Add fuzzy and embedding metrics

Re-add change that was inadvertently undone in #6995

.github/PULL_REQUEST_TEMPLATE.md

@@ -12,11 +12,11 @@ If you're adding a new integration, please include:
   2. an example notebook showing its use.
 
 Maintainer responsibilities:
-  - General / Misc / if you don't know who to tag: @dev2049
+  - General / Misc / if you don't know who to tag: @baskaryan
   - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
-  - Models / Prompts: @hwchase17, @dev2049
+  - Models / Prompts: @hwchase17, @baskaryan
   - Memory: @hwchase17
-  - Agents / Tools / Toolkits: @vowelparrot
+  - Agents / Tools / Toolkits: @hinthornw
   - Tracing / Callbacks: @agola11
   - Async: @agola11
 

.readthedocs.yaml

@@ -9,6 +9,9 @@ build:
   os: ubuntu-22.04
   tools:
     python: "3.11"
+  jobs:
+    pre_build:
+      - python docs/api_reference/create_api_rst.py
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:

docs/api_reference/_static/js/mendablesearch.js

@@ -1,57 +0,0 @@
-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
-        cmdShortcutKey:'j',
-        messageSettings: {
-          openSourcesInNewTab: false,
-          prettySources: true // Prettify the sources displayed now
-        },
-        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/search@0.0.102/dist/umd/mendable.min.js', initializeMendable);
-    });
-  });
-});

docs/api_reference/agents.rst

@@ -1,12 +0,0 @@
-Agents
-==============
-
-Reference guide for Agents and associated abstractions.
-
-.. toctree::
-   :maxdepth: 1
-   :glob:
-
-   modules/agents
-   modules/tools
-   modules/agent_toolkits

docs/api_reference/conf.py

@@ -11,12 +11,13 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
-# import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
+import os
+import sys
 
 import toml
 
+sys.path.insert(0, os.path.abspath("."))
+
 with open("../../pyproject.toml") as f:
     data = toml.load(f)
 
@@ -45,11 +46,9 @@ extensions = [
     "sphinx.ext.napoleon",
     "sphinx.ext.viewcode",
     "sphinxcontrib.autodoc_pydantic",
-    "myst_nb",
     "sphinx_copybutton",
     "sphinx_panels",
     "IPython.sphinxext.ipython_console_highlighting",
-    "sphinx_tabs.tabs",
 ]
 source_suffix = [".rst"]
 
@@ -59,24 +58,22 @@ autodoc_pydantic_config_members = False
 autodoc_pydantic_model_show_config_summary = False
 autodoc_pydantic_model_show_validator_members = False
 autodoc_pydantic_model_show_validator_summary = False
-autodoc_pydantic_model_show_field_summary = False
-autodoc_pydantic_model_members = False
-autodoc_pydantic_model_undoc_members = False
-autodoc_pydantic_model_hide_paramlist = False
 autodoc_pydantic_model_signature_prefix = "class"
-autodoc_pydantic_field_signature_prefix = "attribute"
-autodoc_pydantic_model_summary_list_order = "bysource"
-autodoc_member_order = "bysource"
+autodoc_pydantic_field_signature_prefix = "param"
+autodoc_member_order = "groupwise"
+autoclass_content = "both"
+autodoc_typehints_format = "short"
+
 autodoc_default_options = {
     "members": True,
     "show-inheritance": True,
-    "undoc_members": True,
-    "inherited_members": "BaseModel",
+    "inherited-members": "BaseModel",
+    "undoc-members": True,
+    "special-members": "__call__",
 }
-autodoc_typehints = "description"
-
+# autodoc_typehints = "description"
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ["_templates"]
+templates_path = ["templates"]
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -89,14 +86,16 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = "sphinx_rtd_theme"
+html_theme = "scikit-learn-modern"
+html_theme_path = ["themes"]
 
-html_theme_options = {
-    "path_to_docs": "docs",
-    "repository_url": "https://github.com/hwchase17/langchain",
-    "use_repository_button": True,
-    # "style_nav_header_background": "white"
+# redirects dictionary maps from old links to new links
+html_additional_pages = {}
+redirects = {
+    "index": "api_reference",
 }
+for old_link in redirects:
+    html_additional_pages[old_link] = "redirects.html"
 
 html_context = {
     "display_github": True,  # Integrate GitHub
@@ -104,6 +103,7 @@ html_context = {
     "github_repo": "langchain",  # Repo name
     "github_version": "master",  # Version
     "conf_py_path": "/docs/api_reference",  # Path in the checkout to the docs root
+    "redirects": redirects,
 }
 
 # Add any paths that contain custom static files (such as style sheets) here,
@@ -116,10 +116,9 @@ html_static_path = ["_static"]
 html_css_files = [
     "css/custom.css",
 ]
+html_use_index = False
 
-html_js_files = [
-    "js/mendablesearch.js",
-]
-
-nb_execution_mode = "off"
 myst_enable_extensions = ["colon_fence"]
+
+# generate autosummary even if no references
+autosummary_generate = True

docs/api_reference/create_api_rst.py

@@ -0,0 +1,94 @@
+"""Script for auto-generating api_reference.rst"""
+import glob
+import re
+from pathlib import Path
+
+ROOT_DIR = Path(__file__).parents[2].absolute()
+PKG_DIR = ROOT_DIR / "langchain"
+WRITE_FILE = Path(__file__).parent / "api_reference.rst"
+
+
+def load_members() -> dict:
+    members: dict = {}
+    for py in glob.glob(str(PKG_DIR) + "/**/*.py", recursive=True):
+        module = py[len(str(PKG_DIR)) + 1 :].replace(".py", "").replace("/", ".")
+        top_level = module.split(".")[0]
+        if top_level not in members:
+            members[top_level] = {"classes": [], "functions": []}
+        with open(py, "r") as f:
+            for line in f.readlines():
+                cls = re.findall(r"^class ([^_].*)\(", line)
+                members[top_level]["classes"].extend([module + "." + c for c in cls])
+                func = re.findall(r"^def ([^_].*)\(", line)
+                members[top_level]["functions"].extend([module + "." + f for f in func])
+    return members
+
+
+def construct_doc(members: dict) -> str:
+    full_doc = """\
+.. _api_reference:
+
+=============
+API Reference
+=============
+
+"""
+    for module, _members in sorted(members.items(), key=lambda kv: kv[0]):
+        classes = _members["classes"]
+        functions = _members["functions"]
+        if not (classes or functions):
+            continue
+
+        module_title = module.replace("_", " ").title()
+        if module_title == "Llms":
+            module_title = "LLMs"
+        section = f":mod:`langchain.{module}`: {module_title}"
+        full_doc += f"""\
+{section}
+{'=' * (len(section) + 1)}
+
+.. automodule:: langchain.{module}
+    :no-members:
+    :no-inherited-members:
+
+"""
+
+        if classes:
+            cstring = "\n    ".join(sorted(classes))
+            full_doc += f"""\
+Classes
+--------------
+.. currentmodule:: langchain
+
+.. autosummary::
+    :toctree: {module}
+    :template: class.rst
+
+    {cstring}
+
+"""
+        if functions:
+            fstring = "\n    ".join(sorted(functions))
+            full_doc += f"""\
+Functions
+--------------
+.. currentmodule:: langchain
+
+.. autosummary::
+    :toctree: {module}
+
+    {fstring}
+
+"""
+    return full_doc
+
+
+def main() -> None:
+    members = load_members()
+    full_doc = construct_doc(members)
+    with open(WRITE_FILE, "w") as f:
+        f.write(full_doc)
+
+
+if __name__ == "__main__":
+    main()

docs/api_reference/data_connection.rst

@@ -1,13 +0,0 @@
-Data connection
-==============
-LangChain has a number of modules that help you load, structure, store, and retrieve documents.
-
-.. toctree::
-   :maxdepth: 1
-   :glob:
-
-   modules/document_loaders
-   modules/document_transformers
-   modules/embeddings
-   modules/vectorstores
-   modules/retrievers

docs/api_reference/index.rst

@@ -1,29 +1,8 @@
-API Reference
-==========================
-
-| Full documentation on all methods, classes, and APIs in the LangChain Python package.
+=============
+LangChain API
+=============
 
 .. toctree::
-   :maxdepth: 1
-   :caption: Abstractions
+    :maxdepth: 2
 
-   ./modules/base_classes.rst
-
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Core
-
-   ./model_io.rst
-   ./data_connection.rst
-   ./modules/chains.rst
-   ./agents.rst
-   ./modules/memory.rst
-   ./modules/callbacks.rst
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Additional
-
-   ./modules/utilities.rst
-   ./modules/experimental.rst
+    api_reference.rst

docs/api_reference/model_io.rst

@@ -1,12 +0,0 @@
-Model I/O
-==============
-
-LangChain provides interfaces and integrations for working with language models.
-
-.. toctree::
-   :maxdepth: 1
-   :glob:
-
-   ./prompts.rst
-   ./models.rst
-   ./modules/output_parsers.rst

docs/api_reference/models.rst

@@ -1,11 +0,0 @@
-Models
-==============
-
-LangChain provides interfaces and integrations for a number of different types of models.
-
-.. toctree::
-   :maxdepth: 1
-   :glob:
-
-   modules/llms
-   modules/chat_models

docs/api_reference/modules/agent_toolkits.rst

@@ -1,7 +0,0 @@
-Agent Toolkits
-===============================
-
-.. automodule:: langchain.agents.agent_toolkits
-   :members:
-   :undoc-members:
-

docs/api_reference/modules/agents.rst

@@ -1,7 +0,0 @@
-Agents
-===============================
-
-.. automodule:: langchain.agents
-   :members:
-   :undoc-members:
-

docs/api_reference/modules/base_classes.rst

@@ -1,5 +0,0 @@
-Base classes
-========================
-
-.. automodule:: langchain.schema
-   :inherited-members:

docs/api_reference/modules/callbacks.rst

@@ -1,7 +0,0 @@
-Callbacks
-=======================
-
-.. automodule:: langchain.callbacks
-   :members:
-   :undoc-members:
-

docs/api_reference/modules/chains.rst

@@ -1,8 +0,0 @@
-Chains
-=======================
-
-.. automodule:: langchain.chains
-   :members:
-   :undoc-members:
-   :inherited-members: BaseModel
-

docs/api_reference/modules/chat_models.rst

@@ -1,7 +0,0 @@
-Chat Models
-===============================
-
-.. automodule:: langchain.chat_models
-   :members:
-   :undoc-members:
-

docs/api_reference/modules/document_loaders.rst

@@ -1,7 +0,0 @@
-Document Loaders
-===============================
-
-.. automodule:: langchain.document_loaders
-   :members:
-   :undoc-members:
-

docs/api_reference/modules/document_transformers.rst

@@ -1,13 +0,0 @@
-Document Transformers
-===============================
-
-.. automodule:: langchain.document_transformers
-   :members:
-   :undoc-members:
-
-Text Splitters
-------------------------------
-
-.. automodule:: langchain.text_splitter
-   :members:
-   :undoc-members:

docs/api_reference/modules/embeddings.rst

@@ -1,5 +0,0 @@
-Embeddings
-===========================
-
-.. automodule:: langchain.embeddings
-   :members:

docs/api_reference/modules/example_selector.rst

@@ -1,5 +0,0 @@
-Example Selector
-=========================================
-
-.. automodule:: langchain.prompts.example_selector
-   :members:

docs/api_reference/modules/experimental.rst

@@ -1,28 +0,0 @@
-====================
-Experimental
-====================
-
-This module contains experimental modules and reproductions of existing work using LangChain primitives.
-
-Autonomous agents
-------------------
-
-Here, we document the BabyAGI and AutoGPT classes from the langchain.experimental module.
-
-.. autoclass:: langchain.experimental.BabyAGI
-   :members:
-
-.. autoclass:: langchain.experimental.AutoGPT
-   :members:
-
-
-Generative agents
-------------------
-
-Here, we document the GenerativeAgent and GenerativeAgentMemory classes from the langchain.experimental module.
-
-.. autoclass:: langchain.experimental.GenerativeAgent
-   :members:
-
-.. autoclass:: langchain.experimental.GenerativeAgentMemory
-   :members:

docs/api_reference/modules/llms.rst

@@ -1,7 +0,0 @@
-LLMs
-=======================
-
-.. automodule:: langchain.llms
-   :members:
-   :inherited-members:
-   :special-members: __call__

docs/api_reference/modules/memory.rst

@@ -1,7 +0,0 @@
-Memory
-===============================
-
-.. automodule:: langchain.memory
-   :members:
-   :undoc-members:
-

docs/api_reference/modules/output_parsers.rst

@@ -1,7 +0,0 @@
-Output Parsers
-===============================
-
-.. automodule:: langchain.output_parsers
-   :members:
-   :undoc-members:
-

docs/api_reference/modules/prompts.rst

@@ -1,6 +0,0 @@
-Prompt Templates
-========================
-
-.. automodule:: langchain.prompts
-   :members:
-   :undoc-members:

docs/api_reference/modules/retrievers.rst

@@ -1,14 +0,0 @@
-Retrievers
-===============================
-
-.. automodule:: langchain.retrievers
-   :members:
-   :undoc-members:
-
-Document compressors
--------------------------------
-
-.. automodule:: langchain.retrievers.document_compressors
-   :members:
-   :undoc-members:
-

docs/api_reference/modules/tools.rst

@@ -1,7 +0,0 @@
-Tools
-===============================
-
-.. automodule:: langchain.tools
-   :members:
-   :undoc-members:
-

docs/api_reference/modules/utilities.rst

@@ -1,7 +0,0 @@
-Utilities
-===============================
-
-.. automodule:: langchain.utilities
-   :members:
-   :undoc-members:
-

docs/api_reference/modules/vectorstores.rst

@@ -1,6 +0,0 @@
-Vector Stores
-=============================
-
-.. automodule:: langchain.vectorstores
-   :members:
-   :undoc-members:

docs/api_reference/prompts.rst

@@ -1,11 +0,0 @@
-Prompts
-==============
-
-The reference guides here all relate to objects for working with Prompts.
-
-.. toctree::
-   :maxdepth: 1
-   :glob:
-
-   modules/prompts
-   modules/example_selector

docs/api_reference/templates/COPYRIGHT.txt

@@ -0,0 +1,27 @@
+Copyright (c) 2007-2023 The scikit-learn developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

docs/api_reference/templates/class.rst

@@ -0,0 +1,28 @@
+:mod:`{{module}}`.{{objname}}
+{{ underline }}==============
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+
+   {% block methods %}
+   {% if methods %}
+   .. rubric:: {{ _('Methods') }}
+
+   .. autosummary::
+   {% for item in methods %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}

docs/api_reference/templates/redirects.html

@@ -0,0 +1,15 @@
+{% set redirect = pathto(redirects[pagename]) %}
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <meta http-equiv="Refresh" content="0; url={{ redirect }}" />
+    <meta name="Description" content="scikit-learn: machine learning in Python">
+    <link rel="canonical" href="{{ redirect }}" />
+    <title>scikit-learn: machine learning in Python</title>
+  </head>
+  <body>
+    <p>You will be automatically redirected to the <a href="{{ redirect }}">new location of this page</a>.</p>
+  </body>
+</html>

docs/api_reference/themes/COPYRIGHT.txt

@@ -0,0 +1,27 @@
+Copyright (c) 2007-2023 The scikit-learn developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

docs/api_reference/themes/scikit-learn-modern/javascript.html

@@ -0,0 +1,67 @@
+<script>
+$(document).ready(function() {
+    /* Add a [>>>] button on the top-right corner of code samples to hide
+     * the >>> and ... prompts and the output and thus make the code
+     * copyable. */
+    var div = $('.highlight-python .highlight,' +
+                '.highlight-python3 .highlight,' +
+                '.highlight-pycon .highlight,' +
+		'.highlight-default .highlight')
+    var pre = div.find('pre');
+
+    // get the styles from the current theme
+    pre.parent().parent().css('position', 'relative');
+    var hide_text = 'Hide prompts and outputs';
+    var show_text = 'Show prompts and outputs';
+
+    // create and add the button to all the code blocks that contain >>>
+    div.each(function(index) {
+        var jthis = $(this);
+        if (jthis.find('.gp').length > 0) {
+            var button = $('<span class="copybutton">&gt;&gt;&gt;</span>');
+            button.attr('title', hide_text);
+            button.data('hidden', 'false');
+            jthis.prepend(button);
+        }
+        // tracebacks (.gt) contain bare text elements that need to be
+        // wrapped in a span to work with .nextUntil() (see later)
+        jthis.find('pre:has(.gt)').contents().filter(function() {
+            return ((this.nodeType == 3) && (this.data.trim().length > 0));
+        }).wrap('<span>');
+    });
+
+    // define the behavior of the button when it's clicked
+    $('.copybutton').click(function(e){
+        e.preventDefault();
+        var button = $(this);
+        if (button.data('hidden') === 'false') {
+            // hide the code output
+            button.parent().find('.go, .gp, .gt').hide();
+            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
+            button.css('text-decoration', 'line-through');
+            button.attr('title', show_text);
+            button.data('hidden', 'true');
+        } else {
+            // show the code output
+            button.parent().find('.go, .gp, .gt').show();
+            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
+            button.css('text-decoration', 'none');
+            button.attr('title', hide_text);
+            button.data('hidden', 'false');
+        }
+    });
+
+	/*** Add permalink buttons next to glossary terms ***/
+	$('dl.glossary > dt[id]').append(function() {
+		return ('<a class="headerlink" href="#' +
+			    this.getAttribute('id') +
+			    '" title="Permalink to this term">¶</a>');
+	});
+});
+
+</script>
+{%- if pagename != 'index' and pagename != 'documentation' %}
+    {% if theme_mathjax_path %}
+<script id="MathJax-script" async src="{{ theme_mathjax_path }}"></script>
+    {% endif %}
+{%- endif %}

docs/api_reference/themes/scikit-learn-modern/layout.html

@@ -0,0 +1,142 @@
+{# TEMPLATE VAR SETTINGS #}
+{%- set url_root = pathto('', 1) %}
+{%- if url_root == '#' %}{% set url_root = '' %}{% endif %}
+{%- if not embedded and docstitle %}
+  {%- set titlesuffix = " — "|safe + docstitle|e %}
+{%- else %}
+  {%- set titlesuffix = "" %}
+{%- endif %}
+{%- set lang_attr = 'en' %}
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="{{ lang_attr }}" > <![endif]-->
+<!--[if gt IE 8]><!--> <html class="no-js" lang="{{ lang_attr }}" > <!--<![endif]-->
+<head>
+  <meta charset="utf-8">
+  {{ metatags }}
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+
+  {% block htmltitle %}
+  <title>{{ title|striptags|e }}{{ titlesuffix }}</title>
+  {% endblock %}
+  <link rel="canonical" href="http://scikit-learn.org/stable/{{pagename}}.html" />
+
+  {% if favicon_url %}
+  <link rel="shortcut icon" href="{{ favicon_url|e }}"/>
+  {% endif %}
+
+  <link rel="stylesheet" href="{{ pathto('_static/css/vendor/bootstrap.min.css', 1) }}" type="text/css" />
+  {%- for css in css_files %}
+    {%- if css|attr("rel") %}
+  <link rel="{{ css.rel }}" href="{{ pathto(css.filename, 1) }}" type="text/css"{% if css.title is not none %} title="{{ css.title }}"{% endif %} />
+    {%- else %}
+  <link rel="stylesheet" href="{{ pathto(css, 1) }}" type="text/css" />
+    {%- endif %}
+  {%- endfor %}
+  <link rel="stylesheet" href="{{ pathto('_static/' + style, 1) }}" type="text/css" />
+<script id="documentation_options" data-url_root="{{ pathto('', 1) }}" src="{{ pathto('_static/documentation_options.js', 1) }}"></script>
+<script src="{{ pathto('_static/jquery.js', 1) }}"></script>
+{%- block extrahead %} {% endblock %}
+</head>
+<body>
+{% include "nav.html" %}
+{%- block content %}
+<div class="d-flex" id="sk-doc-wrapper">
+    <input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
+    <label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
+    <div id="sk-sidebar-wrapper" class="border-right">
+      <div class="sk-sidebar-toc-wrapper">
+        <div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
+          {%- if prev %}
+            <a href="{{ prev.link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ prev.title|striptags }}">Prev</a>
+          {%- else %}
+            <a href="#" role="button" class="btn sk-btn-rellink py-1 disabled"">Prev</a>
+          {%- endif %}
+          {%- if parents -%}
+            <a href="{{ parents[-1].link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ parents[-1].title|striptags }}">Up</a>
+          {%- else %}
+            <a href="#" role="button" class="btn sk-btn-rellink disabled py-1">Up</a>
+          {%- endif %}
+          {%- if next %}
+            <a href="{{ next.link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ next.title|striptags }}">Next</a>
+          {%- else %}
+            <a href="#" role="button" class="btn sk-btn-rellink py-1 disabled"">Next</a>
+          {%- endif %}
+        </div>
+        {%- if pagename != "install" %}
+        <div class="alert alert-warning p-1 mb-2" role="alert">
+          <p class="text-center mb-0">
+          <strong>LangChain {{ release }}</strong><br/>
+          </p>
+        </div>
+        {%- endif %}
+            {%- if meta and meta['parenttoc']|tobool %}
+            <div class="sk-sidebar-toc">
+            {% set nav = get_nav_object(maxdepth=3, collapse=True, numbered=True) %}
+              <ul>
+              {% for main_nav_item in nav %}
+              {% if main_nav_item.active %}
+              <li>
+                <a href="{{ main_nav_item.url }}" class="sk-toc-active">{{ main_nav_item.title }}</a>
+              </li>
+              <ul>
+              {% for nav_item in main_nav_item.children %}
+                <li>
+                  <a href="{{ nav_item.url }}" class="{% if nav_item.active %}sk-toc-active{% endif %}">{{ nav_item.title }}</a>
+                  {% if nav_item.children %}
+                  <ul>
+                    {% for inner_child in nav_item.children %}
+                      <li class="sk-toctree-l3">
+                        <a href="{{ inner_child.url }}">{{ inner_child.title }}</a>
+                      </li>
+                    {% endfor %}
+                  </ul>
+                  {% endif %}
+                </li>
+              {% endfor %}
+              </ul>
+              {% endif %}
+              {% endfor %}
+              </ul>
+            </div>
+            {%- elif meta and meta['globalsidebartoc']|tobool %}
+            <div class="sk-sidebar-toc sk-sidebar-global-toc">
+              {{ toctree(maxdepth=2, titles_only=True) }}
+            </div>
+            {%- else %}
+            <div class="sk-sidebar-toc">
+              {{ toc }}
+            </div>
+            {%- endif %}
+      </div>
+    </div>
+    <div id="sk-page-content-wrapper">
+      <div class="sk-page-content container-fluid body px-md-3" role="main">
+        {% block body %}{% endblock %}
+      </div>
+    <div class="container">
+      <footer class="sk-content-footer">
+        {%- if pagename != 'index' %}
+        {%- if show_copyright %}
+          {%- if hasdoc('copyright') %}
+            {% trans path=pathto('copyright'), copyright=copyright|e %}&copy; {{ copyright }}.{% endtrans %}
+          {%- else %}
+            {% trans copyright=copyright|e %}&copy; {{ copyright }}.{% endtrans %}
+          {%- endif %}
+        {%- endif %}
+        {%- if last_updated %}
+          {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %}
+        {%- endif %}
+        {%- if show_source and has_source and sourcename %}
+          <a href="{{ pathto('_sources/' + sourcename, true)|e }}" rel="nofollow">{{ _('Show this page source') }}</a>
+        {%- endif %}
+        {%- endif %}
+      </footer>
+    </div>
+  </div>
+</div>
+{%- endblock %}
+<script src="{{ pathto('_static/js/vendor/bootstrap.min.js', 1) }}"></script>
+{% include "javascript.html" %}
+</body>
+</html>

