add everyting for docs

docs/.local_build.sh

@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+
+set -o errexit
+set -o nounset
+set -o pipefail
+set -o xtrace
+
+SCRIPT_DIR="$(cd "$(dirname "$0")"; pwd)"
+cd "${SCRIPT_DIR}"
+
+mkdir -p _dist/docs_skeleton
+cp -r {docs_skeleton,snippets} _dist
+cp -r extras/* _dist/docs_skeleton/docs
+cd _dist/docs_skeleton
+poetry run nbdoc_build
+poetry run python generate_api_reference_links.py
+yarn install
+yarn start

docs/api_reference/Makefile

@@ -0,0 +1,21 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?= 
+SPHINXBUILD   ?= sphinx-build
+SPHINXAUTOBUILD   ?= sphinx-autobuild
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/api_reference/_static/css/custom.css

@@ -0,0 +1,17 @@
+pre {
+  white-space: break-spaces;
+}
+
+@media (min-width: 1200px) {
+  .container,
+  .container-lg,
+  .container-md,
+  .container-sm,
+  .container-xl {
+    max-width: 2560px !important;
+  }
+}
+
+#my-component-root *, #headlessui-portal-root * {
+  z-index: 10000;
+}

docs/api_reference/conf.py

@@ -0,0 +1,172 @@
+"""Configuration file for the Sphinx documentation builder."""
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+import json
+import os
+import sys
+from pathlib import Path
+
+import toml
+from docutils import nodes
+from sphinx.util.docutils import SphinxDirective
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# 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.
+
+_DIR = Path(__file__).parent.absolute()
+sys.path.insert(0, os.path.abspath("."))
+sys.path.insert(0, os.path.abspath("../../libs/langchain"))
+sys.path.insert(0, os.path.abspath("../../libs/experimental"))
+
+with (_DIR.parents[1] / "libs" / "langchain" / "pyproject.toml").open("r") as f:
+    data = toml.load(f)
+with (_DIR / "guide_imports.json").open("r") as f:
+    imported_classes = json.load(f)
+
+
+class ExampleLinksDirective(SphinxDirective):
+    """Directive to generate a list of links to examples.
+
+    We have a script that extracts links to API reference docs
+    from our notebook examples. This directive uses that information
+    to backlink to the examples from the API reference docs."""
+
+    has_content = False
+    required_arguments = 1
+
+    def run(self):
+        """Run the directive.
+
+        Called any time :example_links:`ClassName` is used
+        in the template *.rst files."""
+        class_or_func_name = self.arguments[0]
+        links = imported_classes.get(class_or_func_name, {})
+        list_node = nodes.bullet_list()
+        for doc_name, link in links.items():
+            item_node = nodes.list_item()
+            para_node = nodes.paragraph()
+            link_node = nodes.reference()
+            link_node["refuri"] = link
+            link_node.append(nodes.Text(doc_name))
+            para_node.append(link_node)
+            item_node.append(para_node)
+            list_node.append(item_node)
+        if list_node.children:
+            title_node = nodes.title()
+            title_node.append(nodes.Text(f"Examples using {class_or_func_name}"))
+            return [title_node, list_node]
+        return [list_node]
+
+
+def setup(app):
+    app.add_directive("example_links", ExampleLinksDirective)
+
+
+# -- Project information -----------------------------------------------------
+
+project = "🦜🔗 LangChain"
+copyright = "2023, Harrison Chase"
+author = "Harrison Chase"
+
+version = data["tool"]["poetry"]["version"]
+release = version
+
+html_title = project + " " + version
+html_last_updated_fmt = "%b %d, %Y"
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autodoc.typehints",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
+    "sphinxcontrib.autodoc_pydantic",
+    "sphinx_copybutton",
+    "sphinx_panels",
+    "IPython.sphinxext.ipython_console_highlighting",
+]
+source_suffix = [".rst"]
+
+autodoc_pydantic_model_show_json = False
+autodoc_pydantic_field_list_validators = False
+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_signature_prefix = "class"
+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,
+    "inherited-members": "BaseModel",
+    "undoc-members": True,
+    "special-members": "__call__",
+}
+# autodoc_typehints = "description"
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["templates"]
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = "scikit-learn-modern"
+html_theme_path = ["themes"]
+
+# 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
+    "github_user": "hwchase17",  # Username
+    "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,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+# These paths are either relative to html_static_path
+# or fully qualified paths (eg. https://...)
+html_css_files = [
+    "css/custom.css",
+]
+html_use_index = False
+
+myst_enable_extensions = ["colon_fence"]
+
+# generate autosummary even if no references
+autosummary_generate = True

