <td>STL</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <td>Flyers</td>\\n      <td>PHI</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <td>Maple Leafs</td>\\n      <td>TOR</td>\\n      <td>13</td>\\n    </tr>\\n  </tbody>\\n</table>', 'category': 'Table', 'source': 'https://drive.google.com/file/d/1aA6L2AR3g0CR-PW03HEZZo4NaVlKpaP7/view'})"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "docs[0]"
    ]
@@ -226,6 +204,309 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83ac576b-48c9-4aad-a35e-e978ea32f746",
+   "metadata": {},
+   "source": [
+    "# Extended usage\n",
+    "An external component can manage the complexity of Google Drive : `langchain-googledrive`\n",
+    "It's compatible with the ̀`langchain.document_loaders.GoogleDriveLoader` and can be used\n",
+    "in its place.\n",
+    "\n",
+    "To be compatible with containers, the authentication uses an environment variable ̀GOOGLE_ACCOUNT_FILE` to credential file (for user or service)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b94f7119-bc1e-4ca3-907f-9d81e837ac59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain-googledrive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4c7474e-49cb-48a1-b3a0-77fba8e2dd70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "folder_id='root'\n",
+    "#folder_id='1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8357f7f1-e2b1-41ef-8e38-48fcc3897dba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the advanced version.\n",
+    "from langchain_googledrive.document_loaders import GoogleDriveLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16ab9d3d-1782-4cb9-ab56-d87edbb25a18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = GoogleDriveLoader(\n",
+    "    folder_id=folder_id,\n",
+    "    recursive=False,\n",
+    "    num_results=2,  # Maximum number of file to load\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebac43aa-dd64-4964-802a-a90172415fd1",
+   "metadata": {},
+   "source": [
+    "By default, all files with these mime-type can be converted to `Document`.\n",
+    "- text/text\n",
+    "- text/plain\n",
+    "- text/html\n",
+    "- text/csv\n",
+    "- text/markdown\n",
+    "- image/png\n",
+    "- image/jpeg\n",
+    "- application/epub+zip\n",
+    "- application/pdf\n",
+    "- application/rtf\n",
+    "- application/vnd.google-apps.document (GDoc)\n",
+    "- application/vnd.google-apps.presentation (GSlide)\n",
+    "- application/vnd.google-apps.spreadsheet (GSheet)\n",
+    "- application/vnd.google.colaboratory (Notebook colab)\n",
+    "- application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX)\n",
+    "- application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX)\n",
+    "\n",
+    "It's possible to update or customize this. See the documentation of `GDriveLoader`.\n",
+    "\n",
+    "But, the corresponding packages must be installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4560f35-a37d-44e2-be0b-adaa245b3b3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install unstructured"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cb08da3-27df-46de-b60e-583bb7e31af4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for doc in loader.load():\n",
+    "    print(\"---\")\n",
+    "    print(doc.page_content.strip()[:60]+\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd13d7d1-db7a-498d-ac98-76ccd9ad9019",
+   "metadata": {},
+   "source": [
+    "## Customize the search pattern\n",
+    "\n",
+    "All parameter compatible with Google [`list()`](https://developers.google.com/drive/api/v3/reference/files/list)\n",
+    "API can be set.\n",
+    "\n",
+    "To specify the new pattern of the Google request, you can use a `PromptTemplate()`.\n",
+    "The variables for the prompt can be set with `kwargs` in the constructor.\n",
+    "Some pre-formated request are proposed (use `{query}`, `{folder_id}` and/or `{mime_type}`):\n",
+    "\n",
+    "You can customize the criteria to select the files. A set of predefined filter are proposed:\n",
+    "| template                               | description                                                           |\n",
+    "| -------------------------------------- | --------------------------------------------------------------------- |\n",
+    "| gdrive-all-in-folder                   | Return all compatible files from a `folder_id`                        |\n",
+    "| gdrive-query                           | Search `query` in all drives                                          |\n",
+    "| gdrive-by-name                         | Search file with name `query`                                        |\n",
+    "| gdrive-query-in-folder                 | Search `query` in `folder_id` (and sub-folders if `recursive=true`)  |\n",
+    "| gdrive-mime-type                       | Search a specific `mime_type`                                         |\n",
+    "| gdrive-mime-type-in-folder             | Search a specific `mime_type` in `folder_id`                          |\n",
+    "| gdrive-query-with-mime-type            | Search `query` with a specific `mime_type`                            |\n",
+    "| gdrive-query-with-mime-type-and-folder | Search `query` with a specific `mime_type` and in `folder_id`         |\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81348d59-8fd6-45d4-9de3-5df5cff5c7e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = GoogleDriveLoader(\n",
+    "    folder_id=folder_id,\n",
+    "    recursive=False,\n",
+    "    template=\"gdrive-query\",  # Default template to use\n",
+    "    query=\"machine learning\",\n",
+    "    num_results=2,            # Maximum number of file to load\n",
+    "    supportsAllDrives=False,  # GDrive `list()` parameter\n",
+    ")\n",
+    "for doc in loader.load():\n",
+    "    print(\"---\")\n",
+    "    print(doc.page_content.strip()[:60]+\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46c6ba5b-d4b1-4f0f-9801-5c1314021605",
+   "metadata": {},
+   "source": [
+    "You can customize your pattern."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a5a323b-8d96-46b7-b46a-fd69bd2c8e04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "loader = GoogleDriveLoader(\n",
+    "    folder_id=folder_id,\n",
+    "    recursive=False,\n",
+    "    template=PromptTemplate(\n",
+    "        input_variables=[\"query\", \"query_name\"],\n",
+    "        template=\"fullText contains '{query}' and name contains '{query_name}' and trashed=false\",\n",
+    "        ),  # Default template to use\n",
+    "    query=\"machine learning\",\n",
+    "    query_name=\"ML\",    \n",
+    "    num_results=2,  # Maximum number of file to load\n",
+    ")\n",
+    "for doc in loader.load():\n",
+    "    print(\"---\")\n",
+    "    print(doc.page_content.strip()[:60]+\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "375bb465-8f69-407b-94bd-ffa3718ef500",
+   "metadata": {},
+   "source": [
+    "### Modes for GSlide and GSheet\n",
+    "The parameter mode accepts different values:\n",
+    "\n",
+    "- \"document\": return the body of each document\n",
+    "- \"snippets\": return the description of each file (set in metadata of Google Drive files).\n",
+    "\n",
+    "\n",
+    "The conversion can manage in Markdown format:\n",
+    "- bullet\n",
+    "- link\n",
+    "- table\n",
+    "- titles\n",
+    "\n",
+    "The parameter `gslide_mode` accepts different values:\n",
+    "\n",
+    "- \"single\" : one document with &lt;PAGE BREAK&gt;\n",
+    "- \"slide\" : one document by slide\n",
+    "- \"elements\" : one document for each elements.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7493d7b0-0600-49af-8107-7f4597c92de7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = GoogleDriveLoader(\n",
+    "    template=\"gdrive-mime-type\",\n",
+    "    mime_type=\"application/vnd.google-apps.presentation\", # Only GSlide files\n",
+    "    gslide_mode=\"slide\",\n",
+    "    num_results=2,  # Maximum number of file to load\n",
+    ")\n",
+    "for doc in loader.load():\n",
+    "    print(\"---\")\n",
+    "    print(doc.page_content.strip()[:60]+\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bf338fb-02d7-452f-8679-c50419b13464",
+   "metadata": {},
+   "source": [
+    "The parameter `gsheet_mode` accepts different values:\n",
+    "- `\"single\"`: Generate one document by line\n",
+    "- `\"elements\"` : one document with markdown array and &lt;PAGE BREAK&gt; tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "469f5af0-67db-4f15-8aee-88cde480729b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = GoogleDriveLoader(\n",
+    "    template=\"gdrive-mime-type\",\n",
+    "    mime_type=\"application/vnd.google-apps.spreadsheet\", # Only GSheet files\n",
+    "    gsheet_mode=\"elements\",\n",
+    "    num_results=2,  # Maximum number of file to load\n",
+    ")\n",
+    "for doc in loader.load():\n",
+    "    print(\"---\")\n",
+    "    print(doc.page_content.strip()[:60]+\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09acb864-e919-4add-9e06-deba6f7f0cd8",
+   "metadata": {},
+   "source": [
+    "## Advanced usage\n",
+    "All Google File have a 'description' in the metadata. This field can be used to memorize a summary of the document or others indexed tags (See method `lazy_update_description_with_summary()`).\n",
+    "\n",
+    "If you use the `mode=\"snippet\"`, only the description will be used for the body. Else, the `metadata['summary']` has the field.\n",
+    "\n",
+    "Sometime, a specific filter can be used to extract some information from the filename, to select some files with specific criteria. You can use a filter.\n",
+    "\n",
+    "Sometimes, many documents are returned. It's not necessary to have all documents in memory at the same time. You can use the lazy versions of methods, to get one document at a time. It's better to use a complex query in place of a recursive search. For each folder, a query must be applied if you activate `recursive=True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5e9c8eb-a266-4ae6-a760-d7826a0aa7c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "loader = GoogleDriveLoader(\n",
+    "                gdrive_api_file=os.environ[\"GOOGLE_ACCOUNT_FILE\"],\n",
+    "                num_results=2,\n",
+    "                template=\"gdrive-query\",\n",
+    "                filter=lambda search, file: \"#test\" not in file.get('description',''),\n",
+    "                query='machine learning',\n",
+    "                supportsAllDrives=False,\n",
+    "                )\n",
+    "for doc in loader.load():\n",
+    "    print(\"---\")\n",
+    "    print(doc.page_content.strip()[:60]+\"...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51efa73a-4e2d-4f9c-abaf-6c9bde2ff69d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -244,7 +525,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/extras/integrations/llms/azure_openai_example.ipynb

@@ -30,7 +30,45 @@
     "```python\n",
     "import os\n",
     "os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
-    "...\n",
+    "```\n",
+    "\n",
+    "## Azure Active Directory Authentication\n",
+    "There are two ways you can authenticate to Azure OpenAI:\n",
+    "- API Key\n",
+    "- Azure Active Directory (AAD)\n",
+    "\n",
+    "Using the API key is the easiest way to get started. You can find your API key in the Azure portal under your Azure OpenAI resource.\n",
+    "\n",
+    "However, if you have complex security requirements - you may want to use Azure Active Directory. You can find more information on how to use AAD with Azure OpenAI [here](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/managed-identity).\n",
+    "\n",
+    "If you are developing locally, you will need to have the Azure CLI installed and be logged in. You can install the Azure CLI [here](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli). Then, run `az login` to log in.\n",
+    "\n",
+    "Add a role an Azure role assignment `Cognitive Services OpenAI User` scoped to your Azure OpenAI resource. This will allow you to get a token from AAD to use with Azure OpenAI. You can grant this role assignment to a user, group, service principal, or managed identity. For more information about Azure OpenAI RBAC roles see [here](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/role-based-access-control).\n",
+    "\n",
+    "To use AAD in Python with LangChain, install the `azure-identity` package. Then, set `OPENAI_API_TYPE` to `azure_ad`. Next, use the `DefaultAzureCredential` class to get a token from AAD by calling `get_token` as shown below. Finally, set the `OPENAI_API_KEY` environment variable to the token value.\n",
+    "\n",
+    "```python\n",
+    "import os\n",
+    "from azure.identity import DefaultAzureCredential\n",
+    "\n",
+    "# Get the Azure Credential\n",
+    "credential = DefaultAzureCredential()\n",
+    "\n",
+    "# Set the API type to `azure_ad`\n",
+    "os.environ[\"OPENAI_API_TYPE\"] = \"azure_ad\"\n",
+    "# Set the API_KEY to the token from the Azure credential\n",
+    "os.environ[\"OPENAI_API_KEY\"] = credential.get_token(\"https://cognitiveservices.azure.com/.default\").token\n",
+    "```\n",
+    "\n",
+    "The `DefaultAzureCredential` class is an easy way to get started with AAD authentication. You can also customize the credential chain if necessary. In the example shown below, we first try Managed Identity, then fall back to the Azure CLI. This is useful if you are running your code in Azure, but want to develop locally.\n",
+    "\n",
+    "```python\n",
+    "from azure.identity import ChainedTokenCredential, ManagedIdentityCredential, AzureCliCredential\n",
+    "\n",
+    "credential = ChainedTokenCredential(\n",
+    "    ManagedIdentityCredential(),\n",
+    "    AzureCliCredential()\n",
+    ")\n",
     "```\n",
     "\n",
     "## Deployments\n",
@@ -144,7 +182,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1mAzureOpenAI\u001b[0m\n",
+      "\u001B[1mAzureOpenAI\u001B[0m\n",
       "Params: {'deployment_name': 'text-davinci-002', 'model_name': 'text-davinci-002', 'temperature': 0.7, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n"
      ]
     }

docs/extras/integrations/llms/google_vertex_ai_palm.ipynb

@@ -1,17 +1,28 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Google Cloud Platform Vertex AI PaLM \n",
+    "# Google Vertex AI PaLM \n",
     "\n",
-    "Note: This is seperate from the Google PaLM integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on Google Cloud. \n",
+    "**Note:** This is seperate from the `Google PaLM` integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on `Google Cloud`. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) customer data to train its foundation models as part of Google Cloud's AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
     "\n",
-    "By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
-    "\n",
-    "To use Vertex AI PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
+    "To use `Vertex AI PaLM` you must have the `google-cloud-aiplatform` Python package installed and either:\n",
     "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
     "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
     "\n",
@@ -19,8 +30,7 @@
     "\n",
     "For more information, see: \n",
     "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
-    "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
-    "\n"
+    "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth"
    ]
   },
   {
@@ -40,7 +50,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms import VertexAI\n",
+    "from langchain.llms import VertexAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question-answering example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "from langchain import PromptTemplate, LLMChain"
    ]
   },
@@ -98,13 +123,21 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can now leverage the Codey API for code generation within Vertex AI. The model names are:\n",
-    "- code-bison: for code suggestion\n",
-    "- code-gecko: for code completion"
+    "## Code generation example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can now leverage the `Codey API` for code generation within `Vertex AI`. \n",
+    "\n",
+    "The model names are:\n",
+    "- `code-bison`: for code suggestion\n",
+    "- `code-gecko`: for code completion"
    ]
   },
   {
@@ -191,7 +224,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/extras/integrations/llms/llamacpp.ipynb