docs/api_reference/themes/scikit-learn-modern/nav.html

@@ -0,0 +1,85 @@
+{%- if pagename != 'index' and pagename != 'documentation' %}
+  {%- set nav_bar_class = "sk-docs-navbar" %}
+  {%- set top_container_cls = "sk-docs-container" %}
+{%- else %}
+  {%- set nav_bar_class = "sk-landing-navbar" %}
+  {%- set top_container_cls = "sk-landing-container" %}
+{%- endif %}
+
+{% if theme_link_to_live_contributing_page|tobool %}
+{# Link to development page for live builds #}
+  {%- set development_link = "https://scikit-learn.org/dev/developers/index.html" %}
+{# Open on a new development page in new window/tab for live builds #}
+  {%- set development_attrs = 'target="_blank" rel="noopener noreferrer"' %}
+{%- else %}
+  {%- set development_link = pathto('developers/index') %}
+  {%- set development_attrs = '' %}
+{%- endif %}
+
+{# title, link, link_attrs #}
+{%- set drop_down_navigation = [
+  ('Getting Started', pathto('getting_started'), ''),
+  ('Tutorial', pathto('tutorial/index'), ''),
+  ("What's new", pathto('whats_new/v' + version), ''),
+  ('Glossary', pathto('glossary'), ''),
+  ('Development', development_link, development_attrs),
+  ('FAQ', pathto('faq'), ''),
+  ('Support', pathto('support'), ''),
+  ('Related packages', pathto('related_projects'), ''),
+  ('Roadmap', pathto('roadmap'), ''),
+  ('Governance', pathto('governance'), ''),
+  ('About us', pathto('about'), ''),
+  ('GitHub', 'https://github.com/scikit-learn/scikit-learn', ''),
+  ('Other Versions and Download', 'https://scikit-learn.org/dev/versions.html', '')]
+-%}
+
+<nav id="navbar" class="{{ nav_bar_class }} navbar navbar-expand-md navbar-light bg-light py-0">
+  <div class="container-fluid {{ top_container_cls }} px-0">
+    {%- if logo_url %}
+      <a class="navbar-brand py-0" href="{{ pathto('index') }}">
+        <img
+          class="sk-brand-img"
+          src="{{ logo_url|e }}"
+          alt="logo"/>
+      </a>
+    {%- endif %}
+    <button
+      id="sk-navbar-toggler"
+      class="navbar-toggler"
+      type="button"
+      data-toggle="collapse"
+      data-target="#navbarSupportedContent"
+      aria-controls="navbarSupportedContent"
+      aria-expanded="false"
+      aria-label="Toggle navigation"
+    >
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
+      <ul class="navbar-nav mr-auto">
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" href="{{ pathto('api_reference') }}">API</a>
+        </li>
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" target="_blank" rel="noopener noreferrer" href="https://python.langchain.com/">Python Docs</a>
+        </li>
+        {%- for title, link, link_attrs in drop_down_navigation %}
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="{{ link }}" {{ link_attrs }}>{{ title }}</a>
+        </li>
+        {%- endfor %}
+      </ul>
+      {%- if pagename != "search"%}
+      <div id="searchbox" role="search">
+          <div class="searchformwrapper">
+          <form class="search" action="{{ pathto('search') }}" method="get">
+            <input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
+            <input class="sk-search-text-btn" type="submit" value="{{ _('Go') }}" />
+          </form>
+          </div>
+      </div>
+      {%- endif %}
+    </div>
+  </div>
+</nav>

docs/api_reference/themes/scikit-learn-modern/search.html

@@ -0,0 +1,16 @@
+{%- extends "basic/search.html" %}
+{% block extrahead %}
+  <script type="text/javascript" src="{{ pathto('_static/underscore.js', 1) }}"></script>
+  <script type="text/javascript" src="{{ pathto('searchindex.js', 1) }}" defer></script>
+  <script type="text/javascript" src="{{ pathto('_static/doctools.js', 1) }}"></script>
+  <script type="text/javascript" src="{{ pathto('_static/language_data.js', 1) }}"></script>
+  <script type="text/javascript" src="{{ pathto('_static/searchtools.js', 1) }}"></script>
+  <!-- <script type="text/javascript" src="{{ pathto('_static/sphinx_highlight.js', 1) }}"></script> -->
+  <script type="text/javascript">
+    $(document).ready(function() {
+      if (!Search.out) {
+        Search.init();
+      }
+    });
+  </script>
+{% endblock %}

docs/api_reference/themes/scikit-learn-modern/theme.conf

@@ -0,0 +1,8 @@
+[theme]
+inherit = basic
+pygments_style = default
+stylesheet = css/theme.css
+
+[options]
+link_to_live_contributing_page = false
+mathjax_path =

docs/docs_skeleton/docs/get_started/quickstart.mdx

@@ -47,7 +47,7 @@ import ChatModel from "@snippets/get_started/quickstart/chat_model.mdx"
 
 ## Prompt templates
 
-Most LLM applications do not pass user input directly into to an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.
+Most LLM applications do not pass user input directly into an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.
 
 In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.
 
@@ -138,7 +138,7 @@ The chains and agents we've looked at so far have been stateless, but for many a
 
 The Memory module gives you a way to maintain application state. The base Memory interface is simple: it lets you update state given the latest run inputs and outputs and it lets you modify (or contextualize) the next input using the stored state.
 
-There are a number of built-in memory systems. The simplest of these are is a buffer memory which just prepends the last few inputs/outputs to the current input - we will use this in the example below.
+There are a number of built-in memory systems. The simplest of these is a buffer memory which just prepends the last few inputs/outputs to the current input - we will use this in the example below.
 
 import MemoryLLM from "@snippets/get_started/quickstart/memory_llms.mdx"
 import MemoryChatModel from "@snippets/get_started/quickstart/memory_chat_models.mdx"
@@ -155,4 +155,4 @@ You can use Memory with chains and agents initialized with chat models. The main
 <MemoryChatModel/>
 
 </TabItem>
-</Tabs>
+</Tabs>

docs/docs_skeleton/docs/modules/data_connection/document_loaders/how_to/json.mdx

@@ -2,6 +2,8 @@
 
 >[JSON (JavaScript Object Notation)](https://en.wikipedia.org/wiki/JSON) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs and arrays (or other serializable values).
 
+>[JSON Lines](https://jsonlines.org/) is a file format where each line is a valid JSON value.
+
 import Example from "@snippets/modules/data_connection/document_loaders/how_to/json.mdx"
 
 <Example/>

docs/docs_skeleton/docs/modules/data_connection/vectorstores/index.mdx

@@ -10,7 +10,7 @@ for you.
 
 ## Get started
 
-This walkthrough showcases basic functionality related to VectorStores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model](/docs/modules/model_io/models/embeddings.html) interfaces before diving into this.
+This walkthrough showcases basic functionality related to VectorStores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model](/docs/modules/data_connection/text_embedding/) interfaces before diving into this.
 
 import GetStarted from "@snippets/modules/data_connection/vectorstores/get_started.mdx"
 

docs/docs_skeleton/docusaurus.config.js

@@ -7,7 +7,10 @@ const { ProvidePlugin } = require("webpack");
 const path = require("path");
 
 const examplesPath = path.resolve(__dirname, "..", "examples", "src");
-const snippetsPath = path.resolve(__dirname, "..", "snippets")
+const snippetsPath = path.resolve(__dirname, "..", "snippets");
+
+const baseLightCodeBlockTheme = require("prism-react-renderer/themes/vsLight");
+const baseDarkCodeBlockTheme = require("prism-react-renderer/themes/vsDark");
 
 /** @type {import('@docusaurus/types').Config} */
 const config = {
@@ -84,7 +87,6 @@ const config = {
       ({
         docs: {
           sidebarPath: require.resolve("./sidebars.js"),
-          editUrl: "https://github.com/hwchase17/langchain/edit/master/docs/",
           remarkPlugins: [
             [require("@docusaurus/remark-plugin-npm2yarn"), { sync: true }],
           ],
@@ -127,8 +129,20 @@ const config = {
         },
       },
       prism: {
-        theme: require("prism-react-renderer/themes/vsLight"),
-        darkTheme: require("prism-react-renderer/themes/vsDark"),
+        theme: {
+          ...baseLightCodeBlockTheme,
+          plain: {
+            ...baseLightCodeBlockTheme.plain,
+            backgroundColor: "#F5F5F5",
+          },
+        },
+        darkTheme: {
+          ...baseDarkCodeBlockTheme,
+          plain: {
+            ...baseDarkCodeBlockTheme.plain,
+            backgroundColor: "#222222",
+          },
+        },
       },
       image: "img/parrot-chainlink-icon.png",
       navbar: {

docs/docs_skeleton/package.json

@@ -23,7 +23,7 @@
     "@docusaurus/preset-classic": "2.4.0",
     "@docusaurus/remark-plugin-npm2yarn": "^2.4.0",
     "@mdx-js/react": "^1.6.22",
-    "@mendable/search": "^0.0.102",
+    "@mendable/search": "^0.0.112-beta.7",
     "clsx": "^1.2.1",
     "json-loader": "^0.5.7",
     "process": "^0.11.10",

docs/extras/ecosystem/integrations/airtable.md

@@ -0,0 +1,28 @@
+# Airtable
+
+>[Airtable](https://en.wikipedia.org/wiki/Airtable) is a cloud collaboration service.
+`Airtable` is a spreadsheet-database hybrid, with the features of a database but applied to a spreadsheet. 
+> The fields in an Airtable table are similar to cells in a spreadsheet, but have types such as 'checkbox', 
+> 'phone number', and 'drop-down list', and can reference file attachments like images.
+
+>Users can create a database, set up column types, add records, link tables to one another, collaborate, sort records
+> and publish views to external websites.
+
+## Installation and Setup
+
+```bash
+pip install pyairtable
+```
+
+* Get your [API key](https://support.airtable.com/docs/creating-and-using-api-keys-and-access-tokens).
+* Get the [ID of your base](https://airtable.com/developers/web/api/introduction).
+* Get the [table ID from the table url](https://www.highviewapps.com/kb/where-can-i-find-the-airtable-base-id-and-table-id/#:~:text=Both%20the%20Airtable%20Base%20ID,URL%20that%20begins%20with%20tbl).
+
+## Document Loader
+
+
+```python
+from langchain.document_loaders import AirtableLoader
+```
+
+See an [example](/docs/modules/data_connection/document_loaders/integrations/airtable.html).

docs/extras/ecosystem/integrations/arthur_tracking.ipynb

@@ -0,0 +1,464 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "944e4194",
+   "metadata": {},
+   "source": [
+    "# Arthur LangChain integration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1ccdfe8",
+   "metadata": {},
+   "source": [
+    "[Arthur](https://www.arthur.ai/) is a model monitoring and observability platform.\n",
+    "\n",
+    "This notebook shows how to register LLMs (chat and non-chat) as models with the Arthur platform. Then we show how to set up langchain LLMs with an Arthur callback that will automatically log model inferences to Arthur.\n",
+    "\n",
+    "For more information about how to use the Arthur SDK, visit our [docs](http://docs.arthur.ai), in particular our [model onboarding guide](https://docs.arthur.ai/user-guide/walkthroughs/model-onboarding/index.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "961c6691",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks import ArthurCallbackHandler\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.chat_models import ChatOpenAI, ChatAnthropic\n",
+    "from langchain.schema import HumanMessage\n",
+    "from langchain.llms import OpenAI, Cohere, HuggingFacePipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a23d1963",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from arthurai import ArthurAI\n",
+    "from arthurai.common.constants import InputType, OutputType, Stage, ValueType\n",
+    "from arthurai.core.attributes import ArthurAttribute, AttributeCategory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d1b90c0",
+   "metadata": {},
+   "source": [
+    "# ArthurModel for chatbot with only input text and output text attributes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a4a4a8a",
+   "metadata": {},
+   "source": [
+    "Connect to Arthur client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f49e9b79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arthur_url = \"https://app.arthur.ai\"\n",
+    "arthur_login = \"your-username-here\"\n",
+    "arthur = ArthurAI(url=arthur_url, login=arthur_login)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6e063bf",
+   "metadata": {},
+   "source": [
+    "Before you can register model inferences to Arthur, you must have a registered model with an ID in the Arthur platform. We will provide this ID to the ArthurCallbackHandler.\n",
+    "\n",
+    "You can register a model with Arthur here in the notebook using this `register_chat_llm()` function. This function returns the ID of the model saved to the platform. To use the function, uncomment `arthur_model_chatbot_id = register_chat_llm()` in the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "31b17b5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def register_chat_llm():\n",
+    "\n",
+    "    arthur_model = arthur.model(\n",
+    "        display_name=\"LangChainChat\",\n",
+    "        input_type=InputType.NLP,\n",
+    "        output_type=OutputType.TokenSequence\n",
+    "    )\n",
+    "\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"my_input_text\",\n",
+    "        stage=Stage.ModelPipelineInput,\n",
+    "        value_type=ValueType.Unstructured_Text,\n",
+    "        categorical=True,\n",
+    "        is_unique=True\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"my_output_text\",\n",
+    "        stage=Stage.PredictedValue,\n",
+    "        value_type=ValueType.Unstructured_Text,\n",
+    "        categorical=True,\n",
+    "        is_unique=False,\n",
+    "    ))\n",
+    "    \n",
+    "    return arthur_model.save()\n",
+    "# arthur_model_chatbot_id = register_chat_llm()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d1d1e60",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can set the `arthur_model_chatbot_id` variable to be the ID of your model on your [model dashboard](https://app.arthur.ai/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cdfa02c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arthur_model_chatbot_id = \"your-model-id-here\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58be5234",
+   "metadata": {},
+   "source": [
+    "This function creates a Langchain chat LLM with the ArthurCallbackHandler to log inferences to Arthur. We provide our `arthur_model_chatbot_id`, as well as the Arthur url and login we are using."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "448a8fee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_langchain_chat_llm(chat_model=ChatOpenAI):\n",
+    "    if chat_model not in [ChatOpenAI, ChatAnthropic]:\n",
+    "        raise ValueError(\"For this notebook, use one of the chat models imported from langchain.chat_models\")\n",
+    "    return chat_model(\n",
+    "        streaming=True, \n",
+    "        temperature=0.1,\n",
+    "        callbacks=[\n",
+    "            StreamingStdOutCallbackHandler(), \n",
+    "            ArthurCallbackHandler.from_credentials(arthur_model_chatbot_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
+    "        ])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17c182da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2dfc00ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat_llm = make_langchain_chat_llm()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "139291f2",
+   "metadata": {},
+   "source": [
+    "Run the chatbot (it will save the chat history in the `history` list so that the conversation can reference earlier messages)\n",
+    "\n",
+    "Type `q` to quit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7480a443",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_langchain_chat_llm(llm):\n",
+    "    history = []\n",
+    "    while True:\n",
+    "        user_input = input(\"\\n>>> input >>>\\n>>>: \")\n",
+    "        if user_input == 'q': break\n",
+    "        history.append(HumanMessage(content=user_input))\n",
+    "        history.append(llm(history))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6868ce71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_langchain_chat_llm(chat_llm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0be7d01",
+   "metadata": {},
+   "source": [
+    "# ArthurModel with input text, output text, token likelihoods, finish reason, and amount of token usage attributes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ee4b741",
+   "metadata": {},
+   "source": [
+    "This function registers an LLM with additional metadata attributes to log to Arthur with each inference\n",
+    "\n",
+    "As above, you can register your callback handler for an LLM using this function here in the notebook or by pasting the ID of an already-registered model from your [model dashboard](https://app.arthur.ai/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e671836c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def register_llm():\n",
+    "\n",
+    "    arthur_model = arthur.model(\n",
+    "        display_name=\"LangChainLLM\",\n",
+    "        input_type=InputType.NLP,\n",
+    "        output_type=OutputType.TokenSequence\n",
+    "    )\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"my_input_text\",\n",
+    "        stage=Stage.ModelPipelineInput,\n",
+    "        value_type=ValueType.Unstructured_Text,\n",
+    "        categorical=True,\n",
+    "        is_unique=True\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"my_output_text\",\n",
+    "        stage=Stage.PredictedValue,\n",
+    "        value_type=ValueType.Unstructured_Text,\n",
+    "        categorical=True,\n",
+    "        is_unique=False,\n",
+    "        token_attribute_link=\"my_output_likelihoods\"\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"my_output_likelihoods\",\n",
+    "        stage=Stage.PredictedValue,\n",
+    "        value_type=ValueType.TokenLikelihoods,\n",
+    "        token_attribute_link=\"my_output_text\"\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"finish_reason\",\n",
+    "        stage=Stage.NonInputData,\n",
+    "        value_type=ValueType.String,\n",
+    "        categorical=True,\n",
+    "        categories=[\n",
+    "            AttributeCategory(value='stop'),\n",
+    "            AttributeCategory(value='length'),\n",
+    "            AttributeCategory(value='content_filter'),\n",
+    "            AttributeCategory(value='null')\n",
+    "        ]\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"prompt_tokens\",\n",
+    "        stage=Stage.NonInputData,\n",
+    "        value_type=ValueType.Integer\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"completion_tokens\",\n",
+    "        stage=Stage.NonInputData,\n",
+    "        value_type=ValueType.Integer\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"duration\",\n",
+    "        stage=Stage.NonInputData,\n",
+    "        value_type=ValueType.Float\n",
+    "    ))\n",
+    "    \n",
+    "    return arthur_model.save()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2a6686f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arthur_model_llm_id = \"your-model-id-here\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dcacb96",
+   "metadata": {},
+   "source": [
+    "These functions create Langchain LLMs with the ArthurCallbackHandler to log inferences to Arthur.\n",
+    "\n",
+    "There are small differences in the underlying Langchain integrations with these libraries and the available metadata for model inputs & outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "34cf0072",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_langchain_openai_llm():\n",
+    "    return OpenAI(\n",
+    "        temperature=0.1,\n",
+    "        model_kwargs = {'logprobs': 3},\n",
+    "        callbacks=[\n",
+    "            ArthurCallbackHandler.from_credentials(arthur_model_llm_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
+    "        ])\n",
+    "\n",
+    "def make_langchain_cohere_llm():\n",
+    "    return Cohere(\n",
+    "        temperature=0.1,\n",
+    "        callbacks=[\n",
+    "            ArthurCallbackHandler.from_credentials(arthur_model_chatbot_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
+    "        ])\n",
+    "\n",
+    "def make_langchain_huggingface_llm():\n",
+    "    llm = HuggingFacePipeline.from_model_id(\n",
+    "        model_id=\"bert-base-uncased\", \n",
+    "        task=\"text-generation\", \n",
+    "        model_kwargs={\"temperature\":2.5, \"max_length\":64})\n",
+    "    llm.callbacks = [\n",
+    "        ArthurCallbackHandler.from_credentials(arthur_model_chatbot_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
+    "    ]\n",
+    "    return llm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f40c3ce0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai_llm = make_langchain_openai_llm()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8476d531",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cohere_llm = make_langchain_cohere_llm()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7483b9d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "huggingface_llm = make_langchain_huggingface_llm()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c17d8e86",
+   "metadata": {},
+   "source": [
+    "Run the LLM (each completion is independent, no chat history is saved as we were doing above with the chat llms)\n",
+    "\n",
+    "Type `q` to quit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "72ee0790",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_langchain_llm(llm):\n",
+    "    while True:\n",
+    "        print(\"Type your text for completion:\\n\")\n",
+    "        user_input = input(\"\\n>>> input >>>\\n>>>: \")\n",
+    "        if user_input == 'q': break\n",
+    "        print(llm(user_input), \"\\n================\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fb864057",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_langchain_llm(openai_llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e6673769",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_langchain_llm(cohere_llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "85541f1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_langchain_llm(huggingface_llm)"
+   ]
+  }
+ ],
+ "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": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/ecosystem/integrations/brave_search.mdx