docs/api_reference/create_api_rst.py

@@ -0,0 +1,97 @@
+"""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 / "libs" / "langchain" / "langchain"
+EXP_DIR = ROOT_DIR / "libs" / "experimental" / "langchain_experimental"
+WRITE_FILE = Path(__file__).parent / "api_reference.rst"
+EXP_WRITE_FILE = Path(__file__).parent / "experimental_api_reference.rst"
+
+
+def load_members(dir: Path) -> dict:
+    members: dict = {}
+    for py in glob.glob(str(dir) + "/**/*.py", recursive=True):
+        module = py[len(str(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)
+                afunc = re.findall(r"^async def ([^_].*)\(", line)
+                func_strings = [module + "." + f for f in func + afunc]
+                members[top_level]["functions"].extend(func_strings)
+    return members
+
+
+def construct_doc(pkg: str, members: dict) -> str:
+    full_doc = f"""\
+=============
+``{pkg}`` 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
+        section = f":mod:`{pkg}.{module}`"
+        full_doc += f"""\
+{section}
+{'=' * (len(section) + 1)}
+
+.. automodule:: {pkg}.{module}
+    :no-members:
+    :no-inherited-members:
+
+"""
+
+        if classes:
+            cstring = "\n    ".join(sorted(classes))
+            full_doc += f"""\
+Classes
+--------------
+.. currentmodule:: {pkg}
+
+.. autosummary::
+    :toctree: {module}
+    :template: class.rst
+
+    {cstring}
+
+"""
+        if functions:
+            fstring = "\n    ".join(sorted(functions))
+            full_doc += f"""\
+Functions
+--------------
+.. currentmodule:: {pkg}
+
+.. autosummary::
+    :toctree: {module}
+    :template: function.rst
+
+    {fstring}
+
+"""
+    return full_doc
+
+
+def main() -> None:
+    lc_members = load_members(PKG_DIR)
+    lc_doc = ".. _api_reference:\n\n" + construct_doc("langchain", lc_members)
+    with open(WRITE_FILE, "w") as f:
+        f.write(lc_doc)
+    exp_members = load_members(EXP_DIR)
+    exp_doc = ".. _experimental_api_reference:\n\n" + construct_doc("langchain_experimental", exp_members)
+    with open(EXP_WRITE_FILE, "w") as f:
+        f.write(exp_doc)
+
+
+if __name__ == "__main__":
+    main()

docs/api_reference/index.rst

@@ -0,0 +1,8 @@
+=============
+LangChain API
+=============
+
+.. toctree::
+    :maxdepth: 2
+
+    api_reference.rst

docs/api_reference/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/api_reference/requirements.txt

@@ -0,0 +1,13 @@
+-e libs/langchain
+autodoc_pydantic==1.8.0
+myst_parser
+nbsphinx==0.8.9
+sphinx==4.5.0
+sphinx-autobuild==2021.3.14
+sphinx_rtd_theme==1.0.0
+sphinx-typlog-theme==0.8.0
+sphinx-panels
+toml
+myst_nb
+sphinx_copybutton
+pydata-sphinx-theme==0.13.1

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 +1,30 @@
+: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 %}
+
+.. example_links:: {{ objname }}

docs/api_reference/templates/function.rst

@@ -0,0 +1,8 @@
+:mod:`{{module}}`.{{objname}}
+{{ underline }}==============
+
+.. currentmodule:: {{ module }}
+
+.. autofunction:: {{ objname }}
+
+.. example_links:: {{ objname }}

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 +1,72 @@
+{%- 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 %}
+
+
+<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" href="{{ pathto('experimental_api_reference') }}">Experimental</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/.gitignore

@@ -0,0 +1,7 @@
+.yarn/
+
+node_modules/
+
+.docusaurus
+.cache-loader
+docs/api

docs/docs_skeleton/README.md

@@ -0,0 +1,49 @@
+# Website
+
+This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
+
+### Installation
+
+```
+$ yarn
+```
+
+### Local Development
+
+```
+$ yarn start
+```
+
+This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
+
+### Build
+
+```
+$ yarn build
+```
+
+This command generates static content into the `build` directory and can be served using any static contents hosting service.
+
+### Deployment
+
+Using SSH:
+
+```
+$ USE_SSH=true yarn deploy
+```
+
+Not using SSH:
+
+```
+$ GIT_USER=<Your GitHub username> yarn deploy
+```
+
+If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
+
+### Continuous Integration
+
+Some common defaults for linting/formatting have been set for you. If you integrate your project with an open source Continuous Integration system (e.g. Travis CI, CircleCI), you may check for issues using the following command.
+
+```
+$ yarn ci
+```

docs/docs_skeleton/babel.config.js

@@ -0,0 +1,12 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ *
+ * @format
+ */
+
+module.exports = {
+  presets: [require.resolve("@docusaurus/core/lib/babel/preset")],
+};

docs/docs_skeleton/code-block-loader.js

@@ -0,0 +1,76 @@
+/* eslint-disable prefer-template */
+/* eslint-disable no-param-reassign */
+// eslint-disable-next-line import/no-extraneous-dependencies
+const babel = require("@babel/core");
+const path = require("path");
+const fs = require("fs");
+
+/**
+ *
+ * @param {string|Buffer} content Content of the resource file
+ * @param {object} [map] SourceMap data consumable by https://github.com/mozilla/source-map
+ * @param {any} [meta] Meta data, could be anything
+ */
+async function webpackLoader(content, map, meta) {
+  const cb = this.async();
+
+  if (!this.resourcePath.endsWith(".ts")) {
+    cb(null, JSON.stringify({ content, imports: [] }), map, meta);
+    return;
+  }
+
+  try {
+    const result = await babel.parseAsync(content, {
+      sourceType: "module",
+      filename: this.resourcePath,
+    });
+
+    const imports = [];
+
+    result.program.body.forEach((node) => {
+      if (node.type === "ImportDeclaration") {
+        const source = node.source.value;
+
+        if (!source.startsWith("langchain")) {
+          return;
+        }
+
+        node.specifiers.forEach((specifier) => {
+          if (specifier.type === "ImportSpecifier") {
+            const local = specifier.local.name;
+            const imported = specifier.imported.name;
+            imports.push({ local, imported, source });
+          } else {
+            throw new Error("Unsupported import type");
+          }
+        });
+      }
+    });
+
+    imports.forEach((imp) => {
+      const { imported, source } = imp;
+      const moduleName = source.split("/").slice(1).join("_");
+      const docsPath = path.resolve(__dirname, "docs", "api", moduleName);
+      const available = fs.readdirSync(docsPath, { withFileTypes: true });
+      const found = available.find(
+        (dirent) =>
+          dirent.isDirectory() &&
+          fs.existsSync(path.resolve(docsPath, dirent.name, imported + ".md"))
+      );
+      if (found) {
+        imp.docs =
+          "/" + path.join("docs", "api", moduleName, found.name, imported);
+      } else {
+        throw new Error(
+          `Could not find docs for ${source}.${imported} in docs/api/`
+        );
+      }
+    });
+
+    cb(null, JSON.stringify({ content, imports }), map, meta);
+  } catch (err) {
+    cb(err);
+  }
+}
+
+module.exports = webpackLoader;