@@ -7,9 +7,20 @@
     "# Llama.cpp\n",
     "\n",
     "[llama-cpp-python](https://github.com/abetlen/llama-cpp-python) is a Python binding for [llama.cpp](https://github.com/ggerganov/llama.cpp). \n",
-    "It supports [several LLMs](https://github.com/ggerganov/llama.cpp).\n",
     "\n",
-    "This notebook goes over how to run `llama-cpp-python` within LangChain."
+    "It supports inference for [many LLMs](https://github.com/ggerganov/llama.cpp), which can be accessed on [HuggingFace](https://huggingface.co/TheBloke).\n",
+    "\n",
+    "This notebook goes over how to run `llama-cpp-python` within LangChain.\n",
+    "\n",
+    "**Note: new versions of `llama-cpp-python` use GGUF model files (see [here](https://github.com/abetlen/llama-cpp-python/pull/633)).**\n",
+    "\n",
+    "This is a breaking change.\n",
+    " \n",
+    "To convert existing GGML models to GGUF you can run the following in [llama.cpp](https://github.com/ggerganov/llama.cpp):\n",
+    "\n",
+    "```\n",
+    "python ./convert-llama-ggmlv3-to-gguf.py --eps 1e-5 --input models/openorca-platypus2-13b.ggmlv3.q4_0.bin --output models/openorca-platypus2-13b.gguf.q4_0.bin\n",
+    "```"
    ]
   },
   {
@@ -19,7 +30,7 @@
     "## Installation\n",
     "\n",
     "There are different options on how to install the llama-cpp package: \n",
-    "- only CPU usage\n",
+    "- CPU usage\n",
     "- CPU + GPU (using one of many BLAS backends)\n",
     "- Metal GPU (MacOS with Apple Silicon Chip) \n",
     "\n",
@@ -171,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "tags": []
    },
@@ -192,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "tags": []
    },
@@ -207,15 +218,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
     "# Callbacks support token-wise streaming\n",
-    "callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])\n",
-    "# Verbose is required to pass to the callback manager"
+    "callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])"
    ]
   },
   {
@@ -240,12 +250,12 @@
    "source": [
     "# Make sure the model path is correct for your system!\n",
     "llm = LlamaCpp(\n",
-    "    model_path=\"/Users/rlm/Desktop/Code/llama/llama-2-7b-ggml/llama-2-7b-chat.ggmlv3.q4_0.bin\",\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin\",\n",
     "    temperature=0.75,\n",
     "    max_tokens=2000,\n",
     "    top_p=1,\n",
-    "    callback_manager=callback_manager,\n",
-    "    verbose=True,\n",
+    "    callback_manager=callback_manager, \n",
+    "    verbose=True, # Verbose is required to pass to the callback manager\n",
     ")"
    ]
   },
@@ -375,7 +385,6 @@
    ],
    "source": [
     "question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
-    "\n",
     "llm_chain.run(question)"
    ]
   },
@@ -397,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -406,46 +415,32 @@
     "\n",
     "# Make sure the model path is correct for your system!\n",
     "llm = LlamaCpp(\n",
-    "    model_path=\"./ggml-model-q4_0.bin\",\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin\",\n",
     "    n_gpu_layers=n_gpu_layers,\n",
     "    n_batch=n_batch,\n",
     "    callback_manager=callback_manager,\n",
-    "    verbose=True,\n",
+    "    verbose=True, # Verbose is required to pass to the callback manager\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " We are looking for an NFL team that won the Super Bowl when Justin Bieber (born March 1, 1994) was born. \n",
       "\n",
-      "First, let's look up which year is closest to when Justin Bieber was born:\n",
       "\n",
-      "* The year before he was born: 1993\n",
-      "* The year of his birth: 1994\n",
-      "* The year after he was born: 1995\n",
+      "1. Identify Justin Bieber's birth date: Justin Bieber was born on March 1, 1994.\n",
       "\n",
-      "We want to know what NFL team won the Super Bowl in the year that is closest to when Justin Bieber was born. Therefore, we should look up the NFL team that won the Super Bowl in either 1993 or 1994.\n",
+      "2. Find the Super Bowl winner of that year: The NFL season of 1993 with the Super Bowl being played in January or of 1994.\n",
       "\n",
-      "Now let's find out which NFL team did win the Super Bowl in either of those years:\n",
+      "3. Determine which team won the game: The Dallas Cowboys faced the Buffalo Bills in Super Bowl XXVII on January 31, 1993 (as the year is mis-labelled due to a error). The Dallas Cowboys won this matchup.\n",
       "\n",
-      "* In 1993, the San Francisco 49ers won the Super Bowl against the Dallas Cowboys by a score of 20-16.\n",
-      "* In 1994, the San Francisco 49ers won the Super Bowl again, this time against the San Diego Chargers by a score of 49-26.\n"
+      "So, Justin Bieber was born when the Dallas Cowboys were the reigning NFL Super Bowl."
      ]
     },
     {
@@ -453,27 +448,27 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =   238.10 ms\n",
-      "llama_print_timings:      sample time =    84.23 ms /   256 runs   (    0.33 ms per token)\n",
-      "llama_print_timings: prompt eval time =   238.04 ms /    49 tokens (    4.86 ms per token)\n",
-      "llama_print_timings:        eval time = 10391.96 ms /   255 runs   (   40.75 ms per token)\n",
-      "llama_print_timings:       total time = 15664.80 ms\n"
+      "llama_print_timings:        load time =   427.63 ms\n",
+      "llama_print_timings:      sample time =   115.85 ms /   164 runs   (    0.71 ms per token,  1415.67 tokens per second)\n",
+      "llama_print_timings: prompt eval time =   427.53 ms /    45 tokens (    9.50 ms per token,   105.26 tokens per second)\n",
+      "llama_print_timings:        eval time =  4526.53 ms /   163 runs   (   27.77 ms per token,    36.01 tokens per second)\n",
+      "llama_print_timings:       total time =  5293.77 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "\" We are looking for an NFL team that won the Super Bowl when Justin Bieber (born March 1, 1994) was born. \\n\\nFirst, let's look up which year is closest to when Justin Bieber was born:\\n\\n* The year before he was born: 1993\\n* The year of his birth: 1994\\n* The year after he was born: 1995\\n\\nWe want to know what NFL team won the Super Bowl in the year that is closest to when Justin Bieber was born. Therefore, we should look up the NFL team that won the Super Bowl in either 1993 or 1994.\\n\\nNow let's find out which NFL team did win the Super Bowl in either of those years:\\n\\n* In 1993, the San Francisco 49ers won the Super Bowl against the Dallas Cowboys by a score of 20-16.\\n* In 1994, the San Francisco 49ers won the Super Bowl again, this time against the San Diego Chargers by a score of 49-26.\\n\""
+       "\"\\n\\n1. Identify Justin Bieber's birth date: Justin Bieber was born on March 1, 1994.\\n\\n2. Find the Super Bowl winner of that year: The NFL season of 1993 with the Super Bowl being played in January or of 1994.\\n\\n3. Determine which team won the game: The Dallas Cowboys faced the Buffalo Bills in Super Bowl XXVII on January 31, 1993 (as the year is mis-labelled due to a error). The Dallas Cowboys won this matchup.\\n\\nSo, Justin Bieber was born when the Dallas Cowboys were the reigning NFL Super Bowl.\""
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
     "question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
-    "\n",
     "llm_chain.run(question)"
    ]
   },
@@ -503,15 +498,14 @@
    "source": [
     "n_gpu_layers = 1  # Metal set to 1 is enough.\n",
     "n_batch = 512  # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.\n",
-    "\n",
     "# Make sure the model path is correct for your system!\n",
     "llm = LlamaCpp(\n",
-    "    model_path=\"./ggml-model-q4_0.bin\",\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin\",\n",
     "    n_gpu_layers=n_gpu_layers,\n",
     "    n_batch=n_batch,\n",
     "    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls\n",
     "    callback_manager=callback_manager,\n",
-    "    verbose=True,\n",
+    "    verbose=True, # Verbose is required to pass to the callback manager\n",
     ")"
    ]
   },
@@ -531,6 +525,144 @@
     "\n",
     "For the first call to the LLM, the performance may be slow due to the model compilation in Metal GPU."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Grammars\n",
+    "\n",
+    "\n",
+    "We can specify [grammars](https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md) to constrain model outputs.\n",
+    "\n",
+    "This will sample tokens according to the grammar.\n",
+    "  \n",
+    "For example, supply the path to the specifed `json.gbnf` file in order to produce JSON."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_gpu_layers = 1  # Metal set to 1 is enough.\n",
+    "n_batch = 512  # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.\n",
+    "# Make sure the model path is correct for your system!\n",
+    "llm = LlamaCpp(\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin\",\n",
+    "    n_gpu_layers=n_gpu_layers,\n",
+    "    n_batch=n_batch,\n",
+    "    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls\n",
+    "    callback_manager=callback_manager,\n",
+    "    verbose=True, # Verbose is required to pass to the callback manager\n",
+    "    grammar_path=\"/Users/rlm/Desktop/Code/langchain-main/langchain/libs/langchain/langchain/llms/grammars/json.gbnf\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"name\": \"John Doe\",\n",
+      "  \"age\": 34,\n",
+      "  \"\": {\n",
+      "    \"title\": \"Software Developer\",\n",
+      "    \"company\": \"Google\"\n",
+      "  },\n",
+      "  \"interests\": [\n",
+      "    \"Sports\",\n",
+      "    \"Music\",\n",
+      "    \"Cooking\"\n",
+      "  ],\n",
+      "  \"address\": {\n",
+      "    \"street_number\": 123,\n",
+      "    \"street_name\": \"Oak Street\",\n",
+      "    \"city\": \"Mountain View\",\n",
+      "    \"state\": \"California\",\n",
+      "    \"postal_code\": 94040\n",
+      "  }}"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time =   357.51 ms\n",
+      "llama_print_timings:      sample time =  1213.30 ms /   144 runs   (    8.43 ms per token,   118.68 tokens per second)\n",
+      "llama_print_timings: prompt eval time =   356.78 ms /     9 tokens (   39.64 ms per token,    25.23 tokens per second)\n",
+      "llama_print_timings:        eval time =  3947.16 ms /   143 runs   (   27.60 ms per token,    36.23 tokens per second)\n",
+      "llama_print_timings:       total time =  5846.21 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%capture captured --no-stdout\n",
+    "result=llm(\"Describe a person in JSON format:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also supply `list.gbnf` to return a list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_gpu_layers = 1 \n",
+    "n_batch = 512\n",
+    "llm = LlamaCpp(\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin\",\n",
+    "    n_gpu_layers=n_gpu_layers,\n",
+    "    n_batch=n_batch,\n",
+    "    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls\n",
+    "    callback_manager=callback_manager,\n",
+    "    verbose=True,\n",
+    "    grammar_path=\"/Users/rlm/Desktop/Code/langchain-main/langchain/libs/langchain/langchain/llms/grammars/list.gbnf\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\"The Catcher in the Rye\", \"Wuthering Heights\", \"Anna Karenina\"]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time =   322.34 ms\n",
+      "llama_print_timings:      sample time =   232.60 ms /    26 runs   (    8.95 ms per token,   111.78 tokens per second)\n",
+      "llama_print_timings: prompt eval time =   321.90 ms /    11 tokens (   29.26 ms per token,    34.17 tokens per second)\n",
+      "llama_print_timings:        eval time =   680.82 ms /    25 runs   (   27.23 ms per token,    36.72 tokens per second)\n",
+      "llama_print_timings:       total time =  1295.27 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%capture captured --no-stdout\n",
+    "result=llm(\"List of top-3 my favourite books:\")"
+   ]
   }
  ],
  "metadata": {
@@ -549,7 +681,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,

docs/extras/integrations/llms/llm_caching.ipynb

@@ -5,8 +5,9 @@
    "id": "f36d938c",
    "metadata": {},
    "source": [
-    "# Caching integrations\n",
-    "This notebook covers how to cache results of individual LLM calls."
+    "# LLM Caching integrations\n",
+    "\n",
+    "This notebook covers how to cache results of individual LLM calls using different caches."
    ]
   },
   {
@@ -26,9 +27,12 @@
   {
    "cell_type": "markdown",
    "id": "b50f0598",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
-    "## In Memory Cache"
+    "## `In Memory` Cache"
    ]
   },
   {
@@ -108,9 +112,12 @@
   {
    "cell_type": "markdown",
    "id": "4bf59c12",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
-    "## SQLite Cache"
+    "## `SQLite` Cache"
    ]
   },
   {
@@ -203,9 +210,12 @@
   {
    "cell_type": "markdown",
    "id": "278ad7ae",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
-    "## Redis Cache"
+    "## `Redis` Cache"
    ]
   },
   {
@@ -385,9 +395,12 @@
   {
    "cell_type": "markdown",
    "id": "684eab55",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
-    "## GPTCache\n",
+    "## `GPTCache`\n",
     "\n",
     "We can use [GPTCache](https://github.com/zilliztech/GPTCache) for exact match caching OR to cache results based on semantic similarity\n",
     "\n",
@@ -614,9 +627,12 @@
   {
    "cell_type": "markdown",
    "id": "726fe754",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
-    "## Momento Cache\n",
+    "## `Momento` Cache\n",
     "Use [Momento](/docs/ecosystem/integrations/momento.html) to cache prompts and responses.\n",
     "\n",
     "Requires momento to use, uncomment below to install:"
@@ -723,9 +739,14 @@
   {
    "cell_type": "markdown",
    "id": "934943dc",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
-    "## SQLAlchemy Cache"
+    "## `SQLAlchemy` Cache\n",
+    "\n",
+    "You can use `SQLAlchemyCache` to cache with any SQL database supported by `SQLAlchemy`."
    ]
   },
   {
@@ -735,8 +756,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# You can use SQLAlchemyCache to cache with any SQL database supported by SQLAlchemy.\n",
-    "\n",
     "# from langchain.cache import SQLAlchemyCache\n",
     "# from sqlalchemy import create_engine\n",
     "\n",
@@ -795,7 +814,10 @@
   {
    "cell_type": "markdown",
    "id": "0c69d84d",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
     "## Optional Caching\n",
     "You can also turn off caching for specific LLMs should you choose. In the example below, even though global caching is enabled, we turn it off for a specific LLM"
@@ -874,7 +896,10 @@
   {
    "cell_type": "markdown",
    "id": "5da41b77",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
     "## Optional Caching in Chains\n",
     "You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, its often easier to construct the chain first, and then edit the LLM afterwards.\n",
@@ -1022,9 +1047,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1036,7 +1061,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/memory/xata_chat_message_history.ipynb

@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install xata==1.0.0rc0 openai langchain"
+    "!pip install xata openai langchain"
    ]
   },
   {

docs/extras/integrations/providers/ainetwork.mdx

@@ -0,0 +1,23 @@
+# AINetwork
+
+>[AI Network](https://www.ainetwork.ai/build-on-ain) is a layer 1 blockchain designed to accommodate 
+> large-scale AI models, utilizing a decentralized GPU network powered by the 
+> [$AIN token](https://www.ainetwork.ai/token), enriching AI-driven `NFTs` (`AINFTs`).
+
+
+## Installation and Setup
+
+You need to install `ain-py` python package.
+
+```bash
+pip install ain-py
+```
+You need to set the `AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY` environmental variable to your AIN Blockchain Account Private Key.
+## Toolkit
+
+See a [usage example](/docs/integrations/toolkits/ainetwork).
+
+```python
+from langchain.agents.agent_toolkits.ainetwork.toolkit import AINetworkToolkit
+```
+

docs/extras/integrations/providers/infino.mdx

@@ -2,10 +2,10 @@
 
 >[Infino](https://github.com/infinohq/infino) is an open-source observability platform that stores both metrics and application logs together.
 
-Key features of infino include:
-- Metrics Tracking: Capture time taken by LLM model to handle request, errors, number of tokens, and costing indication for the particular LLM.
-- Data Tracking: Log and store prompt, request, and response data for each LangChain interaction.
-- Graph Visualization: Generate basic graphs over time, depicting metrics such as request duration, error occurrences, token count, and cost.
+Key features of `Infino` include:
+- **Metrics Tracking**: Capture time taken by LLM model to handle request, errors, number of tokens, and costing indication for the particular LLM.
+- **Data Tracking**: Log and store prompt, request, and response data for each LangChain interaction.
+- **Graph Visualization**: Generate basic graphs over time, depicting metrics such as request duration, error occurrences, token count, and cost.
 
 ## Installation and Setup
 
@@ -15,7 +15,7 @@ First, you'll need to install the  `infinopy` Python package as follows:
 pip install infinopy
 ```
 
-If you already have an Infino Server running, then you're good to go; but if
+If you already have an `Infino Server` running, then you're good to go; but if
 you don't, follow the next steps to start it:
 
 - Make sure you have Docker installed
@@ -28,7 +28,7 @@ you don't, follow the next steps to start it:
 
 ## Using Infino
 
-See a [usage example of `InfinoCallbackHandler`](/docs/modules/callbacks/integrations/infino.html).
+See a [usage example of `InfinoCallbackHandler`](/docs/integrations/callbacks/infino.html).
 
 ```python
 from langchain.callbacks import InfinoCallbackHandler

docs/extras/integrations/providers/neo4j.mdx

@@ -0,0 +1,44 @@
+# Neo4j
+
+This page covers how to use the Neo4j ecosystem within LangChain.
+
+What is Neo4j?
+
+**Neo4j in a nutshell:**
+
+- Neo4j is an open-source database management system that specializes in graph database technology.
+- Neo4j allows you to represent and store data in nodes and edges, making it ideal for handling connected data and relationships.
+- Neo4j provides a Cypher Query Language, making it easy to interact with and query your graph data.
+- With Neo4j, you can achieve high-performance graph traversals and queries, suitable for production-level systems.
+- Get started quickly with Neo4j by visiting [their website](https://neo4j.com/).
+
+## Installation and Setup
+
+- Install the Python SDK with `pip install neo4j`
+
+## Wrappers
+
+### VectorStore
+
+There exists a wrapper around Neo4j vector index, allowing you to use it as a vectorstore,
+whether for semantic search or example selection.
+
+To import this vectorstore:
+
+```python
+from langchain.vectorstores import Neo4jVector
+```
+
+For a more detailed walkthrough of the Neo4j vector index wrapper, see [this notebook](/docs/integrations/vectorstores/neo4jvector.html)
+
+### GraphCypherQAChain
+
+There exists a wrapper around Neo4j graph database that allows you to generate Cypher statements based on the user input
+and use them to retrieve relevant information from the database.
+
+```python
+from langchain.graphs import Neo4jGraph
+from langchain.chains import GraphCypherQAChain
+```
+
+For a more detailed walkthrough of Cypher generating chain, see [this notebook](/docs/extras/use_cases/more/graph/graph_cypher_qa.html)

docs/extras/integrations/retrievers/google_drive.ipynb

@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b0ed136e-6983-4893-ae1b-b75753af05f8",
+   "metadata": {},
+   "source": [
+    "# Google Drive Retriever\n",
+    "This notebook covers how to retrieve documents from Google Drive.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "1. Create a Google Cloud project or use an existing project\n",
+    "1. Enable the [Google Drive API](https://console.cloud.google.com/flows/enableapi?apiid=drive.googleapis.com)\n",
+    "1. [Authorize credentials for desktop app](https://developers.google.com/drive/api/quickstart/python#authorize_credentials_for_a_desktop_application)\n",
+    "1. `pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib`\n",
+    "\n",
+    "## Instructions for retrieving your Google Docs data\n",
+    "By default, the `GoogleDriveRetriever` expects the `credentials.json` file to be `~/.credentials/credentials.json`, but this is configurable using the `GOOGLE_ACCOUNT_FILE` environment variable. \n",
+    "The location of `token.json` use the same directory (or use the parameter `token_path`). Note that `token.json` will be created automatically the first time you use the retriever.\n",
+    "\n",
+    "`GoogleDriveRetriever` can retrieve a selection of files with some requests. \n",
+    "\n",
+    "By default, If you use a `folder_id`, all the files inside this folder can be retrieved to `Document`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35b94a93-97de-4af8-9cca-de9ffb7930c3",
+   "metadata": {},
+   "source": [
+    "You can obtain your folder and document id from the URL:\n",
+    "* Folder: https://drive.google.com/drive/u/0/folders/1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5 -> folder id is `\"1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5\"`\n",
+    "* Document: https://docs.google.com/document/d/1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw/edit -> document id is `\"1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw\"`\n",
+    "\n",
+    "The special value `root` is for your personal home."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c9665c9-a023-4078-9d95-e43021cecb6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "878928a6-a5ae-4f74-b351-64e3b01733fe",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-09T10:45:59.438650905Z",
+     "start_time": "2023-05-09T10:45:57.955900302Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.retrievers import GoogleDriveRetriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "755907c2-145d-4f0f-9b15-07a628a2d2d2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-09T10:45:59.442890834Z",
+     "start_time": "2023-05-09T10:45:59.440941528Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "folder_id=\"root\"\n",
+    "#folder_id='1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2216c83f-68e4-4d2f-8ea2-5878fb18bbe7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-09T10:45:59.795842403Z",
+     "start_time": "2023-05-09T10:45:59.445262457Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "retriever = GoogleDriveRetriever(\n",
+    "    num_results=2,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa339ca0-f478-440c-ba80-0e5f41a19ce1",
+   "metadata": {},
+   "source": [
+    "By default, all files with these mime-type can be converted to `Document`.\n",
+    "- text/text\n",
+    "- text/plain\n",
+    "- text/html\n",
+    "- text/csv\n",
+    "- text/markdown\n",
+    "- image/png\n",
+    "- image/jpeg\n",
+    "- application/epub+zip\n",
+    "- application/pdf\n",
+    "- application/rtf\n",
+    "- application/vnd.google-apps.document (GDoc)\n",
+    "- application/vnd.google-apps.presentation (GSlide)\n",
+    "- application/vnd.google-apps.spreadsheet (GSheet)\n",
+    "- application/vnd.google.colaboratory (Notebook colab)\n",
+    "- application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX)\n",
+    "- application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX)\n",
+    "\n",
+    "It's possible to update or customize this. See the documentation of `GDriveRetriever`.\n",
+    "\n",
+    "But, the corresponding packages must be installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9dadec48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install unstructured"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f3b6aa0-b45d-4e37-8c50-5bebe70fdb9d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-09T10:46:00.990310466Z",
+     "start_time": "2023-05-09T10:45:59.798774595Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "retriever.get_relevant_documents(\"machine learning\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ff33817-8619-4897-8742-2216b9934d2a",
+   "metadata": {},
+   "source": [
+    "You can customize the criteria to select the files. A set of predefined filter are proposed:\n",
+    "| template                               | description                                                           |\n",
+    "| -------------------------------------- | --------------------------------------------------------------------- |\n",
+    "| gdrive-all-in-folder                   | Return all compatible files from a `folder_id`                        |\n",
+    "| gdrive-query                           | Search `query` in all drives                                          |\n",
+    "| gdrive-by-name                         | Search file with name `query`)                                        |\n",
+    "| gdrive-query-in-folder                 | Search `query` in `folder_id` (and sub-folders in `_recursive=true`)  |\n",
+    "| gdrive-mime-type                       | Search a specific `mime_type`                                         |\n",
+    "| gdrive-mime-type-in-folder             | Search a specific `mime_type` in `folder_id`                          |\n",
+    "| gdrive-query-with-mime-type            | Search `query` with a specific `mime_type`                            |\n",
+    "| gdrive-query-with-mime-type-and-folder | Search `query` with a specific `mime_type` and in `folder_id`         |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9977c712-9659-4959-b508-f59cc7d49d44",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "retriever = GoogleDriveRetriever(\n",
+    "    template=\"gdrive-query\", # Search everywhere\n",
+    "    num_results=2,  # But take only 2 documents\n",
+    ")\n",
+    "for doc in retriever.get_relevant_documents(\"machine learning\"):\n",
+    "    print(\"---\")\n",
+    "    print(doc.page_content.strip()[:60]+\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5a0f3ef-26fb-4a5c-85f0-5aba90b682b1",
+   "metadata": {},
+   "source": [
+    "Else, you can customize the prompt with a specialized `PromptTemplate`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0bbebde-0487-4d20-9d77-8070e4f0e0d6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain import PromptTemplate\n",
+    "retriever = GoogleDriveRetriever(\n",
+    "    template=PromptTemplate(input_variables=['query'],\n",
+    "                            # See https://developers.google.com/drive/api/guides/search-files\n",
+    "                            template=\"(fullText contains '{query}') \"\n",
+    "                              \"and mimeType='application/vnd.google-apps.document' \"\n",
+    "                              \"and modifiedTime > '2000-01-01T00:00:00' \"\n",
+    "                              \"and trashed=false\"),\n",
+    "    num_results=2,\n",
+    "    # See https://developers.google.com/drive/api/v3/reference/files/list\n",
+    "    includeItemsFromAllDrives=False,\n",
+    "    supportsAllDrives=False,\n",
+    ")\n",
+    "for doc in retriever.get_relevant_documents(\"machine learning\"):\n",
+    "    print(f\"{doc.metadata['name']}:\")\n",
+    "    print(\"---\")\n",
+    "    print(doc.page_content.strip()[:60]+\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b6fed29-1666-452e-b677-401613270388",
+   "metadata": {},
+   "source": [
+    "# Use GDrive 'description' metadata\n",
+    "Each Google Drive has a `description` field in metadata (see the *details of a file*).\n",
+    "Use the `snippets` mode to return the description of selected files.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "342dbe12-ed83-40f4-8957-0cc8c4609542",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "retriever = GoogleDriveRetriever(\n",
+    "    template='gdrive-mime-type-in-folder',\n",
+    "    folder_id=folder_id,\n",
+    "    mime_type='application/vnd.google-apps.document',  # Only Google Docs\n",
+    "    num_results=2,\n",
+    "    mode='snippets',\n",
+    "    includeItemsFromAllDrives=False,\n",
+    "    supportsAllDrives=False,\n",
+    ")\n",
+    "retriever.get_relevant_documents(\"machine learning\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "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",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/text_embedding/sagemaker-endpoint.ipynb