@@ -0,0 +1,36 @@
+# Brave Search
+
+
+>[Brave Search](https://en.wikipedia.org/wiki/Brave_Search) is a search engine developed by Brave Software.
+> - `Brave Search` uses its own web index. As of May 2022, it covered over 10 billion pages and was used to serve 92% 
+> of search results without relying on any third-parties, with the remainder being retrieved 
+> server-side from the Bing API or (on an opt-in basis) client-side from Google. According 
+> to Brave, the index was kept "intentionally smaller than that of Google or Bing" in order to 
+> help avoid spam and other low-quality content, with the disadvantage that "Brave Search is 
+> not yet as good as Google in recovering long-tail queries."
+>- `Brave Search Premium`: As of April 2023 Brave Search is an ad-free website, but it will 
+> eventually switch to a new model that will include ads and premium users will get an ad-free experience.
+> User data including IP addresses won't be collected from its users by default. A premium account 
+> will be required for opt-in data-collection.
+
+
+## Installation and Setup
+
+To get access to the Brave Search API, you need to [create an account and get an API key](https://api.search.brave.com/app/dashboard).
+
+
+## Document Loader
+
+See a [usage example](/docs/modules/data_connection/document_loaders/integrations/brave_search.html).
+
+```python
+from langchain.document_loaders import BraveSearchLoader
+```
+
+## Tool
+
+See a [usage example](/docs/modules/agents/tools/integrations/brave_search.html).
+
+```python
+from langchain.tools import BraveSearch
+```

docs/extras/ecosystem/integrations/cassandra.mdx

@@ -1,19 +1,31 @@
 # Cassandra
 
->[Cassandra](https://en.wikipedia.org/wiki/Apache_Cassandra) is a free and open-source, distributed, wide-column 
+>[Apache Cassandra®](https://cassandra.apache.org/) is a free and open-source, distributed, wide-column
 > store, NoSQL database management system designed to handle large amounts of data across many commodity servers, 
-> providing high availability with no single point of failure. `Cassandra` offers support for clusters spanning 
+> providing high availability with no single point of failure. Cassandra offers support for clusters spanning
 > multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients. 
-> `Cassandra` was designed to implement a combination of `Amazon's Dynamo` distributed storage and replication 
-> techniques combined with `Google's Bigtable` data and storage engine model.
+> Cassandra was designed to implement a combination of _Amazon's Dynamo_ distributed storage and replication
+> techniques combined with _Google's Bigtable_ data and storage engine model.
  
 ## Installation and Setup
 
 ```bash
-pip install cassandra-drive
+pip install cassandra-driver
+pip install cassio
 ```
 
 
+
+## Vector Store
+
+See a [usage example](/docs/modules/data_connection/vectorstores/integrations/cassandra.html).
+
+```python
+from langchain.memory import CassandraChatMessageHistory
+```
+
+
+
 ## Memory
 
 See a [usage example](/docs/modules/memory/integrations/cassandra_chat_message_history.html).

docs/extras/ecosystem/integrations/flyte.mdx

@@ -0,0 +1,153 @@
+# Flyte
+
+> [Flyte](https://github.com/flyteorg/flyte) is an open-source orchestrator that facilitates building production-grade data and ML pipelines.
+> It is built for scalability and reproducibility, leveraging Kubernetes as its underlying platform.
+
+The purpose of this notebook is to demonstrate the integration of a `FlyteCallback` into your Flyte task, enabling you to effectively monitor and track your LangChain experiments.
+
+## Installation & Setup
+
+- Install the Flytekit library by running the command `pip install flytekit`.
+- Install the Flytekit-Envd plugin by running the command `pip install flytekitplugins-envd`.
+- Install LangChain by running the command `pip install langchain`.
+- Install [Docker](https://docs.docker.com/engine/install/) on your system.
+
+## Flyte Tasks
+
+A Flyte [task](https://docs.flyte.org/projects/cookbook/en/latest/auto/core/flyte_basics/task.html) serves as the foundational building block of Flyte.
+To execute LangChain experiments, you need to write Flyte tasks that define the specific steps and operations involved.
+
+NOTE: The [getting started guide](https://docs.flyte.org/projects/cookbook/en/latest/index.html) offers detailed, step-by-step instructions on installing Flyte locally and running your initial Flyte pipeline.
+
+First, import the necessary dependencies to support your LangChain experiments.
+
+```python
+import os
+
+from flytekit import ImageSpec, task
+from langchain.agents import AgentType, initialize_agent, load_tools
+from langchain.callbacks import FlyteCallbackHandler
+from langchain.chains import LLMChain
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from langchain.schema import HumanMessage
+```
+
+Set up the necessary environment variables to utilize the OpenAI API and Serp API:
+
+```python
+# Set OpenAI API key
+os.environ["OPENAI_API_KEY"] = "<your_openai_api_key>"
+
+# Set Serp API key
+os.environ["SERPAPI_API_KEY"] = "<your_serp_api_key>"
+```
+
+Replace `<your_openai_api_key>` and `<your_serp_api_key>` with your respective API keys obtained from OpenAI and Serp API.
+
+To guarantee reproducibility of your pipelines, Flyte tasks are containerized.
+Each Flyte task must be associated with an image, which can either be shared across the entire Flyte [workflow](https://docs.flyte.org/projects/cookbook/en/latest/auto/core/flyte_basics/basic_workflow.html) or provided separately for each task.
+
+To streamline the process of supplying the required dependencies for each Flyte task, you can initialize an [`ImageSpec`](https://docs.flyte.org/projects/cookbook/en/latest/auto/core/image_spec/image_spec.html) object.
+This approach automatically triggers a Docker build, alleviating the need for users to manually create a Docker image.
+
+```python
+custom_image = ImageSpec(
+    name="langchain-flyte",
+    packages=[
+        "langchain",
+        "openai",
+        "spacy",
+        "https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0.tar.gz",
+        "textstat",
+        "google-search-results",
+    ],
+    registry="<your-registry>",
+)
+```
+
+You have the flexibility to push the Docker image to a registry of your preference.
+[Docker Hub](https://hub.docker.com/) or [GitHub Container Registry (GHCR)](https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry) is a convenient option to begin with.
+
+Once you have selected a registry, you can proceed to create Flyte tasks that log the LangChain metrics to Flyte Deck.
+
+The following examples demonstrate tasks related to OpenAI LLM, chains and agent with tools:
+
+### LLM
+
+```python
+@task(disable_deck=False, container_image=custom_image)
+def langchain_llm() -> str:
+    llm = ChatOpenAI(
+        model_name="gpt-3.5-turbo",
+        temperature=0.2,
+        callbacks=[FlyteCallbackHandler()],
+    )
+    return llm([HumanMessage(content="Tell me a joke")]).content
+```
+
+### Chain
+
+```python
+@task(disable_deck=False, container_image=custom_image)
+def langchain_chain() -> list[dict[str, str]]:
+    template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
+Title: {title}
+Playwright: This is a synopsis for the above play:"""
+    llm = ChatOpenAI(
+        model_name="gpt-3.5-turbo",
+        temperature=0,
+        callbacks=[FlyteCallbackHandler()],
+    )
+    prompt_template = PromptTemplate(input_variables=["title"], template=template)
+    synopsis_chain = LLMChain(
+        llm=llm, prompt=prompt_template, callbacks=[FlyteCallbackHandler()]
+    )
+    test_prompts = [
+        {
+            "title": "documentary about good video games that push the boundary of game design"
+        },
+    ]
+    return synopsis_chain.apply(test_prompts)
+```
+
+### Agent
+
+```python
+@task(disable_deck=False, container_image=custom_image)
+def langchain_agent() -> str:
+    llm = OpenAI(
+        model_name="gpt-3.5-turbo",
+        temperature=0,
+        callbacks=[FlyteCallbackHandler()],
+    )
+    tools = load_tools(
+        ["serpapi", "llm-math"], llm=llm, callbacks=[FlyteCallbackHandler()]
+    )
+    agent = initialize_agent(
+        tools,
+        llm,
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        callbacks=[FlyteCallbackHandler()],
+        verbose=True,
+    )
+    return agent.run(
+        "Who is Leonardo DiCaprio's girlfriend? Could you calculate her current age and raise it to the power of 0.43?"
+    )
+```
+
+These tasks serve as a starting point for running your LangChain experiments within Flyte.
+
+## Execute the Flyte Tasks on Kubernetes
+
+To execute the Flyte tasks on the configured Flyte backend, use the following command:
+
+```bash
+pyflyte run --image <your-image> langchain_flyte.py langchain_llm
+```
+
+This command will initiate the execution of the `langchain_llm` task on the Flyte backend. You can trigger the remaining two tasks in a similar manner.
+
+The metrics will be displayed on the Flyte UI as follows:
+
+![LangChain LLM](https://ik.imagekit.io/c8zl7irwkdda/Screenshot_2023-06-20_at_1.23.29_PM_MZYeG0dKa.png?updatedAt=1687247642993)

docs/extras/ecosystem/integrations/grobid.mdx

@@ -0,0 +1,44 @@
+# Grobid
+
+This page covers how to use the Grobid to parse articles for LangChain.
+It is seperated into two parts: installation and running the server
+
+## Installation and Setup
+#Ensure You have Java installed
+!apt-get install -y openjdk-11-jdk -q
+!update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java
+
+#Clone and install the Grobid Repo
+import os
+!git clone https://github.com/kermitt2/grobid.git
+os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-11-openjdk-amd64"
+os.chdir('grobid')
+!./gradlew clean install
+
+#Run the server, 
+get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')
+
+You can now use the GrobidParser to produce documents
+```python
+from langchain.document_loaders.parsers import GrobidParser
+from langchain.document_loaders.generic import GenericLoader
+
+#Produce chunks from article paragraphs
+loader = GenericLoader.from_filesystem(
+    "/Users/31treehaus/Desktop/Papers/",
+    glob="*",
+    suffixes=[".pdf"],
+    parser= GrobidParser(segment_sentences=False)
+)
+docs = loader.load()
+
+#Produce chunks from article sentences
+loader = GenericLoader.from_filesystem(
+    "/Users/31treehaus/Desktop/Papers/",
+    glob="*",
+    suffixes=[".pdf"],
+    parser= GrobidParser(segment_sentences=True)
+)
+docs = loader.load()
+```
+Chunk metadata will include bboxes although these are a bit funky to parse, see https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/

docs/extras/ecosystem/integrations/unstructured.mdx

@@ -23,11 +23,15 @@ its dependencies running locally.
 If you want to get up and running with less set up, you can
 simply run `pip install unstructured` and use `UnstructuredAPIFileLoader` or
 `UnstructuredAPIFileIOLoader`. That will process your document using the hosted Unstructured API.
-Note that currently (as of 1 May 2023) the Unstructured API is open, but it will soon require
-an API. The [Unstructured documentation page](https://unstructured-io.github.io/) will have
-instructions on how to generate an API key once they're available. Check out the instructions
-[here](https://github.com/Unstructured-IO/unstructured-api#dizzy-instructions-for-using-the-docker-image)
-if you'd like to self-host the Unstructured API or run it locally.
+
+
+The Unstructured API requires API keys to make requests.
+You can generate a free API key [here](https://www.unstructured.io/api-key) and start using it today!
+Checkout the README [here](https://github.com/Unstructured-IO/unstructured-api) here to get started making API calls.
+We'd love to hear your feedback, let us know how it goes in our [community slack](https://join.slack.com/t/unstructuredw-kbe4326/shared_invite/zt-1x7cgo0pg-PTptXWylzPQF9xZolzCnwQ).
+And stay tuned for improvements to both quality and performance!
+Check out the instructions
+[here](https://github.com/Unstructured-IO/unstructured-api#dizzy-instructions-for-using-the-docker-image) if you'd like to self-host the Unstructured API or run it locally.
 
 ## Wrappers
 

docs/extras/ecosystem/integrations/vectara/index.mdx

@@ -39,6 +39,21 @@ vectara = Vectara(
 ```
 The customer_id, corpus_id and api_key are optional, and if they are not supplied will be read from the environment variables `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY`, respectively.
 
+Afer you have the vectorstore, you can `add_texts` or `add_documents` as per the standard `VectorStore` interface, for example:
+
+```python
+vectara.add_texts(["to be or not to be", "that is the question"])
+```
+
+
+Since Vectara supports file-upload, we also added the ability to upload files (PDF, TXT, HTML, PPT, DOC, etc) directly as file. When using this method, the file is uploaded directly to the Vectara backend, processed and chunked optimally there, so you don't have to use the LangChain document loader or chunking mechanism.
+
+As an example:
+
+```python
+vectara.add_files(["path/to/file1.pdf", "path/to/file2.pdf",...])
+```
+
 To query the vectorstore, you can use the `similarity_search` method (or `similarity_search_with_score`), which takes a query string and returns a list of results:
 ```python
 results = vectara.similarity_score("what is LangChain?")

docs/extras/guides/evaluation/comparisons.ipynb

@@ -243,8 +243,8 @@
     "            pred_a, pred_b = res_b, res_a\n",
     "            a, b = \"b\", \"a\"\n",
     "        eval_res = eval_chain.evaluate_string_pairs(\n",
-    "            output_a=pred_a['output'] if isinstance(pred_a, dict) else str(pred_a),\n",
-    "            output_b=pred_b['output'] if isinstance(pred_b, dict) else str(pred_b),\n",
+    "            prediction=pred_a['output'] if isinstance(pred_a, dict) else str(pred_a),\n",
+    "            prediction_b=pred_b['output'] if isinstance(pred_b, dict) else str(pred_b),\n",
     "            input=input_\n",
     "        )\n",
     "        if eval_res[\"value\"] == \"A\":\n",

docs/extras/guides/evaluation/openapi_eval.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Evaluating an OpenAPI Chain\n",
     "\n",
-    "This notebook goes over ways to semantically evaluate an [OpenAPI Chain](/docs/modules/chains/additiona/openapi.html), which calls an endpoint defined by the OpenAPI specification using purely natural language."
+    "This notebook goes over ways to semantically evaluate an [OpenAPI Chain](/docs/modules/chains/additional/openapi.html), which calls an endpoint defined by the OpenAPI specification using purely natural language."
    ]
   },
   {

docs/extras/modules/agents/how_to/custom-functions-with-openai-functions-agent.ipynb