docs/docs_skeleton/docs/_static/css/custom.css

@@ -0,0 +1,21 @@
+pre {
+  white-space: break-spaces;
+}
+
+@media (min-width: 1200px) {
+  .container,
+  .container-lg,
+  .container-md,
+  .container-sm,
+  .container-xl {
+    max-width: 2560px !important;
+  }
+}
+
+#my-component-root *, #headlessui-portal-root * {
+  z-index: 10000;
+}
+
+.content-container p {
+    margin: revert;
+}

docs/docs_skeleton/docs/_static/js/mendablesearch.js

@@ -0,0 +1,56 @@
+document.addEventListener('DOMContentLoaded', () => {
+  // Load the external dependencies
+  function loadScript(src, onLoadCallback) {
+    const script = document.createElement('script');
+    script.src = src;
+    script.onload = onLoadCallback;
+    document.head.appendChild(script);
+  }
+
+  function createRootElement() {
+    const rootElement = document.createElement('div');
+    rootElement.id = 'my-component-root';
+    document.body.appendChild(rootElement);
+    return rootElement;
+  }
+
+  
+
+  function initializeMendable() {
+    const rootElement = createRootElement();
+    const { MendableFloatingButton } = Mendable;
+    
+
+    const iconSpan1 = React.createElement('span', {
+    }, '🦜');
+
+    const iconSpan2 = React.createElement('span', {
+    }, '🔗');
+
+    const icon = React.createElement('p', {
+      style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
+    }, [iconSpan1, iconSpan2]);
+    
+    const mendableFloatingButton = React.createElement(
+      MendableFloatingButton,
+      {
+        style: { darkMode: false, accentColor: '#010810' },
+        floatingButtonStyle: { color: '#ffffff', backgroundColor: '#010810' },
+        anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
+        messageSettings: {
+          openSourcesInNewTab: false,
+          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/docs_skeleton/docs/get_started/installation.mdx

@@ -0,0 +1,5 @@
+# Installation
+
+import Installation from "@snippets/get_started/installation.mdx"
+
+<Installation/>

docs/docs_skeleton/docs/get_started/introduction.mdx

@@ -0,0 +1,65 @@
+---
+sidebar_position: 0
+---
+
+# Introduction
+
+**LangChain** is a framework for developing applications powered by language models. It enables applications that are:
+- **Data-aware**: connect a language model to other sources of data
+- **Agentic**: allow a language model to interact with its environment
+
+The main value props of LangChain are:
+1. **Components**: abstractions for working with language models, along with a collection of implementations for each abstraction. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
+2. **Off-the-shelf chains**: a structured assembly of components for accomplishing specific higher-level tasks
+
+Off-the-shelf chains make it easy to get started. For more complex applications and nuanced use-cases, components make it easy to customize existing chains or build new ones.
+
+## Get started
+
+[Here’s](/docs/get_started/installation.html) how to install LangChain, set up your environment, and start building.
+
+We recommend following our [Quickstart](/docs/get_started/quickstart.html) guide to familiarize yourself with the framework by building your first LangChain application.
+
+_**Note**: These docs are for the LangChain [Python package](https://github.com/hwchase17/langchain). For documentation on [LangChain.js](https://github.com/hwchase17/langchainjs), the JS/TS version, [head here](https://js.langchain.com/docs)._
+
+## Modules
+
+LangChain provides standard, extendable interfaces and external integrations for the following modules, listed from least to most complex:
+
+#### [Model I/O](/docs/modules/model_io/)
+Interface with language models
+#### [Data connection](/docs/modules/data_connection/)
+Interface with application-specific data
+#### [Chains](/docs/modules/chains/)
+Construct sequences of calls
+#### [Agents](/docs/modules/agents/)
+Let chains choose which tools to use given high-level directives
+#### [Memory](/docs/modules/memory/)
+Persist application state between runs of a chain
+#### [Callbacks](/docs/modules/callbacks/)
+Log and stream intermediate steps of any chain
+
+## Examples, ecosystem, and resources
+### [Use cases](/docs/use_cases/)
+Walkthroughs and best-practices for common end-to-end use cases, like:
+- [Chatbots](/docs/use_cases/chatbots/)
+- [Answering questions using sources](/docs/use_cases/question_answering/)
+- [Analyzing structured data](/docs/use_cases/tabular.html)
+- and much more...
+
+### [Guides](/docs/guides/)
+Learn best practices for developing with LangChain.
+
+### [Ecosystem](/docs/ecosystem/)
+LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/) and [dependent repos](/docs/ecosystem/dependents).
+
+### [Additional resources](/docs/additional_resources/)
+Our community is full of prolific developers, creative builders, and fantastic teachers. Check out [YouTube tutorials](/docs/additional_resources/youtube.html) for great tutorials from folks in the community, and [Gallery](https://github.com/kyrolabs/awesome-langchain) for a list of awesome LangChain projects, compiled by the folks at [KyroLabs](https://kyrolabs.com).
+
+<h3><span style={{color:"#2e8555"}}> Support </span></h3>
+
+Join us on [GitHub](https://github.com/hwchase17/langchain) or [Discord](https://discord.gg/6adMQxSpJS) to ask questions, share feedback, meet other developers building with LangChain, and dream about the future of LLM’s.
+
+## API reference
+
+Head to the [reference](https://api.python.langchain.com) section for full documentation of all classes and methods in the LangChain Python package.