@@ -48,10 +48,31 @@
     "    accepts = \"application/json\"\n",
     "\n",
     "    def transform_input(self, inputs: list[str], model_kwargs: Dict) -> bytes:\n",
-    "        input_str = json.dumps({\"inputs\": inputs, **model_kwargs})\n",
+    "        \"\"\"\n",
+    "        Transforms the input into bytes that can be consumed by SageMaker endpoint.\n",
+    "        Args:\n",
+    "            inputs: List of input strings.\n",
+    "            model_kwargs: Additional keyword arguments to be passed to the endpoint.\n",
+    "        Returns:\n",
+    "            The transformed bytes input.\n",
+    "        \"\"\"\n",
+    "        # Example: inference.py expects a JSON string with a \"inputs\" key:\n",
+    "        input_str = json.dumps({\"inputs\": inputs, **model_kwargs})  \n",
     "        return input_str.encode(\"utf-8\")\n",
     "\n",
     "    def transform_output(self, output: bytes) -> List[List[float]]:\n",
+    "        \"\"\"\n",
+    "        Transforms the bytes output from the endpoint into a list of embeddings.\n",
+    "        Args:\n",
+    "            output: The bytes output from SageMaker endpoint.\n",
+    "        Returns:\n",
+    "            The transformed output - list of embeddings\n",
+    "        Note:\n",
+    "            The length of the outer list is the number of input strings.\n",
+    "            The length of the inner lists is the embedding dimension.\n",
+    "        \"\"\"\n",
+    "        # Example: inference.py returns a JSON string with the list of\n",
+    "        # embeddings in a \"vectors\" key:\n",
     "        response_json = json.loads(output.read().decode(\"utf-8\"))\n",
     "        return response_json[\"vectors\"]\n",
     "\n",
@@ -60,7 +81,6 @@
     "\n",
     "\n",
     "embeddings = SagemakerEndpointEmbeddings(\n",
-    "    # endpoint_name=\"endpoint-name\",\n",
     "    # credentials_profile_name=\"credentials-profile-name\",\n",
     "    endpoint_name=\"huggingface-pytorch-inference-2023-03-21-16-14-03-834\",\n",
     "    region_name=\"us-east-1\",\n",

docs/extras/integrations/toolkits/ainetwork.ipynb

@@ -1,17 +1,17 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# AINetwork Toolkit\n",
+    "# AINetwork\n",
     "\n",
-    "The AINetwork Toolkit is a set of tools for interacting with the AINetwork Blockchain. These tools allow you to transfer AIN, read and write values, create apps, and set permissions for specific paths within the blockchain database."
+    ">[AI Network](https://www.ainetwork.ai/build-on-ain) is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the [$AIN token](https://www.ainetwork.ai/token), enriching AI-driven `NFTs` (`AINFTs`).\n",
+    ">\n",
+    ">The `AINetwork Toolkit` is a set of tools for interacting with the [AINetwork Blockchain](https://www.ainetwork.ai/public/whitepaper.pdf). These tools allow you to transfer `AIN`, read and write values, create apps, and set permissions for specific paths within the blockchain database."
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -30,7 +30,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -51,7 +50,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -96,7 +94,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -119,7 +116,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -147,7 +143,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -157,7 +152,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -174,7 +168,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -213,7 +206,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -250,7 +242,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -290,7 +281,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -337,7 +327,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -362,7 +351,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -397,7 +385,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -438,7 +425,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -453,9 +440,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.10.12"
-  },
-  "orig_nbformat": 4
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/integrations/toolkits/google_drive.ipynb

@@ -0,0 +1,215 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Google Drive tool\n",
+    "\n",
+    "This notebook walks through connecting a LangChain to the Google Drive API.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "1. Create a Google Cloud project or use an existing project\n",
+    "1. Enable the [Google Drive API](https://console.cloud.google.com/flows/enableapi?apiid=drive.googleapis.com)\n",
+    "1. [Authorize credentials for desktop app](https://developers.google.com/drive/api/quickstart/python#authorize_credentials_for_a_desktop_application)\n",
+    "1. `pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib`\n",
+    "\n",
+    "## Instructions for retrieving your Google Docs data\n",
+    "By default, the `GoogleDriveTools` and `GoogleDriveWrapper` expects the `credentials.json` file to be `~/.credentials/credentials.json`, but this is configurable using the `GOOGLE_ACCOUNT_FILE` environment variable. \n",
+    "The location of `token.json` use the same directory (or use the parameter `token_path`). Note that `token.json` will be created automatically the first time you use the tool.\n",
+    "\n",
+    "`GoogleDriveSearchTool` can retrieve a selection of files with some requests. \n",
+    "\n",
+    "By default, If you use a `folder_id`, all the files inside this folder can be retrieved to `Document`, if the name match the query.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can obtain your folder and document id from the URL:\n",
+    "* Folder: https://drive.google.com/drive/u/0/folders/1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5 -> folder id is `\"1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5\"`\n",
+    "* Document: https://docs.google.com/document/d/1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw/edit -> document id is `\"1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw\"`\n",
+    "\n",
+    "The special value `root` is for your personal home."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "folder_id=\"root\"\n",
+    "#folder_id='1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, all files with these mime-type can be converted to `Document`.\n",
+    "- text/text\n",
+    "- text/plain\n",
+    "- text/html\n",
+    "- text/csv\n",
+    "- text/markdown\n",
+    "- image/png\n",
+    "- image/jpeg\n",
+    "- application/epub+zip\n",
+    "- application/pdf\n",
+    "- application/rtf\n",
+    "- application/vnd.google-apps.document (GDoc)\n",
+    "- application/vnd.google-apps.presentation (GSlide)\n",
+    "- application/vnd.google-apps.spreadsheet (GSheet)\n",
+    "- application/vnd.google.colaboratory (Notebook colab)\n",
+    "- application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX)\n",
+    "- application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX)\n",
+    "\n",
+    "It's possible to update or customize this. See the documentation of `GoogleDriveAPIWrapper`.\n",
+    "\n",
+    "But, the corresponding packages must installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install unstructured"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.utilities.google_drive import GoogleDriveAPIWrapper\n",
+    "from langchain.tools.google_drive.tool import GoogleDriveSearchTool\n",
+    "\n",
+    "# By default, search only in the filename.\n",
+    "tool = GoogleDriveSearchTool(\n",
+    "    api_wrapper=GoogleDriveAPIWrapper(\n",
+    "        folder_id=folder_id,\n",
+    "        num_results=2,\n",
+    "        template=\"gdrive-query-in-folder\", # Search in the body of documents\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "logging.basicConfig(level=logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tool.run(\"machine learning\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tool.description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import load_tools\n",
+    "tools = load_tools([\"google-drive-search\"],\n",
+    "                   folder_id=folder_id,\n",
+    "                   template=\"gdrive-query-in-folder\",\n",
+    "                  )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use within an Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI\n",
+    "from langchain.agents import initialize_agent, AgentType\n",
+    "llm = OpenAI(temperature=0)\n",
+    "agent = initialize_agent(\n",
+    "    tools=tools,\n",
+    "    llm=llm,\n",
+    "    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "agent.run(\n",
+    "    \"Search in google drive, who is 'Yann LeCun' ?\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "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",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/extras/integrations/tools/arxiv.ipynb

@@ -5,9 +5,9 @@
    "id": "245a954a",
    "metadata": {},
    "source": [
-    "# ArXiv API Tool\n",
+    "# ArXiv\n",
     "\n",
-    "This notebook goes over how to use the `arxiv` component. \n",
+    "This notebook goes over how to use the `arxiv` tool with an agent. \n",
     "\n",
     "First, you need to install `arxiv` python package."
    ]
@@ -110,7 +110,7 @@
    "source": [
     "## The ArXiv API Wrapper\n",
     "\n",
-    "The tool wraps the API Wrapper. Below, we can explore some of the features it provides."
+    "The tool uses the `API Wrapper`. Below, we explore some of the features it provides."
    ]
   },
   {
@@ -167,7 +167,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "840f70c9-8f80-4680-bb38-46198e931bcf",
    "metadata": {},
@@ -250,7 +249,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/tools/awslambda.ipynb

@@ -1,25 +1,23 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# AWS Lambda API"
+    "# AWS Lambda"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This notebook goes over how to use the AWS Lambda Tool component.\n",
+    ">`Amazon AWS Lambda` is a serverless computing service provided by `Amazon Web Services` (`AWS`). It helps developers to build and run applications and services without provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.\n",
     "\n",
-    "AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS), designed to allow developers to build and run applications and services without the need for provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.\n",
+    "This notebook goes over how to use the `AWS Lambda` Tool.\n",
     "\n",
     "By including a `awslambda` in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.\n",
     "\n",
-    "When an Agent uses the awslambda tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.\n",
+    "When an Agent uses the `AWS Lambda` tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.\n",
     "\n",
     "First, you need to install `boto3` python package."
    ]
@@ -38,7 +36,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -48,7 +45,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -98,7 +94,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -112,10 +108,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  },
-  "orig_nbformat": 4
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/integrations/tools/bash.ipynb

@@ -5,11 +5,13 @@
    "id": "8f210ec3",
    "metadata": {},
    "source": [
-    "# Shell Tool\n",
+    "# Shell (bash)\n",
     "\n",
     "Giving agents access to the shell is powerful (though risky outside a sandboxed environment).\n",
     "\n",
-    "The LLM can use it to execute any shell commands. A common use case for this is letting the LLM interact with your local file system."
+    "The LLM can use it to execute any shell commands. A common use case for this is letting the LLM interact with your local file system.\n",
+    "\n",
+    "**Note:** Shell tool does not work with Windows OS."
    ]
   },
   {
@@ -184,7 +186,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/tools/dataforseo.ipynb

@@ -1,12 +1,12 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# DataForSeo API Wrapper\n",
-    "This notebook demonstrates how to use the DataForSeo API wrapper to obtain search engine results. The DataForSeo API allows users to retrieve SERP from most popular search engines like Google, Bing, Yahoo. It also allows to get SERPs from different search engine types like Maps, News, Events, etc.\n"
+    "# DataForSeo\n",
+    "\n",
+    "This notebook demonstrates how to use the `DataForSeo API` to obtain search engine results. The `DataForSeo API` retrieves `SERP` from most popular search engines like `Google`, `Bing`, `Yahoo`. It also allows to get SERPs from different search engine types like `Maps`, `News`, `Events`, etc.\n"
    ]
   },
   {
@@ -19,12 +19,12 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setting up the API wrapper with your credentials\n",
-    "You can obtain your API credentials by registering on the DataForSeo website."
+    "## Setting up the API credentials\n",
+    "\n",
+    "You can obtain your API credentials by registering on the `DataForSeo` website."
    ]
   },
   {
@@ -42,7 +42,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -59,7 +58,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -72,7 +70,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -103,7 +100,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -127,7 +123,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -151,7 +146,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -178,7 +172,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -214,7 +207,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -228,10 +221,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  },
-  "orig_nbformat": 4
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/integrations/tools/filesystem.ipynb

@@ -4,11 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# File System Tools\n",
+    "# File System\n",
     "\n",
     "LangChain provides tools for interacting with a local file system out of the box. This notebook walks through some of them.\n",
     "\n",
-    "Note: these tools are not recommended for use outside a sandboxed environment! "
+    "**Note:** these tools are not recommended for use outside a sandboxed environment! "
    ]
   },
   {
@@ -187,7 +187,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/tools/google_serper.ipynb

@@ -5,32 +5,35 @@
    "id": "dc23c48e",
    "metadata": {},
    "source": [
-    "# Google Serper API\n",
+    "# Google Serper\n",
     "\n",
-    "This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at [serper.dev](https://serper.dev) and get your api key."
+    "This notebook goes over how to use the `Google Serper` component to search the web. First you need to sign up for a free account at [serper.dev](https://serper.dev) and get your api key."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
+   "id": "a8acfb24",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:56:29.336521Z",
+     "start_time": "2023-05-04T00:56:29.334173Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "is_executing": true
+    }
+   },
    "outputs": [],
    "source": [
     "import os\n",
     "import pprint\n",
     "\n",
     "os.environ[\"SERPER_API_KEY\"] = \"\""
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:56:29.336521Z",
-     "start_time": "2023-05-04T00:56:29.334173Z"
-    }
-   },
-   "id": "a8acfb24"
+   ]
   },
   {
    "cell_type": "code",
@@ -75,7 +78,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "'Barack Hussein Obama II'"
+      "text/plain": [
+       "'Barack Hussein Obama II'"
+      ]
      },
      "execution_count": 4,
      "metadata": {},
@@ -88,33 +93,41 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1f1c6c22",
+   "metadata": {},
    "source": [
     "## As part of a Self Ask With Search Chain"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "1f1c6c22"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "outputs": [],
-   "source": [
-    "os.environ[\"OPENAI_API_KEY\"] = \"\""
-   ],
+   "id": "c1b5edd7",
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2023-05-04T00:54:14.311773Z",
      "start_time": "2023-05-04T00:54:14.304389Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "id": "c1b5edd7"
+   "outputs": [],
+   "source": [
+    "os.environ[\"OPENAI_API_KEY\"] = \"\""
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "a8ccea61",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -135,7 +148,9 @@
     },
     {
      "data": {
-      "text/plain": "'El Palmar, Spain'"
+      "text/plain": [
+       "'El Palmar, Spain'"
+      ]
      },
      "execution_count": 5,
      "metadata": {},
@@ -164,26 +179,34 @@
     "self_ask_with_search.run(\n",
     "    \"What is the hometown of the reigning men's U.S. Open champion?\"\n",
     ")"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "a8ccea61"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "3aee3682",
+   "metadata": {},
    "source": [
     "## Obtaining results with metadata\n",
     "If you would also like to obtain the results in a structured way including metadata. For this we will be using the `results` method of the wrapper."
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "3aee3682"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "073c3fc5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:54:22.863413Z",
+     "start_time": "2023-05-04T00:54:20.827395Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "is_executing": true
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -344,33 +367,31 @@
     "search = GoogleSerperAPIWrapper()\n",
     "results = search.results(\"Apple Inc.\")\n",
     "pprint.pp(results)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:22.863413Z",
-     "start_time": "2023-05-04T00:54:20.827395Z"
-    }
-   },
-   "id": "073c3fc5"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "b402c308",
+   "metadata": {},
    "source": [
     "## Searching for Google Images\n",
     "We can also query Google Images using this wrapper. For example:"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "b402c308"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "7fb2b7e2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:54:27.879867Z",
+     "start_time": "2023-05-04T00:54:26.380022Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -501,30 +522,31 @@
     "search = GoogleSerperAPIWrapper(type=\"images\")\n",
     "results = search.results(\"Lion\")\n",
     "pprint.pp(results)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:27.879867Z",
-     "start_time": "2023-05-04T00:54:26.380022Z"
-    }
-   },
-   "id": "7fb2b7e2"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "85a3bed3",
+   "metadata": {},
    "source": [
     "## Searching for Google News\n",
     "We can also query Google News using this wrapper. For example:"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "85a3bed3"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "afc48b39",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:54:34.984087Z",
+     "start_time": "2023-05-04T00:54:33.369231Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -630,29 +652,30 @@
     "search = GoogleSerperAPIWrapper(type=\"news\")\n",
     "results = search.results(\"Tesla Inc.\")\n",
     "pprint.pp(results)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:34.984087Z",
-     "start_time": "2023-05-04T00:54:33.369231Z"
-    }
-   },
-   "id": "afc48b39"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "d42ee7b5",
+   "metadata": {},
    "source": [
     "If you want to only receive news articles published in the last hour, you can do the following:"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "d42ee7b5"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "id": "8e3824cb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:54:41.786864Z",
+     "start_time": "2023-05-04T00:54:40.691905Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -701,18 +724,12 @@
     "search = GoogleSerperAPIWrapper(type=\"news\", tbs=\"qdr:h\")\n",
     "results = search.results(\"Tesla Inc.\")\n",
     "pprint.pp(results)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:54:41.786864Z",
-     "start_time": "2023-05-04T00:54:40.691905Z"
-    }
-   },
-   "id": "8e3824cb"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "3f13e9f9",
+   "metadata": {},
    "source": [
     "Some examples of the `tbs` parameter:\n",
     "\n",
@@ -730,26 +747,31 @@
     "`qdr:m2` (past 2 years)\n",
     "\n",
     "For all supported filters simply go to [Google Search](https://google.com), search for something, click on \"Tools\", add your date filter and check the URL for \"tbs=\".\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "3f13e9f9"
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "38d4402c",
+   "metadata": {},
    "source": [
     "## Searching for Google Places\n",
     "We can also query Google Places using this wrapper. For example:"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "38d4402c"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "e7881203",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-04T00:56:07.271164Z",
+     "start_time": "2023-05-04T00:56:05.645847Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -858,15 +880,7 @@
     "search = GoogleSerperAPIWrapper(type=\"places\")\n",
     "results = search.results(\"Italian restaurants in Upper East Side\")\n",
     "pprint.pp(results)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-05-04T00:56:07.271164Z",
-     "start_time": "2023-05-04T00:56:05.645847Z"
-    }
-   },
-   "id": "e7881203"
+   ]
   }
  ],
  "metadata": {
@@ -885,9 +899,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

