Resolve merge conflicts

docs/_static/css/custom.css

@@ -11,3 +11,7 @@ pre {
     max-width: 2560px !important;
   }
 }
+
+#my-component-root *, #headlessui-portal-root * {
+  z-index: 1000000000000;
+}

docs/_static/js/mendablesearch.js

@@ -0,0 +1,58 @@
+document.addEventListener('DOMContentLoaded', () => {
+  // Load the external dependencies
+  function loadScript(src, onLoadCallback) {
+    const script = document.createElement('script');
+    script.src = src;
+    script.onload = onLoadCallback;
+    document.head.appendChild(script);
+  }
+
+  function createRootElement() {
+    const rootElement = document.createElement('div');
+    rootElement.id = 'my-component-root';
+    document.body.appendChild(rootElement);
+    return rootElement;
+  }
+
+
+
+  function initializeMendable() {
+    const rootElement = createRootElement();
+    const { MendableFloatingButton } = Mendable;
+
+
+    const iconSpan1 = React.createElement('span', {
+    }, '🦜');
+
+    const iconSpan2 = React.createElement('span', {
+    }, '🔗');
+
+    const icon = React.createElement('p', {
+      style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
+    }, [iconSpan1, iconSpan2]);
+
+
+
+
+    const mendableFloatingButton = React.createElement(
+      MendableFloatingButton,
+      {
+        style: { darkMode: false, accentColor: '#010810' },
+        floatingButtonStyle: { color: '#ffffff', backgroundColor: '#010810' },
+        anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
+        messageSettings: {
+          openSourcesInNewTab: false,
+        },
+        icon: icon,
+      }
+    );
+
+    ReactDOM.render(mendableFloatingButton, rootElement);
+  }
+
+  loadScript('https://unpkg.com/react@17/umd/react.production.min.js', () => {
+    loadScript('https://unpkg.com/react-dom@17/umd/react-dom.production.min.js', () => {
+      loadScript('https://unpkg.com/@mendable/[email protected]/dist/umd/mendable.min.js', initializeMendable);
+    });
+  });
+});

docs/conf.py

@@ -103,5 +103,10 @@
 html_css_files = [
     "css/custom.css",
 ]
+
+html_js_files = [
+    "js/mendablesearch.js",
+]
+
 nb_execution_mode = "off"
 myst_enable_extensions = ["colon_fence"]

docs/deployments.md

@@ -33,12 +33,17 @@ It implements a Question Answering app and contains instructions for deploying t
 
 A minimal example on how to run LangChain on Vercel using Flask.
 
+## [Digitalocean App Platform](https://github.com/homanp/digitalocean-langchain)
+
+A minimal example on how to deploy LangChain to DigitalOcean App Platform.
 
 ## [SteamShip](https://github.com/steamship-core/steamship-langchain/)
+
 This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship.
 This includes: production ready endpoints, horizontal scaling across dependencies, persistant storage of app state, multi-tenancy support, etc.
 
 ## [Langchain-serve](https://github.com/jina-ai/langchain-serve)
+
 This repository allows users to serve local chains and agents as RESTful, gRPC, or Websocket APIs thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
 