docs/docs_skeleton/docs/get_started/quickstart.mdx

@@ -0,0 +1,162 @@
+# Quickstart
+
+## Installation
+
+To install LangChain run:
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Install from "@snippets/get_started/quickstart/installation.mdx"
+
+<Install/>
+
+For more details, see our [Installation guide](/docs/get_started/installation.html).
+
+## Environment setup
+
+Using LangChain will usually require integrations with one or more model providers, data stores, APIs, etc. For this example, we'll use OpenAI's model APIs.
+
+import OpenAISetup from "@snippets/get_started/quickstart/openai_setup.mdx"
+
+<OpenAISetup/>
+
+## Building an application
+
+Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications.
+Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
+
+The core building block of LangChain applications is the LLMChain.
+This combines three things:
+- LLM: The language model is the core reasoning engine here. In order to work with LangChain, you need to understand the different types of language models and how to work with them.
+- Prompt Templates: This provides instructions to the language model. This controls what the language model outputs, so understanding how to construct prompts and different prompting strategies is crucial.
+- Output Parsers: These translate the raw response from the LLM to a more workable format, making it easy to use the output downstream.
+
+In this getting started guide we will cover those three components by themselves, and then cover the LLMChain which combines all of them.
+Understanding these concepts will set you up well for being able to use and customize LangChain applications.
+Most LangChain applications allow you to configure the LLM and/or the prompt used, so knowing how to take advantage of this will be a big enabler.
+
+## LLMs
+
+There are two types of language models, which in LangChain are called:
+
+- LLMs: this is a language model which takes a string as input and returns a string
+- ChatModels: this is a language model which takes a list of messages as input and returns a message
+
+The input/output for LLMs is simple and easy to understand - a string.
+But what about ChatModels? The input there is a list of `ChatMessage`s, and the output is a single `ChatMessage`.
+A `ChatMessage` has two required components:
+
+- `content`: This is the content of the message.
+- `role`: This is the role of the entity from which the `ChatMessage` is coming from.
+
+LangChain provides several objects to easily distinguish between different roles:
+
+- `HumanMessage`: A `ChatMessage` coming from a human/user.
+- `AIMessage`: A `ChatMessage` coming from an AI/assistant.
+- `SystemMessage`: A `ChatMessage` coming from the system.
+- `FunctionMessage`: A `ChatMessage` coming from a function call.
+
+If none of those roles sound right, there is also a `ChatMessage` class where you can specify the role manually.
+For more information on how to use these different messages most effectively, see our prompting guide.
+
+LangChain exposes a standard interface for both, but it's useful to understand this difference in order to construct prompts for a given language model.
+The standard interface that LangChain exposes has two methods:
+- `predict`: Takes in a string, returns a string
+- `predict_messages`: Takes in a list of messages, returns a message.
+
+Let's see how to work with these different types of models and these different types of inputs.
+First, let's import an LLM and a ChatModel.
+
+import ImportLLMs from "@snippets/get_started/quickstart/import_llms.mdx"
+
+<ImportLLMs/>
+
+The `OpenAI` and `ChatOpenAI` objects are basically just configuration objects.
+You can initialize them with parameters like `temperature` and others, and pass them around.
+
+Next, let's use the `predict` method to run over a string input.
+
+import InputString from "@snippets/get_started/quickstart/input_string.mdx"
+
+<InputString/>
+
+Finally, let's use the `predict_messages` method to run over a list of messages.
+
+import InputMessages from "@snippets/get_started/quickstart/input_messages.mdx"
+
+<InputMessages/>
+
+For both these methods, you can also pass in parameters as key word arguments.
+For example, you could pass in `temperature=0` to adjust the temperature that is used from what the object was configured with.
+Whatever values are passed in during run time will always override what the object was configured with.
+
+
+## Prompt templates
+
+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.
+
+PromptTemplates help with exactly this!
+They bundle up all the logic for going from user input into a fully formatted prompt.
+This can start off very simple - for example, a prompt to produce the above string would just be:
+
+import PromptTemplateLLM from "@snippets/get_started/quickstart/prompt_templates_llms.mdx"
+import PromptTemplateChatModel from "@snippets/get_started/quickstart/prompt_templates_chat_models.mdx"
+
+<PromptTemplateLLM/>
+
+However, the advantages of using these over raw string formatting are several.
+You can "partial" out variables - eg you can format only some of the variables at a time.
+You can compose them together, easily combining different templates into a single prompt.
+For explanations of these functionalities, see the [section on prompts](/docs/modules/model_io/prompts) for more detail.
+
+PromptTemplates can also be used to produce a list of messages.
+In this case, the prompt not only contains information about the content, but also each message (its role, its position in the list, etc)
+Here, what happens most often is a ChatPromptTemplate is a list of ChatMessageTemplates.
+Each ChatMessageTemplate contains instructions for how to format that ChatMessage - its role, and then also its content.
+Let's take a look at this below:
+
+<PromptTemplateChatModel/>
+
+ChatPromptTemplates can also include other things besides ChatMessageTemplates - see the [section on prompts](/docs/modules/model_io/prompts) for more detail.
+
+## Output Parsers
+
+OutputParsers convert the raw output of an LLM into a format that can be used downstream.
+There are few main type of OutputParsers, including:
+
+- Convert text from LLM -> structured information (eg JSON)
+- Convert a ChatMessage into just a string
+- Convert the extra information returned from a call besides the message (like OpenAI function invocation) into a string.
+
+For full information on this, see the [section on output parsers](/docs/modules/model_io/output_parsers)
+
+In this getting started guide, we will write our own output parser - one that converts a comma separated list into a list.
+
+import OutputParser from "@snippets/get_started/quickstart/output_parser.mdx"
+
+<OutputParser/>
+
+## LLMChain
+
+We can now combine all these into one chain.
+This chain will take input variables, pass those to a prompt template to create a prompt, pass the prompt to an LLM, and then pass the output through an (optional) output parser.
+This is a convenient way to bundle up a modular piece of logic.
+Let's see it in action!
+
+import LLMChain from "@snippets/get_started/quickstart/llm_chain.mdx"
+
+<LLMChain/>
+
+## Next Steps
+
+This is it!
+We've now gone over how to create the core building block of LangChain applications - the LLMChains.
+There is a lot more nuance in all these components (LLMs, prompts, output parsers) and a lot more different components to learn about as well.
+To continue on your journey:
+
+- [Dive deeper](/docs/modules/model_io) into LLMs, prompts, and output parsers
+- Learn the other [key components](/docs/modules)
+- Check out our [helpful guides](/docs/guides) for detailed walkthroughs on particular topics
+- Explore [end-to-end use cases](/docs/use_cases)