docs/extras/integrations/tools/gradio_tools.ipynb

@@ -5,11 +5,11 @@
    "id": "c613812f",
    "metadata": {},
    "source": [
-    "# Gradio Tools\n",
+    "# Gradio\n",
     "\n",
-    "There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM's fingers 🦾\n",
+    "There are many 1000s of `Gradio` apps on `Hugging Face Spaces`. This library puts them at the tips of your LLM's fingers 🦾\n",
     "\n",
-    "Specifically, gradio-tools is a Python library for converting Gradio apps into tools that can be leveraged by a large language model (LLM)-based agent to complete its task. For example, an LLM could use a Gradio tool to transcribe a voice recording it finds online and then summarize it for you. Or it could use a different Gradio tool to apply OCR to a document on your Google Drive and then answer questions about it.\n",
+    "Specifically, `gradio-tools` is a Python library for converting `Gradio` apps into tools that can be leveraged by a large language model (LLM)-based agent to complete its task. For example, an LLM could use a `Gradio` tool to transcribe a voice recording it finds online and then summarize it for you. Or it could use a different `Gradio` tool to apply OCR to a document on your Google Drive and then answer questions about it.\n",
     "\n",
     "It's very easy to create you own tool if you want to use a space that's not one of the pre-built tools. Please see this section of the gradio-tools documentation for information on how to do that. All contributions are welcome!"
    ]
@@ -99,9 +99,7 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "98e1e602",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -244,7 +242,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/tools/graphql.ipynb

@@ -4,17 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# GraphQL\n",
     "\n",
-    "# GraphQL tool\n",
-    "This Jupyter Notebook demonstrates how to use the BaseGraphQLTool component with an Agent.\n",
+    ">[GraphQL](https://graphql.org/) is a query language for APIs and a runtime for executing those queries against your data. `GraphQL` provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.\n",
     "\n",
-    "GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.\n",
+    "By including a `BaseGraphQLTool` in the list of tools provided to an Agent, you can grant your Agent the ability to query data from GraphQL APIs for any purposes you need.\n",
     "\n",
-    "By including a BaseGraphQLTool in the list of tools provided to an Agent, you can grant your Agent the ability to query data from GraphQL APIs for any purposes you need.\n",
+    "This Jupyter Notebook demonstrates how to use the `GraphQLAPIWrapper` component with an Agent.\n",
     "\n",
-    "In this example, we'll be using the public Star Wars GraphQL API available at the following endpoint: https://swapi-graphql.netlify.app/.netlify/functions/index.\n",
+    "In this example, we'll be using the public `Star Wars GraphQL API` available at the following endpoint: https://swapi-graphql.netlify.app/.netlify/functions/index.\n",
     "\n",
-    "First, you need to install httpx and gql Python packages."
+    "First, you need to install `httpx` and `gql` Python packages."
    ]
   },
   {
@@ -131,7 +131,7 @@
    "hash": "f85209c3c4c190dca7367d6a1e623da50a9a4392fd53313a7cf9d4bda9c4b85b"
   },
   "kernelspec": {
-   "display_name": "Python 3.9.16 ('langchain')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -145,10 +145,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  },
-  "orig_nbformat": 4
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/integrations/tools/huggingface_tools.ipynb

@@ -5,9 +5,9 @@
    "id": "40a27d3c-4e5c-4b96-b290-4c49d4fd7219",
    "metadata": {},
    "source": [
-    "## HuggingFace Tools\n",
+    "# HuggingFace Hub Tools\n",
     "\n",
-    "[Huggingface Tools](https://huggingface.co/docs/transformers/v4.29.0/en/custom_tools) supporting text I/O can be\n",
+    ">[Huggingface Tools](https://huggingface.co/docs/transformers/v4.29.0/en/custom_tools) that supporting text I/O can be\n",
     "loaded directly using the `load_huggingface_tool` function."
    ]
   },
@@ -94,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/tools/lemonai.ipynb

@@ -1,24 +1,23 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "16763ed3",
    "metadata": {},
    "source": [
-    "# Lemon AI NLP Workflow Automation\n",
-    "\\\n",
-    "Full docs are available at: https://github.com/felixbrock/lemonai-py-client\n",
+    "# Lemon Agent\n",
+    "\n",
+    ">[Lemon Agent](https://github.com/felixbrock/lemon-agent) helps you build powerful AI assistants in minutes and automate workflows by allowing for accurate and reliable read and write operations in tools like `Airtable`, `Hubspot`, `Discord`, `Notion`, `Slack` and `Github`.\n",
+    "\n",
+    "See [full docs here](https://github.com/felixbrock/lemonai-py-client).\n",
     "\n",
-    "**Lemon AI helps you build powerful AI assistants in minutes and automate workflows by allowing for accurate and reliable read and write operations in tools like Airtable, Hubspot, Discord, Notion, Slack and Github.**\n",
     "\n",
     "Most connectors available today are focused on read-only operations, limiting the potential of LLMs. Agents, on the other hand, have a tendency to hallucinate from time to time due to missing context or instructions.\n",
     "\n",
-    "With Lemon AI, it is possible to give your agents access to well-defined APIs for reliable read and write operations. In addition, Lemon AI functions allow you to further reduce the risk of hallucinations by providing a way to statically define workflows that the model can rely on in case of uncertainty."
+    "With `Lemon AI`, it is possible to give your agents access to well-defined APIs for reliable read and write operations. In addition, `Lemon AI` functions allow you to further reduce the risk of hallucinations by providing a way to statically define workflows that the model can rely on in case of uncertainty."
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "4881b484-1b97-478f-b206-aec407ceff66",
    "metadata": {},
@@ -29,7 +28,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "ff91b41a",
    "metadata": {},
@@ -46,7 +44,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "340ff63d",
    "metadata": {},
@@ -57,7 +54,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "e845f402",
    "metadata": {},
@@ -66,7 +62,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "d3ae6a82",
    "metadata": {},
@@ -75,7 +70,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "43476a22",
    "metadata": {},
@@ -84,7 +78,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "cb038670",
    "metadata": {},
@@ -93,7 +86,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "e423ebbb",
    "metadata": {},
@@ -110,7 +102,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "3fdb36ce",
    "metadata": {},
@@ -119,7 +110,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "ebfb8b5d",
    "metadata": {},
@@ -140,7 +130,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c9d082cb",
    "metadata": {},
@@ -189,7 +178,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "aef3e801",
    "metadata": {},
@@ -225,7 +213,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/tools/nuclia.ipynb

@@ -1,17 +1,16 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Nuclia Understanding API tool\n",
+    "# Nuclia Understanding\n",
     "\n",
-    "[Nuclia](https://nuclia.com) automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.\n",
+    ">[Nuclia](https://nuclia.com) automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.\n",
     "\n",
-    "The Nuclia Understanding API supports the processing of unstructured data, including text, web pages, documents, and audio/video contents. It extracts all texts wherever it is (using speech-to-text or OCR when needed), it identifies entities, it aslo extracts metadata, embedded files (like images in a PDF), and web links. It also provides a summary of the content.\n",
+    "The `Nuclia Understanding API` supports the processing of unstructured data, including text, web pages, documents, and audio/video contents. It extracts all texts wherever it is (using speech-to-text or OCR when needed), it identifies entities, it aslo extracts metadata, embedded files (like images in a PDF), and web links. It also provides a summary of the content.\n",
     "\n",
-    "To use the Nuclia Understanding API, you need to have a Nuclia account. You can create one for free at [https://nuclia.cloud](https://nuclia.cloud), and then [create a NUA key](https://docs.nuclia.dev/docs/docs/using/understanding/intro)."
+    "To use the `Nuclia Understanding API`, you need to have a `Nuclia` account. You can create one for free at [https://nuclia.cloud](https://nuclia.cloud), and then [create a NUA key](https://docs.nuclia.dev/docs/docs/using/understanding/intro)."
    ]
   },
   {
@@ -48,7 +47,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -66,7 +64,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -94,7 +91,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -121,7 +117,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -150,7 +145,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "langchain",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -164,10 +159,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/integrations/tools/openweathermap.ipynb

@@ -5,11 +5,11 @@
    "id": "245a954a",
    "metadata": {},
    "source": [
-    "# OpenWeatherMap API\n",
+    "# OpenWeatherMap\n",
     "\n",
-    "This notebook goes over how to use the OpenWeatherMap component to fetch weather information.\n",
+    "This notebook goes over how to use the `OpenWeatherMap` component to fetch weather information.\n",
     "\n",
-    "First, you need to sign up for an OpenWeatherMap API key:\n",
+    "First, you need to sign up for an `OpenWeatherMap API` key:\n",
     "\n",
     "1. Go to OpenWeatherMap and sign up for an API key [here](https://openweathermap.org/api/)\n",
     "2. pip install pyowm\n",
@@ -162,7 +162,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/tools/pubmed.ipynb

@@ -5,11 +5,11 @@
    "id": "64f20f38",
    "metadata": {},
    "source": [
-    "# PubMed Tool\n",
+    "# PubMed\n",
     "\n",
-    "This notebook goes over how to use PubMed as a tool\n",
+    ">[PubMed®](https://pubmed.ncbi.nlm.nih.gov/) comprises more than 35 million citations for biomedical literature from `MEDLINE`, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.\n",
     "\n",
-    "PubMed® comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites."
+    "This notebook goes over how to use `PubMed` as a tool."
    ]
   },
   {
@@ -78,7 +78,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/tools/searx_search.ipynb

@@ -6,11 +6,11 @@
     "jukit_cell_id": "DUXgyWySl5"
    },
    "source": [
-    "# SearxNG Search API\n",
+    "# SearxNG Search\n",
     "\n",
-    "This notebook goes over how to use a self hosted SearxNG search API to search the web.\n",
+    "This notebook goes over how to use a self hosted `SearxNG` search API to search the web.\n",
     "\n",
-    "You can [check this link](https://docs.searxng.org/dev/search_api.html) for more informations about Searx API parameters."
+    "You can [check this link](https://docs.searxng.org/dev/search_api.html) for more informations about `Searx API` parameters."
    ]
   },
   {
@@ -611,7 +611,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/tools/youtube.ipynb

@@ -5,9 +5,9 @@
    "id": "acb64858",
    "metadata": {},
    "source": [
-    "# YouTubeSearchTool\n",
+    "# YouTube (youtube_search)\n",
     "\n",
-    "This notebook shows how to use a tool to search YouTube\n",
+    "This notebook shows how to use a tool to search `YouTube` using `youtube_search` package.\n",
     "\n",
     "Adapted from [https://github.com/venuv/langchain_yt_tools](https://github.com/venuv/langchain_yt_tools)"
    ]
@@ -117,7 +117,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/tools/zapier.ipynb

@@ -5,15 +5,12 @@
    "id": "16763ed3",
    "metadata": {},
    "source": [
-    "# Zapier Natural Language Actions API\n",
-    "\\\n",
-    "Full docs here: https://nla.zapier.com/start/\n",
+    "# Zapier Natural Language Actions\n",
     "\n",
-    "**Zapier Natural Language Actions** gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface.\n",
-    "\n",
-    "NLA supports apps like Gmail, Salesforce, Trello, Slack, Asana, HubSpot, Google Sheets, Microsoft Teams, and thousands more apps: https://zapier.com/apps\n",
-    "\n",
-    "Zapier NLA handles ALL the underlying API auth and translation from natural language --> underlying API call --> return simplified output for LLMs. The key idea is you, or your users, expose a set of actions via an oauth-like setup window, which you can then query and execute via a REST API.\n",
+    ">[Zapier Natural Language Actions](https://nla.zapier.com/start/) gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface.\n",
+    ">\n",
+    ">NLA supports apps like `Gmail`, `Salesforce`, `Trello`, `Slack`, `Asana`, `HubSpot`, `Google Sheets`, `Microsoft Teams`, and thousands more apps: https://zapier.com/apps\n",
+    ">`Zapier NLA` handles ALL the underlying API auth and translation from natural language --> underlying API call --> return simplified output for LLMs. The key idea is you, or your users, expose a set of actions via an oauth-like setup window, which you can then query and execute via a REST API.\n",
     "\n",
     "NLA offers both API Key and OAuth for signing NLA API requests.\n",
     "\n",
@@ -21,7 +18,7 @@
     "\n",
     "2. User-facing (Oauth): for production scenarios where you are deploying an end-user facing application and LangChain needs access to end-user's exposed actions and connected accounts on Zapier.com\n",
     "\n",
-    "This quick start will focus mostly on the server-side use case for brevity. Jump to [Example Using OAuth Access Token](#oauth) to see a short example how to set up Zapier for user-facing situations. Review [full docs](https://nla.zapier.com/start/) for full user-facing oauth developer support.\n",
+    "This quick start focus mostly on the server-side use case for brevity. Jump to [Example Using OAuth Access Token](#oauth) to see a short example how to set up Zapier for user-facing situations. Review [full docs](https://nla.zapier.com/start/) for full user-facing oauth developer support.\n",
     "\n",
     "This example goes over how to use the Zapier integration with a `SimpleSequentialChain`, then an `Agent`.\n",
     "In code, below:"
@@ -369,7 +366,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/vectorstores/neo4jvector.ipynb