@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g9EmNu5DD9YI"
+   },
+   "source": [
+    "# Custom functions with OpenAI Functions Agent\n",
+    "\n",
+    "This notebook goes through how to integrate custom functions with OpenAI Functions agent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LFKylC3CPtTl"
+   },
+   "source": [
+    "Install libraries which are required to run this example notebook\n",
+    "\n",
+    "`pip install -q openai langchain yfinance`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E2DqzmEGDPak"
+   },
+   "source": [
+    "## Define custom functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "SiucthMs6SIK"
+   },
+   "outputs": [],
+   "source": [
+    "import yfinance as yf\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "def get_current_stock_price(ticker):\n",
+    "    \"\"\"Method to get current stock price\"\"\"\n",
+    "\n",
+    "    ticker_data = yf.Ticker(ticker)\n",
+    "    recent = ticker_data.history(period='1d')\n",
+    "    return {\n",
+    "        'price': recent.iloc[0]['Close'],\n",
+    "        'currency': ticker_data.info['currency']\n",
+    "    }\n",
+    "\n",
+    "def get_stock_performance(ticker, days):\n",
+    "    \"\"\"Method to get stock price change in percentage\"\"\"\n",
+    "\n",
+    "    past_date = datetime.today() - timedelta(days=days)\n",
+    "    ticker_data = yf.Ticker(ticker)\n",
+    "    history = ticker_data.history(start=past_date)\n",
+    "    old_price = history.iloc[0]['Close']\n",
+    "    current_price = history.iloc[-1]['Close']\n",
+    "    return {\n",
+    "        'percent_change': ((current_price - old_price)/old_price)*100\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vRLINGvQR1rO",
+    "outputId": "68230a4b-dda2-4273-b956-7439661e3785"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'price': 334.57000732421875, 'currency': 'USD'}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_current_stock_price('MSFT')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "57T190q235mD",
+    "outputId": "c6ee66ec-0659-4632-85d1-263b08826e68"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'percent_change': 1.014466941163018}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_stock_performance('MSFT', 30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MT8QsdyBDhwg"
+   },
+   "source": [
+    "## Make custom tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "NvLOUv-XP3Ap"
+   },
+   "outputs": [],
+   "source": [
+    "from typing import Type\n",
+    "from pydantic import BaseModel, Field\n",
+    "from langchain.tools import BaseTool\n",
+    "\n",
+    "class CurrentStockPriceInput(BaseModel):\n",
+    "    \"\"\"Inputs for get_current_stock_price\"\"\"\n",
+    "    ticker: str = Field(description=\"Ticker symbol of the stock\")\n",
+    "\n",
+    "class CurrentStockPriceTool(BaseTool):\n",
+    "    name = \"get_current_stock_price\"\n",
+    "    description = \"\"\"\n",
+    "        Useful when you want to get current stock price.\n",
+    "        You should enter the stock ticker symbol recognized by the yahoo finance\n",
+    "        \"\"\"\n",
+    "    args_schema: Type[BaseModel] = CurrentStockPriceInput\n",
+    "\n",
+    "    def _run(self, ticker: str):\n",
+    "        price_response = get_current_stock_price(ticker)\n",
+    "        return price_response\n",
+    "\n",
+    "    def _arun(self, ticker: str):\n",
+    "        raise NotImplementedError(\"get_current_stock_price does not support async\")\n",
+    "\n",
+    "\n",
+    "class StockPercentChangeInput(BaseModel):\n",
+    "    \"\"\"Inputs for get_stock_performance\"\"\"\n",
+    "    ticker: str = Field(description=\"Ticker symbol of the stock\")\n",
+    "    days: int = Field(description='Timedelta days to get past date from current date')\n",
+    "\n",
+    "class StockPerformanceTool(BaseTool):\n",
+    "    name = \"get_stock_performance\"\n",
+    "    description = \"\"\"\n",
+    "        Useful when you want to check performance of the stock.\n",
+    "        You should enter the stock ticker symbol recognized by the yahoo finance.\n",
+    "        You should enter days as number of days from today from which performance needs to be check.\n",
+    "        output will be the change in the stock price represented as a percentage.\n",
+    "        \"\"\"\n",
+    "    args_schema: Type[BaseModel] = StockPercentChangeInput\n",
+    "\n",
+    "    def _run(self, ticker: str, days: int):\n",
+    "        response = get_stock_performance(ticker, days)\n",
+    "        return response\n",
+    "\n",
+    "    def _arun(self, ticker: str):\n",
+    "        raise NotImplementedError(\"get_stock_performance does not support async\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PVKoqeCyFKHF"
+   },
+   "source": [
+    "## Create Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "yY7qNB7vSQGh"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.agents import AgentType\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.agents import initialize_agent\n",
+    "\n",
+    "llm = ChatOpenAI(\n",
+    "    model=\"gpt-3.5-turbo-0613\",\n",
+    "    temperature=0\n",
+    ")\n",
+    "\n",
+    "tools = [\n",
+    "    CurrentStockPriceTool(),\n",
+    "    StockPerformanceTool()\n",
+    "]\n",
+    "\n",
+    "agent = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 321
+    },
+    "id": "4X96xmgwRkcC",
+    "outputId": "a91b13ef-9643-4f60-d067-c4341e0b285e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "Invoking: `get_current_stock_price` with `{'ticker': 'MSFT'}`\n",
+      "\n",
+      "\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3m{'price': 334.57000732421875, 'currency': 'USD'}\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "Invoking: `get_stock_performance` with `{'ticker': 'MSFT', 'days': 180}`\n",
+      "\n",
+      "\n",
+      "\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 40.163963297187905}\u001b[0m\u001b[32;1m\u001b[1;3mThe current price of Microsoft stock is $334.57 USD. \n",
+      "\n",
+      "Over the past 6 months, Microsoft stock has performed well with a 40.16% increase in its price.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The current price of Microsoft stock is $334.57 USD. \\n\\nOver the past 6 months, Microsoft stock has performed well with a 40.16% increase in its price.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"What is the current price of Microsoft stock? How it has performed over past 6 months?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 285
+    },
+    "id": "nkZ_vmAcT7Al",
+    "outputId": "092ebc55-4d28-4a4b-aa2a-98ae47ceec20"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "Invoking: `get_current_stock_price` with `{'ticker': 'GOOGL'}`\n",
+      "\n",
+      "\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3m{'price': 118.33000183105469, 'currency': 'USD'}\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "Invoking: `get_current_stock_price` with `{'ticker': 'META'}`\n",
+      "\n",
+      "\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3m{'price': 287.04998779296875, 'currency': 'USD'}\u001b[0m\u001b[32;1m\u001b[1;3mThe recent stock price of Google (GOOGL) is $118.33 USD and the recent stock price of Meta (META) is $287.05 USD.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The recent stock price of Google (GOOGL) is $118.33 USD and the recent stock price of Meta (META) is $287.05 USD.'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"Give me recent stock prices of Google and Meta?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 466
+    },
+    "id": "jLU-HjMq7n1o",
+    "outputId": "a42194dd-26ed-4b5a-d4a2-1038420045c4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m\n",
+      "Invoking: `get_stock_performance` with `{'ticker': 'MSFT', 'days': 90}`\n",
+      "\n",
+      "\n",
+      "\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 18.043096235165596}\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "Invoking: `get_stock_performance` with `{'ticker': 'GOOGL', 'days': 90}`\n",
+      "\n",
+      "\n",
+      "\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 17.286155760642853}\u001b[0m\u001b[32;1m\u001b[1;3mIn the past 3 months, Microsoft (MSFT) has performed better than Google (GOOGL). Microsoft's stock price has increased by 18.04% while Google's stock price has increased by 17.29%.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"In the past 3 months, Microsoft (MSFT) has performed better than Google (GOOGL). Microsoft's stock price has increased by 18.04% while Google's stock price has increased by 17.29%.\""
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run('In the past 3 months, which stock between Microsoft and Google has performed the best?')"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/extras/modules/agents/tools/integrations/brave_search.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "a4c896e5",
    "metadata": {},
    "outputs": [],
@@ -27,7 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "api_key = \"...\""
+    "api_key = \"BSAv1neIuQOsxqOyy0sEe_ie2zD_n_V\""
    ]
   },
   {
@@ -49,7 +49,7 @@
     {
      "data": {
       "text/plain": [
-       "'[{\"title\": \"Barack Obama - Wikipedia\", \"link\": \"https://en.wikipedia.org/wiki/Barack_Obama\", \"snippet\": \"Outside of politics, <strong>Obama</strong> has published three bestselling books: Dreams from My Father (1995), The Audacity of Hope (2006) and A Promised Land (2020). Rankings by scholars and historians, in which he has been featured since 2010, place him in the <strong>middle</strong> to upper tier of American presidents.\"}, {\"title\": \"Obama\\'s Middle Name -- My Last Name -- is \\'Hussein.\\' So?\", \"link\": \"https://www.cair.com/cair_in_the_news/obamas-middle-name-my-last-name-is-hussein-so/\", \"snippet\": \"Many Americans understand that common names don\\\\u2019t only come in the form of a \\\\u201cSmith\\\\u201d or a \\\\u201cJohnson.\\\\u201d Perhaps, they have a neighbor, mechanic or teacher named Hussein. Or maybe they\\\\u2019ve seen fashion designer Hussein Chalayan in the pages of Vogue or recall <strong>King Hussein</strong>, our ally in the Middle East.\"}, {\"title\": \"What\\'s up with Obama\\'s middle name? - Quora\", \"link\": \"https://www.quora.com/Whats-up-with-Obamas-middle-name\", \"snippet\": \"Answer (1 of 15): A better question would be, \\\\u201cWhat\\\\u2019s up with Obama\\\\u2019s first name?\\\\u201d President <strong>Barack Hussein Obama</strong>\\\\u2019s father\\\\u2019s name was <strong>Barack Hussein Obama</strong>. He was named after his father. Hussein, Obama\\\\u2019s middle name, is a very common Arabic name, meaning &quot;good,&quot; &quot;handsome,&quot; or &quot;beautiful.&quot;\"}]'"
+       "'[{\"title\": \"Obama\\'s Middle Name -- My Last Name -- is \\'Hussein.\\' So?\", \"link\": \"https://www.cair.com/cair_in_the_news/obamas-middle-name-my-last-name-is-hussein-so/\", \"snippet\": \"I wasn\\\\u2019t sure whether to laugh or cry a few days back listening to radio talk show host Bill Cunningham repeatedly scream Barack <strong>Obama</strong>\\\\u2019<strong>s</strong> <strong>middle</strong> <strong>name</strong> \\\\u2014 my last <strong>name</strong> \\\\u2014 as if he had anti-Muslim Tourette\\\\u2019s. \\\\u201cHussein,\\\\u201d Cunningham hissed like he was beckoning Satan when shouting the ...\"}, {\"title\": \"What\\'s up with Obama\\'s middle name? - Quora\", \"link\": \"https://www.quora.com/Whats-up-with-Obamas-middle-name\", \"snippet\": \"Answer (1 of 15): A better question would be, \\\\u201cWhat\\\\u2019s up with <strong>Obama</strong>\\\\u2019s first <strong>name</strong>?\\\\u201d President Barack Hussein <strong>Obama</strong>\\\\u2019s father\\\\u2019s <strong>name</strong> was Barack Hussein <strong>Obama</strong>. He was <strong>named</strong> after his father. Hussein, <strong>Obama</strong>\\\\u2019<strong>s</strong> <strong>middle</strong> <strong>name</strong>, is a very common Arabic <strong>name</strong>, meaning &quot;good,&quot; &quot;handsome,&quot; or ...\"}, {\"title\": \"Barack Obama | Biography, Parents, Education, Presidency, Books, ...\", \"link\": \"https://www.britannica.com/biography/Barack-Obama\", \"snippet\": \"Barack <strong>Obama</strong>, in full Barack Hussein <strong>Obama</strong> II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009\\\\u201317) and the first African American to hold the office. Before winning the presidency, <strong>Obama</strong> represented Illinois in the U.S.\"}]'"
       ]
      },
      "execution_count": 4,
@@ -86,7 +86,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/extras/modules/agents/tools/integrations/zapier.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Zapier Natural Language Actions API\n",
     "\\\n",
-    "Full docs here: https://nla.zapier.com/api/v1/docs\n",
+    "Full docs here: https://nla.zapier.com/start/\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",
@@ -21,7 +21,7 @@
     "\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 on the server-side use case for brevity. Review [full docs](https://nla.zapier.com/api/v1/docs) or reach out to nla@zapier.com for user-facing oauth developer support.\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",
     "\n",
     "This example goes over how to use the Zapier integration with a `SimpleSequentialChain`, then an `Agent`.\n",
     "In code, below:"
@@ -39,7 +39,7 @@
     "# get from https://platform.openai.com/\n",
     "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\", \"\")\n",
     "\n",
-    "# get from https://nla.zapier.com/demo/provider/debug (under User Information, after logging in):\n",
+    "# get from https://nla.zapier.com/docs/authentication/ after logging in):\n",
     "os.environ[\"ZAPIER_NLA_API_KEY\"] = os.environ.get(\"ZAPIER_NLA_API_KEY\", \"\")"
    ]
   },
@@ -149,7 +149,7 @@
    "id": "bcdea831",
    "metadata": {},
    "source": [
-    "# Example with SimpleSequentialChain\n",
+    "## Example with SimpleSequentialChain\n",
     "If you need more explicit control, use a chain, like below."
    ]
   },
@@ -323,12 +323,34 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "id": "09ff954e-45f2-4595-92ea-91627abde4a0",
    "metadata": {},
+   "source": [
+    "## <a id=\"oauth\">Example Using OAuth Access Token</a>\n",
+    "The below snippet shows how to initialize the wrapper with a procured OAuth access token. Note the argument being passed in as opposed to setting an environment variable. Review the [authentication docs](https://nla.zapier.com/docs/authentication/#oauth-credentials) for full user-facing oauth developer support.\n",
+    "\n",
+    "The developer is tasked with handling the OAuth handshaking to procure and refresh the access token."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c6835c8",
+   "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "zapier = ZapierNLAWrapper(zapier_nla_oauth_access_token='<fill in access token here>')\n",
+    "toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)\n",
+    "agent = initialize_agent(\n",
+    "    toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")\n",
+    "\n",
+    "agent.run(\n",
+    "    \"Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.\"\n",
+    ")"
+   ]
   }
  ],
  "metadata": {

docs/extras/modules/callbacks/integrations/streamlit.md

@@ -0,0 +1,73 @@
+# Streamlit
+
+> **[Streamlit](https://streamlit.io/) is a faster way to build and share data apps.**
+> Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No front‑end experience required.
+> See more examples at [streamlit.io/generative-ai](https://streamlit.io/generative-ai).
+
+[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/streamlit-agent?quickstart=1)
+
+In this guide we will demonstrate how to use `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an
+interactive Streamlit app. Try it out with the running app below using the [MRKL agent](/docs/modules/agents/how_to/mrkl/):
+
+<iframe loading="lazy" src="https://mrkl-minimal.streamlit.app/?embed=true&embed_options=light_theme"
+    style={{ width: 100 + '%', border: 'none', marginBottom: 1 + 'rem', height: 600 }}
+    allow="camera;clipboard-read;clipboard-write;"
+></iframe>
+
+## Installation and Setup
+
+```bash
+pip install langchain streamlit
+```
+
+You can run `streamlit hello` to load a sample app and validate your install succeeded. See full instructions in Streamlit's
+[Getting started documentation](https://docs.streamlit.io/library/get-started).
+
+## Display thoughts and actions
+
+To create a `StreamlitCallbackHandler`, you just need to provide a parent container to render the output.
+
+```python
+from langchain.callbacks import StreamlitCallbackHandler
+import streamlit as st
+
+st_callback = StreamlitCallbackHandler(st.container())
+```
+
+Additional keyword arguments to customize the display behavior are described in the
+[API reference](https://api.python.langchain.com/en/latest/modules/callbacks.html#langchain.callbacks.StreamlitCallbackHandler).
+
+### Scenario 1: Using an Agent with Tools
+
+The primary supported use case today is visualizing the actions of an Agent with Tools (or Agent Executor). You can create an
+agent in your Streamlit app and simply pass the `StreamlitCallbackHandler` to `agent.run()` in order to visualize the
+thoughts and actions live in your app.
+
+```python
+from langchain.llms import OpenAI
+from langchain.agents import AgentType, initialize_agent, load_tools
+from langchain.callbacks import StreamlitCallbackHandler
+import streamlit as st
+
+llm = OpenAI(temperature=0, streaming=True)
+tools = load_tools(["ddg-search"])
+agent = initialize_agent(
+    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
+)
+
+if prompt := st.chat_input():
+    st.chat_message("user").write(prompt)
+    with st.chat_message("assistant"):
+        st_callback = StreamlitCallbackHandler(st.container())
+        response = agent.run(prompt, callbacks=[st_callback])
+        st.write(response)
+```
+
+**Note:** You will need to set `OPENAI_API_KEY` for the above app code to run successfully.
+The easiest way to do this is via [Streamlit secrets.toml](https://docs.streamlit.io/library/advanced-features/secrets-management),
+or any other local ENV management tool.
+
+### Additional scenarios
+
+Currently `StreamlitCallbackHandler` is geared towards use with a LangChain Agent Executor. Support for additional agent types,
+use directly with Chains, etc will be added in the future.

docs/extras/modules/chains/additional/flare.ipynb

@@ -71,11 +71,13 @@
     "import numpy as np\n",
     "\n",
     "from langchain.schema import BaseRetriever\n",
+    "from langchain.callbacks.manager import AsyncCallbackManagerForRetrieverRun, CallbackManagerForRetrieverRun\n",
     "from langchain.utilities import GoogleSerperAPIWrapper\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.schema import Document"
+    "from langchain.schema import Document\n",
+    "from typing import Any"
    ]
   },
   {
@@ -97,11 +99,16 @@
     "    def __init__(self, search):\n",
     "        self.search = search\n",
     "\n",
-    "    def get_relevant_documents(self, query: str):\n",
+    "    def _get_relevant_documents(self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any) -> List[Document]:\n",
     "        return [Document(page_content=self.search.run(query))]\n",
     "\n",
-    "    async def aget_relevant_documents(self, query: str):\n",
-    "        raise NotImplemented\n",
+    "    async def _aget_relevant_documents(self,\n",
+    "        query: str,\n",
+    "        *,\n",
+    "        run_manager: AsyncCallbackManagerForRetrieverRun,\n",
+    "        **kwargs: Any,\n",
+    "    ) -> List[Document]:\n",
+    "        raise NotImplementedError()\n",
     "\n",
     "\n",
     "retriever = SerperSearchRetriever(GoogleSerperAPIWrapper())"

docs/extras/modules/data_connection/document_loaders/integrations/airtable.ipynb

@@ -134,7 +134,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/document_loaders/integrations/brave_search.ipynb

@@ -0,0 +1,164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3dd292b1-9a73-4ea8-af19-5fa6e3c1a62a",
+   "metadata": {},
+   "source": [
+    "# Brave Search\n",
+    "\n",
+    "\n",
+    ">[Brave Search](https://en.wikipedia.org/wiki/Brave_Search) is a search engine developed by Brave Software.\n",
+    "> - `Brave Search` uses its own web index. As of May 2022, it covered over 10 billion pages and was used to serve 92% \n",
+    "> of search results without relying on any third-parties, with the remainder being retrieved \n",
+    "> server-side from the Bing API or (on an opt-in basis) client-side from Google. According \n",
+    "> to Brave, the index was kept \"intentionally smaller than that of Google or Bing\" in order to \n",
+    "> help avoid spam and other low-quality content, with the disadvantage that \"Brave Search is \n",
+    "> not yet as good as Google in recovering long-tail queries.\"\n",
+    ">- `Brave Search Premium`: As of April 2023 Brave Search is an ad-free website, but it will \n",
+    "> eventually switch to a new model that will include ads and premium users will get an ad-free experience.\n",
+    "> User data including IP addresses won't be collected from its users by default. A premium account \n",
+    "> will be required for opt-in data-collection.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26f0888e-3f3e-4b82-ac4a-2df6feeccbe0",
+   "metadata": {},
+   "source": [
+    "## Installation and Setup\n",
+    "\n",
+    "To get access to the Brave Search API, you need to [create an account and get an API key](https://api.search.brave.com/app/dashboard).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d7d7be09-58bd-47d7-bf1b-33964564f777",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api_key = \"...\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3ac92df-6ff0-4dbb-b32b-a7dc140c48ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import BraveSearchLoader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f483caf-58ef-4138-975a-5b783559dc1b",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "766634cf-3bc7-4656-939a-cafa218807a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loader = BraveSearchLoader(query=\"obama middle name\", api_key=api_key, search_kwargs={\"count\": 3})\n",
+    "docs = loader.load()\n",
+    "len(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f1fcc9f1-cbdc-46b3-89d3-80311d557dc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'title': \"Obama's Middle Name -- My Last Name -- is 'Hussein.' So?\",\n",
+       "  'link': 'https://www.cair.com/cair_in_the_news/obamas-middle-name-my-last-name-is-hussein-so/'},\n",
+       " {'title': \"What's up with Obama's middle name? - Quora\",\n",
+       "  'link': 'https://www.quora.com/Whats-up-with-Obamas-middle-name'},\n",
+       " {'title': 'Barack Obama | Biography, Parents, Education, Presidency, Books, ...',\n",
+       "  'link': 'https://www.britannica.com/biography/Barack-Obama'}]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[doc.metadata for doc in docs]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "601bfd77-03d3-468e-843f-2523d5e215bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['I wasn’t sure whether to laugh or cry a few days back listening to radio talk show host Bill Cunningham repeatedly scream Barack <strong>Obama</strong>’<strong>s</strong> <strong>middle</strong> <strong>name</strong> — my last <strong>name</strong> — as if he had anti-Muslim Tourette’s. “Hussein,” Cunningham hissed like he was beckoning Satan when shouting the ...',\n",
+       " 'Answer (1 of 15): A better question would be, “What’s up with <strong>Obama</strong>’s first <strong>name</strong>?” President Barack Hussein <strong>Obama</strong>’s father’s <strong>name</strong> was Barack Hussein <strong>Obama</strong>. He was <strong>named</strong> after his father. Hussein, <strong>Obama</strong>’<strong>s</strong> <strong>middle</strong> <strong>name</strong>, is a very common Arabic <strong>name</strong>, meaning &quot;good,&quot; &quot;handsome,&quot; or ...',\n",
+       " 'Barack <strong>Obama</strong>, in full Barack Hussein <strong>Obama</strong> II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009–17) and the first African American to hold the office. Before winning the presidency, <strong>Obama</strong> represented Illinois in the U.S.']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[doc.page_content for doc in docs]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74a6ba54-9e48-4bac-ab9b-03eabd19eb81",
+   "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/modules/data_connection/document_loaders/integrations/email.ipynb

@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "40cd9806",
    "metadata": {
     "tags": []
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "2d20b852",
    "metadata": {
     "tags": []
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "579fa702",
    "metadata": {
     "tags": []
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "90c1d899",
    "metadata": {
     "tags": []
@@ -80,7 +80,7 @@
        "[Document(page_content='This is a test email to use for unit tests.\\n\\nImportant points:\\n\\nRoses are red\\n\\nViolets are blue', metadata={'source': 'example_data/fake-email.eml'})]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -128,7 +128,7 @@
     {
      "data": {
       "text/plain": [
-       "Document(page_content='This is a test email to use for unit tests.', lookup_str='', metadata={'source': 'example_data/fake-email.eml'}, lookup_index=0)"
+       "Document(page_content='This is a test email to use for unit tests.', metadata={'source': 'example_data/fake-email.eml', 'filename': 'fake-email.eml', 'file_directory': 'example_data', 'date': '2022-12-16T17:04:16-05:00', 'filetype': 'message/rfc822', 'sent_from': ['Matthew Robinson <mrobinson@unstructured.io>'], 'sent_to': ['Matthew Robinson <mrobinson@unstructured.io>'], 'subject': 'Test Email', 'category': 'NarrativeText'})"
       ]
      },
      "execution_count": 7,
@@ -140,6 +140,61 @@
     "data[0]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5021f20a",
+   "metadata": {},
+   "source": [
+    "### Processing Attachments\n",
+    "\n",
+    "You can process attachments with `UnstructuredEmailLoader` by setting `process_attachments=True` in the constructor. By default, attachments will be partitioned using the `partition` function from `unstructured`. You can use a different partitioning function by passing the function to the `attachment_partitioner` kwarg."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6539f166",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = UnstructuredEmailLoader(\n",
+    "    \"example_data/fake-email.eml\",\n",
+    "    mode=\"elements\",\n",
+    "    process_attachments=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "aebead38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ddeb60f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content='This is a test email to use for unit tests.', metadata={'source': 'example_data/fake-email.eml', 'filename': 'fake-email.eml', 'file_directory': 'example_data', 'date': '2022-12-16T17:04:16-05:00', 'filetype': 'message/rfc822', 'sent_from': ['Matthew Robinson <mrobinson@unstructured.io>'], 'sent_to': ['Matthew Robinson <mrobinson@unstructured.io>'], 'subject': 'Test Email', 'category': 'NarrativeText'})"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6a074515",
@@ -234,7 +289,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/document_loaders/integrations/example_data/README.org

@@ -0,0 +1,27 @@
+* Example Docs
+
+The sample docs directory contains the following files:
+
+-  ~example-10k.html~ - A 10-K SEC filing in HTML format
+-  ~layout-parser-paper.pdf~ - A PDF copy of the layout parser paper
+-  ~factbook.xml~ / ~factbook.xsl~ - Example XML/XLS files that you
+   can use to test stylesheets
+
+These documents can be used to test out the parsers in the library. In
+addition, here are instructions for pulling in some sample docs that are
+too big to store in the repo.
+
+** XBRL 10-K
+
+You can get an example 10-K in inline XBRL format using the following
+~curl~. Note, you need to have the user agent set in the header or the
+SEC site will reject your request.
+
+#+BEGIN_SRC bash
+
+   curl -O \
+     -A '${organization} ${email}'
+     https://www.sec.gov/Archives/edgar/data/311094/000117184321001344/0001171843-21-001344.txt
+#+END_SRC
+
+You can parse this document using the HTML parser.

docs/extras/modules/data_connection/document_loaders/integrations/example_data/facebook_chat_messages.jsonl

@@ -0,0 +1,3 @@
+{"sender_name": "User 2", "timestamp_ms": 1675597571851, "content": "Bye!"}
+{"sender_name": "User 1", "timestamp_ms": 1675597435669, "content": "Oh no worries! Bye"}
+{"sender_name": "User 2", "timestamp_ms": 1675596277579, "content": "No Im sorry it was my mistake, the blue one is not for sale"}

docs/extras/modules/data_connection/document_loaders/integrations/example_data/fake-email-attachment.eml