docs/docs_skeleton/docs/guides/evaluation/comparison/index.mdx

@@ -0,0 +1,24 @@
+---
+sidebar_position: 3 
+---
+# Comparison Evaluators
+
+Comparison evaluators in LangChain help measure two different chain or LLM outputs. These evaluators are helpful for comparative analyses, such as A/B testing between two language models, or comparing different versions of the same model. They can also be useful for things like generating preference scores for ai-assisted reinforcement learning.
+
+These evaluators inherit from the `PairwiseStringEvaluator` class, providing a comparison interface for two strings - typically, the outputs from two different prompts or models, or two versions of the same model. In essence, a comparison evaluator performs an evaluation on a pair of strings and returns a dictionary containing the evaluation score and other relevant details.
+
+To create a custom comparison evaluator, inherit from the `PairwiseStringEvaluator` class and overwrite the `_evaluate_string_pairs` method. If you require asynchronous evaluation, also overwrite the `_aevaluate_string_pairs` method.
+
+Here's a summary of the key methods and properties of a comparison evaluator:
+
+- `evaluate_string_pairs`: Evaluate the output string pairs. This function should be overwritten when creating custom evaluators.
+- `aevaluate_string_pairs`: Asynchronously evaluate the output string pairs. This function should be overwritten for asynchronous evaluation.
+- `requires_input`: This property indicates whether this evaluator requires an input string.
+- `requires_reference`: This property specifies whether this evaluator requires a reference label.
+
+Detailed information about creating custom evaluators and the available built-in comparison evaluators are provided in the following sections.
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />
+

docs/docs_skeleton/docs/guides/evaluation/examples/index.mdx

@@ -0,0 +1,12 @@
+---
+sidebar_position: 5
+---
+# Examples
+
+🚧 _Docs under construction_ 🚧
+
+Below are some examples for inspecting and checking different chains.
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />

docs/docs_skeleton/docs/guides/evaluation/index.mdx

@@ -0,0 +1,31 @@
+---
+sidebar_position: 6
+---
+
+import DocCardList from "@theme/DocCardList";
+
+# Evaluation
+
+Building applications with language models involves many moving parts. One of the most critical components is ensuring that the outcomes produced by your models are reliable and useful across a broad array of inputs, and that they work well with your application's other software components. Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks. 
+
+The guides in this section review the APIs and functionality LangChain provides to help yous better evaluate your applications. Evaluation and testing are both critical when thinking about deploying LLM applications, since production environments require repeatable and useful outcomes.
+
+LangChain offers various types of evaluators to help you measure performance and integrity on diverse data, and we hope to encourage the the community to create and share other useful evaluators so everyone can improve. These docs will introduce the evaluator types, how to use them, and provide some examples of their use in real-world scenarios.
+
+Each evaluator type in LangChain comes with ready-to-use implementations and an extensible API that allows for customization according to your unique requirements. Here are some of the types of evaluators we offer:
+
+- [String Evaluators](/docs/guides/evaluation/string/): These evaluators assess the predicted string for a given input, usually comparing it against a reference string.
+- [Trajectory Evaluators](/docs/guides/evaluation/trajectory/): These are used to evaluate the entire trajectory of agent actions.
+- [Comparison Evaluators](/docs/guides/evaluation/comparison/): These evaluators are designed to compare predictions from two runs on a common input.
+
+These evaluators can be used across various scenarios and can be applied to different chain and LLM implementations in the LangChain library.
+
+We also are working to share guides and cookbooks that demonstrate how to use these evaluators in real-world scenarios, such as:
+
+- [Chain Comparisons](/docs/guides/evaluation/examples/comparisons): This example uses a comparison evaluator to predict the preferred output. It reviews ways to measure confidence intervals to select statistically significant differences in aggregate preference scores across different models or prompts.
+
+## Reference Docs
+
+For detailed information on the available evaluators, including how to instantiate, configure, and customize them, check out the [reference documentation](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.evaluation) directly.
+
+<DocCardList />