@@ -0,0 +1,440 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Neo4j Vector Index\n",
+    "\n",
+    ">[Neo4j](https://neo4j.com/) is an open-source graph database with integrated support for vector similarity search\n",
+    "\n",
+    "It supports:\n",
+    "- approximate nearest neighbor search\n",
+    "- L2 distance and cosine distance\n",
+    "\n",
+    "This notebook shows how to use the Neo4j vector index (`Neo4jVector`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See the [installation instruction](https://neo4j.com/docs/operations-manual/current/installation/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: neo4j in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (5.11.0)\n",
+      "Requirement already satisfied: pytz in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from neo4j) (2023.3)\n",
+      "Requirement already satisfied: openai in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (0.27.6)\n",
+      "Requirement already satisfied: requests>=2.20 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from openai) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from openai) (4.66.1)\n",
+      "Requirement already satisfied: aiohttp in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from openai) (3.8.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.20->openai) (3.2.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.20->openai) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.20->openai) (2.0.4)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.20->openai) (2023.7.22)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from aiohttp->openai) (23.1.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from aiohttp->openai) (6.0.4)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from aiohttp->openai) (4.0.3)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from aiohttp->openai) (1.9.2)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from aiohttp->openai) (1.4.0)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from aiohttp->openai) (1.3.1)\n",
+      "Requirement already satisfied: tiktoken in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (0.4.0)\n",
+      "Requirement already satisfied: regex>=2022.1.18 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from tiktoken) (2023.8.8)\n",
+      "Requirement already satisfied: requests>=2.26.0 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from tiktoken) (2.31.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.26.0->tiktoken) (3.2.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.26.0->tiktoken) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.26.0->tiktoken) (2.0.4)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/tomaz/anaconda3/envs/myenv/lib/python3.11/site-packages (from requests>=2.26.0->tiktoken) (2023.7.22)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pip install necessary package\n",
+    "!pip install neo4j\n",
+    "!pip install openai\n",
+    "!pip install tiktoken"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Neo4jVector\n",
+    "from langchain.document_loaders import TextLoader\n",
+    "from langchain.docstore.document import Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Neo4jVector requires the Neo4j database credentials\n",
+    "\n",
+    "url = \"bolt://localhost:7687\"\n",
+    "username = \"neo4j\"\n",
+    "password = \"pleaseletmein\"\n",
+    "\n",
+    "# You can also use environment variables instead of directly passing named parameters\n",
+    "#os.environ[\"NEO4J_URL\"] = \"bolt://localhost:7687\"\n",
+    "#os.environ[\"NEO4J_USERNAME\"] = \"neo4j\"\n",
+    "#os.environ[\"NEO4J_PASSWORD\"] = \"pleaseletmein\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Similarity Search with Cosine Distance (Default)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The Neo4jVector Module will connect to Neo4j and create a vector index if needed.\n",
+    "\n",
+    "db = Neo4jVector.from_documents(\n",
+    "    docs, OpenAIEmbeddings(), url=url, username=username, password=password\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs_with_score = db.similarity_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.9077161550521851\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.9077161550521851\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.891287088394165\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
+      "\n",
+      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
+      "\n",
+      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
+      "\n",
+      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
+      "\n",
+      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.891287088394165\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
+      "\n",
+      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
+      "\n",
+      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
+      "\n",
+      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
+      "\n",
+      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "for doc, score in docs_with_score:\n",
+    "    print(\"-\" * 80)\n",
+    "    print(\"Score: \", score)\n",
+    "    print(doc.page_content)\n",
+    "    print(\"-\" * 80)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with vectorstore\n",
+    "\n",
+    "Above, we created a vectorstore from scratch. However, often times we want to work with an existing vectorstore.\n",
+    "In order to do that, we can initialize it directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index_name = \"vector\"  # default index name\n",
+    "\n",
+    "store = Neo4jVector.from_existing_index(\n",
+    "    OpenAIEmbeddings(),\n",
+    "    url=url,\n",
+    "    username=username,\n",
+    "    password=password,\n",
+    "    index_name=index_name,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add documents\n",
+    "We can add documents to the existing vectorstore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['2f70679a-4416-11ee-b7c3-d46a6aa24f5b']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "store.add_documents([Document(page_content=\"foo\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_with_score = store.similarity_search_with_score(\"foo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Document(page_content='foo', metadata={}), 1.0)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_with_score[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retriever options\n",
+    "\n",
+    "This section shows how to use `Neo4jVector` as a retriever."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt'})"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever = store.as_retriever()\n",
+    "retriever.get_relevant_documents(query)[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question Answering with Sources\n",
+    "\n",
+    "This section goes over how to do question-answering with sources over an Index. It does this by using the `RetrievalQAWithSourcesChain`, which does the lookup of the documents from an Index. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import RetrievalQAWithSourcesChain\n",
+    "from langchain.chat_models import ChatOpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
+    "    ChatOpenAI(temperature=0), chain_type=\"stuff\", retriever=retriever\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': \"The president honored Justice Stephen Breyer, who is retiring from the United States Supreme Court, and thanked him for his service. The president also mentioned that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy of excellence. \\n\",\n",
+       " 'sources': '../../modules/state_of_the_union.txt'}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain(\n",
+    "    {\"question\": \"What did the president say about Justice Breyer\"},\n",
+    "    return_only_outputs=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "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",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/extras/integrations/vectorstores/vectara.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "683953b3",
    "metadata": {},
@@ -60,7 +59,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "eeead681",
    "metadata": {},
@@ -73,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "04a1f1a0",
    "metadata": {},
    "outputs": [],
@@ -86,12 +84,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "be0a4973",
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
+    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
     "documents = loader.load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
     "docs = text_splitter.split_documents(documents)"
@@ -99,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "8429667e",
    "metadata": {
     "ExecuteTime": {
@@ -118,7 +116,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "90dbf3e7",
    "metadata": {},
@@ -133,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "85ef3468",
    "metadata": {},
    "outputs": [],
@@ -165,7 +162,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "1f9215c8",
    "metadata": {
@@ -182,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "a8c513ab",
    "metadata": {
     "ExecuteTime": {
@@ -201,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "fc516993",
    "metadata": {
     "ExecuteTime": {
@@ -215,13 +211,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson.\n"
      ]
     }
    ],
@@ -230,7 +220,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "1bda9bf5",
    "metadata": {},
@@ -242,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "8804a21d",
    "metadata": {
     "ExecuteTime": {
@@ -254,13 +243,13 @@
    "source": [
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "found_docs = vectara.similarity_search_with_score(\n",
-    "    query, filter=\"doc.speech = 'state-of-the-union'\"\n",
+    "    query, filter=\"doc.speech = 'state-of-the-union'\", score_threshold=0.2,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "756a6887",
    "metadata": {
     "ExecuteTime": {
@@ -273,15 +262,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice.\n",
       "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "\n",
-      "Score: 0.4917977\n"
+      "Score: 0.786569\n"
      ]
     }
    ],
@@ -292,7 +275,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "1f9876a8",
    "metadata": {},
@@ -302,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "47784de5",
    "metadata": {},
    "outputs": [
@@ -310,22 +292,43 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(Document(page_content='We must forever conduct our struggle on the high plane of dignity and discipline.', metadata={'section': '1'}), 0.7962591)\n",
-      "(Document(page_content='We must not allow our\\ncreative protests to degenerate into physical violence. . . .', metadata={'section': '1'}), 0.25983918)\n"
+      "With this threshold of 1.2 we have 0 documents\n"
      ]
     }
    ],
    "source": [
     "query = \"We must forever conduct our struggle\"\n",
+    "min_score = 1.2\n",
     "found_docs = vectara.similarity_search_with_score(\n",
-    "    query, filter=\"doc.speech = 'I-have-a-dream'\"\n",
+    "    query, filter=\"doc.speech = 'I-have-a-dream'\", score_threshold=min_score,\n",
     ")\n",
-    "print(found_docs[0])\n",
-    "print(found_docs[1])"
+    "print(f\"With this threshold of {min_score} we have {len(found_docs)} documents\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3e22949f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With this threshold of 0.2 we have 3 documents\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"We must forever conduct our struggle\"\n",
+    "min_score = 0.2\n",
+    "found_docs = vectara.similarity_search_with_score(\n",
+    "    query, filter=\"doc.speech = 'I-have-a-dream'\", score_threshold=min_score,\n",
+    ")\n",
+    "print(f\"With this threshold of {min_score} we have {len(found_docs)} documents\")\n"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "691a82d6",
    "metadata": {},
@@ -349,7 +352,7 @@
     {
      "data": {
       "text/plain": [
-       "VectaraRetriever(vectorstore=<langchain.vectorstores.vectara.Vectara object at 0x12772caf0>, search_type='similarity', search_kwargs={'lambda_val': 0.025, 'k': 5, 'filter': '', 'n_sentence_context': '0'})"
+       "VectaraRetriever(tags=['Vectara'], metadata=None, vectorstore=<langchain.vectorstores.vectara.Vectara object at 0x1586bd330>, search_type='similarity', search_kwargs={'lambda_val': 0.025, 'k': 5, 'filter': '', 'n_sentence_context': '2'})"
       ]
      },
      "execution_count": 11,
@@ -376,7 +379,7 @@
     {
      "data": {
       "text/plain": [
-       "Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})"
+       "Document(page_content='Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice.', metadata={'source': 'langchain', 'lang': 'eng', 'offset': '596', 'len': '97', 'speech': 'state-of-the-union'})"
       ]
      },
      "execution_count": 12,
@@ -414,7 +417,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/extras/integrations/vectorstores/xata.ipynb

@@ -52,7 +52,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install xata==1.0.0a7 openai tiktoken langchain"
+    "!pip install xata openai tiktoken langchain"
    ]
   },
   {

docs/extras/modules/memory/adding_memory.ipynb

@@ -7,9 +7,11 @@
     "tags": []
    },
    "source": [
-    "# How to add Memory to an LLMChain\n",
+    "# Memory in LLMChain\n",
     "\n",
-    "This notebook goes over how to use the Memory class with an LLMChain. For the purposes of this walkthrough, we will add  the [ConversationBufferMemory](https://api.python.langchain.com/en/latest/memory/langchain.memory.buffer.ConversationBufferMemory.html#langchain.memory.buffer.ConversationBufferMemory) class, although this can be any memory class."
+    "This notebook goes over how to use the Memory class with an LLMChain. \n",
+    "\n",
+    "We will add  the [ConversationBufferMemory](https://api.python.langchain.com/en/latest/memory/langchain.memory.buffer.ConversationBufferMemory.html#langchain.memory.buffer.ConversationBufferMemory) class, although this can be any memory class."
    ]
   },
   {
@@ -321,7 +323,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/adding_memory_chain_multiple_inputs.ipynb

@@ -5,9 +5,9 @@
    "id": "e42733c5",
    "metadata": {},
    "source": [
-    "# How to add memory to a Multi-Input Chain\n",
+    "# Memory in the Multi-Input Chain\n",
     "\n",
-    "Most memory objects assume a single input. In this notebook, we go over how to add memory to a chain that has multiple inputs. As an example of such a chain, we will add memory to a question/answering chain. This chain takes as inputs both related documents and a user question."
+    "Most memory objects assume a single input. In this notebook, we go over how to add memory to a chain that has multiple inputs. We will add memory to a question/answering chain. This chain takes as inputs both related documents and a user question."
    ]
   },
   {
@@ -178,7 +178,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/agent_with_memory.ipynb

@@ -5,11 +5,11 @@
    "id": "fa6802ac",
    "metadata": {},
    "source": [
-    "# How to add Memory to an Agent\n",
+    "# Memory in Agent\n",
     "\n",
     "This notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:\n",
     "\n",
-    "- [Adding memory to an LLM Chain](/docs/modules/memory/how_to/adding_memory.html)\n",
+    "- [Memory in LLMChain](/docs/modules/memory/how_to/adding_memory.html)\n",
     "- [Custom Agents](/docs/modules/agents/how_to/custom_agent.html)\n",
     "\n",
     "In order to add a memory to an agent we are going to the the following steps:\n",
@@ -317,7 +317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/agent_with_memory_in_db.ipynb

@@ -5,13 +5,13 @@
    "id": "fa6802ac",
    "metadata": {},
    "source": [
-    "# Adding Message Memory backed by a database to an Agent\n",
+    "# Message Memory in Agent backed by a database\n",
     "\n",
     "This notebook goes over adding memory to an Agent where the memory uses an external message store. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:\n",
     "\n",
-    "- [Adding memory to an LLM Chain](/docs/modules/memory/how_to/adding_memory.html)\n",
+    "- [Memory in LLMChain](/docs/modules/memory/how_to/adding_memory.html)\n",
     "- [Custom Agents](/docs/modules/agents/how_to/custom_agent.html)\n",
-    "- [Agent with Memory](/docs/modules/memory/how_to/agent_with_memory.html)\n",
+    "- [Memory in Agent](/docs/modules/memory/how_to/agent_with_memory.html)\n",
     "\n",
     "In order to add a memory with an external message store to an agent we are going to do the following steps:\n",
     "\n",
@@ -348,7 +348,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/conversational_customization.ipynb

@@ -5,7 +5,7 @@
    "id": "69e35d6f",
    "metadata": {},
    "source": [
-    "# How to customize conversational memory\n",
+    "# Customizing Conversational Memory\n",
     "\n",
     "This notebook walks through a few ways to customize conversational memory."
    ]
@@ -373,7 +373,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/custom_memory.ipynb

@@ -5,7 +5,8 @@
    "id": "94e33ebe",
    "metadata": {},
    "source": [
-    "# How to create a custom Memory class\n",
+    "# Custom Memory\n",
+    "\n",
     "Although there are a few predefined types of memory in LangChain, it is highly possible you will want to add your own type of memory that is optimal for your application. This notebook covers how to do that."
    ]
   },
@@ -295,7 +296,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/multiple_memory.ipynb

@@ -5,8 +5,9 @@
    "id": "d9fec22e",
    "metadata": {},
    "source": [
-    "# How to use multiple memory classes in the same chain\n",
-    "It is also possible to use multiple memory classes in the same chain. To combine multiple memory classes, we can initialize the `CombinedMemory` class, and then use that."
+    "# Multiple Memory classes\n",
+    "\n",
+    "We can use multiple memory classes in the same chain. To combine multiple memory classes, we initialize and use the `CombinedMemory` class."
    ]
   },
   {
@@ -158,7 +159,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/types/kg.ipynb

@@ -5,11 +5,17 @@
    "id": "44c9933a",
    "metadata": {},
    "source": [
-    "# Conversation Knowledge Graph Memory\n",
+    "# Conversation Knowledge Graph\n",
     "\n",
-    "This type of memory uses a knowledge graph to recreate memory.\n",
-    "\n",
-    "Let's first walk through how to use the utilities"
+    "This type of memory uses a knowledge graph to recreate memory.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c798006-ca04-4de3-83eb-cf167fb2bd01",
+   "metadata": {},
+   "source": [
+    "## Using memory with LLM"
    ]
   },
   {
@@ -162,6 +168,7 @@
    "metadata": {},
    "source": [
     "## Using in a chain\n",
+    "\n",
     "Let's now use this in a chain!"
    ]
   },
@@ -348,7 +355,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/types/summary_buffer.ipynb

@@ -5,13 +5,22 @@
    "id": "ff4be5f3",
    "metadata": {},
    "source": [
-    "# ConversationSummaryBufferMemory\n",
+    "# Conversation Summary Buffer\n",
     "\n",
-    "`ConversationSummaryBufferMemory` combines the last two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses both. Unlike the previous implementation though, it uses token length rather than number of interactions to determine when to flush interactions.\n",
+    "`ConversationSummaryBufferMemory` combines the two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses both. \n",
+    "It uses token length rather than number of interactions to determine when to flush interactions.\n",
     "\n",
     "Let's first walk through how to use the utilities"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0309636e-a530-4d2a-ba07-0916ea18bb20",
+   "metadata": {},
+   "source": [
+    "## Using memory with LLM"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -320,7 +329,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/types/token_buffer.ipynb

@@ -5,13 +5,21 @@
    "id": "ff4be5f3",
    "metadata": {},
    "source": [
-    "# ConversationTokenBufferMemory\n",
+    "# Conversation Token Buffer\n",
     "\n",
     "`ConversationTokenBufferMemory` keeps a buffer of recent interactions in memory, and uses token length rather than number of interactions to determine when to flush interactions.\n",
     "\n",
     "Let's first walk through how to use the utilities"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0e528ef0-7b04-4a4a-8ff2-493c02027e83",
+   "metadata": {},
+   "source": [
+    "## Using memory with LLM"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -286,7 +294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/use_cases/apis.ipynb

@@ -62,7 +62,7 @@
     "\n",
     "# Set env var OPENAI_API_KEY or load from a .env file:\n",
     "# import dotenv\n",
-    "# dotenv.load_env()"
+    "# dotenv.load_dotenv()"
    ]
   },
   {
@@ -145,7 +145,7 @@
    "source": [
     "## Functions \n",
     "\n",
-    "We can unpack what is hapening when we use the functions to calls external APIs.\n",
+    "We can unpack what is happening when we use the functions to call external APIs.\n",
     "\n",
     "Let's look at the [LangSmith trace](https://smith.langchain.com/public/76a58b85-193f-4eb7-ba40-747f0d5dd56e/r):\n",
     "\n",
@@ -155,10 +155,10 @@
     "https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\n",
     "```\n",
     "\n",
-    "* The prompt then tells the LLM to use the API spec wiith input question:\n",
+    "* The prompt then tells the LLM to use the API spec with input question:\n",
     "\n",
     "```\n",
-    "Use the provided API's to respond to this user query:\n",
+    "Use the provided APIs to respond to this user query:\n",
     "What are some options for a men's large blue button down shirt\n",
     "```\n",
     "\n",
@@ -278,7 +278,7 @@
     "![Image description](/img/api_chain.png)\n",
     "\n",
     "* [Here](https://github.com/langchain-ai/langchain/blob/bbd22b9b761389a5e40fc45b0570e1830aabb707/libs/langchain/langchain/chains/api/base.py#L82) we make the API request with the API url.\n",
-    "* The `api_answer_chain` takes the response from the API and provides us with a natural langugae response:\n",
+    "* The `api_answer_chain` takes the response from the API and provides us with a natural language response:\n",
     "\n",
     "![Image description](/img/api_chain_response.png)"
    ]

docs/extras/use_cases/chatbots.ipynb

@@ -54,7 +54,7 @@
     "\n",
     "# Set env var OPENAI_API_KEY or load from a .env file:\n",
     "# import dotenv\n",
-    "# dotenv.load_env()"
+    "# dotenv.load_dotenv()"
    ]
   },
   {

docs/extras/use_cases/code_understanding.ipynb

@@ -25,7 +25,7 @@
     "In particular, we can employ a [splitting strategy](https://python.langchain.com/docs/integrations/document_loaders/source_code) that does a few things:\n",
     "\n",
     "* Keeps each top-level function and class in the code is loaded into separate documents. \n",
-    "* Puts remaining into a seperate document.\n",
+    "* Puts remaining into a separate document.\n",
     "* Retains metadata about where each split comes from\n",
     "\n",
     "## Quickstart"
@@ -42,7 +42,7 @@
     "# Set env var OPENAI_API_KEY or load from a .env file\n",
     "# import dotenv\n",
     "\n",
-    "# dotenv.load_env()"
+    "# dotenv.load_dotenv()"
    ]
   },
   {
@@ -94,7 +94,7 @@
     "We load the py code using [`LanguageParser`](https://python.langchain.com/docs/integrations/document_loaders/source_code), which will:\n",
     "\n",
     "* Keep top-level functions and classes together (into a single document)\n",
-    "* Put remaining code into a seperate document\n",
+    "* Put remaining code into a separate document\n",
     "* Retains metadata about where each split comes from"
    ]
   },

docs/extras/use_cases/extraction.ipynb

@@ -73,7 +73,7 @@
     "\n",
     "# Set env var OPENAI_API_KEY or load from a .env file:\n",
     "# import dotenv\n",
-    "# dotenv.load_env()"
+    "# dotenv.load_dotenv()"
    ]
   },
   {

docs/extras/use_cases/more/agents/agents.ipynb

@@ -70,7 +70,7 @@
     "# Set env var OPENAI_API_KEY and SERPAPI_API_KEY or load from a .env file\n",
     "# import dotenv\n",
     "\n",
-    "# dotenv.load_env()"
+    "# dotenv.load_dotenv()"
    ]
   },
   {

docs/extras/use_cases/more/graph/graph_falkordb_qa.ipynb

@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FalkorDBQAChain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook shows how to use LLMs to provide a natural language interface to FalkorDB database.\n",
+    "\n",
+    "FalkorDB is a low latency property graph database management system. You can simply run its docker locally:\n",
+    "\n",
+    "```bash\n",
+    "docker run -p 6379:6379 -it --rm falkordb/falkordb:edge\n",
+    "```\n",
+    "\n",
+    "Once launched, you can simply start creating a database on the local machine and connect to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.graphs import FalkorDBGraph\n",
+    "from langchain.chains import FalkorDBQAChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph = FalkorDBGraph(database=\"movies\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph.query(\n",
+    "    \"\"\"\n",
+    "MERGE (m:Movie {name:\"Top Gun\"})\n",
+    "WITH m\n",
+    "UNWIND [\"Tom Cruise\", \"Val Kilmer\", \"Anthony Edwards\", \"Meg Ryan\"] AS actor\n",
+    "MERGE (a:Actor {name:actor})\n",
+    "MERGE (a)-[:ACTED_IN]->(m)\n",
+    "\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph.refresh_schema()\n",
+    "import os\n",
+    "os.environ['OPENAI_API_KEY']='API_KEY_HERE'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = FalkorDBQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new FalkorDBQAChain chain...\u001b[0m\n",
+      "Generated Cypher:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (:Movie {title: 'Top Gun'})<-[:ACTED_IN]-(actor:Person)\n",
+      "RETURN actor.name AS output\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The actor who played in Top Gun is Tom Cruise.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"Who played in Top Gun?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/extras/use_cases/question_answering/question_answering.ipynb