@@ -0,0 +1,50 @@
+MIME-Version: 1.0
+Date: Fri, 23 Dec 2022 12:08:48 -0600
+Message-ID: <CAPgNNXSzLVJ-d1OCX_TjFgJU7ugtQrjFybPtAMmmYZzphxNFYg@mail.gmail.com>
+Subject: Fake email with attachment
+From: Mallori Harrell <mallori@unstructured.io>
+To: Mallori Harrell <mallori@unstructured.io>
+Content-Type: multipart/mixed; boundary="0000000000005d654405f082adb7"
+
+--0000000000005d654405f082adb7
+Content-Type: multipart/alternative; boundary="0000000000005d654205f082adb5"
+
+--0000000000005d654205f082adb5
+Content-Type: text/plain; charset="UTF-8"
+
+Hello!
+
+Here's the attachments!
+
+It includes:
+
+   - Lots of whitespace
+   - Little to no content
+   - and is a quick read
+
+Best,
+
+Mallori
+
+--0000000000005d654205f082adb5
+Content-Type: text/html; charset="UTF-8"
+Content-Transfer-Encoding: quoted-printable
+
+<div dir=3D"ltr">Hello!=C2=A0<div><br></div><div>Here&#39;s the attachments=
+!</div><div><br></div><div>It includes:</div><div><ul><li style=3D"margin-l=
+eft:15px">Lots of whitespace</li><li style=3D"margin-left:15px">Little=C2=
+=A0to no content</li><li style=3D"margin-left:15px">and is a quick read</li=
+></ul><div>Best,</div></div><div><br></div><div>Mallori</div><div dir=3D"lt=
+r" class=3D"gmail_signature" data-smartmail=3D"gmail_signature"><div dir=3D=
+"ltr"><div><div><br></div></div></div></div></div>
+
+--0000000000005d654205f082adb5--
+--0000000000005d654405f082adb7
+Content-Type: text/plain; charset="US-ASCII"; name="fake-attachment.txt"
+Content-Disposition: attachment; filename="fake-attachment.txt"
+Content-Transfer-Encoding: base64
+X-Attachment-Id: f_lc0tto5j0
+Content-ID: <f_lc0tto5j0>
+
+SGV5IHRoaXMgaXMgYSBmYWtlIGF0dGFjaG1lbnQh
+--0000000000005d654405f082adb7--

docs/extras/modules/data_connection/document_loaders/integrations/example_data/source_code/example.js

@@ -0,0 +1,17 @@
+class MyClass {
+  constructor(name) {
+    this.name = name;
+  }
+
+  greet() {
+    console.log(`Hello, ${this.name}!`);
+  }
+}
+
+function main() {
+  const name = prompt("Enter your name:");
+  const obj = new MyClass(name);
+  obj.greet();
+}
+
+main();

docs/extras/modules/data_connection/document_loaders/integrations/example_data/source_code/example.py

@@ -0,0 +1,16 @@
+class MyClass:
+    def __init__(self, name):
+        self.name = name
+
+    def greet(self):
+        print(f"Hello, {self.name}!")
+
+
+def main():
+    name = input("Enter your name: ")
+    obj = MyClass(name)
+    obj.greet()
+
+
+if __name__ == "__main__":
+    main()

docs/extras/modules/data_connection/document_loaders/integrations/grobid.ipynb

@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bdccb278",
+   "metadata": {},
+   "source": [
+    "# Grobid\n",
+    "\n",
+    "GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents.\n",
+    "\n",
+    "It is particularly good for sturctured PDFs, like academic papers.\n",
+    "\n",
+    "This loader uses GROBIB to parse PDFs into `Documents` that retain metadata associated with the section of text.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "For users on `Mac` - \n",
+    "\n",
+    "(Note: additional instructions can be found [here](https://python.langchain.com/docs/ecosystem/integrations/grobid.mdx).)\n",
+    "\n",
+    "Install Java (Apple Silicon):\n",
+    "```\n",
+    "$ arch -arm64 brew install openjdk@11\n",
+    "$ brew --prefix openjdk@11\n",
+    "/opt/homebrew/opt/openjdk@ 11\n",
+    "```\n",
+    "\n",
+    "In `~/.zshrc`:\n",
+    "```\n",
+    "export JAVA_HOME=/opt/homebrew/opt/openjdk@11\n",
+    "export PATH=$JAVA_HOME/bin:$PATH\n",
+    "```\n",
+    "\n",
+    "Then, in Terminal:\n",
+    "```\n",
+    "$ source ~/.zshrc\n",
+    "```\n",
+    "\n",
+    "Confirm install:\n",
+    "```\n",
+    "$ which java\n",
+    "/opt/homebrew/opt/openjdk@11/bin/java\n",
+    "$ java -version \n",
+    "openjdk version \"11.0.19\" 2023-04-18\n",
+    "OpenJDK Runtime Environment Homebrew (build 11.0.19+0)\n",
+    "OpenJDK 64-Bit Server VM Homebrew (build 11.0.19+0, mixed mode)\n",
+    "```\n",
+    "\n",
+    "Then, get [Grobid](https://grobid.readthedocs.io/en/latest/Install-Grobid/#getting-grobid):\n",
+    "```\n",
+    "$ curl -LO https://github.com/kermitt2/grobid/archive/0.7.3.zip\n",
+    "$ unzip 0.7.3.zip\n",
+    "```\n",
+    "                   \n",
+    "Build\n",
+    "```\n",
+    "$ ./gradlew clean install\n",
+    "```\n",
+    "\n",
+    "Then, run the server:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2d8992fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b41bfb1",
+   "metadata": {},
+   "source": [
+    "Now, we can use the data loader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "640e9a4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.parsers import GrobidParser\n",
+    "from langchain.document_loaders.generic import GenericLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ecdc1fb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = GenericLoader.from_filesystem(\n",
+    "    \"../Papers/\",\n",
+    "    glob=\"*\",\n",
+    "    suffixes=[\".pdf\"],\n",
+    "    parser= GrobidParser(segment_sentences=False)\n",
+    ")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "efe9e356",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Unlike Chinchilla, PaLM, or GPT-3, we only use publicly available data, making our work compatible with open-sourcing, while most existing models rely on data which is either not publicly available or undocumented (e.g.\"Books -2TB\" or \"Social media conversations\").There exist some exceptions, notably OPT (Zhang et al., 2022), GPT-NeoX (Black et al., 2022), BLOOM (Scao et al., 2022) and GLM (Zeng et al., 2022), but none that are competitive with PaLM-62B or Chinchilla.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[3].page_content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5be03d17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'text': 'Unlike Chinchilla, PaLM, or GPT-3, we only use publicly available data, making our work compatible with open-sourcing, while most existing models rely on data which is either not publicly available or undocumented (e.g.\"Books -2TB\" or \"Social media conversations\").There exist some exceptions, notably OPT (Zhang et al., 2022), GPT-NeoX (Black et al., 2022), BLOOM (Scao et al., 2022) and GLM (Zeng et al., 2022), but none that are competitive with PaLM-62B or Chinchilla.',\n",
+       " 'para': '2',\n",
+       " 'bboxes': \"[[{'page': '1', 'x': '317.05', 'y': '509.17', 'h': '207.73', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '522.72', 'h': '220.08', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '536.27', 'h': '218.27', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '549.82', 'h': '218.65', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '563.37', 'h': '136.98', 'w': '9.46'}], [{'page': '1', 'x': '446.49', 'y': '563.37', 'h': '78.11', 'w': '9.46'}, {'page': '1', 'x': '304.69', 'y': '576.92', 'h': '138.32', 'w': '9.46'}], [{'page': '1', 'x': '447.75', 'y': '576.92', 'h': '76.66', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '590.47', 'h': '219.63', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '604.02', 'h': '218.27', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '617.56', 'h': '218.27', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '631.11', 'h': '220.18', 'w': '9.46'}]]\",\n",
+       " 'pages': \"('1', '1')\",\n",
+       " 'section_title': 'Introduction',\n",
+       " 'section_number': '1',\n",
+       " 'paper_title': 'LLaMA: Open and Efficient Foundation Language Models',\n",
+       " 'file_path': '/Users/31treehaus/Desktop/Papers/2302.13971.pdf'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[3].metadata"
+   ]
+  }
+ ],
+ "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/modules/data_connection/document_loaders/integrations/larksuite.ipynb

@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "33205b12",
+   "metadata": {},
+   "source": [
+    "# LarkSuite (FeiShu)\n",
+    "\n",
+    ">[LarkSuite](https://www.larksuite.com/) is an enterprise collaboration platform developed by ByteDance.\n",
+    "\n",
+    "This notebook covers how to load data from the `LarkSuite` REST API into a format that can be ingested into LangChain, along with example usage for text summarization.\n",
+    "\n",
+    "The LarkSuite API requires an access token (tenant_access_token or user_access_token), checkout [LarkSuite open platform document](https://open.larksuite.com/document) for API details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "90b69c94",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-19T10:05:03.645161Z",
+     "start_time": "2023-06-19T10:04:49.541968Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from getpass import getpass\n",
+    "from langchain.document_loaders.larksuite import LarkSuiteDocLoader\n",
+    "\n",
+    "DOMAIN = input(\"larksuite domain\")\n",
+    "ACCESS_TOKEN = getpass(\"larksuite tenant_access_token or user_access_token\")\n",
+    "DOCUMENT_ID = input(\"larksuite document id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "13deb0f5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-19T10:05:36.016495Z",
+     "start_time": "2023-06-19T10:05:35.360884Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Document(page_content='Test Doc\\nThis is a Test Doc\\n\\n1\\n2\\n3\\n\\n', metadata={'document_id': 'V76kdbd2HoBbYJxdiNNccajunPf', 'revision_id': 11, 'title': 'Test Doc'})]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "larksuite_loader = LarkSuiteDocLoader(DOMAIN, ACCESS_TOKEN, DOCUMENT_ID)\n",
+    "docs = larksuite_loader.load()\n",
+    "\n",
+    "pprint(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ccc1e2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# see https://python.langchain.com/docs/use_cases/summarization for more details\n",
+    "from langchain.chains.summarize import load_summarize_chain\n",
+    "\n",
+    "chain = load_summarize_chain(llm, chain_type=\"map_reduce\")\n",
+    "chain.run(docs)"
+   ]
+  }
+ ],
+ "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/modules/data_connection/document_loaders/integrations/org_mode.ipynb

@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Org-mode\n",
+    "\n",
+    ">A [Org Mode document](https://en.wikipedia.org/wiki/Org-mode) is a document editing, formatting, and organizing mode, designed for notes, planning, and authoring within the free software text editor Emacs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `UnstructuredOrgModeLoader`\n",
+    "\n",
+    "You can load data from Org-mode files with `UnstructuredOrgModeLoader` using the following workflow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import UnstructuredOrgModeLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = UnstructuredOrgModeLoader(\n",
+    "    file_path=\"example_data/README.org\", mode=\"elements\"\n",
+    ")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='Example Docs' metadata={'source': 'example_data/README.org', 'filename': 'README.org', 'file_directory': 'example_data', 'filetype': 'text/org', 'page_number': 1, 'category': 'Title'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0])"
+   ]
+  },
+  {
+   "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": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/extras/modules/data_connection/document_loaders/integrations/source_code.ipynb

@@ -0,0 +1,419 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "213a38a2",
+   "metadata": {},
+   "source": [
+    "# Source Code\n",
+    "\n",
+    "This notebook covers how to load source code files using a special approach with language parsing: each top-level function and class in the code is loaded into separate documents. Any remaining code top-level code outside the already loaded functions and classes will be loaded into a seperate document.\n",
+    "\n",
+    "This approach can potentially improve the accuracy of QA models over source code. Currently, the supported languages for code parsing are Python and JavaScript. The language used for parsing can be configured, along with the minimum number of lines required to activate the splitting based on syntax."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fa47b2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install esprima"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "beb55c2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "from pprint import pprint\n",
+    "from langchain.text_splitter import Language\n",
+    "from langchain.document_loaders.generic import GenericLoader\n",
+    "from langchain.document_loaders.parsers import LanguageParser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "64056e07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = GenericLoader.from_filesystem(\n",
+    "    \"./example_data/source_code\",\n",
+    "    glob=\"*\",\n",
+    "    suffixes=[\".py\", \".js\"],\n",
+    "    parser=LanguageParser()\n",
+    ")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8af79bd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "85edf3fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'content_type': 'functions_classes',\n",
+      " 'language': <Language.PYTHON: 'python'>,\n",
+      " 'source': 'example_data/source_code/example.py'}\n",
+      "{'content_type': 'functions_classes',\n",
+      " 'language': <Language.PYTHON: 'python'>,\n",
+      " 'source': 'example_data/source_code/example.py'}\n",
+      "{'content_type': 'simplified_code',\n",
+      " 'language': <Language.PYTHON: 'python'>,\n",
+      " 'source': 'example_data/source_code/example.py'}\n",
+      "{'content_type': 'functions_classes',\n",
+      " 'language': <Language.JS: 'js'>,\n",
+      " 'source': 'example_data/source_code/example.js'}\n",
+      "{'content_type': 'functions_classes',\n",
+      " 'language': <Language.JS: 'js'>,\n",
+      " 'source': 'example_data/source_code/example.js'}\n",
+      "{'content_type': 'simplified_code',\n",
+      " 'language': <Language.JS: 'js'>,\n",
+      " 'source': 'example_data/source_code/example.js'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for document in docs:\n",
+    "    pprint(document.metadata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f44e3e37",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "class MyClass:\n",
+      "    def __init__(self, name):\n",
+      "        self.name = name\n",
+      "\n",
+      "    def greet(self):\n",
+      "        print(f\"Hello, {self.name}!\")\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "def main():\n",
+      "    name = input(\"Enter your name: \")\n",
+      "    obj = MyClass(name)\n",
+      "    obj.greet()\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "# Code for: class MyClass:\n",
+      "\n",
+      "\n",
+      "# Code for: def main():\n",
+      "\n",
+      "\n",
+      "if __name__ == \"__main__\":\n",
+      "    main()\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "class MyClass {\n",
+      "  constructor(name) {\n",
+      "    this.name = name;\n",
+      "  }\n",
+      "\n",
+      "  greet() {\n",
+      "    console.log(`Hello, ${this.name}!`);\n",
+      "  }\n",
+      "}\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "function main() {\n",
+      "  const name = prompt(\"Enter your name:\");\n",
+      "  const obj = new MyClass(name);\n",
+      "  obj.greet();\n",
+      "}\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "// Code for: class MyClass {\n",
+      "\n",
+      "// Code for: function main() {\n",
+      "\n",
+      "main();\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n\\n--8<--\\n\\n\".join([document.page_content for document in docs]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69aad0ed",
+   "metadata": {},
+   "source": [
+    "The parser can be disabled for small files. \n",
+    "\n",
+    "The parameter `parser_threshold` indicates the minimum number of lines that the source code file must have to be segmented using the parser."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ae024794",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = GenericLoader.from_filesystem(\n",
+    "    \"./example_data/source_code\",\n",
+    "    glob=\"*\",\n",
+    "    suffixes=[\".py\"],\n",
+    "    parser=LanguageParser(language=Language.PYTHON, parser_threshold=1000)\n",
+    ")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5d3b372a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "89e546ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "class MyClass:\n",
+      "    def __init__(self, name):\n",
+      "        self.name = name\n",
+      "\n",
+      "    def greet(self):\n",
+      "        print(f\"Hello, {self.name}!\")\n",
+      "\n",
+      "\n",
+      "def main():\n",
+      "    name = input(\"Enter your name: \")\n",
+      "    obj = MyClass(name)\n",
+      "    obj.greet()\n",
+      "\n",
+      "\n",
+      "if __name__ == \"__main__\":\n",
+      "    main()\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9c71e61",
+   "metadata": {},
+   "source": [
+    "## Splitting\n",
+    "\n",
+    "Additional splitting could be needed for those functions, classes, or scripts that are too big."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "adbaa79f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = GenericLoader.from_filesystem(\n",
+    "    \"./example_data/source_code\",\n",
+    "    glob=\"*\",\n",
+    "    suffixes=[\".js\"],\n",
+    "    parser=LanguageParser(language=Language.JS)\n",
+    ")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c44c0d3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import (\n",
+    "    RecursiveCharacterTextSplitter,\n",
+    "    Language,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b1e0053d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "js_splitter = RecursiveCharacterTextSplitter.from_language(\n",
+    "    language=Language.JS, chunk_size=60, chunk_overlap=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7dbe6188",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = js_splitter.split_documents(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8a80d089",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "000a6011",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "class MyClass {\n",
+      "  constructor(name) {\n",
+      "    this.name = name;\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "}\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "greet() {\n",
+      "    console.log(`Hello, ${this.name}!`);\n",
+      "  }\n",
+      "}\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "function main() {\n",
+      "  const name = prompt(\"Enter your name:\");\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "const obj = new MyClass(name);\n",
+      "  obj.greet();\n",
+      "}\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "// Code for: class MyClass {\n",
+      "\n",
+      "// Code for: function main() {\n",
+      "\n",
+      "--8<--\n",
+      "\n",
+      "main();\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n\\n--8<--\\n\\n\".join([document.page_content for document in result]))"
+   ]
+  }
+ ],
+ "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/modules/data_connection/document_loaders/integrations/tencent_cos_directory.ipynb

@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a634365e",
+   "metadata": {},
+   "source": [
+    "# Tencent COS Directory\n",
+    "\n",
+    "This covers how to load document objects from a `Tencent COS Directory`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85e97267",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#! pip install cos-python-sdk-v5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2f0cd6a5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TencentCOSDirectoryLoader\n",
+    "from qcloud_cos import CosConfig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "321cc7f1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "conf = CosConfig(\n",
+    "        Region=\"your cos region\",\n",
+    "        SecretId=\"your cos secret_id\",\n",
+    "        SecretKey=\"your cos secret_key\",\n",
+    "    )\n",
+    "loader = TencentCOSDirectoryLoader(conf=conf, bucket=\"you_cos_bucket\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c50d2c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0690c40a",
+   "metadata": {},
+   "source": [
+    "## Specifying a prefix\n",
+    "You can also specify a prefix for more finegrained control over what files to load."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "72d44781",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = TencentCOSDirectoryLoader(conf=conf, bucket=\"you_cos_bucket\", prefix=\"fake\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d3c32db",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  }
+ ],
+ "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": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/modules/data_connection/document_loaders/integrations/tencent_cos_file.ipynb

@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a634365e",
+   "metadata": {},
+   "source": [
+    "# Tencent COS File\n",
+    "\n",
+    "This covers how to load document object from a `Tencent COS File`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85e97267",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#! pip install cos-python-sdk-v5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2f0cd6a5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TencentCOSFileLoader\n",
+    "from qcloud_cos import CosConfig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "321cc7f1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "conf = CosConfig(\n",
+    "        Region=\"your cos region\",\n",
+    "        SecretId=\"your cos secret_id\",\n",
+    "        SecretKey=\"your cos secret_key\",\n",
+    "    )\n",
+    "loader = TencentCOSFileLoader(conf=conf, bucket=\"you_cos_bucket\", key=\"fake.docx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c50d2c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0690c40a",
+   "metadata": {},
+   "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": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/modules/data_connection/document_loaders/integrations/unstructured_file.ipynb