docs/docs_skeleton/docs/guides/evaluation/string/index.mdx

@@ -0,0 +1,27 @@
+---
+sidebar_position: 2 
+---
+# String Evaluators
+
+A string evaluator is a component within LangChain designed to assess the performance of a language model by comparing its generated outputs (predictions) to a reference string or an input. This comparison is a crucial step in the evaluation of language models, providing a measure of the accuracy or quality of the generated text.
+
+In practice, string evaluators are typically used to evaluate a predicted string against a given input, such as a question or a prompt. Often, a reference label or context string is provided to define what a correct or ideal response would look like. These evaluators can be customized to tailor the evaluation process to fit your application's specific requirements.
+
+To create a custom string evaluator, inherit from the `StringEvaluator` class and implement the `_evaluate_strings` method. If you require asynchronous support, also implement the `_aevaluate_strings` method.
+
+Here's a summary of the key attributes and methods associated with a string evaluator:
+
+- `evaluation_name`: Specifies the name of the evaluation.
+- `requires_input`: Boolean attribute that indicates whether the evaluator requires an input string. If True, the evaluator will raise an error when the input isn't provided. If False, a warning will be logged if an input _is_ provided, indicating that it will not be considered in the evaluation.
+- `requires_reference`: Boolean attribute specifying whether the evaluator requires a reference label. If True, the evaluator will raise an error when the reference isn't provided. If False, a warning will be logged if a reference _is_ provided, indicating that it will not be considered in the evaluation.
+
+String evaluators also implement the following methods:
+
+- `aevaluate_strings`: Asynchronously evaluates the output of the Chain or Language Model, with support for optional input and label.
+- `evaluate_strings`: Synchronously evaluates the output of the Chain or Language Model, with support for optional input and label.
+
+The following sections provide detailed information on available string evaluator implementations as well as how to create a custom string evaluator.
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />

docs/docs_skeleton/docs/guides/evaluation/trajectory/index.mdx

@@ -0,0 +1,28 @@
+---
+sidebar_position: 4
+---
+# Trajectory Evaluators
+
+Trajectory Evaluators in LangChain provide a more holistic approach to evaluating an agent. These evaluators assess the full sequence of actions taken by an agent and their corresponding responses, which we refer to as the "trajectory". This allows you to better measure an agent's effectiveness and capabilities.
+
+A Trajectory Evaluator implements the `AgentTrajectoryEvaluator` interface, which requires two main methods:
+
+- `evaluate_agent_trajectory`: This method synchronously evaluates an agent's trajectory.
+- `aevaluate_agent_trajectory`: This asynchronous counterpart allows evaluations to be run in parallel for efficiency.
+
+Both methods accept three main parameters:
+
+- `input`: The initial input given to the agent.
+- `prediction`: The final predicted response from the agent.
+- `agent_trajectory`: The intermediate steps taken by the agent, given as a list of tuples.
+
+These methods return a dictionary. It is recommended that custom implementations return a `score` (a float indicating the effectiveness of the agent) and `reasoning` (a string explaining the reasoning behind the score).
+
+You can capture an agent's trajectory by initializing the agent with the `return_intermediate_steps=True` parameter. This lets you collect all intermediate steps without relying on special callbacks.
+
+For a deeper dive into the implementation and use of Trajectory Evaluators, refer to the sections below.
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />
+

docs/docs_skeleton/docs/guides/langsmith/index.md

@@ -0,0 +1,12 @@
+# LangSmith
+
+import DocCardList from "@theme/DocCardList";
+
+LangSmith helps you trace and evaluate your language model applications and intelligent agents to help you
+move from prototype to production.
+
+Check out the [interactive walkthrough](walkthrough) below to get started.
+
+For more information, please refer to the [LangSmith documentation](https://docs.smith.langchain.com/)
+
+<DocCardList />

docs/docs_skeleton/docs/guides/safety/constitutional_chain.mdx

@@ -0,0 +1,7 @@
+# Self-critique chain with constitutional AI
+The ConstitutionalChain is a chain that ensures the output of a language model adheres to a predefined set of constitutional principles. By incorporating specific rules and guidelines, the ConstitutionalChain filters and modifies the generated content to align with these principles, thus providing more controlled, ethical, and contextually appropriate responses. This mechanism helps maintain the integrity of the output while minimizing the risk of generating content that may violate guidelines, be offensive, or deviate from the desired context.
+
+
+import Example from "@snippets/modules/chains/additional/constitutional_chain.mdx"
+
+<Example/>

docs/docs_skeleton/docs/guides/safety/index.mdx

@@ -0,0 +1,6 @@
+# Preventing harmful outputs
+
+One of the key concerns with using LLMs is that they may generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer.
+
+- [Moderation chain](/docs/use_cases/safety/moderation): Explicitly check if any output text is harmful and flag it.
+- [Constitutional chain](/docs/use_cases/safety/constitutional_chain): Prompt the model with a set of principles which should guide it's behavior.

docs/docs_skeleton/docs/guides/safety/moderation.mdx

@@ -0,0 +1,8 @@
+# Moderation
+This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model. Some API providers, like OpenAI, [specifically prohibit](https://beta.openai.com/docs/usage-policies/use-case-policy) you, or your end users, from generating some types of harmful content. To comply with this (and to just generally prevent your application from being harmful) you may often want to append a moderation chain to any LLMChains, in order to make sure any output the LLM generates is not harmful.
+
+If the content passed into the moderation chain is harmful, there is not one best way to handle it, it probably depends on your application. Sometimes you may want to throw an error in the Chain (and have your application handle that). Other times, you may want to return something to the user explaining that the text was harmful. There could even be other ways to handle it! We will cover all these ways in this walkthrough.
+
+import Example from "@snippets/modules/chains/additional/moderation.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/agents/agent_types/chat_conversation_agent.mdx