 ## [BentoML](https://github.com/ssheng/BentoChain)

docs/ecosystem/comet_tracking.ipynb

@@ -1,23 +1,20 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Comet"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "![](https://user-images.githubusercontent.com/7529846/230328046-a8b18c51-12e3-4617-9b39-97614a571a2d.png)"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -31,15 +28,13 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "<img width=\"1280\" alt=\"comet-langchain\" src=\"https://user-images.githubusercontent.com/7529846/230326720-a9711435-9c6f-4edb-a707-94b67271ab25.png\">\n"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -52,22 +47,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install comet_ml\n",
-    "!pip install langchain\n",
-    "!pip install openai\n",
-    "!pip install google-search-results"
+    "%pip install comet_ml langchain openai google-search-results spacy textstat pandas\n",
+    "\n",
+    "import sys\n",
+    "!{sys.executable} -m spacy download en_core_web_sm"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Initialize Comet and Set your Credentials"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -86,15 +79,13 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Set OpenAI and SerpAPI credentials"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -109,12 +100,12 @@
    "source": [
     "import os\n",
     "\n",
-    "%env OPENAI_API_KEY=\"...\"\n",
-    "%env SERPAPI_API_KEY=\"...\""
+    "os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
+    "#os.environ[\"OPENAI_ORGANIZATION\"] = \"...\"\n",
+    "os.environ[\"SERPAPI_API_KEY\"] = \"...\""
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -149,7 +140,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -185,12 +175,11 @@
     "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
     "\n",
     "test_prompts = [{\"title\": \"Documentary about Bigfoot in Paris\"}]\n",
-    "synopsis_chain.apply(test_prompts)\n",
+    "print(synopsis_chain.apply(test_prompts))\n",
     "comet_callback.flush_tracker(synopsis_chain, finish=True)"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -232,15 +221,13 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Scenario 4: Using Custom Evaluation Metrics"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -256,7 +243,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install rouge-score"
+    "%pip install rouge-score"
    ]
   },
   {
@@ -336,16 +323,29 @@
     "                 \"\"\"\n",
     "    }\n",
     "]\n",
-    "synopsis_chain.apply(test_prompts)\n",
+    "print(synopsis_chain.apply(test_prompts))\n",
     "comet_callback.flush_tracker(synopsis_chain, finish=True)"
    ]
   }
  ],
  "metadata": {
-  "language_info": {
-   "name": "python"
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "orig_nbformat": 4
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2

docs/ecosystem/gpt4all.md

@@ -36,12 +36,12 @@ from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
 model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8, callback_handler=callback_handler, verbose=True)
 
-# Generate text. Tokens are streamed throught the callback manager.
+# Generate text. Tokens are streamed through the callback manager.
 model("Once upon a time, ")
 ```
 
 ## Model File
 
 You can find links to model file downloads in the [pyllamacpp](https://github.com/nomic-ai/pyllamacpp) repository.
 
-For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/gpt4all.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/gpt4all.ipynb)

docs/gallery.rst

@@ -1,5 +1,5 @@
 LangChain Gallery
-=============
+=================
 
 Lots of people have built some pretty awesome stuff with LangChain.
 This is a collection of our favorites.
@@ -223,7 +223,7 @@ Open Source
     Answer questions about the documentation of any project    
 
 Misc. Colab Notebooks
-~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 .. panels::
     :body: text-center

docs/modules/chains/examples/sqlite.ipynb

@@ -9,9 +9,9 @@
     }
    },
    "source": [
-    "# SQLite example\n",
+    "# SQL Chain example\n",
     "\n",
-    "This example showcases hooking up an LLM to answer questions over a database."
+    "This example demonstrates the use of the `SQLDatabaseChain` for answering questions over a database."
    ]
   },
   {
@@ -23,8 +23,10 @@
     }
    },
    "source": [
-    "This uses the example Chinook database.\n",
-    "To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
+    "Under the hood, LangChain uses SQLAlchemy to connect to SQL databases. The `SQLDatabaseChain` can therefore be used with any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, and SQLite. Please refer to the SQLAlchemy documentation for more information about requirements for connecting to your database. For example, a connection to MySQL requires an appropriate connector such as PyMySQL. A URI for a MySQL connection might look like: `mysql+pymysql://user:pass@some_mysql_db_address/db_name`\n",
+    "\n",
+    "This demonstration uses SQLite and the example Chinook database.\n",
+    "To set it up, follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
    ]
   },
   {
@@ -679,7 +681,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

docs/modules/indexes/document_loaders/examples/csv.ipynb

@@ -106,7 +106,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Specify a column to be used identify the document source\n",
+    "## Specify a column to be used identify the document source\n",
     "\n",
     "Use the `source_column` argument to specify a column to be set as the source for the document created from each row. Otherwise `file_path` will be used as the source for all documents created from the csv file.\n",
     "\n",