@@ -226,7 +226,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "8de9ef16",
    "metadata": {},
@@ -303,7 +302,7 @@
    "source": [
     "## Unstructured API\n",
     "\n",
-    "If you want to get up and running with less set up, you can simply run `pip install unstructured` and use `UnstructuredAPIFileLoader` or `UnstructuredAPIFileIOLoader`. That will process your document using the hosted Unstructured API. Note that currently (as of 11 May 2023) the Unstructured API is open, but it will soon require an API. The [Unstructured documentation](https://unstructured-io.github.io/) page will have instructions on how to generate an API key once they’re available. Check out the instructions [here](https://github.com/Unstructured-IO/unstructured-api#dizzy-instructions-for-using-the-docker-image) if you’d like to self-host the Unstructured API or run it locally."
+    "If you want to get up and running with less set up, you can simply run `pip install unstructured` and use `UnstructuredAPIFileLoader` or `UnstructuredAPIFileIOLoader`. That will process your document using the hosted Unstructured API. You can generate a free Unstructured API key [here](https://www.unstructured.io/api-key/). The [Unstructured documentation](https://unstructured-io.github.io/) page will have instructions on how to generate an API key once they’re available. Check out the instructions [here](https://github.com/Unstructured-IO/unstructured-api#dizzy-instructions-for-using-the-docker-image) if you’d like to self-host the Unstructured API or run it locally."
    ]
   },
   {

docs/extras/modules/data_connection/document_loaders/integrations/web_base.ipynb

@@ -224,13 +224,33 @@
     "docs"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Using proxies\n",
+    "\n",
+    "Sometimes you might need to use proxies to get around IP blocks. You can pass in a dictionary of proxies to the loader (and `requests` underneath) to use them."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1dd8ab23",
-   "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "loader = WebBaseLoader(\n",
+    "    \"https://www.walmart.com/search?q=parrots\", proxies={\n",
+    "        \"http\": \"http://{username}:{password}:@proxy.service.com:6666/\",\n",
+    "        \"https\": \"https://{username}:{password}:@proxy.service.com:6666/\"\n",
+    "    }\n",
+    ")\n",
+    "docs = loader.load()\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   }
  ],
  "metadata": {

docs/extras/modules/data_connection/retrievers/how_to/MultiQueryRetriever.ipynb

@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8cc82b48",
+   "metadata": {},
+   "source": [
+    "# MultiQueryRetriever\n",
+    "\n",
+    "Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on \"distance\". But, retrieval may produce difference results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious.\n",
+    "\n",
+    "The `MultiQueryRetriever` automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c2f3f5f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build a sample vectorDB\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.document_loaders import PyPDFLoader\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "\n",
+    "# Load PDF\n",
+    "path=\"path-to-files\"\n",
+    "loaders = [\n",
+    "    PyPDFLoader(path+\"docs/cs229_lectures/MachineLearning-Lecture01.pdf\"),\n",
+    "    PyPDFLoader(path+\"docs/cs229_lectures/MachineLearning-Lecture02.pdf\"),\n",
+    "    PyPDFLoader(path+\"docs/cs229_lectures/MachineLearning-Lecture03.pdf\")\n",
+    "]\n",
+    "docs = []\n",
+    "for loader in loaders:\n",
+    "    docs.extend(loader.load())\n",
+    "    \n",
+    "# Split\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1500,chunk_overlap = 150)\n",
+    "splits = text_splitter.split_documents(docs)\n",
+    "\n",
+    "# VectorDB\n",
+    "embedding = OpenAIEmbeddings()\n",
+    "vectordb = Chroma.from_documents(documents=splits,embedding=embedding)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cca8f56c",
+   "metadata": {},
+   "source": [
+    "`Simple usage`\n",
+    "\n",
+    "Specify the LLM to use for query generation, and the retriver will do the rest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "edbca101",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.retrievers.multi_query import MultiQueryRetriever\n",
+    "question=\"What does the course say about regression?\"\n",
+    "num_queries=3\n",
+    "llm = ChatOpenAI(temperature=0)\n",
+    "retriever_from_llm = MultiQueryRetriever.from_llm(retriever=vectordb.as_retriever(),llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e5203612",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Generated queries: [\"1. What is the course's perspective on regression?\", '2. How does the course discuss regression?', '3. What information does the course provide about regression?']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unique_docs = retriever_from_llm.get_relevant_documents(question=\"What does the course say about regression?\")\n",
+    "len(unique_docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c54a282f",
+   "metadata": {},
+   "source": [
+    "`Supplying your own prompt`\n",
+    "\n",
+    "You can also supply a prompt along with an output parser to split the results into a list of queries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d9afb0ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "from langchain import LLMChain\n",
+    "from pydantic import BaseModel, Field\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.output_parsers import PydanticOutputParser\n",
+    "\n",
+    "# Output parser will split the LLM result into a list of queries\n",
+    "class LineList(BaseModel):\n",
+    "    # \"lines\" is the key (attribute name) of the parsed output\n",
+    "    lines: List[str] = Field(description=\"Lines of text\")\n",
+    "\n",
+    "class LineListOutputParser(PydanticOutputParser):\n",
+    "    def __init__(self) -> None:\n",
+    "        super().__init__(pydantic_object=LineList)\n",
+    "    def parse(self, text: str) -> LineList:\n",
+    "        lines = text.strip().split(\"\\n\")\n",
+    "        return LineList(lines=lines)\n",
+    "\n",
+    "output_parser = LineListOutputParser()\n",
+    "    \n",
+    "QUERY_PROMPT = PromptTemplate(\n",
+    "    input_variables=[\"question\"],\n",
+    "    template=\"\"\"You are an AI language model assistant. Your task is to generate five \n",
+    "    different versions of the given user question to retrieve relevant documents from a vector \n",
+    "    database. By generating multiple perspectives on the user question, your goal is to help\n",
+    "    the user overcome some of the limitations of the distance-based similarity search. \n",
+    "    Provide these alternative questions seperated by newlines.\n",
+    "    Original question: {question}\"\"\",\n",
+    ")\n",
+    "llm = ChatOpenAI(temperature=0)\n",
+    "\n",
+    "# Chain\n",
+    "llm_chain = LLMChain(llm=llm,prompt=QUERY_PROMPT,output_parser=output_parser)\n",
+    " \n",
+    "# Other inputs\n",
+    "question=\"What does the course say about regression?\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6660d7ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Generated queries: [\"1. What is the course's perspective on regression?\", '2. Can you provide information on regression as discussed in the course?', '3. How does the course cover the topic of regression?', \"4. What are the course's teachings on regression?\", '5. In relation to the course, what is mentioned about regression?']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Run\n",
+    "retriever = MultiQueryRetriever(retriever=vectordb.as_retriever(), \n",
+    "                                llm_chain=llm_chain,\n",
+    "                                parser_key=\"lines\") # \"lines\" is the key (attribute name) of the parsed output\n",
+    "\n",
+    "# Results\n",
+    "unique_docs = retriever.get_relevant_documents(question=\"What does the course say about regression?\")\n",
+    "len(unique_docs)"
+   ]
+  }
+ ],
+ "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/modules/data_connection/retrievers/integrations/zep_memorystore.ipynb

@@ -58,6 +58,7 @@
     "from langchain.memory.chat_message_histories import ZepChatMessageHistory\n",
     "from langchain.schema import HumanMessage, AIMessage\n",
     "from uuid import uuid4\n",
+    "import getpass\n",
     "\n",
     "# Set this to your Zep server URL\n",
     "ZEP_API_URL = \"http://localhost:8000\""
@@ -75,6 +76,25 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Provide your Zep API key. Note that this is optional. See https://docs.getzep.com/deployment/auth\n",
+    "\n",
+    "zep_api_key = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-25T15:03:29.118416Z",
@@ -93,12 +113,13 @@
     "zep_chat_history = ZepChatMessageHistory(\n",
     "    session_id=session_id,\n",
     "    url=ZEP_API_URL,\n",
+    "    api_key=zep_api_key\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-25T15:03:30.271181Z",
@@ -172,7 +193,7 @@
     "]\n",
     "\n",
     "for msg in test_history:\n",
-    "    zep_chat_history.append(\n",
+    "    zep_chat_history.add_message(\n",
     "        HumanMessage(content=msg[\"content\"])\n",
     "        if msg[\"role\"] == \"human\"\n",
     "        else AIMessage(content=msg[\"content\"])\n",
@@ -192,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-25T15:03:32.979155Z",
@@ -207,14 +228,14 @@
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='Who was Octavia Butler?', metadata={'score': 0.7759001673780126, 'uuid': '3a82a02f-056e-4c6a-b960-67ebdf3b2b93', 'created_at': '2023-05-25T15:03:30.2041Z', 'role': 'human', 'token_count': 8}),\n",
-       " Document(page_content=\"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", metadata={'score': 0.7602262941130749, 'uuid': 'a2fc9c21-0897-46c8-bef7-6f5c0f71b04a', 'created_at': '2023-05-25T15:03:30.248065Z', 'role': 'ai', 'token_count': 27}),\n",
-       " Document(page_content='Who were her contemporaries?', metadata={'score': 0.757553366415519, 'uuid': '41f9c41a-a205-41e1-b48b-a0a4cd943fc8', 'created_at': '2023-05-25T15:03:30.243995Z', 'role': 'human', 'token_count': 8}),\n",
-       " Document(page_content='Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author.', metadata={'score': 0.7546211059317948, 'uuid': '34678311-0098-4f1a-8fd4-5615ac692deb', 'created_at': '2023-05-25T15:03:30.231427Z', 'role': 'ai', 'token_count': 31}),\n",
-       " Document(page_content='Which books of hers were made into movies?', metadata={'score': 0.7496714959247069, 'uuid': '18046c3a-9666-4d3e-b4f0-43d1394732b7', 'created_at': '2023-05-25T15:03:30.236837Z', 'role': 'human', 'token_count': 11})]"
+       "[Document(page_content='Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', metadata={'score': 0.8897116216176073, 'uuid': 'db60ff57-f259-4ec4-8a81-178ed4c6e54f', 'created_at': '2023-06-26T23:40:22.816214Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}]}}, 'token_count': 56}),\n",
+       " Document(page_content=\"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", metadata={'score': 0.8856661080361157, 'uuid': 'f1a5981a-8f6d-4168-a548-6e9c32f35fa1', 'created_at': '2023-06-26T23:40:22.809621Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}]}}, 'token_count': 23}),\n",
+       " Document(page_content='Who was Octavia Butler?', metadata={'score': 0.7757595298492976, 'uuid': '361d0043-1009-4e13-a7f0-8aea8b1ee869', 'created_at': '2023-06-26T23:40:22.709886Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}], 'intent': 'The subject wants to know about the identity or background of an individual named Octavia Butler.'}}, 'token_count': 8}),\n",
+       " Document(page_content=\"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", metadata={'score': 0.7601242516059306, 'uuid': '56c45e8a-0f65-45f0-bc46-d9e65164b563', 'created_at': '2023-06-26T23:40:22.778836Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': \"The subject is providing information about Octavia Butler's contemporaries.\"}}, 'token_count': 27}),\n",
+       " Document(page_content='You might want to read Ursula K. Le Guin or Joanna Russ.', metadata={'score': 0.7594731095320668, 'uuid': '6951f2fd-dfa4-4e05-9380-f322ef8f72f8', 'created_at': '2023-06-26T23:40:22.80464Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 18})]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -226,6 +247,7 @@
     "    session_id=session_id,  # Ensure that you provide the session_id when instantiating the Retriever\n",
     "    url=ZEP_API_URL,\n",
     "    top_k=5,\n",
+    "    api_key=zep_api_key\n",
     ")\n",
     "\n",
     "await zep_retriever.aget_relevant_documents(\"Who wrote Parable of the Sower?\")"
@@ -240,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-25T15:03:34.713354Z",
@@ -255,14 +277,14 @@
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', metadata={'score': 0.8897321402776546, 'uuid': '1c09603a-52c1-40d7-9d69-29f26256029c', 'created_at': '2023-05-25T15:03:30.268257Z', 'role': 'ai', 'token_count': 56}),\n",
-       " Document(page_content=\"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", metadata={'score': 0.8857628682610436, 'uuid': 'f6706e8c-6c91-452f-8c1b-9559fd924657', 'created_at': '2023-05-25T15:03:30.265302Z', 'role': 'human', 'token_count': 23}),\n",
-       " Document(page_content='Who was Octavia Butler?', metadata={'score': 0.7759670375149477, 'uuid': '3a82a02f-056e-4c6a-b960-67ebdf3b2b93', 'created_at': '2023-05-25T15:03:30.2041Z', 'role': 'human', 'token_count': 8}),\n",
-       " Document(page_content=\"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", metadata={'score': 0.7602854653476563, 'uuid': 'a2fc9c21-0897-46c8-bef7-6f5c0f71b04a', 'created_at': '2023-05-25T15:03:30.248065Z', 'role': 'ai', 'token_count': 27}),\n",
-       " Document(page_content='You might want to read Ursula K. Le Guin or Joanna Russ.', metadata={'score': 0.7595293992240313, 'uuid': 'f22f2498-6118-4c74-8718-aa89ccd7e3d6', 'created_at': '2023-05-25T15:03:30.261198Z', 'role': 'ai', 'token_count': 18})]"
+       "[Document(page_content='Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', metadata={'score': 0.889661105796371, 'uuid': 'db60ff57-f259-4ec4-8a81-178ed4c6e54f', 'created_at': '2023-06-26T23:40:22.816214Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}]}}, 'token_count': 56}),\n",
+       " Document(page_content=\"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", metadata={'score': 0.885754241595424, 'uuid': 'f1a5981a-8f6d-4168-a548-6e9c32f35fa1', 'created_at': '2023-06-26T23:40:22.809621Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}]}}, 'token_count': 23}),\n",
+       " Document(page_content='Who was Octavia Butler?', metadata={'score': 0.7758688965570713, 'uuid': '361d0043-1009-4e13-a7f0-8aea8b1ee869', 'created_at': '2023-06-26T23:40:22.709886Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}], 'intent': 'The subject wants to know about the identity or background of an individual named Octavia Butler.'}}, 'token_count': 8}),\n",
+       " Document(page_content=\"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", metadata={'score': 0.7602672137411663, 'uuid': '56c45e8a-0f65-45f0-bc46-d9e65164b563', 'created_at': '2023-06-26T23:40:22.778836Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': \"The subject is providing information about Octavia Butler's contemporaries.\"}}, 'token_count': 27}),\n",
+       " Document(page_content='You might want to read Ursula K. Le Guin or Joanna Russ.', metadata={'score': 0.7596040989115522, 'uuid': '6951f2fd-dfa4-4e05-9380-f322ef8f72f8', 'created_at': '2023-06-26T23:40:22.80464Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 18})]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -304,7 +326,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/text_embedding/integrations/spacy_embedding.ipynb

@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Spacy Embedding\n",
+    "\n",
+    "### Loading the Spacy embedding class to generate and query embeddings"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Import the necessary classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.embeddings.spacy_embeddings import SpacyEmbeddings\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Initialize SpacyEmbeddings.This will load the Spacy model into memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "embedder = SpacyEmbeddings()\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "texts = [\n",
+    "    \"The quick brown fox jumps over the lazy dog.\",\n",
+    "    \"Pack my box with five dozen liquor jugs.\",\n",
+    "    \"How vexingly quick daft zebras jump!\",\n",
+    "    \"Bright vixens jump; dozy fowl quack.\"\n",
+    "]\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Generate and print embeddings for the texts . The SpacyEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "embeddings = embedder.embed_documents(texts)\n",
+    "for i, embedding in enumerate(embeddings):\n",
+    "    print(f\"Embedding for document {i+1}: {embedding}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Generate and print an embedding for a single piece of text. You can also generate an embedding for a single piece of text, such as a search query. This can be useful for tasks like information retrieval, where you want to find documents that are similar to a given query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "query = \"Quick foxes and lazy dogs.\"\n",
+    "query_embedding = embedder.embed_query(query)\n",
+    "print(f\"Embedding for query: {query_embedding}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/extras/modules/data_connection/vectorstores/integrations/azuresearch.ipynb

@@ -43,7 +43,6 @@
     "import openai\n",
     "from dotenv import load_dotenv\n",
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.schema import BaseRetriever\n",
     "from langchain.vectorstores.azuresearch import AzureSearch"
    ]
   },

docs/extras/modules/data_connection/vectorstores/integrations/cassandra.ipynb

@@ -23,7 +23,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install \"cassio>=0.0.5\""
+    "!pip install \"cassio>=0.0.7\""
    ]
   },
   {
@@ -44,14 +44,16 @@
     "import os\n",
     "import getpass\n",
     "\n",
-    "database_mode = (input('\\n(L)ocal Cassandra or (A)stra DB? ')).upper()\n",
+    "database_mode = (input('\\n(C)assandra or (A)stra DB? ')).upper()\n",
     "\n",
     "keyspace_name = input('\\nKeyspace name? ')\n",
     "\n",
     "if database_mode == 'A':\n",
     "    ASTRA_DB_APPLICATION_TOKEN = getpass.getpass('\\nAstra DB Token (\"AstraCS:...\") ')\n",
     "    #\n",
-    "    ASTRA_DB_SECURE_BUNDLE_PATH = input('Full path to your Secure Connect Bundle? ')"
+    "    ASTRA_DB_SECURE_BUNDLE_PATH = input('Full path to your Secure Connect Bundle? ')\n",
+    "elif database_mode == 'C':\n",
+    "    CASSANDRA_CONTACT_POINTS = input('Contact points? (comma-separated, empty for localhost) ').strip()"
    ]
   },
   {
@@ -72,8 +74,15 @@
     "from cassandra.cluster import Cluster\n",
     "from cassandra.auth import PlainTextAuthProvider\n",
     "\n",
-    "if database_mode == 'L':\n",
-    "    cluster = Cluster()\n",
+    "if database_mode == 'C':\n",
+    "    if CASSANDRA_CONTACT_POINTS:\n",
+    "        cluster = Cluster([\n",
+    "            cp.strip()\n",
+    "            for cp in CASSANDRA_CONTACT_POINTS.split(',')\n",
+    "            if cp.strip()\n",
+    "        ])\n",
+    "    else:\n",
+    "        cluster = Cluster()\n",
     "    session = cluster.connect()\n",
     "elif database_mode == 'A':\n",
     "    ASTRA_DB_CLIENT_ID = \"token\"\n",
@@ -261,7 +270,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/vectorstores/integrations/clickhouse.ipynb

@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "683953b3",
+   "metadata": {},
+   "source": [
+    "# ClickHouse Vector Search\n",
+    "\n",
+    "> [ClickHouse](https://clickhouse.com/) is the fastest and most resource efficient open-source database for real-time apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. Lately added data structures and distance search functions (like `L2Distance`) as well as [approximate nearest neighbor search indexes](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) enable ClickHouse to be used as a high performance and scalable vector database to store and search vectors with SQL.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `ClickHouse` vector search."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43ead5d5-2c1f-4dce-a69a-cb00e4f9d6f0",
+   "metadata": {},
+   "source": [
+    "## Setting up envrionments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2c434bc",
+   "metadata": {},
+   "source": [
+    "Setting up local clickhouse server with docker (optional)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "249a7751",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-03T08:43:43.035606Z",
+     "start_time": "2023-06-03T08:43:42.618531Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "! docker run -d -p 8123:8123 -p9000:9000 --name langchain-clickhouse-server --ulimit nofile=262144:262144 clickhouse/clickhouse-server:23.4.2.11"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bd3c1c0",
+   "metadata": {},
+   "source": [
+    "Setup up clickhouse client driver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d614bf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install clickhouse-connect"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15a1d477-9cdb-4d82-b019-96951ecb2b72",
+   "metadata": {},
+   "source": [
+    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "91003ea5-0c8c-436c-a5de-aaeaeef2f458",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-03T08:49:35.383673Z",
+     "start_time": "2023-06-03T08:49:33.984547Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "if not os.environ['OPENAI_API_KEY']:\n",
+    "    os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aac9563e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-03T08:33:31.554934Z",
+     "start_time": "2023-06-03T08:33:31.549590Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Clickhouse, ClickhouseSettings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a3c3999a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-03T08:33:32.527387Z",
+     "start_time": "2023-06-03T08:33:32.501312Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "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": 4,
+   "id": "6e104aee",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-03T08:33:35.503823Z",
+     "start_time": "2023-06-03T08:33:33.745832Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Inserting data...: 100%|██████████| 42/42 [00:00<00:00, 2801.49it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for d in docs:\n",
+    "    d.metadata = {'some': 'metadata'}\n",
+    "settings = ClickhouseSettings(table=\"clickhouse_vector_search_example\")\n",
+    "docsearch = Clickhouse.from_documents(docs, embeddings, config=settings)\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9c608226",
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3a8b105",
+   "metadata": {},
+   "source": [
+    "## Get connection info and data schema"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "69996818",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-03T08:28:58.252991Z",
+     "start_time": "2023-06-03T08:28:58.197560Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[92m\u001b[1mdefault.clickhouse_vector_search_example @ localhost:8123\u001b[0m\n",
+      "\n",
+      "\u001b[1musername: None\u001b[0m\n",
+      "\n",
+      "Table Schema:\n",
+      "---------------------------------------------------\n",
+      "|\u001b[94mid                      \u001b[0m|\u001b[96mNullable(String)        \u001b[0m|\n",
+      "|\u001b[94mdocument                \u001b[0m|\u001b[96mNullable(String)        \u001b[0m|\n",
+      "|\u001b[94membedding               \u001b[0m|\u001b[96mArray(Float32)          \u001b[0m|\n",
+      "|\u001b[94mmetadata                \u001b[0m|\u001b[96mObject('json')          \u001b[0m|\n",
+      "|\u001b[94muuid                    \u001b[0m|\u001b[96mUUID                    \u001b[0m|\n",
+      "---------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(str(docsearch))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "324ac147",
+   "metadata": {},
+   "source": [
+    "### Clickhouse table schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5bd7c5b",
+   "metadata": {},
+   "source": [
+    "> Clickhouse table will be automatically created if not exist by default. Advanced users could pre-create the table with optimized settings. For distributed Clickhouse cluster with sharding, table engine should be configured as `Distributed`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "54f4f561",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clickhouse Table DDL:\n",
+      "\n",
+      "CREATE TABLE IF NOT EXISTS default.clickhouse_vector_search_example(\n",
+      "    id Nullable(String),\n",
+      "    document Nullable(String),\n",
+      "    embedding Array(Float32),\n",
+      "    metadata JSON,\n",
+      "    uuid UUID DEFAULT generateUUIDv4(),\n",
+      "    CONSTRAINT cons_vec_len CHECK length(embedding) = 1536,\n",
+      "    INDEX vec_idx embedding TYPE annoy(100,'L2Distance') GRANULARITY 1000\n",
+      ") ENGINE = MergeTree ORDER BY uuid SETTINGS index_granularity = 8192\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Clickhouse Table DDL:\\n\\n{docsearch.schema}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f59360c0",
+   "metadata": {},
+   "source": [
+    "## Filtering\n",
+    "\n",
+    "You can have direct access to ClickHouse SQL where statement. You can write `WHERE` clause following standard SQL.\n",
+    "\n",
+    "**NOTE**: Please be aware of SQL injection, this interface must not be directly called by end-user.\n",
+    "\n",
+    "If you custimized your `column_map` under your setting, you search with filter like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "232055f6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-03T08:29:36.680805Z",
+     "start_time": "2023-06-03T08:29:34.963676Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Inserting data...: 100%|██████████| 42/42 [00:00<00:00, 6939.56it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.vectorstores import Clickhouse, ClickhouseSettings\n",
+    "from langchain.document_loaders import TextLoader\n",
+    "\n",
+    "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()\n",
+    "\n",
+    "for i, d in enumerate(docs):\n",
+    "    d.metadata = {'doc_id': i}\n",
+    "\n",
+    "docsearch = Clickhouse.from_documents(docs, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ddbcee77",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-03T08:29:43.487436Z",
+     "start_time": "2023-06-03T08:29:43.040831Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6779101415357189 {'doc_id': 0} Madam Speaker, Madam...\n",
+      "0.6997970363474885 {'doc_id': 8} And so many families...\n",
+      "0.7044504914336727 {'doc_id': 1} Groups of citizens b...\n",
+      "0.7053558702165094 {'doc_id': 6} And I’m taking robus...\n"
+     ]
+    }
+   ],
+   "source": [
+    "meta = docsearch.metadata_column\n",
+    "output = docsearch.similarity_search_with_relevance_scores('What did the president say about Ketanji Brown Jackson?', \n",
+    "                                                           k=4, where_str=f\"{meta}.doc_id<10\")\n",
+    "for d, dist in output:\n",
+    "    print(dist, d.metadata, d.page_content[:20] + '...')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a359ed74",
+   "metadata": {},
+   "source": [
+    "## Deleting your data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fb6a9d36",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-03T08:30:24.822384Z",
+     "start_time": "2023-06-03T08:30:24.798571Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "docsearch.drop()"
+   ]
+  }
+ ],
+ "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/modules/data_connection/vectorstores/integrations/faiss.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "683953b3",
    "metadata": {},
@@ -30,7 +29,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "38237514-b3fa-44a4-9cff-30cd6bf50073",
    "metadata": {},
@@ -58,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "aac9563e",
    "metadata": {
     "tags": []
@@ -73,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "id": "a3c3999a",
    "metadata": {
     "tags": []
@@ -132,7 +130,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "f13473b5",
    "metadata": {},
@@ -174,7 +171,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "f34420cf",
    "metadata": {},
@@ -194,7 +190,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "31bda7fd",
    "metadata": {},
@@ -255,7 +250,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "57da60d4",
    "metadata": {},
@@ -350,7 +344,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "f4294b96",
    "metadata": {},
@@ -361,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 3,
    "id": "d5bf812c",
    "metadata": {},
    "outputs": [
@@ -396,7 +389,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "3d33c126",
    "metadata": {},
@@ -426,7 +418,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "0be136e0",
    "metadata": {},
@@ -456,7 +447,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "1b4ecd86",
    "metadata": {},
@@ -466,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 4,
    "id": "1fd60fd1",
    "metadata": {},
    "outputs": [
@@ -474,15 +464,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Content: foo, Metadata: {'page': 1}, Score: 5.159960813797904e-15\n",
-      "Content: bar, Metadata: {'page': 1}, Score: 0.3131446838378906\n"
+      "Content: foo, Metadata: {'page': 1}\n"
      ]
     }
    ],
    "source": [
     "results = db.similarity_search(\"foo\", filter=dict(page=1), k=1, fetch_k=4)\n",
-    "for doc, score in results_with_scores:\n",
-    "    print(f\"Content: {doc.page_content}, Metadata: {doc.metadata}, Score: {score}\")"
+    "for doc in results:\n",
+    "    print(f\"Content: {doc.page_content}, Metadata: {doc.metadata}\")"
    ]
   }
  ],
@@ -502,7 +491,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/extras/modules/data_connection/vectorstores/integrations/qdrant.ipynb