@@ -0,0 +1,13 @@
+# Conversational
+
+This walkthrough demonstrates how to use an agent optimized for conversation. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.
+
+import Example from "@snippets/modules/agents/agent_types/conversational_agent.mdx"
+
+<Example/>
+
+import ChatExample from "@snippets/modules/agents/agent_types/chat_conversation_agent.mdx"
+
+## Using a chat model
+
+<ChatExample/>

docs/docs_skeleton/docs/modules/agents/agent_types/index.mdx

@@ -0,0 +1,57 @@
+---
+sidebar_position: 0
+---
+
+# Agent types
+
+## Action agents
+
+Agents use an LLM to determine which actions to take and in what order.
+An action can either be using a tool and observing its output, or returning a response to the user.
+Here are the agents available in LangChain.
+
+### [Zero-shot ReAct](/docs/modules/agents/agent_types/react.html)
+
+This agent uses the [ReAct](https://arxiv.org/pdf/2205.00445.pdf) framework to determine which tool to use
+based solely on the tool's description. Any number of tools can be provided.
+This agent requires that a description is provided for each tool.
+
+**Note**: This is the most general purpose action agent.
+
+### [Structured input ReAct](/docs/modules/agents/agent_types/structured_chat.html)
+
+The structured tool chat agent is capable of using multi-input tools.
+Older agents are configured to specify an action input as a single string, but this agent can use a tools' argument
+schema to create a structured action input. This is useful for more complex tool usage, like precisely
+navigating around a browser.
+
+### [OpenAI Functions](/docs/modules/agents/agent_types/openai_functions_agent.html)
+
+Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been explicitly fine-tuned to detect when a
+function should to be called and respond with the inputs that should be passed to the function.
+The OpenAI Functions Agent is designed to work with these models.
+
+### [Conversational](/docs/modules/agents/agent_types/chat_conversation_agent.html)
+
+This agent is designed to be used in conversational settings.
+The prompt is designed to make the agent helpful and conversational.
+It uses the ReAct framework to decide which tool to use, and uses memory to remember the previous conversation interactions.
+
+### [Self ask with search](/docs/modules/agents/agent_types/self_ask_with_search.html)
+
+This agent utilizes a single tool that should be named `Intermediate Answer`.
+This tool should be able to lookup factual answers to questions. This agent
+is equivalent to the original [self ask with search paper](https://ofir.io/self-ask.pdf),
+where a Google search API was provided as the tool.
+
+### [ReAct document store](/docs/modules/agents/agent_types/react_docstore.html)
+
+This agent uses the ReAct framework to interact with a docstore. Two tools must
+be provided: a `Search` tool and a `Lookup` tool (they must be named exactly as so).
+The `Search` tool should search for a document, while the `Lookup` tool should lookup
+a term in the most recently found document.
+This agent is equivalent to the
+original [ReAct paper](https://arxiv.org/pdf/2210.03629.pdf), specifically the Wikipedia example.
+
+## [Plan-and-execute agents](/docs/modules/agents/agent_types/plan_and_execute.html)
+Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).

docs/docs_skeleton/docs/modules/agents/agent_types/openai_functions_agent.mdx

@@ -0,0 +1,11 @@
+# OpenAI functions
+
+Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should to be called and respond with the inputs that should be passed to the function.
+In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions.
+The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API.
+
+The OpenAI Functions Agent is designed to work with these models.
+
+import Example from "@snippets/modules/agents/agent_types/openai_functions_agent.mdx";
+
+<Example/>

docs/docs_skeleton/docs/modules/agents/agent_types/plan_and_execute.mdx

@@ -0,0 +1,11 @@
+# Plan and execute
+
+Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).
+
+The planning is almost always done by an LLM.
+
+The execution is usually done by a separate agent (equipped with tools).
+
+import Example from "@snippets/modules/agents/agent_types/plan_and_execute.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/agents/agent_types/react.mdx

@@ -0,0 +1,15 @@
+# ReAct
+
+This walkthrough showcases using an agent to implement the [ReAct](https://react-lm.github.io/) logic.
+
+import Example from "@snippets/modules/agents/agent_types/react.mdx"
+
+<Example/>
+
+## Using chat models
+
+You can also create ReAct agents that use chat models instead of LLMs as the agent driver.
+
+import ChatExample from "@snippets/modules/agents/agent_types/react_chat.mdx"
+
+<ChatExample/>

docs/docs_skeleton/docs/modules/agents/agent_types/structured_chat.mdx

@@ -0,0 +1,10 @@
+# Structured tool chat
+
+The structured tool chat agent is capable of using multi-input tools.
+
+Older agents are configured to specify an action input as a single string, but this agent can use the provided tools' `args_schema` to populate the action input.
+
+
+import Example from "@snippets/modules/agents/agent_types/structured_chat.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/agents/how_to/_category_.yml

@@ -0,0 +1,2 @@
+label: 'How-to'
+position: 1

docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_agent.mdx

@@ -0,0 +1,14 @@
+# Custom LLM Agent
+
+This notebook goes through how to create your own custom LLM agent.
+
+An LLM agent consists of three parts:
+
+- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
+- LLM: This is the language model that powers the agent
+- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
+- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
+
+import Example from "@snippets/modules/agents/how_to/custom_llm_agent.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_chat_agent.mdx

@@ -0,0 +1,14 @@
+# Custom LLM Agent (with a ChatModel)
+
+This notebook goes through how to create your own custom agent based on a chat model.
+
+An LLM chat agent consists of three parts:
+
+- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
+- ChatModel: This is the language model that powers the agent
+- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
+- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
+
+import Example from "@snippets/modules/agents/how_to/custom_llm_chat_agent.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/agents/how_to/mrkl.mdx