@@ -47,7 +47,7 @@
     "# Set env var OPENAI_API_KEY or load from a .env file\n",
     "# import dotenv\n",
     "\n",
-    "# dotenv.load_env()"
+    "# dotenv.load_dotenv()"
    ]
   },
   {

docs/extras/use_cases/sql.ipynb

@@ -50,7 +50,7 @@
     "# Set env var OPENAI_API_KEY or load from a .env file\n",
     "# import dotenv\n",
     "\n",
-    "# dotenv.load_env()"
+    "# dotenv.load_dotenv()"
    ]
   },
   {

docs/extras/use_cases/summarization.ipynb

@@ -76,7 +76,7 @@
     "# Set env var OPENAI_API_KEY or load from a .env file\n",
     "# import dotenv\n",
     "\n",
-    "# dotenv.load_env()"
+    "# dotenv.load_dotenv()"
    ]
   },
   {

docs/extras/use_cases/tagging.ipynb

@@ -44,7 +44,7 @@
     "\n",
     "# Set env var OPENAI_API_KEY or load from a .env file:\n",
     "# import dotenv\n",
-    "# dotenv.load_env()"
+    "# dotenv.load_dotenv()"
    ]
   },
   {

docs/extras/use_cases/web_scraping.ipynb

@@ -41,7 +41,7 @@
     "\n",
     "# Set env var OPENAI_API_KEY or load from a .env file:\n",
     "# import dotenv\n",
-    "# dotenv.load_env()"
+    "# dotenv.load_dotenv()"
    ]
   },
   {
@@ -143,7 +143,7 @@
     "\n",
     "Chromium is one of the browsers supported by Playwright, a library used to control browser automation. \n",
     "\n",
-    "Headless mode means that the browser is running without a graphical user interface, which is commonly used for web scrapin."
+    "Headless mode means that the browser is running without a graphical user interface, which is commonly used for web scraping."
    ]
   },
   {