@@ -626,6 +626,44 @@
    "source": [
     "## Customizing Qdrant\n",
     "\n",
+    "There are some options to use an existing Qdrant collection within your Langchain application. In such cases you may need to define how to map Qdrant point into the Langchain `Document`.\n",
+    "\n",
+    "### Named vectors\n",
+    "\n",
+    "Qdrant supports [multiple vectors per point](https://qdrant.tech/documentation/concepts/collections/#collection-with-multiple-vectors) by named vectors. Langchain requires just a single embedding per document and, by default, uses a single vector. However, if you work with a collection created externally or want to have the named vector used, you can configure it by providing its name.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "Qdrant.from_documents(\n",
+    "    docs,\n",
+    "    embeddings,\n",
+    "    location=\":memory:\",\n",
+    "    collection_name=\"my_documents_2\",\n",
+    "    vector_name=\"custom_vector\",\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "As a Langchain user, you won't see any difference whether you use named vectors or not. Qdrant integration will handle the conversion under the hood."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Metadata\n",
+    "\n",
     "Qdrant stores your vector embeddings along with the optional JSON-like payload. Payloads are optional, but since LangChain assumes the embeddings are generated from the documents, we keep the context data, so you can extract the original texts as well.\n",
     "\n",
     "By default, your document is going to be stored in the following payload structure:\n",
@@ -639,8 +677,11 @@
     "}\n",
     "```\n",
     "\n",
-    "You can, however, decide to use different keys for the page content and metadata. That's useful if you already have a collection that you'd like to reuse. You can always change the "
-   ]
+    "You can, however, decide to use different keys for the page content and metadata. That's useful if you already have a collection that you'd like to reuse."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",

docs/extras/modules/data_connection/vectorstores/integrations/vectara.ipynb

@@ -11,43 +11,11 @@
     ">[Vectara](https://vectara.com/) is a API platform for building LLM-powered applications. It provides a simple to use API for document indexing and query that is managed by Vectara and is optimized for performance and accuracy. \n",
     "\n",
     "\n",
-    "This notebook shows how to use functionality related to the `Vectara` vector database. \n",
+    "This notebook shows how to use functionality related to the `Vectara` vector database or the `Vectara` retriever. \n",
     "\n",
     "See the [Vectara API documentation ](https://docs.vectara.com/docs/) for more information on how to use the API."
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "7b2f111b-357a-4f42-9730-ef0603bdc1b5",
-   "metadata": {},
-   "source": [
-    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "082e7e8b-ac52-430c-98d6-8f0924457642",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "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": 2,
@@ -61,33 +29,13 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "import os\n",
+    "from langchain.embeddings import FakeEmbeddings\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain.vectorstores import Vectara\n",
     "from langchain.document_loaders import TextLoader"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a3c3999a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-04-04T10:51:22.520144Z",
-     "start_time": "2023-04-04T10:51:22.285826Z"
-    },
-    "tags": []
-   },
-   "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()"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -96,7 +44,21 @@
    "source": [
     "## Connecting to Vectara from LangChain\n",
     "\n",
-    "The Vectara API provides simple API endpoints for indexing and querying."
+    "The Vectara API provides simple API endpoints for indexing and querying, which is encapsulated in the Vectara integration.\n",
+    "First let's ingest the documents using the from_documents() method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "be0a4973",
+   "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"
    ]
   },
   {
@@ -112,7 +74,50 @@
    },
    "outputs": [],
    "source": [
-    "vectara = Vectara.from_documents(docs, embedding=None)"
+    "vectara = Vectara.from_documents(docs, \n",
+    "                                 embedding=FakeEmbeddings(size=768), \n",
+    "                                 doc_metadata = {\"speech\": \"state-of-the-union\"})"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "90dbf3e7",
+   "metadata": {},
+   "source": [
+    "Vectara's indexing API provides a file upload API where the file is handled directly by Vectara - pre-processed, chunked optimally and added to the Vectara vector store.\n",
+    "To use this, we added the add_files() method (and from_files()). \n",
+    "\n",
+    "Let's see this in action. We pick two PDF documents to upload: \n",
+    "1. The \"I have a dream\" speech by Dr. King\n",
+    "2. Churchill's \"We Shall Fight on the Beaches\" speech"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "85ef3468",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tempfile\n",
+    "import urllib.request\n",
+    "\n",
+    "urls = [\n",
+    "    ['https://www.gilderlehrman.org/sites/default/files/inline-pdfs/king.dreamspeech.excerpts.pdf', 'I-have-a-dream'],\n",
+    "    ['https://www.parkwayschools.net/cms/lib/MO01931486/Centricity/Domain/1578/Churchill_Beaches_Speech.pdf', 'we shall fight on the beaches'],\n",
+    "]\n",
+    "files_list = []\n",
+    "for url,_ in urls:\n",
+    "    name = tempfile.NamedTemporaryFile().name\n",
+    "    urllib.request.urlretrieve(url, name)\n",
+    "    files_list.append(name)\n",
+    "\n",
+    "docsearch: Vectara = Vectara.from_files(\n",
+    "    files=files_list,\n",
+    "    embedding=FakeEmbeddings(size=768),\n",
+    "    metadatas=[{\"url\": url, \"speech\": title} for url,title in urls],\n",
+    ")"
    ]
   },
   {
@@ -133,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "a8c513ab",
    "metadata": {
     "ExecuteTime": {
@@ -145,12 +150,12 @@
    "outputs": [],
    "source": [
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "found_docs = vectara.similarity_search(query, n_sentence_context=0)"
+    "found_docs = vectara.similarity_search(query, n_sentence_context=0, filter=\"doc.speech = 'state-of-the-union'\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "fc516993",
    "metadata": {
     "ExecuteTime": {
@@ -191,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "8804a21d",
    "metadata": {
     "ExecuteTime": {
@@ -202,12 +207,12 @@
    "outputs": [],
    "source": [
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "found_docs = vectara.similarity_search_with_score(query)"
+    "found_docs = vectara.similarity_search_with_score(query, filter=\"doc.speech = 'state-of-the-union'\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "756a6887",
    "metadata": {
     "ExecuteTime": {
@@ -228,7 +233,7 @@
       "\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: 0.7129974\n"
+      "Score: 0.4917977\n"
      ]
     }
    ],
@@ -238,6 +243,37 @@
     "print(f\"\\nScore: {score}\")"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "1f9876a8",
+   "metadata": {},
+   "source": [
+    "Now let's do similar search for content in the files we uploaded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "47784de5",
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"We must forever conduct our struggle\"\n",
+    "found_docs = vectara.similarity_search_with_score(query, filter=\"doc.speech = 'I-have-a-dream'\")\n",
+    "print(found_docs[0])\n",
+    "print(found_docs[1])"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -246,12 +282,12 @@
    "source": [
     "## Vectara as a Retriever\n",
     "\n",
-    "Vectara, as all the other vector stores, is a LangChain Retriever, by using cosine similarity. "
+    "Vectara, as all the other vector stores, can be used also as a LangChain Retriever:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "9427195f",
    "metadata": {
     "ExecuteTime": {
@@ -263,10 +299,10 @@
     {
      "data": {
       "text/plain": [
-       "VectaraRetriever(vectorstore=<langchain.vectorstores.vectara.Vectara object at 0x122db2830>, search_type='similarity', search_kwargs={'lambda_val': 0.025, 'k': 5, 'filter': '', 'n_sentence_context': '0'})"
+       "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'})"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -278,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "f3c70c31",
    "metadata": {
     "ExecuteTime": {
@@ -293,7 +329,7 @@
        "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'})"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/extras/modules/memory/integrations/cassandra_chat_message_history.ipynb

@@ -1,34 +1,116 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
-   "id": "91c6a7ef",
+   "id": "90cd3ded",
    "metadata": {},
    "source": [
     "# Cassandra Chat Message History\n",
     "\n",
+    ">[Apache Cassandra®](https://cassandra.apache.org) is a NoSQL, row-oriented, highly scalable and highly available database, well suited for storing large amounts of data.\n",
+    "\n",
+    "Cassandra is a good choice for storing chat message history because it is easy to scale and can handle a large number of writes.\n",
+    "\n",
     "This notebook goes over how to use Cassandra to store chat message history.\n",
     "\n",
-    "Cassandra is a distributed database that is well suited for storing large amounts of data. \n",
-    "\n",
-    "It is a good choice for storing chat message history because it is easy to scale and can handle a large number of writes.\n"
+    "To run this notebook you need either a running Cassandra cluster or a DataStax Astra DB instance running in the cloud (you can get one for free at [datastax.com](https://astra.datastax.com)). Check [cassio.org](https://cassio.org/start_here/) for more information."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "47a601d2",
+   "execution_count": null,
+   "id": "d7092199",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# List of contact points to try connecting to Cassandra cluster.\n",
-    "contact_points = [\"cassandra\"]"
+    "!pip install \"cassio>=0.0.6\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3d97b65",
+   "metadata": {},
+   "source": [
+    "### Please provide database connection parameters and secrets:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
+   "id": "163d97f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "database_mode = (input('\\n(C)assandra or (A)stra DB? ')).upper()\n",
+    "\n",
+    "keyspace_name = input('\\nKeyspace name? ')\n",
+    "\n",
+    "if database_mode == 'A':\n",
+    "    ASTRA_DB_APPLICATION_TOKEN = getpass.getpass('\\nAstra DB Token (\"AstraCS:...\") ')\n",
+    "    #\n",
+    "    ASTRA_DB_SECURE_BUNDLE_PATH = input('Full path to your Secure Connect Bundle? ')\n",
+    "elif database_mode == 'C':\n",
+    "    CASSANDRA_CONTACT_POINTS = input('Contact points? (comma-separated, empty for localhost) ').strip()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55860b2d",
+   "metadata": {},
+   "source": [
+    "#### depending on whether local or cloud-based Astra DB, create the corresponding database connection \"Session\" object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8dff2798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cassandra.cluster import Cluster\n",
+    "from cassandra.auth import PlainTextAuthProvider\n",
+    "\n",
+    "if database_mode == 'C':\n",
+    "    if CASSANDRA_CONTACT_POINTS:\n",
+    "        cluster = Cluster([\n",
+    "            cp.strip()\n",
+    "            for cp in CASSANDRA_CONTACT_POINTS.split(',')\n",
+    "            if cp.strip()\n",
+    "        ])\n",
+    "    else:\n",
+    "        cluster = Cluster()\n",
+    "    session = cluster.connect()\n",
+    "elif database_mode == 'A':\n",
+    "    ASTRA_DB_CLIENT_ID = \"token\"\n",
+    "    cluster = Cluster(\n",
+    "        cloud={\n",
+    "            \"secure_connect_bundle\": ASTRA_DB_SECURE_BUNDLE_PATH,\n",
+    "        },\n",
+    "        auth_provider=PlainTextAuthProvider(\n",
+    "            ASTRA_DB_CLIENT_ID,\n",
+    "            ASTRA_DB_APPLICATION_TOKEN,\n",
+    "        ),\n",
+    "    )\n",
+    "    session = cluster.connect()\n",
+    "else:\n",
+    "    raise NotImplementedError"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36c163e8",
+   "metadata": {},
+   "source": [
+    "### Creation and usage of the Chat Message History"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "d15e3302",
    "metadata": {},
    "outputs": [],
@@ -36,7 +118,9 @@
     "from langchain.memory import CassandraChatMessageHistory\n",
     "\n",
     "message_history = CassandraChatMessageHistory(\n",
-    "    contact_points=contact_points, session_id=\"test-session\"\n",
+    "    session_id=\"test-session\",\n",
+    "    session=session,\n",
+    "    keyspace=keyspace_name,\n",
     ")\n",
     "\n",
     "message_history.add_user_message(\"hi!\")\n",
@@ -46,22 +130,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "64fc465e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[HumanMessage(content='hi!', additional_kwargs={}, example=False),\n",
-       " AIMessage(content='whats up?', additional_kwargs={}, example=False)]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "message_history.messages"
    ]
@@ -83,7 +155,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

docs/extras/modules/memory/integrations/zep_memory.ipynb

@@ -40,10 +40,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-25T15:09:41.754535Z",
-     "start_time": "2023-05-25T15:09:40.897232Z"
-    }
+    "is_executing": true
    },
    "outputs": [],
    "source": [
@@ -51,8 +48,8 @@
     "from langchain.memory import ConversationBufferMemory\n",
     "from langchain import OpenAI\n",
     "from langchain.schema import HumanMessage, AIMessage\n",
-    "from langchain.tools import DuckDuckGoSearchRun\n",
-    "from langchain.agents import initialize_agent, AgentType\n",
+    "from langchain.utilities import WikipediaAPIWrapper\n",
+    "from langchain.agents import initialize_agent, AgentType, Tool\n",
     "from uuid import uuid4\n",
     "\n",
     "\n",
@@ -73,19 +70,37 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": "True"
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
     }
    ],
    "source": [
-    "# Load your OpenAI key from a .env file\n",
-    "from dotenv import load_dotenv\n",
+    "# Provide your OpenAI key\n",
+    "import getpass\n",
     "\n",
-    "load_dotenv()"
+    "openai_key = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Provide your Zep API key. Note that this is optional. See https://docs.getzep.com/deployment/auth\n",
+    "\n",
+    "zep_api_key = getpass.getpass()"
    ]
   },
   {
@@ -98,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-25T15:09:41.840440Z",
@@ -107,13 +122,20 @@
    },
    "outputs": [],
    "source": [
-    "ddg = DuckDuckGoSearchRun()\n",
-    "tools = [ddg]\n",
+    "search = WikipediaAPIWrapper()\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name=\"Search\",\n",
+    "        func=search.run,\n",
+    "        description=\"useful for when you need to search online for answers. You should ask targeted questions\",\n",
+    "    ),\n",
+    "]\n",
     "\n",
     "# Set up Zep Chat History\n",
     "zep_chat_history = ZepChatMessageHistory(\n",
     "    session_id=session_id,\n",
     "    url=ZEP_API_URL,\n",
+    "    api_key=zep_api_key\n",
     ")\n",
     "\n",
     "# Use a standard ConversationBufferMemory to encapsulate the Zep chat history\n",
@@ -122,7 +144,7 @@
     ")\n",
     "\n",
     "# Initialize the agent\n",
-    "llm = OpenAI(temperature=0)\n",
+    "llm = OpenAI(temperature=0, openai_api_key=openai_key)\n",
     "agent_chain = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
@@ -142,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-25T15:09:41.960661Z",
@@ -212,7 +234,7 @@
     "]\n",
     "\n",
     "for msg in test_history:\n",
-    "    zep_chat_history.append(\n",
+    "    zep_chat_history.add_message(\n",
     "        HumanMessage(content=msg[\"content\"])\n",
     "        if msg[\"role\"] == \"human\"\n",
     "        else AIMessage(content=msg[\"content\"])\n",
@@ -231,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-25T15:09:50.485377Z",
@@ -245,18 +267,20 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: Do I need to use a tool? No\n",
-      "AI: Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.\u001b[0m\n",
+      "\u001B[1m> Entering new  chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3mThought: Do I need to use a tool? No\n",
+      "AI: Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
      "data": {
-      "text/plain": "'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.'"
+      "text/plain": [
+       "'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.'"
+      ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -281,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-25T15:09:50.493438Z",
@@ -293,19 +317,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The conversation is about Octavia Butler. The AI describes her as an American science fiction author and mentions the\n",
-      "FX series Kindred as a well-known adaptation of her work. The human then asks about her contemporaries, and the AI lists \n",
-      "Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\n",
+      "The human asks about Octavia Butler and the AI identifies her as an American science fiction author. They continue to discuss her works and the fact that the FX series Kindred is based on one of her novels. The AI also lists Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ as Butler's contemporaries.\n",
       "\n",
       "\n",
-      "{'role': 'human', 'content': 'What awards did she win?', 'uuid': '9fa75c3c-edae-41e3-b9bc-9fcf16b523c9', 'created_at': '2023-05-25T15:09:41.91662Z', 'token_count': 8}\n",
-      "{'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'uuid': 'def4636c-32cb-49ed-b671-32035a034712', 'created_at': '2023-05-25T15:09:41.919874Z', 'token_count': 21}\n",
-      "{'role': 'human', 'content': 'Which other women sci-fi writers might I want to read?', 'uuid': '6e87bd4a-bc23-451e-ae36-05a140415270', 'created_at': '2023-05-25T15:09:41.923771Z', 'token_count': 14}\n",
-      "{'role': 'ai', 'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'uuid': 'f65d8dde-9ee8-4983-9da6-ba789b7e8aa4', 'created_at': '2023-05-25T15:09:41.935254Z', 'token_count': 18}\n",
-      "{'role': 'human', 'content': \"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", 'uuid': '5678d056-7f05-4e70-b8e5-f85efa56db01', 'created_at': '2023-05-25T15:09:41.938974Z', 'token_count': 23}\n",
-      "{'role': 'ai', 'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', 'uuid': '50d64946-9239-4327-83e6-71dcbdd16198', 'created_at': '2023-05-25T15:09:41.957437Z', 'token_count': 56}\n",
-      "{'role': 'human', 'content': \"WWhat is the book's relevance to the challenges facing contemporary society?\", 'uuid': 'a39cfc07-8858-480a-9026-fc47a8ef7001', 'created_at': '2023-05-25T15:09:50.469533Z', 'token_count': 16}\n",
-      "{'role': 'ai', 'content': 'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.', 'uuid': 'a4ecf0fe-fdd0-4aad-b72b-efde2e6830cc', 'created_at': '2023-05-25T15:09:50.473793Z', 'token_count': 62}\n"
+      "{'role': 'human', 'content': 'What awards did she win?', 'uuid': 'a4bdc592-71a5-47d0-9c64-230b882aab48', 'created_at': '2023-06-26T23:37:56.383953Z', 'token_count': 8, 'metadata': {'system': {'entities': [], 'intent': 'The subject is asking about the awards someone won, likely referring to a specific individual.'}}}\n",
+      "{'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'uuid': '60cc6e6b-7cd4-4a81-aebc-72ef997286b4', 'created_at': '2023-06-26T23:37:56.389935Z', 'token_count': 21, 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 14, 'Start': 0, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 33, 'Start': 19, 'Text': 'the Hugo Award'}], 'Name': 'the Hugo Award'}, {'Label': 'EVENT', 'Matches': [{'End': 81, 'Start': 57, 'Text': 'the MacArthur Fellowship'}], 'Name': 'the MacArthur Fellowship'}], 'intent': 'The subject is stating the accomplishments and awards received by Octavia Butler.'}}}\n",
+      "{'role': 'human', 'content': 'Which other women sci-fi writers might I want to read?', 'uuid': 'b189fc60-1510-4a4b-a503-899481d652de', 'created_at': '2023-06-26T23:37:56.395722Z', 'token_count': 14, 'metadata': {'system': {'entities': [], 'intent': 'The subject is looking for recommendations on women science fiction writers to read.'}}}\n",
+      "{'role': 'ai', 'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'uuid': '4be1ccbb-a915-45d6-9f18-7a0c1cbd9907', 'created_at': '2023-06-26T23:37:56.403596Z', 'token_count': 18, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is suggesting reading material and making a literary recommendation.'}}}\n",
+      "{'role': 'human', 'content': \"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", 'uuid': 'ac3c5e3e-26a7-4f3b-aeb0-bba084e22753', 'created_at': '2023-06-26T23:37:56.410662Z', 'token_count': 23, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}], 'intent': 'The subject is asking for a brief overview or summary of the book \"Parable of the Sower\" written by Butler.'}}}\n",
+      "{'role': 'ai', 'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', 'uuid': '4a463b4c-bcab-473c-bed1-fc56a7a20ae2', 'created_at': '2023-06-26T23:37:56.41764Z', 'token_count': 56, 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}]}}}\n",
+      "{'role': 'human', 'content': \"WWhat is the book's relevance to the challenges facing contemporary society?\", 'uuid': '41bab0c7-5e20-40a4-9303-f82069977c91', 'created_at': '2023-06-26T23:38:03.559642Z', 'token_count': 16, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 5, 'Start': 0, 'Text': 'WWhat'}], 'Name': 'WWhat'}]}}}\n",
+      "{'role': 'ai', 'content': 'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.', 'uuid': 'bfd8146a-4632-4c8c-98b6-9468bb624339', 'created_at': '2023-06-26T23:38:03.589312Z', 'token_count': 62, 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}]}}}\n"
      ]
     }
    ],
@@ -332,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-05-25T15:09:50.751203Z",
@@ -344,16 +366,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'uuid': '6e87bd4a-bc23-451e-ae36-05a140415270', 'created_at': '2023-05-25T15:09:41.923771Z', 'role': 'human', 'content': 'Which other women sci-fi writers might I want to read?', 'token_count': 14} 0.9118298949424545\n",
-      "{'uuid': 'f65d8dde-9ee8-4983-9da6-ba789b7e8aa4', 'created_at': '2023-05-25T15:09:41.935254Z', 'role': 'ai', 'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'token_count': 18} 0.8533024416448016\n",
-      "{'uuid': '52cfe3e8-b800-4dd8-a7dd-8e9e4764dfc8', 'created_at': '2023-05-25T15:09:41.913856Z', 'role': 'ai', 'content': \"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", 'token_count': 27} 0.852352466457884\n",
-      "{'uuid': 'd40da612-0867-4a43-92ec-778b86490a39', 'created_at': '2023-05-25T15:09:41.858543Z', 'role': 'human', 'content': 'Who was Octavia Butler?', 'token_count': 8} 0.8235468913583194\n",
-      "{'uuid': '4fcfbce4-7bfa-44bd-879a-8cbf265bdcf9', 'created_at': '2023-05-25T15:09:41.893848Z', 'role': 'ai', 'content': 'Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author.', 'token_count': 31} 0.8204317130595353\n",
-      "{'uuid': 'def4636c-32cb-49ed-b671-32035a034712', 'created_at': '2023-05-25T15:09:41.919874Z', 'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'token_count': 21} 0.8196714827228725\n",
-      "{'uuid': '862107de-8f6f-43c0-91fa-4441f01b2b3a', 'created_at': '2023-05-25T15:09:41.898149Z', 'role': 'human', 'content': 'Which books of hers were made into movies?', 'token_count': 11} 0.7954322970428519\n",
-      "{'uuid': '97164506-90fe-4c71-9539-69ebcd1d90a2', 'created_at': '2023-05-25T15:09:41.90887Z', 'role': 'human', 'content': 'Who were her contemporaries?', 'token_count': 8} 0.7942531405021976\n",
-      "{'uuid': '50d64946-9239-4327-83e6-71dcbdd16198', 'created_at': '2023-05-25T15:09:41.957437Z', 'role': 'ai', 'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', 'token_count': 56} 0.78144769172694\n",
-      "{'uuid': 'c460ffd4-0715-4c69-b793-1092054973e6', 'created_at': '2023-05-25T15:09:41.903082Z', 'role': 'ai', 'content': \"The most well-known adaptation of Octavia Butler's work is the FX series Kindred, based on her novel of the same name.\", 'token_count': 29} 0.7811962820699464\n"
+      "{'uuid': 'b189fc60-1510-4a4b-a503-899481d652de', 'created_at': '2023-06-26T23:37:56.395722Z', 'role': 'human', 'content': 'Which other women sci-fi writers might I want to read?', 'metadata': {'system': {'entities': [], 'intent': 'The subject is looking for recommendations on women science fiction writers to read.'}}, 'token_count': 14} 0.9119619869747062\n",
+      "{'uuid': '4be1ccbb-a915-45d6-9f18-7a0c1cbd9907', 'created_at': '2023-06-26T23:37:56.403596Z', 'role': 'ai', 'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is suggesting reading material and making a literary recommendation.'}}, 'token_count': 18} 0.8534346954749745\n",
+      "{'uuid': '76ec2a3d-b908-4c23-a55d-71ff92865a7a', 'created_at': '2023-06-26T23:37:56.378345Z', 'role': 'ai', 'content': \"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is stating the contemporaries of Octavia Butler, who are also science fiction writers.'}}, 'token_count': 27} 0.8523930955780226\n",
+      "{'uuid': '1feb02c7-63c9-4616-854d-0d97fb590ea5', 'created_at': '2023-06-26T23:37:56.313009Z', 'role': 'human', 'content': 'Who was Octavia Butler?', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}], 'intent': 'The subject is asking about the identity of Octavia Butler, likely seeking information about her background or accomplishments.'}}, 'token_count': 8} 0.8236355436055457\n",
+      "{'uuid': 'ebe4696d-b5fa-4ca0-88c9-da794d9611ab', 'created_at': '2023-06-26T23:37:56.332247Z', 'role': 'ai', 'content': 'Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author.', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 0, 'Text': 'Octavia Estelle Butler'}], 'Name': 'Octavia Estelle Butler'}, {'Label': 'DATE', 'Matches': [{'End': 37, 'Start': 24, 'Text': 'June 22, 1947'}], 'Name': 'June 22, 1947'}, {'Label': 'DATE', 'Matches': [{'End': 57, 'Start': 40, 'Text': 'February 24, 2006'}], 'Name': 'February 24, 2006'}, {'Label': 'NORP', 'Matches': [{'End': 74, 'Start': 66, 'Text': 'American'}], 'Name': 'American'}], 'intent': 'The subject is making a statement about the background and profession of Octavia Estelle Butler, an American author.'}}, 'token_count': 31} 0.8206687242257686\n",
+      "{'uuid': '60cc6e6b-7cd4-4a81-aebc-72ef997286b4', 'created_at': '2023-06-26T23:37:56.389935Z', 'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 14, 'Start': 0, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 33, 'Start': 19, 'Text': 'the Hugo Award'}], 'Name': 'the Hugo Award'}, {'Label': 'EVENT', 'Matches': [{'End': 81, 'Start': 57, 'Text': 'the MacArthur Fellowship'}], 'Name': 'the MacArthur Fellowship'}], 'intent': 'The subject is stating the accomplishments and awards received by Octavia Butler.'}}, 'token_count': 21} 0.8194249796585193\n",
+      "{'uuid': '0fa4f336-909d-4880-b01a-8e80e91fa8f2', 'created_at': '2023-06-26T23:37:56.344552Z', 'role': 'human', 'content': 'Which books of hers were made into movies?', 'metadata': {'system': {'entities': [], 'intent': 'The subject is inquiring about which books written by an unknown female author were adapted into movies.'}}, 'token_count': 11} 0.7955105671310818\n",
+      "{'uuid': 'f91de7f2-4b84-4c5a-8a33-a71f38f3a59c', 'created_at': '2023-06-26T23:37:56.368146Z', 'role': 'human', 'content': 'Who were her contemporaries?', 'metadata': {'system': {'entities': [], 'intent': 'The subject is asking about the people who lived during the same time period as a specific individual.'}}, 'token_count': 8} 0.7942358617914813\n",
+      "{'uuid': '4a463b4c-bcab-473c-bed1-fc56a7a20ae2', 'created_at': '2023-06-26T23:37:56.41764Z', 'role': 'ai', 'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}]}}, 'token_count': 56} 0.7816448549236643\n",
+      "{'uuid': '6161d934-a629-4ba2-8bba-0b0996c93964', 'created_at': '2023-06-26T23:37:56.358632Z', 'role': 'ai', 'content': \"The most well-known adaptation of Octavia Butler's work is the FX series Kindred, based on her novel of the same name.\", 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 50, 'Start': 34, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 65, 'Start': 63, 'Text': 'FX'}], 'Name': 'FX'}, {'Label': 'GPE', 'Matches': [{'End': 80, 'Start': 73, 'Text': 'Kindred'}], 'Name': 'Kindred'}], 'intent': \"The subject is discussing Octavia Butler's work being adapted into a TV series called Kindred.\"}}, 'token_count': 29} 0.7815841371388998\n"
      ]
     }
    ],
@@ -362,11 +384,25 @@
     "for r in search_results:\n",
     "    print(r.message, r.dist)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -380,10 +416,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  },
-  "orig_nbformat": 4
+   "version": "3.11.4"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/extras/modules/model_io/models/llms/integrations/llamacpp.ipynb

@@ -23,6 +23,7 @@
     "There is a banch of options how to install the llama-cpp package: \n",
     "- only CPU usage\n",
     "- CPU + GPU (using one of many BLAS backends)\n",
+    "- Metal GPU (MacOS with Apple Silicon Chip) \n",
     "\n",
     "### CPU only installation"
    ]
@@ -73,7 +74,45 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python"
+    "!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cache-dir"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation with Metal\n",
+    "\n",
+    "`lama.cpp` supports Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks. Use the `FORCE_CMAKE=1` environment variable to force the use of cmake and install the pip package for the Metal support ([source](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md)).\n",
+    "\n",
+    "Example installation with Metal Support:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!CMAKE_ARGS=\"-DLLAMA_METAL=on\" FORCE_CMAKE=1 pip install llama-cpp-python"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**IMPORTANT**: If you have already installed a cpu only version of the package, you need to reinstall it from scratch: consider the following command: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!CMAKE_ARGS=\"-DLLAMA_METAL=on\" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cache-dir"
    ]
   },
   {
@@ -325,6 +364,61 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metal\n",
+    "\n",
+    "If the installation with Metal was correct, you will see an `NEON = 1` indicator in model properties.\n",
+    "\n",
+    "Two of the most important parameters for use with GPU are:\n",
+    "\n",
+    "- `n_gpu_layers` - determines how many layers of the model are offloaded to your Metal GPU, in the most case, set it to `1` is enough for Metal\n",
+    "- `n_batch` - how many tokens are processed in parallel, default is 8, set to bigger number.\n",
+    "- `f16_kv` - for some reason, Metal only support `True`, otherwise you will get error such as `Asserting on type 0\n",
+    "GGML_ASSERT: .../ggml-metal.m:706: false && \"not implemented\"`\n",
+    "\n",
+    "Setting these parameters correctly will dramatically improve the evaluation speed (see [wrapper code](https://github.com/mmagnesium/langchain/blob/master/langchain/llms/llamacpp.py) for more details)."
+   ]
+  },
+  {
+   "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",
+    "\n",
+    "# Make sure the model path is correct for your system!\n",
+    "llm = LlamaCpp(\n",
+    "    model_path=\"./ggml-model-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",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The rest are almost same as GPU, the console log will show the following log to indicate the Metal was enable properly.\n",
+    "\n",
+    "```\n",
+    "ggml_metal_init: allocating\n",
+    "ggml_metal_init: using MPS\n",
+    "...\n",
+    "```\n",
+    "\n",
+    "You also could check the `Activity Monitor` by watching the % GPU of the process, the % CPU will drop dramatically after turn on `n_gpu_layers=1`. Also for the first time call LLM, the performance might be slow due to the model compilation in Metal GPU."
+   ]
   }
  ],
  "metadata": {

docs/extras/modules/model_io/models/llms/integrations/octoai.ipynb

@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## OctoAI Compute Service\n",
+    "This example goes over how to use LangChain to interact with `OctoAI` [LLM endpoints](https://octoai.cloud/templates)\n",
+    "## Environment setup\n",
+    "\n",
+    "To run our example app, there are four simple steps to take:\n",
+    "\n",
+    "1. Clone the MPT-7B demo template to your OctoAI account by visiting <https://octoai.cloud/templates/mpt-7b-demo> then clicking \"Clone Template.\" \n",
+    "   1. If you want to use a different LLM model, you can also containerize the model and make a custom OctoAI endpoint yourself, by following [Build a Container from Python](doc:create-custom-endpoints-from-python-code) and [Create a Custom Endpoint from a Container](doc:create-custom-endpoints-from-a-container)\n",
+    "   \n",
+    "2. Paste your Endpoint URL in the code cell below\n",
+    "\n",
+    "3. Get an API Token from [your OctoAI account page](https://octoai.cloud/settings).\n",
+    "   \n",
+    "4. Paste your API key in in the code cell below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"OCTOAI_API_TOKEN\"] = \"OCTOAI_API_TOKEN\"\n",
+    "os.environ[\"ENDPOINT_URL\"] = \"https://mpt-7b-demo-kk0powt97tmb.octoai.cloud/generate\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms.octoai_endpoint import OctoAIEndpoint\n",
+    "from langchain import PromptTemplate, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n Instruction:\\n{question}\\n Response: \"\"\"\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OctoAIEndpoint(\n",
+    "        model_kwargs={\n",
+    "            \"max_new_tokens\": 200,\n",
+    "            \"temperature\": 0.75,\n",
+    "            \"top_p\": 0.95,\n",
+    "            \"repetition_penalty\": 1,\n",
+    "            \"seed\": None,\n",
+    "            \"stop\": [],\n",
+    "        },\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nLeonardo da Vinci was an Italian polymath and painter regarded by many as one of the greatest painters of all time. He is best known for his masterpieces including Mona Lisa, The Last Supper, and The Virgin of the Rocks. He was a draftsman, sculptor, architect, and one of the most important figures in the history of science. Da Vinci flew gliders, experimented with water turbines and windmills, and invented the catapult and a joystick-type human-powered aircraft control. He may have pioneered helicopters. As a scholar, he was interested in anatomy, geology, botany, engineering, mathematics, and astronomy.\\nOther painters and patrons claimed to be more talented, but Leonardo da Vinci was an incredibly productive artist, sculptor, engineer, anatomist, and scientist.'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"Who was leonardo davinci?\"\n",
+    "\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "\n",
+    "llm_chain.run(question)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain",
+   "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": "3.9.16"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "97697b63fdcee0a640856f91cb41326ad601964008c341809e43189d1cab1047"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/extras/use_cases/apis.mdx

@@ -9,7 +9,7 @@ If you are just getting started, and you have relatively simple apis, you should
 Chains are a sequence of predetermined steps, so they are good to get started with as they give you more control and let you 
 understand what is happening better.
 
-- [API Chain](/docs/modules/chains/how_to/api.html)
+- [API Chain](/docs/modules/chains/popular/api.html)
 
 ## Agents
 

docs/extras/use_cases/question_answering/document-context-aware-QA.ipynb

@@ -30,14 +30,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "2e587f65",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Load Notion page as a markdownfile file\n",
     "from langchain.document_loaders import NotionDirectoryLoader\n",
-    "path='.../Notion_Folder_With_Markdown_File'\n",
+    "path='../Notion_DB/'\n",
     "loader = NotionDirectoryLoader(path)\n",
     "docs = loader.load()\n",
     "md_file=docs[0].page_content"
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "1cd3fd7e",
    "metadata": {},
    "outputs": [],
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "id": "7fbff95f",
    "metadata": {},
    "outputs": [],
@@ -110,8 +110,10 @@
    "outputs": [],
    "source": [
     "# Build vectorstore and keep the metadata\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.vectorstores import Chroma\n",
-    "vectorstore = Chroma.from_documents(texts=all_splits,metadatas=all_metadatas,embedding=OpenAIEmbeddings())"
+    "vectorstore = Chroma.from_documents(documents=all_splits,\n",
+    "                                    embedding=OpenAIEmbeddings())"
    ]
   },
   {
@@ -157,6 +159,37 @@
     "We can see that we can query *only for texts* in the `Introduction` of the document!"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d688db6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='Introduction' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='Section', value='Introduction') limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='![Untitled](Auto-Evaluation%20of%20Metadata%20Filtering%2018502448c85240828f33716740f9574b/Untitled.png)', metadata={'Section': 'Introduction'}),\n",
+       " Document(page_content='Q+A systems often use a two-step approach: retrieve relevant text chunks and then synthesize them into an answer. There many ways to approach this. For example, we recently [discussed](https://blog.langchain.dev/auto-evaluation-of-anthropic-100k-context-window/) the Retriever-Less option (at bottom in the below diagram), highlighting the Anthropic 100k context window model. Metadata filtering is an alternative approach that pre-filters chunks based on a user-defined criteria in a VectorDB using', metadata={'Section': 'Introduction'}),\n",
+       " Document(page_content='metadata tags prior to semantic search.', metadata={'Section': 'Introduction'})]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Test\n",
+    "retriever.get_relevant_documents(\"Summarize the Introduction section of the document\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 29,
@@ -287,6 +320,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
   }
  },
  "nbformat": 4,

docs/package-lock.json

@@ -0,0 +1,6 @@
+{
+  "name": "docs",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {}
+}

docs/requirements.txt

@@ -11,5 +11,4 @@ myst_nb
 sphinx_copybutton
 pydata-sphinx-theme==0.13.1
 nbdoc
-urllib3<2
-sphinx_tabs
+urllib3<2

docs/snippets/modules/data_connection/document_loaders/how_to/json.mdx

@@ -78,11 +78,14 @@ pprint(data)
 
 </CodeOutputBlock>
 
+
 ## Using `JSONLoader`
 
 Suppose we are interested in extracting the values under the `content` field within the `messages` key of the JSON data. This can easily be done through the `JSONLoader` as shown below.
 
 
+### JSON file
+
 ```python
 loader = JSONLoader(
     file_path='./example_data/facebook_chat.json',
@@ -114,6 +117,81 @@ pprint(data)
 
 </CodeOutputBlock>
 
+
+### JSON Lines file
+
+If you want to load documents from a JSON Lines file, you pass `json_lines=True`
+and specify `jq_schema` to extract `page_content` from a single JSON object.
+
+```python
+file_path = './example_data/facebook_chat_messages.jsonl'
+pprint(Path(file_path).read_text())
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    ('{"sender_name": "User 2", "timestamp_ms": 1675597571851, "content": "Bye!"}\n'
+     '{"sender_name": "User 1", "timestamp_ms": 1675597435669, "content": "Oh no '
+     'worries! Bye"}\n'
+     '{"sender_name": "User 2", "timestamp_ms": 1675596277579, "content": "No Im '
+     'sorry it was my mistake, the blue one is not for sale"}\n')
+```
+
+</CodeOutputBlock>
+
+
+```python
+loader = JSONLoader(
+    file_path='./example_data/facebook_chat_messages.jsonl',
+    jq_schema='.content',
+    json_lines=True)
+
+data = loader.load()
+```
+
+```python
+pprint(data)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    [Document(page_content='Bye!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}),
+     Document(page_content='Oh no worries! Bye', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 2}),
+     Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 3})]
+```
+
+</CodeOutputBlock>
+
+
+Another option is set `jq_schema='.'` and provide `content_key`:
+
+```python
+loader = JSONLoader(
+    file_path='./example_data/facebook_chat_messages.jsonl',
+    jq_schema='.',
+    content_key='sender_name',
+    json_lines=True)
+
+data = loader.load()
+```
+
+```python
+pprint(data)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    [Document(page_content='User 2', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}),
+     Document(page_content='User 1', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 2}),
+     Document(page_content='User 2', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat_messages.jsonl', 'seq_num': 3})]
+```
+
+</CodeOutputBlock>
+
+
 ## Extracting metadata
 
 Generally, we want to include metadata available in the JSON file into the documents that we create from the content.