docs/snippets/modules/data_connection/retrievers/get_started.mdx

@@ -33,7 +33,7 @@ class BaseRetriever(ABC):
         ...
 ```
 
-It's that simple! You can call `get_relevant_documents` or the async `get_relevant_documents` methods to retrieve documents relevant to a query, where "relevance" is defined by
+It's that simple! You can call `get_relevant_documents` or the async `aget_relevant_documents` methods to retrieve documents relevant to a query, where "relevance" is defined by
 the specific retriever object you are calling.
 
 Of course, we also help construct what we think useful Retrievers are. The main type of Retriever that we focus on is a Vectorstore retriever. We will focus on that for the rest of this guide.

libs/experimental/README.md

@@ -1,3 +1,16 @@
 # 🦜️🧪 LangChain Experimental
 
-This repository holds more experimental LangChain code.
+This package holds experimental LangChain code, intended for research and experimental
+uses.
+
+> [!WARNING]
+> Portions of the code in this package may be dangerous if not properly deployed
+> in a sandboxed environment. Please be wary of deploying experimental code
+> to production unless you've taken appropriate precautions and
+> have already discussed it with your security team.
+
+Some of the code here may be marked with security notices. However,
+given the exploratory and experimental nature of the code in this package,
+the lack of a security notice on a piece of code does not mean that
+the code in question does not require additional security considerations
+in order to be safe to use.

libs/experimental/langchain_experimental/cpal/base.py

@@ -131,13 +131,34 @@ class InterventionChain(_BaseStoryElementChain):
 
 
 class QueryChain(_BaseStoryElementChain):
-    """Query the outcome table using SQL."""
+    """Query the outcome table using SQL.
+
+    *Security note*: This class implements an AI technique that generates SQL code.
+        If those SQL commands are executed, it's critical to ensure they use credentials
+        that are narrowly-scoped to only include the permissions this chain needs.
+        Failure to do so may result in data corruption or loss, since this chain may
+        attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this chain.
+    """
 
     pydantic_model: ClassVar[Type[pydantic.BaseModel]] = QueryModel
     template: ClassVar[str] = query_template  # TODO: incl. table schema
 
 
 class CPALChain(_BaseStoryElementChain):
+    """Causal program-aided language (CPAL) chain implementation.
+
+    *Security note*: The building blocks of this class include the implementation
+        of an AI technique that generates SQL code. If those SQL commands
+        are executed, it's critical to ensure they use credentials that
+        are narrowly-scoped to only include the permissions this chain needs.
+        Failure to do so may result in data corruption or loss, since this chain may
+        attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this chain.
+    """
+
     llm: BaseLanguageModel
     narrative_chain: Optional[NarrativeChain] = None
     causal_chain: Optional[CausalChain] = None
@@ -151,7 +172,17 @@ class CPALChain(_BaseStoryElementChain):
         llm: BaseLanguageModel,
         **kwargs: Any,
     ) -> CPALChain:
-        """instantiation depends on component chains"""
+        """instantiation depends on component chains
+
+        *Security note*: The building blocks of this class include the implementation
+            of an AI technique that generates SQL code. If those SQL commands
+            are executed, it's critical to ensure they use credentials that
+            are narrowly-scoped to only include the permissions this chain needs.
+            Failure to do so may result in data corruption or loss, since this chain may
+            attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
+            The best way to guard against such negative outcomes is to (as appropriate)
+            limit the permissions granted to the credentials used with this chain.
+        """
         return cls(
             llm=llm,
             chain=LLMChain(

libs/experimental/langchain_experimental/pal_chain/base.py

@@ -90,6 +90,15 @@ class PALChain(Chain):
     This class implements the Program-Aided Language Models (PAL) for generating code
     solutions. PAL is a technique described in the paper "Program-Aided Language Models"
     (https://arxiv.org/pdf/2211.10435.pdf).
+
+    *Security note*: This class implements an AI technique that generates and evaluates
+        Python code, which can be dangerous and requires a specially sandboxed
+        environment to be safely used. While this class implements some basic guardrails
+        by limiting available locals/globals and by parsing and inspecting
+        the generated Python AST using `PALValidation`, those guardrails will not
+        deter sophisticated attackers and are not a replacement for a proper sandbox.
+        Do not use this class on untrusted inputs, with elevated permissions,
+        or without consulting your security team about proper sandboxing!
     """
 
     llm_chain: LLMChain

libs/experimental/langchain_experimental/sql/base.py

@@ -29,6 +29,15 @@ class SQLDatabaseChain(Chain):
             from langchain import OpenAI, SQLDatabase
             db = SQLDatabase(...)
             db_chain = SQLDatabaseChain.from_llm(OpenAI(), db)
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include the permissions this chain needs.
+        Failure to do so may result in data corruption or loss, since this chain may
+        attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this chain.
+        This issue shows an example negative outcome if these steps are not taken:
+        https://github.com/langchain-ai/langchain/issues/5923
     """
 
     llm_chain: LLMChain
@@ -49,7 +58,7 @@ class SQLDatabaseChain(Chain):
     return_direct: bool = False
     """Whether or not to return the result of querying the SQL table directly."""
     use_query_checker: bool = False
-    """Whether or not the query checker tool should be used to attempt 
+    """Whether or not the query checker tool should be used to attempt
     to fix the initial SQL from the LLM."""
     query_checker_prompt: Optional[BasePromptTemplate] = None
     """The prompt template that should be used by the query checker"""
@@ -197,6 +206,17 @@ class SQLDatabaseChain(Chain):
         prompt: Optional[BasePromptTemplate] = None,
         **kwargs: Any,
     ) -> SQLDatabaseChain:
+        """Create a SQLDatabaseChain from an LLM and a database connection.
+
+        *Security note*: Make sure that the database connection uses credentials
+            that are narrowly-scoped to only include the permissions this chain needs.
+            Failure to do so may result in data corruption or loss, since this chain may
+            attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
+            The best way to guard against such negative outcomes is to (as appropriate)
+            limit the permissions granted to the credentials used with this chain.
+            This issue shows an example negative outcome if these steps are not taken:
+            https://github.com/langchain-ai/langchain/issues/5923
+        """
         prompt = prompt or SQL_PROMPTS.get(db.dialect, PROMPT)
         llm_chain = LLMChain(llm=llm, prompt=prompt)
         return cls(llm_chain=llm_chain, database=db, **kwargs)

libs/experimental/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-experimental"
-version = "0.0.11"
+version = "0.0.12"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"

libs/experimental/tests/unit_tests/conftest.py

@@ -0,0 +1,83 @@
+"""Configuration for unit tests."""
+from importlib import util
+from typing import Dict, Sequence
+
+import pytest
+from pytest import Config, Function, Parser
+
+
+def pytest_addoption(parser: Parser) -> None:
+    """Add custom command line options to pytest."""
+    parser.addoption(
+        "--only-extended",
+        action="store_true",
+        help="Only run extended tests. Does not allow skipping any extended tests.",
+    )
+    parser.addoption(
+        "--only-core",
+        action="store_true",
+        help="Only run core tests. Never runs any extended tests.",
+    )
+
+
+def pytest_collection_modifyitems(config: Config, items: Sequence[Function]) -> None:
+    """Add implementations for handling custom markers.
+
+    At the moment, this adds support for a custom `requires` marker.
+
+    The `requires` marker is used to denote tests that require one or more packages
+    to be installed to run. If the package is not installed, the test is skipped.
+
+    The `requires` marker syntax is:
+
+    .. code-block:: python
+
+        @pytest.mark.requires("package1", "package2")
+        def test_something():
+            ...
+    """
+    # Mapping from the name of a package to whether it is installed or not.
+    # Used to avoid repeated calls to `util.find_spec`
+    required_pkgs_info: Dict[str, bool] = {}
+
+    only_extended = config.getoption("--only-extended") or False
+    only_core = config.getoption("--only-core") or False
+
+    if only_extended and only_core:
+        raise ValueError("Cannot specify both `--only-extended` and `--only-core`.")
+
+    for item in items:
+        requires_marker = item.get_closest_marker("requires")
+        if requires_marker is not None:
+            if only_core:
+                item.add_marker(pytest.mark.skip(reason="Skipping not a core test."))
+                continue
+
+            # Iterate through the list of required packages
+            required_pkgs = requires_marker.args
+            for pkg in required_pkgs:
+                # If we haven't yet checked whether the pkg is installed
+                # let's check it and store the result.
+                if pkg not in required_pkgs_info:
+                    required_pkgs_info[pkg] = util.find_spec(pkg) is not None
+
+                if not required_pkgs_info[pkg]:
+                    if only_extended:
+                        pytest.fail(
+                            f"Package `{pkg}` is not installed but is required for "
+                            f"extended tests. Please install the given package and "
+                            f"try again.",
+                        )
+
+                    else:
+                        # If the package is not installed, we immediately break
+                        # and mark the test as skipped.
+                        item.add_marker(
+                            pytest.mark.skip(reason=f"Requires pkg: `{pkg}`")
+                        )
+                        break
+        else:
+            if only_extended:
+                item.add_marker(
+                    pytest.mark.skip(reason="Skipping not an extended test.")
+                )

libs/langchain/langchain/agents/agent.py

@@ -686,12 +686,15 @@ s
         cls,
         agent: Union[BaseSingleActionAgent, BaseMultiActionAgent],
         tools: Sequence[BaseTool],
-        callback_manager: Optional[BaseCallbackManager] = None,
+        callbacks: Callbacks = None,
         **kwargs: Any,
     ) -> AgentExecutor:
         """Create from agent and tools."""
         return cls(
-            agent=agent, tools=tools, callback_manager=callback_manager, **kwargs
+            agent=agent,
+            tools=tools,
+            callbacks=callbacks,
+            **kwargs,
         )
 
     @root_validator()

libs/langchain/langchain/agents/agent_toolkits/spark_sql/base.py

@@ -6,7 +6,7 @@ from langchain.agents.agent_toolkits.spark_sql.prompt import SQL_PREFIX, SQL_SUF
 from langchain.agents.agent_toolkits.spark_sql.toolkit import SparkSQLToolkit
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
-from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.base import BaseCallbackManager, Callbacks
 from langchain.chains.llm import LLMChain
 from langchain.schema.language_model import BaseLanguageModel
 
@@ -15,6 +15,7 @@ def create_spark_sql_agent(
     llm: BaseLanguageModel,
     toolkit: SparkSQLToolkit,
     callback_manager: Optional[BaseCallbackManager] = None,
+    callbacks: Callbacks = None,
     prefix: str = SQL_PREFIX,
     suffix: str = SQL_SUFFIX,
     format_instructions: str = FORMAT_INSTRUCTIONS,
@@ -41,6 +42,7 @@ def create_spark_sql_agent(
         llm=llm,
         prompt=prompt,
         callback_manager=callback_manager,
+        callbacks=callbacks,
     )
     tool_names = [tool.name for tool in tools]
     agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
@@ -48,6 +50,7 @@ def create_spark_sql_agent(
         agent=agent,
         tools=tools,
         callback_manager=callback_manager,
+        callbacks=callbacks,
         verbose=verbose,
         max_iterations=max_iterations,
         max_execution_time=max_execution_time,

libs/langchain/langchain/callbacks/__init__.py

@@ -19,7 +19,9 @@ from langchain.callbacks.flyte_callback import FlyteCallbackHandler
 from langchain.callbacks.human import HumanApprovalCallbackHandler
 from langchain.callbacks.infino_callback import InfinoCallbackHandler
 from langchain.callbacks.labelstudio_callback import LabelStudioCallbackHandler
+from langchain.callbacks.llmonitor_callback import LLMonitorCallbackHandler
 from langchain.callbacks.manager import (
+    collect_runs,
     get_openai_callback,
     tracing_enabled,
     tracing_v2_enabled,
@@ -53,6 +55,7 @@ __all__ = [
     "HumanApprovalCallbackHandler",
     "InfinoCallbackHandler",
     "MlflowCallbackHandler",
+    "LLMonitorCallbackHandler",
     "OpenAICallbackHandler",
     "StdOutCallbackHandler",
     "AsyncIteratorCallbackHandler",
@@ -66,6 +69,7 @@ __all__ = [
     "get_openai_callback",
     "tracing_enabled",
     "tracing_v2_enabled",
+    "collect_runs",
     "wandb_tracing_enabled",
     "FlyteCallbackHandler",
     "SageMakerCallbackHandler",

libs/langchain/langchain/callbacks/llmonitor_callback.py

@@ -0,0 +1,329 @@
+import os
+import traceback
+from datetime import datetime
+from typing import Any, Dict, List, Literal, Union
+from uuid import UUID
+
+import requests
+
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.schema.agent import AgentAction, AgentFinish
+from langchain.schema.messages import BaseMessage
+from langchain.schema.output import LLMResult
+
+DEFAULT_API_URL = "https://app.llmonitor.com"
+
+
+def _parse_lc_role(
+    role: str,
+) -> Union[Literal["user", "ai", "system", "function"], None]:
+    if role == "human":
+        return "user"
+    elif role == "ai":
+        return "ai"
+    elif role == "system":
+        return "system"
+    elif role == "function":
+        return "function"
+    else:
+        return None
+
+
+def _serialize_lc_message(message: BaseMessage) -> Dict[str, Any]:
+    return {"text": message.content, "role": _parse_lc_role(message.type)}
+
+
+class LLMonitorCallbackHandler(BaseCallbackHandler):
+    """Initializes the `LLMonitorCallbackHandler`.
+    #### Parameters:
+        - `app_id`: The app id of the app you want to report to. Defaults to
+        `None`, which means that `LLMONITOR_APP_ID` will be used.
+        - `api_url`: The url of the LLMonitor API. Defaults to `None`,
+        which means that either `LLMONITOR_API_URL` environment variable
+        or `https://app.llmonitor.com` will be used.
+
+    #### Raises:
+        - `ValueError`: if `app_id` is not provided either as an
+        argument or as an environment variable.
+        - `ConnectionError`: if the connection to the API fails.
+
+
+    #### Example:
+    ```python
+    from langchain.llms import OpenAI
+    from langchain.callbacks import LLMonitorCallbackHandler
+
+    llmonitor_callback = LLMonitorCallbackHandler()
+    llm = OpenAI(callbacks=[llmonitor_callback],
+                 metadata={"userId": "user-123"})
+    llm.predict("Hello, how are you?")
+    ```
+    """
+
+    __api_url: str
+    __app_id: str
+
+    def __init__(
+        self, app_id: Union[str, None] = None, api_url: Union[str, None] = None
+    ) -> None:
+        super().__init__()
+
+        self.__api_url = api_url or os.getenv("LLMONITOR_API_URL") or DEFAULT_API_URL
+
+        _app_id = app_id or os.getenv("LLMONITOR_APP_ID")
+        if _app_id is None:
+            raise ValueError(
+                """app_id must be provided either as an argument or as 
+                an environment variable"""
+            )
+        self.__app_id = _app_id
+
+        try:
+            res = requests.get(f"{self.__api_url}/api/app/{self.__app_id}")
+            if not res.ok:
+                raise ConnectionError()
+        except Exception as e:
+            raise ConnectionError(
+                f"Could not connect to the LLMonitor API at {self.__api_url}"
+            ) from e
+
+    def __send_event(self, event: Dict[str, Any]) -> None:
+        headers = {"Content-Type": "application/json"}
+        event = {**event, "app": self.__app_id, "timestamp": str(datetime.utcnow())}
+        data = {"events": event}
+        requests.post(headers=headers, url=f"{self.__api_url}/api/report", json=data)
+
+    def on_llm_start(
+        self,
+        serialized: Dict[str, Any],
+        prompts: List[str],
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        tags: Union[List[str], None] = None,
+        metadata: Union[Dict[str, Any], None] = None,
+        **kwargs: Any,
+    ) -> None:
+        event = {
+            "event": "start",
+            "type": "llm",
+            "userId": (metadata or {}).get("userId"),
+            "runId": str(run_id),
+            "parentRunId": str(parent_run_id) if parent_run_id else None,
+            "input": prompts[0],
+            "name": kwargs.get("invocation_params", {}).get("model_name"),
+            "tags": tags,
+            "metadata": metadata,
+        }
+        self.__send_event(event)
+
+    def on_chat_model_start(
+        self,
+        serialized: Dict[str, Any],
+        messages: List[List[BaseMessage]],
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        tags: Union[List[str], None] = None,
+        metadata: Union[Dict[str, Any], None] = None,
+        **kwargs: Any,
+    ) -> Any:
+        event = {
+            "event": "start",
+            "type": "llm",
+            "userId": (metadata or {}).get("userId"),
+            "runId": str(run_id),
+            "parentRunId": str(parent_run_id) if parent_run_id else None,
+            "input": [_serialize_lc_message(message[0]) for message in messages],
+            "name": kwargs.get("invocation_params", {}).get("model_name"),
+            "tags": tags,
+            "metadata": metadata,
+        }
+        self.__send_event(event)
+
+    def on_llm_end(
+        self,
+        response: LLMResult,
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        **kwargs: Any,
+    ) -> None:
+        token_usage = (response.llm_output or {}).get("token_usage", {})
+
+        event = {
+            "event": "end",
+            "type": "llm",
+            "runId": str(run_id),
+            "parent_run_id": str(parent_run_id) if parent_run_id else None,
+            "output": {"text": response.generations[0][0].text, "role": "ai"},
+            "tokensUsage": {
+                "prompt": token_usage.get("prompt_tokens", 0),
+                "completion": token_usage.get("completion_tokens", 0),
+            },
+        }
+        self.__send_event(event)
+
+    def on_llm_error(
+        self,
+        error: Union[Exception, KeyboardInterrupt],
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        **kwargs: Any,
+    ) -> Any:
+        event = {
+            "event": "error",
+            "type": "llm",
+            "runId": str(run_id),
+            "parent_run_id": str(parent_run_id) if parent_run_id else None,
+            "error": {"message": str(error), "stack": traceback.format_exc()},
+        }
+        self.__send_event(event)
+
+    def on_tool_start(
+        self,
+        serialized: Dict[str, Any],
+        input_str: str,
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        tags: Union[List[str], None] = None,
+        metadata: Union[Dict[str, Any], None] = None,
+        **kwargs: Any,
+    ) -> None:
+        event = {
+            "event": "start",
+            "type": "tool",
+            "userId": (metadata or {}).get("userId"),
+            "runId": str(run_id),
+            "parentRunId": str(parent_run_id) if parent_run_id else None,
+            "name": serialized.get("name"),
+            "input": input_str,
+            "tags": tags,
+            "metadata": metadata,
+        }
+        self.__send_event(event)
+
+    def on_tool_end(
+        self,
+        output: str,
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        tags: Union[List[str], None] = None,
+        **kwargs: Any,
+    ) -> None:
+        event = {
+            "event": "end",
+            "type": "tool",
+            "runId": str(run_id),
+            "parent_run_id": str(parent_run_id) if parent_run_id else None,
+            "output": output,
+        }
+        self.__send_event(event)
+
+    def on_chain_start(
+        self,
+        serialized: Dict[str, Any],
+        inputs: Dict[str, Any],
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        tags: Union[List[str], None] = None,
+        metadata: Union[Dict[str, Any], None] = None,
+        **kwargs: Any,
+    ) -> Any:
+        name = serialized.get("id", [None, None, None, None])[3]
+        type = "chain"
+
+        agentName = (metadata or {}).get("agentName")
+        if agentName is not None:
+            type = "agent"
+            name = agentName
+        if name == "AgentExecutor" or name == "PlanAndExecute":
+            type = "agent"
+        event = {
+            "event": "start",
+            "type": type,
+            "userId": (metadata or {}).get("userId"),
+            "runId": str(run_id),
+            "parentRunId": str(parent_run_id) if parent_run_id else None,
+            "input": inputs.get("input", inputs),
+            "tags": tags,
+            "metadata": metadata,
+            "name": serialized.get("id", [None, None, None, None])[3],
+        }
+
+        self.__send_event(event)
+
+    def on_chain_end(
+        self,
+        outputs: Dict[str, Any],
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        **kwargs: Any,
+    ) -> Any:
+        event = {
+            "event": "end",
+            "type": "chain",
+            "runId": str(run_id),
+            "output": outputs.get("output", outputs),
+        }
+        self.__send_event(event)
+
+    def on_chain_error(
+        self,
+        error: Union[Exception, KeyboardInterrupt],
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        **kwargs: Any,
+    ) -> Any:
+        event = {
+            "event": "error",
+            "type": "chain",
+            "runId": str(run_id),
+            "parent_run_id": str(parent_run_id) if parent_run_id else None,
+            "error": {"message": str(error), "stack": traceback.format_exc()},
+        }
+        self.__send_event(event)
+
+    def on_agent_action(
+        self,
+        action: AgentAction,
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        **kwargs: Any,
+    ) -> Any:
+        event = {
+            "event": "start",
+            "type": "tool",
+            "runId": str(run_id),
+            "parentRunId": str(parent_run_id) if parent_run_id else None,
+            "name": action.tool,
+            "input": action.tool_input,
+        }
+        self.__send_event(event)
+
+    def on_agent_finish(
+        self,
+        finish: AgentFinish,
+        *,
+        run_id: UUID,
+        parent_run_id: Union[UUID, None] = None,
+        **kwargs: Any,
+    ) -> Any:
+        event = {
+            "event": "end",
+            "type": "agent",
+            "runId": str(run_id),
+            "parentRunId": str(parent_run_id) if parent_run_id else None,
+            "output": finish.return_values,
+        }
+        self.__send_event(event)
+
+
+__all__ = ["LLMonitorCallbackHandler"]

libs/langchain/langchain/callbacks/manager.py

@@ -38,6 +38,7 @@ from langchain.callbacks.base import (
 )
 from langchain.callbacks.openai_info import OpenAICallbackHandler
 from langchain.callbacks.stdout import StdOutCallbackHandler
+from langchain.callbacks.tracers import run_collector
 from langchain.callbacks.tracers.langchain import LangChainTracer
 from langchain.callbacks.tracers.langchain_v1 import LangChainTracerV1, TracerSessionV1
 from langchain.callbacks.tracers.stdout import ConsoleCallbackHandler
@@ -75,6 +76,11 @@ tracing_v2_callback_var: ContextVar[
 ] = ContextVar(  # noqa: E501
     "tracing_callback_v2", default=None
 )
+run_collector_var: ContextVar[
+    Optional[run_collector.RunCollectorCallbackHandler]
+] = ContextVar(  # noqa: E501
+    "run_collector", default=None
+)
 
 
 def _get_debug() -> bool:
@@ -184,6 +190,24 @@ def tracing_v2_enabled(
     tracing_v2_callback_var.set(None)
 
 
+@contextmanager
+def collect_runs() -> Generator[run_collector.RunCollectorCallbackHandler, None, None]:
+    """Collect all run traces in context.
+
+    Returns:
+        run_collector.RunCollectorCallbackHandler: The run collector callback handler.
+
+    Example:
+        >>> with collect_runs() as runs_cb:
+                chain.invoke("foo")
+                run_id = runs_cb.traced_runs[0].id
+    """
+    cb = run_collector.RunCollectorCallbackHandler()
+    run_collector_var.set(cb)
+    yield cb
+    run_collector_var.set(None)
+
+
 @contextmanager
 def trace_as_chain_group(
     group_name: str,
@@ -1712,6 +1736,7 @@ def _configure(
     tracer_project = os.environ.get(
         "LANGCHAIN_PROJECT", os.environ.get("LANGCHAIN_SESSION", "default")
     )
+    run_collector_ = run_collector_var.get()
     debug = _get_debug()
     if (
         verbose
@@ -1774,4 +1799,6 @@ def _configure(
             for handler in callback_manager.handlers
         ):
             callback_manager.add_handler(open_ai, True)
+    if run_collector_ is not None:
+        callback_manager.add_handler(run_collector_, False)
     return callback_manager

libs/langchain/langchain/callbacks/mlflow_callback.py

@@ -242,7 +242,7 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         self,
         name: Optional[str] = "langchainrun-%",
         experiment: Optional[str] = "langchain",
-        tags: Optional[Dict] = {},
+        tags: Optional[Dict] = None,
         tracking_uri: Optional[str] = None,
     ) -> None:
         """Initialize callback handler."""
@@ -254,7 +254,7 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
         self.name = name
         self.experiment = experiment
-        self.tags = tags
+        self.tags = tags or {}
         self.tracking_uri = tracking_uri
 
         self.temp_dir = tempfile.TemporaryDirectory()

libs/langchain/langchain/callbacks/promptlayer_callback.py

@@ -40,12 +40,12 @@ class PromptLayerCallbackHandler(BaseCallbackHandler):
     def __init__(
         self,
         pl_id_callback: Optional[Callable[..., Any]] = None,
-        pl_tags: Optional[List[str]] = [],
+        pl_tags: Optional[List[str]] = None,
     ) -> None:
         """Initialize the PromptLayerCallbackHandler."""
         _lazy_import_promptlayer()
         self.pl_id_callback = pl_id_callback
-        self.pl_tags = pl_tags
+        self.pl_tags = pl_tags or []
         self.runs: Dict[UUID, Dict[str, Any]] = {}
 
     def on_chat_model_start(

libs/langchain/langchain/callbacks/tracers/evaluation.py

@@ -3,10 +3,11 @@ from __future__ import annotations
 
 import logging
 from concurrent.futures import Future, ThreadPoolExecutor, wait
-from typing import Any, List, Optional, Sequence, Set, Union
+from typing import Any, Dict, List, Optional, Sequence, Set, Union
 from uuid import UUID
 
-from langsmith import Client, RunEvaluator
+import langsmith
+from langsmith import schemas as langsmith_schemas
 
 from langchain.callbacks.manager import tracing_v2_enabled
 from langchain.callbacks.tracers.base import BaseTracer
@@ -62,13 +63,13 @@ class EvaluatorCallbackHandler(BaseTracer):
         The LangSmith project name to be organize eval chain runs under.
     """
 
-    name: str = "evaluator_callback_handler"
+    name = "evaluator_callback_handler"
 
     def __init__(
         self,
-        evaluators: Sequence[RunEvaluator],
+        evaluators: Sequence[langsmith.RunEvaluator],
         max_workers: Optional[int] = None,
-        client: Optional[Client] = None,
+        client: Optional[langsmith.Client] = None,
         example_id: Optional[Union[UUID, str]] = None,
         skip_unfinished: bool = True,
         project_name: Optional[str] = "evaluators",
@@ -86,10 +87,11 @@ class EvaluatorCallbackHandler(BaseTracer):
         self.futures: Set[Future] = set()
         self.skip_unfinished = skip_unfinished
         self.project_name = project_name
+        self.logged_feedback: Dict[str, List[langsmith_schemas.Feedback]] = {}
         global _TRACERS
         _TRACERS.append(self)
 
-    def _evaluate_in_project(self, run: Run, evaluator: RunEvaluator) -> None:
+    def _evaluate_in_project(self, run: Run, evaluator: langsmith.RunEvaluator) -> None:
         """Evaluate the run in the project.
 
         Parameters
@@ -102,11 +104,11 @@ class EvaluatorCallbackHandler(BaseTracer):
         """
         try:
             if self.project_name is None:
-                self.client.evaluate_run(run, evaluator)
+                feedback = self.client.evaluate_run(run, evaluator)
             with tracing_v2_enabled(
                 project_name=self.project_name, tags=["eval"], client=self.client
             ):
-                self.client.evaluate_run(run, evaluator)
+                feedback = self.client.evaluate_run(run, evaluator)
         except Exception as e:
             logger.error(
                 f"Error evaluating run {run.id} with "
@@ -114,6 +116,8 @@ class EvaluatorCallbackHandler(BaseTracer):
                 exc_info=True,
             )
             raise e
+        example_id = str(run.reference_example_id)
+        self.logged_feedback.setdefault(example_id, []).append(feedback)
 
     def _persist_run(self, run: Run) -> None:
         """Run the evaluator on the run.

libs/langchain/langchain/callbacks/tracers/langchain.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 
 import logging
 import os
+import weakref
 from concurrent.futures import Future, ThreadPoolExecutor, wait
 from datetime import datetime
 from typing import Any, Callable, Dict, List, Optional, Set, Union
@@ -18,8 +19,10 @@ from langchain.schema.messages import BaseMessage
 
 logger = logging.getLogger(__name__)
 _LOGGED = set()
-_TRACERS: List[LangChainTracer] = []
+_TRACERS: weakref.WeakSet[LangChainTracer] = weakref.WeakSet()
 _CLIENT: Optional[Client] = None
+_MAX_EXECUTORS = 10  # TODO: Remove once write queue is implemented
+_EXECUTORS: List[ThreadPoolExecutor] = []
 
 
 def log_error_once(method: str, exception: Exception) -> None:
@@ -34,8 +37,9 @@ def log_error_once(method: str, exception: Exception) -> None:
 def wait_for_all_tracers() -> None:
     """Wait for all tracers to finish."""
     global _TRACERS
-    for tracer in _TRACERS:
-        tracer.wait_for_futures()
+    for tracer in list(_TRACERS):
+        if tracer is not None:
+            tracer.wait_for_futures()
 
 
 def _get_client() -> Client:
@@ -68,17 +72,22 @@ class LangChainTracer(BaseTracer):
             "LANGCHAIN_PROJECT", os.getenv("LANGCHAIN_SESSION", "default")
         )
         if use_threading:
-            # set max_workers to 1 to process tasks in order
-            self.executor: Optional[ThreadPoolExecutor] = ThreadPoolExecutor(
-                max_workers=1
-            )
+            global _MAX_EXECUTORS
+            if len(_EXECUTORS) < _MAX_EXECUTORS:
+                self.executor: Optional[ThreadPoolExecutor] = ThreadPoolExecutor(
+                    max_workers=1
+                )
+                _EXECUTORS.append(self.executor)
+            else:
+                self.executor = _EXECUTORS.pop(0)
+                _EXECUTORS.append(self.executor)
         else:
             self.executor = None
         self.client = client or _get_client()
         self._futures: Set[Future] = set()
         self.tags = tags or []
         global _TRACERS
-        _TRACERS.append(self)
+        _TRACERS.add(self)
 
     def on_chat_model_start(
         self,

libs/langchain/langchain/chains/__init__.py

@@ -36,6 +36,7 @@ from langchain.chains.flare.base import FlareChain
 from langchain.chains.graph_qa.arangodb import ArangoGraphQAChain
 from langchain.chains.graph_qa.base import GraphQAChain
 from langchain.chains.graph_qa.cypher import GraphCypherQAChain
+from langchain.chains.graph_qa.falkordb import FalkorDBQAChain
 from langchain.chains.graph_qa.hugegraph import HugeGraphQAChain
 from langchain.chains.graph_qa.kuzu import KuzuQAChain
 from langchain.chains.graph_qa.nebulagraph import NebulaGraphQAChain
@@ -85,6 +86,7 @@ __all__ = [
     "ConstitutionalChain",
     "ConversationChain",
     "ConversationalRetrievalChain",
+    "FalkorDBQAChain",
     "FlareChain",
     "GraphCypherQAChain",
     "GraphQAChain",

libs/langchain/langchain/chains/base.py

@@ -136,6 +136,12 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC):
     def raise_callback_manager_deprecation(cls, values: Dict) -> Dict:
         """Raise deprecation warning if callback_manager is used."""
         if values.get("callback_manager") is not None:
+            if values.get("callbacks") is not None:
+                raise ValueError(
+                    "Cannot specify both callback_manager and callbacks. "
+                    "callback_manager is deprecated, callbacks is the preferred "
+                    "parameter to pass in."
+                )
             warnings.warn(
                 "callback_manager is deprecated. Please use callbacks instead.",
                 DeprecationWarning,

libs/langchain/langchain/chains/graph_qa/falkordb.py

@@ -0,0 +1,141 @@
+"""Question answering over a graph."""
+from __future__ import annotations
+
+import re
+from typing import Any, Dict, List, Optional
+
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.chains.base import Chain
+from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
+from langchain.chains.llm import LLMChain
+from langchain.graphs import FalkorDBGraph
+from langchain.pydantic_v1 import Field
+from langchain.schema import BasePromptTemplate
+
+INTERMEDIATE_STEPS_KEY = "intermediate_steps"
+
+
+def extract_cypher(text: str) -> str:
+    """
+    Extract Cypher code from a text.
+    Args:
+        text: Text to extract Cypher code from.
+
+    Returns:
+        Cypher code extracted from the text.
+    """
+    # The pattern to find Cypher code enclosed in triple backticks
+    pattern = r"```(.*?)```"
+
+    # Find all matches in the input text
+    matches = re.findall(pattern, text, re.DOTALL)
+
+    return matches[0] if matches else text
+
+
+class FalkorDBQAChain(Chain):
+    """Chain for question-answering against a graph by generating Cypher statements."""
+
+    graph: FalkorDBGraph = Field(exclude=True)
+    cypher_generation_chain: LLMChain
+    qa_chain: LLMChain
+    input_key: str = "query"  #: :meta private:
+    output_key: str = "result"  #: :meta private:
+    top_k: int = 10
+    """Number of results to return from the query"""
+    return_intermediate_steps: bool = False
+    """Whether or not to return the intermediate steps along with the final answer."""
+    return_direct: bool = False
+    """Whether or not to return the result of querying the graph directly."""
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Return the input keys.
+
+        :meta private:
+        """
+        return [self.input_key]
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Return the output keys.
+
+        :meta private:
+        """
+        _output_keys = [self.output_key]
+        return _output_keys
+
+    @property
+    def _chain_type(self) -> str:
+        return "graph_cypher_chain"
+
+    @classmethod
+    def from_llm(
+        cls,
+        llm: BaseLanguageModel,
+        *,
+        qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
+        cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT,
+        **kwargs: Any,
+    ) -> FalkorDBQAChain:
+        """Initialize from LLM."""
+        qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
+        cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt)
+
+        return cls(
+            qa_chain=qa_chain,
+            cypher_generation_chain=cypher_generation_chain,
+            **kwargs,
+        )
+
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        """Generate Cypher statement, use it to look up in db and answer question."""
+        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
+        callbacks = _run_manager.get_child()
+        question = inputs[self.input_key]
+
+        intermediate_steps: List = []
+
+        generated_cypher = self.cypher_generation_chain.run(
+            {"question": question, "schema": self.graph.schema}, callbacks=callbacks
+        )
+
+        # Extract Cypher code if it is wrapped in backticks
+        generated_cypher = extract_cypher(generated_cypher)
+
+        _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
+        _run_manager.on_text(
+            generated_cypher, color="green", end="\n", verbose=self.verbose
+        )
+
+        intermediate_steps.append({"query": generated_cypher})
+
+        # Retrieve and limit the number of results
+        context = self.graph.query(generated_cypher)[: self.top_k]
+
+        if self.return_direct:
+            final_result = context
+        else:
+            _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
+            _run_manager.on_text(
+                str(context), color="green", end="\n", verbose=self.verbose
+            )
+
+            intermediate_steps.append({"context": context})
+
+            result = self.qa_chain(
+                {"question": question, "context": context},
+                callbacks=callbacks,
+            )
+            final_result = result[self.qa_chain.output_key]
+
+        chain_result: Dict[str, Any] = {self.output_key: final_result}
+        if self.return_intermediate_steps:
+            chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
+
+        return chain_result

libs/langchain/langchain/chains/qa_with_sources/base.py

@@ -120,9 +120,9 @@ class BaseQAWithSourcesChain(Chain, ABC):
 
     def _split_sources(self, answer: str) -> Tuple[str, str]:
         """Split sources from answer."""
-        if re.search(r"SOURCES?[:\s]", answer, re.IGNORECASE):
+        if re.search(r"SOURCES?:", answer, re.IGNORECASE):
             answer, sources = re.split(
-                r"SOURCES?[:\s]|QUESTION:\s", answer, flags=re.IGNORECASE
+                r"SOURCES?:|QUESTION:\s", answer, flags=re.IGNORECASE
             )[:2]
             sources = re.split(r"\n", sources)[0].strip()
         else:

libs/langchain/langchain/chat_loaders/gmail.py

@@ -0,0 +1,110 @@
+import base64
+import re
+from typing import Any, Iterator
+
+from langchain.chat_loaders.base import BaseChatLoader, ChatSession
+from langchain.schema.messages import HumanMessage
+
+
+def _extract_email_content(msg: Any) -> HumanMessage:
+    from_email = None
+    for values in msg["payload"]["headers"]:
+        name = values["name"]
+        if name == "From":
+            from_email = values["value"]
+    if from_email is None:
+        raise ValueError
+    for part in msg["payload"]["parts"]:
+        if part["mimeType"] == "text/plain":
+            data = part["body"]["data"]
+            data = base64.urlsafe_b64decode(data).decode("utf-8")
+            # Regular expression to split the email body at the first
+            # occurrence of a line that starts with "On ... wrote:"
+            pattern = re.compile(r"\r\nOn .+(\r\n)*wrote:\r\n")
+            # Split the email body and extract the first part
+            newest_response = re.split(pattern, data)[0]
+            message = HumanMessage(
+                content=newest_response, additional_kwargs={"sender": from_email}
+            )
+            return message
+    raise ValueError
+
+
+def _get_message_data(service: Any, message: Any) -> ChatSession:
+    msg = service.users().messages().get(userId="me", id=message["id"]).execute()
+    message_content = _extract_email_content(msg)
+    in_reply_to = None
+    email_data = msg["payload"]["headers"]
+    for values in email_data:
+        name = values["name"]
+        if name == "In-Reply-To":
+            in_reply_to = values["value"]
+    if in_reply_to is None:
+        raise ValueError
+
+    thread_id = msg["threadId"]
+
+    thread = service.users().threads().get(userId="me", id=thread_id).execute()
+    messages = thread["messages"]
+
+    response_email = None
+    for message in messages:
+        email_data = message["payload"]["headers"]
+        for values in email_data:
+            if values["name"] == "Message-ID":
+                message_id = values["value"]
+                if message_id == in_reply_to:
+                    response_email = message
+    if response_email is None:
+        raise ValueError
+    starter_content = _extract_email_content(response_email)
+    return ChatSession(messages=[starter_content, message_content])
+
+
+class GMailLoader(BaseChatLoader):
+    """This loader goes over how to load data from GMail.
+
+    There are many ways you could want to load data from GMail.
+    This loader is currently fairly opinionated in how to do so.
+    The way it does it is it first looks for all messages that you have sent.
+    It then looks for messages where you are responding to a previous email.
+    It then fetches that previous email, and creates a training example
+    of that email, followed by your email.
+
+    Note that there are clear limitations here. For example,
+    all examples created are only looking at the previous email for context.
+
+    To use:
+
+    - Set up a Google Developer Account:
+        Go to the Google Developer Console, create a project,
+        and enable the Gmail API for that project.
+        This will give you a credentials.json file that you'll need later.
+    """
+
+    def __init__(self, creds: Any, n: int = 100, raise_error: bool = False) -> None:
+        super().__init__()
+        self.creds = creds
+        self.n = n
+        self.raise_error = raise_error
+
+    def lazy_load(self) -> Iterator[ChatSession]:
+        from googleapiclient.discovery import build
+
+        service = build("gmail", "v1", credentials=self.creds)
+        results = (
+            service.users()
+            .messages()
+            .list(userId="me", labelIds=["SENT"], maxResults=self.n)
+            .execute()
+        )
+        messages = results.get("messages", [])
+        for message in messages:
+            try:
+                yield _get_message_data(service, message)
+            except Exception as e:
+                # TODO: handle errors better
+                if self.raise_error:
+                    raise e
+                else:
+                    pass

libs/langchain/langchain/chat_loaders/imessage.py

@@ -0,0 +1,118 @@
+"""IMessage Chat Loader.
+
+This class is used to load chat sessions from the iMessage chat.db SQLite file.
+It only works on macOS when you have iMessage enabled and have the chat.db file.
+
+The chat.db file is likely located at ~/Library/Messages/chat.db. However, your
+terminal may not have permission to access this file. To resolve this, you can
+copy the file to a different location, change the permissions of the file, or
+grant full disk access for your terminal emulator in System Settings > Security
+and Privacy > Full Disk Access.
+"""
+from __future__ import annotations
+
+from pathlib import Path
+from typing import TYPE_CHECKING, Iterator, List, Optional, Union
+
+from langchain import schema
+from langchain.chat_loaders import base as chat_loaders
+
+if TYPE_CHECKING:
+    import sqlite3
+
+
+class IMessageChatLoader(chat_loaders.BaseChatLoader):
+    def __init__(self, path: Optional[Union[str, Path]] = None):
+        """
+        Initialize the IMessageChatLoader.
+
+        Args:
+            path (str or Path, optional): Path to the chat.db SQLite file.
+                Defaults to None, in which case the default path
+                ~/Library/Messages/chat.db will be used.
+        """
+        if path is None:
+            path = Path.home() / "Library" / "Messages" / "chat.db"
+        self.db_path = path if isinstance(path, Path) else Path(path)
+        if not self.db_path.exists():
+            raise FileNotFoundError(f"File {self.db_path} not found")
+        try:
+            import sqlite3  # noqa: F401
+        except ImportError as e:
+            raise ImportError(
+                "The sqlite3 module is required to load iMessage chats.\n"
+                "Please install it with `pip install pysqlite3`"
+            ) from e
+
+    def _load_single_chat_session(
+        self, cursor: "sqlite3.Cursor", chat_id: int
+    ) -> chat_loaders.ChatSession:
+        """
+        Load a single chat session from the iMessage chat.db.
+
+        Args:
+            cursor: SQLite cursor object.
+            chat_id (int): ID of the chat session to load.
+
+        Returns:
+            ChatSession: Loaded chat session.
+        """
+        results: List[schema.HumanMessage] = []
+
+        query = """
+        SELECT message.date, handle.id, message.text
+        FROM message
+        JOIN chat_message_join ON message.ROWID = chat_message_join.message_id
+        JOIN handle ON message.handle_id = handle.ROWID
+        WHERE chat_message_join.chat_id = ?
+        ORDER BY message.date ASC;
+        """
+        cursor.execute(query, (chat_id,))
+        messages = cursor.fetchall()
+
+        for date, sender, text in messages:
+            if text:  # Skip empty messages
+                results.append(
+                    schema.HumanMessage(
+                        role=sender,
+                        content=text,
+                        additional_kwargs={
+                            "message_time": date,
+                            "sender": sender,
+                        },
+                    )
+                )
+
+        return chat_loaders.ChatSession(messages=results)
+
+    def lazy_load(self) -> Iterator[chat_loaders.ChatSession]:
+        """
+        Lazy load the chat sessions from the iMessage chat.db
+        and yield them in the required format.
+
+        Yields:
+            ChatSession: Loaded chat session.
+        """
+        import sqlite3
+
+        try:
+            conn = sqlite3.connect(self.db_path)
+        except sqlite3.OperationalError as e:
+            raise ValueError(
+                f"Could not open iMessage DB file {self.db_path}.\n"
+                "Make sure your terminal emulator has disk access to this file.\n"
+                "   You can either copy the DB file to an accessible location"
+                " or grant full disk access for your terminal emulator."
+                "  You can grant full disk access for your terminal emulator"
+                " in System Settings > Security and Privacy > Full Disk Access."
+            ) from e
+        cursor = conn.cursor()
+
+        # Fetch the list of chat IDs
+        cursor.execute("SELECT ROWID FROM chat")
+        chat_ids = [row[0] for row in cursor.fetchall()]
+
+        for chat_id in chat_ids:
+            yield self._load_single_chat_session(cursor, chat_id)
+
+        conn.close()

libs/langchain/langchain/chat_models/ernie.py

@@ -57,12 +57,25 @@ class ErnieBotChat(BaseChatModel):
     """
 
     ernie_client_id: Optional[str] = None
+    """Baidu application client id"""
+
     ernie_client_secret: Optional[str] = None
+    """Baidu application client secret"""
+
     access_token: Optional[str] = None
+    """access token is generated by client id and client secret, 
+    setting this value directly will cause an error"""
 
     model_name: str = "ERNIE-Bot-turbo"
+    """model name of ernie, default is `ERNIE-Bot-turbo`.
+      Currently supported `ERNIE-Bot-turbo`, `ERNIE-Bot`"""
+
+    request_timeout: Optional[int] = 60
+    """request timeout for chat http requests"""
 
     streaming: Optional[bool] = False
+    """streaming mode. not supported yet."""
+
     top_p: Optional[float] = 0.8
     temperature: Optional[float] = 0.95
     penalty_score: Optional[float] = 1
@@ -93,6 +106,7 @@ class ErnieBotChat(BaseChatModel):
             raise ValueError(f"Got unknown model_name {self.model_name}")
         resp = requests.post(
             url,
+            timeout=self.request_timeout,
             headers={
                 "Content-Type": "application/json",
             },
@@ -107,6 +121,7 @@ class ErnieBotChat(BaseChatModel):
             base_url: str = "https://aip.baidubce.com/oauth/2.0/token"
             resp = requests.post(
                 base_url,
+                timeout=10,
                 headers={
                     "Content-Type": "application/json",
                     "Accept": "application/json",

libs/langchain/langchain/document_loaders/async_html.py

@@ -33,7 +33,7 @@ class AsyncHtmlLoader(BaseLoader):
         verify_ssl: Optional[bool] = True,
         proxies: Optional[dict] = None,
         requests_per_second: int = 2,
-        requests_kwargs: Dict[str, Any] = {},
+        requests_kwargs: Optional[Dict[str, Any]] = None,
         raise_for_status: bool = False,
     ):
         """Initialize with a webpage path."""
@@ -67,7 +67,7 @@ class AsyncHtmlLoader(BaseLoader):
             self.session.proxies.update(proxies)
 
         self.requests_per_second = requests_per_second
-        self.requests_kwargs = requests_kwargs
+        self.requests_kwargs = requests_kwargs or {}
         self.raise_for_status = raise_for_status
 
     async def _fetch(

libs/langchain/langchain/document_loaders/cube_semantic.py

@@ -113,27 +113,39 @@ class CubeSemanticLoader(BaseLoader):
                     - column_title
                     - column_description
                     - column_values
+                    - cube_data_obj_type
         """
         headers = {
             "Content-Type": "application/json",
             "Authorization": self.cube_api_token,
         }
 
+        logger.info(f"Loading metadata from {self.cube_api_url}...")
         response = requests.get(f"{self.cube_api_url}/meta", headers=headers)
         response.raise_for_status()
         raw_meta_json = response.json()
-        cubes = raw_meta_json.get("cubes", [])
+        cube_data_objects = raw_meta_json.get("cubes", [])
+
+        logger.info(f"Found {len(cube_data_objects)} cube data objects in metadata.")
+
+        if not cube_data_objects:
+            raise ValueError("No cubes found in metadata.")
+
         docs = []
 
-        for cube in cubes:
-            if cube.get("type") != "view":
+        for cube_data_obj in cube_data_objects:
+            cube_data_obj_name = cube_data_obj.get("name")
+            cube_data_obj_type = cube_data_obj.get("type")
+            cube_data_obj_is_public = cube_data_obj.get("public")
+            measures = cube_data_obj.get("measures", [])
+            dimensions = cube_data_obj.get("dimensions", [])
+
+            logger.info(f"Processing {cube_data_obj_name}...")
+
+            if not cube_data_obj_is_public:
+                logger.info(f"Skipping {cube_data_obj_name} because it is not public.")
                 continue
 
-            cube_name = cube.get("name")
-
-            measures = cube.get("measures", [])
-            dimensions = cube.get("dimensions", [])
-
             for item in measures + dimensions:
                 column_member_type = "measure" if item in measures else "dimension"
                 dimension_values = []
@@ -148,13 +160,14 @@ class CubeSemanticLoader(BaseLoader):
                     dimension_values = self._get_dimension_values(item_name)
 
                 metadata = dict(
-                    table_name=str(cube_name),
+                    table_name=str(cube_data_obj_name),
                     column_name=item_name,
                     column_data_type=item_type,
                     column_title=str(item.get("title")),
                     column_description=str(item.get("description")),
                     column_member_type=column_member_type,
                     column_values=dimension_values,
+                    cube_data_obj_type=cube_data_obj_type,
                 )
 
                 page_content = f"{str(item.get('title'))}, "

libs/langchain/langchain/document_loaders/url_selenium.py

@@ -74,6 +74,7 @@ class SeleniumURLLoader(BaseLoader):
         if self.browser.lower() == "chrome":
             from selenium.webdriver import Chrome
             from selenium.webdriver.chrome.options import Options as ChromeOptions
+            from selenium.webdriver.chrome.service import Service
 
             chrome_options = ChromeOptions()
 
@@ -87,10 +88,14 @@ class SeleniumURLLoader(BaseLoader):
                 chrome_options.binary_location = self.binary_location
             if self.executable_path is None:
                 return Chrome(options=chrome_options)
-            return Chrome(executable_path=self.executable_path, options=chrome_options)
+            return Chrome(
+                options=chrome_options,
+                service=Service(executable_path=self.executable_path),
+            )
         elif self.browser.lower() == "firefox":
             from selenium.webdriver import Firefox
             from selenium.webdriver.firefox.options import Options as FirefoxOptions
+            from selenium.webdriver.firefox.service import Service
 
             firefox_options = FirefoxOptions()
 
@@ -104,7 +109,8 @@ class SeleniumURLLoader(BaseLoader):
             if self.executable_path is None:
                 return Firefox(options=firefox_options)
             return Firefox(
-                executable_path=self.executable_path, options=firefox_options
+                options=firefox_options,
+                service=Service(executable_path=self.executable_path),
             )
         else:
             raise ValueError("Invalid browser specified. Use 'chrome' or 'firefox'.")

libs/langchain/langchain/graphs/__init__.py

@@ -1,6 +1,7 @@
 """**Graphs** provide a natural language interface to graph databases."""
 
 from langchain.graphs.arangodb_graph import ArangoGraph
+from langchain.graphs.falkordb_graph import FalkorDBGraph
 from langchain.graphs.hugegraph import HugeGraph
 from langchain.graphs.kuzu_graph import KuzuGraph
 from langchain.graphs.memgraph_graph import MemgraphGraph
@@ -20,4 +21,5 @@ __all__ = [
     "HugeGraph",
     "RdfGraph",
     "ArangoGraph",
+    "FalkorDBGraph",
 ]

libs/langchain/langchain/graphs/falkordb_graph.py

@@ -0,0 +1,67 @@
+from typing import Any, Dict, List
+
+node_properties_query = """
+MATCH (n)
+UNWIND labels(n) as l
+UNWIND keys(n) as p
+RETURN {label:l, properties: collect(distinct p)} AS output
+"""
+
+rel_properties_query = """
+MATCH ()-[r]->()
+UNWIND keys(r) as p
+RETURN {type:type(r), properties: collect(distinct p)} AS output
+"""
+
+rel_query = """
+MATCH (n)-[r]->(m)
+WITH labels(n)[0] AS src, labels(m)[0] AS dst, type(r) AS type
+RETURN DISTINCT "(:" + src + ")-[:" + type + "]->(:" + dst + ")" AS output
+"""
+
+
+class FalkorDBGraph:
+    """FalkorDB wrapper for graph operations."""
+
+    def __init__(
+        self, database: str, host: str = "localhost", port: int = 6379
+    ) -> None:
+        """Create a new FalkorDB graph wrapper instance."""
+        try:
+            import redis
+            from redis.commands.graph import Graph
+        except ImportError:
+            raise ImportError(
+                "Could not import redis python package. "
+                "Please install it with `pip install redis`."
+            )
+
+        self._driver = redis.Redis(host=host, port=port)
+        self._graph = Graph(self._driver, database)
+
+        try:
+            self.refresh_schema()
+        except Exception as e:
+            raise ValueError(f"Could not refresh schema. Error: {e}")
+
+    @property
+    def get_schema(self) -> str:
+        """Returns the schema of the FalkorDB database"""
+        return self.schema
+
+    def refresh_schema(self) -> None:
+        """Refreshes the schema of the FalkorDB database"""
+        self.schema = (
+            f"Node properties: {node_properties_query}\n"
+            f"Relationships properties: {rel_properties_query}\n"
+            f"Relationships: {rel_query}\n"
+        )
+
+    def query(self, query: str, params: dict = {}) -> List[Dict[str, Any]]:
+        """Query FalkorDB database."""
+
+        try:
+            data = self._graph.query(query, params)
+            return data.result_set
+        except Exception as e:
+            raise ValueError("Generated Cypher Statement is not valid\n" f"{e}")

libs/langchain/langchain/graphs/nebula_graph.py

@@ -1,6 +1,6 @@
 import logging
 from string import Template
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 
 logger = logging.getLogger(__name__)
 
@@ -106,11 +106,12 @@ class NebulaGraph:
         """Returns the schema of the NebulaGraph database"""
         return self.schema
 
-    def execute(self, query: str, params: dict = {}, retry: int = 0) -> Any:
+    def execute(self, query: str, params: Optional[dict] = None, retry: int = 0) -> Any:
         """Query NebulaGraph database."""
         from nebula3.Exception import IOErrorException, NoValidSessionException
         from nebula3.fbthrift.transport.TTransport import TTransportException
 
+        params = params or {}
         try:
             result = self.session_pool.execute_parameter(query, params)
             if not result.is_succeeded():