@@ -0,0 +1,16 @@
+# Replicating MRKL
+
+This walkthrough demonstrates how to replicate the [MRKL](https://arxiv.org/pdf/2205.00445.pdf) system using agents.
+
+This uses the example Chinook database.
+To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository.
+
+import Example from "@snippets/modules/agents/how_to/mrkl.mdx"
+
+<Example/>
+
+## With a chat model
+
+import ChatExample from "@snippets/modules/agents/how_to/mrkl_chat.mdx"
+
+<ChatExample/>

docs/docs_skeleton/docs/modules/agents/index.mdx

@@ -0,0 +1,85 @@
+---
+sidebar_position: 4
+---
+# Agents
+
+The core idea of agents is to use an LLM to choose a sequence of actions to take.
+In chains, a sequence of actions is hardcoded (in code).
+In agents, a language model is used as a reasoning engine to determine which actions to take and in which order.
+
+There are several key components here:
+
+## Agent
+
+This is the class responsible for deciding what step to take next.
+This is powered by a language model and a prompt.
+This prompt can include things like:
+
+1. The personality of the agent (useful for having it respond in a certain way)
+2. Background context for the agent (useful for giving it more context on the types of tasks it's being asked to do)
+3. Prompting strategies to invoke better reasoning (the most famous/widely used being [ReAct](https://arxiv.org/abs/2210.03629))
+
+LangChain provides a few different types of agents to get started.
+Even then, you will likely want to customize those agents with parts (1) and (2).
+For a full list of agent types see [agent types](/docs/modules/agents/agent_types/)
+
+## Tools
+
+Tools are functions that an agent calls.
+There are two important considerations here:
+
+1. Giving the agent access to the right tools
+2. Describing the tools in a way that is most helpful to the agent
+
+Without both, the agent you are trying to build will not work.
+If you don't give the agent access to a correct set of tools, it will never be able to accomplish the objective.
+If you don't describe the tools properly, the agent won't know how to properly use them.
+
+LangChain provides a wide set of tools to get started, but also makes it easy to define your own (including custom descriptions).
+For a full list of tools, see [here](/docs/modules/agents/tools/)
+
+## Toolkits
+
+Often the set of tools an agent has access to is more important than a single tool.
+For this LangChain provides the concept of toolkits - groups of tools needed to accomplish specific objectives.
+There are generally around 3-5 tools in a toolkit.
+
+LangChain provides a wide set of toolkits to get started.
+For a full list of toolkits, see [here](/docs/modules/agents/toolkits/)
+
+## AgentExecutor
+
+The agent executor is the runtime for an agent.
+This is what actually calls the agent and executes the actions it chooses.
+Pseudocode for this runtime is below:
+
+```python
+next_action = agent.get_action(...)
+while next_action != AgentFinish:
+    observation = run(next_action)
+    next_action = agent.get_action(..., next_action, observation)
+return next_action
+```
+
+While this may seem simple, there are several complexities this runtime handles for you, including:
+
+1. Handling cases where the agent selects a non-existent tool
+2. Handling cases where the tool errors
+3. Handling cases where the agent produces output that cannot be parsed into a tool invocation
+4. Logging and observability at all levels (agent decisions, tool calls) either to stdout or [LangSmith](https://smith.langchain.com).
+
+## Other types of agent runtimes
+
+The `AgentExecutor` class is the main agent runtime supported by LangChain.
+However, there are other, more experimental runtimes we also support.
+These include:
+
+- [Plan-and-execute Agent](/docs/modules/agents/agent_types/plan_and_execute.html)
+- [Baby AGI](/docs/use_cases/autonomous_agents/baby_agi.html)
+- [Auto GPT](/docs/use_cases/autonomous_agents/autogpt.html)
+
+## Get started
+
+import GetStarted from "@snippets/modules/agents/get_started.mdx"
+
+<GetStarted/>

docs/docs_skeleton/docs/modules/agents/toolkits/index.mdx

@@ -0,0 +1,10 @@
+---
+sidebar_position: 3
+---
+# Toolkits
+
+:::info
+Head to [Integrations](/docs/integrations/toolkits/) for documentation on built-in toolkit integrations.
+:::
+
+Toolkits are collections of tools that are designed to be used together for specific tasks and have convenience loading methods.

docs/docs_skeleton/docs/modules/agents/tools/index.mdx

@@ -0,0 +1,21 @@
+---
+sidebar_position: 2
+---
+# Tools
+
+:::info
+Head to [Integrations](/docs/integrations/tools/) for documentation on built-in tool integrations.
+:::
+
+Tools are interfaces that an agent can use to interact with the world.
+
+## Get started
+
+Tools are functions that agents can use to interact with the world.
+These tools can be generic utilities (e.g. search), other chains, or even other agents.
+
+Currently, tools can be loaded with the following snippet:
+
+import GetStarted from "@snippets/modules/agents/tools/get_started.mdx"
+
+<GetStarted/>

docs/docs_skeleton/docs/modules/callbacks/index.mdx

@@ -0,0 +1,14 @@
+---
+sidebar_position: 5
+---
+# Callbacks
+
+:::info
+Head to [Integrations](/docs/integrations/callbacks/) for documentation on built-in callbacks integrations with 3rd-party tools.
+:::
+
+LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
+
+import GetStarted from "@snippets/modules/callbacks/get_started.mdx"
+
+<GetStarted/>

docs/docs_skeleton/docs/modules/chains/document/index.mdx

@@ -0,0 +1,16 @@
+---
+sidebar_position: 2
+---
+# Documents
+
+These are the core chains for working with Documents. They are useful for summarizing documents, answering questions over documents, extracting information from documents, and more.
+
+These chains all implement a common interface:
+
+import Interface from "@snippets/modules/chains/document/combine_docs.mdx"
+
+<Interface/>
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />

docs/docs_skeleton/docs/modules/chains/document/map_reduce.mdx

@@ -0,0 +1,5 @@
+# Map reduce
+
+The map reduce documents chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combine documents chain to get a single output (the Reduce step). It can optionally first compress, or collapse, the mapped documents to make sure that they fit in the combine documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
+
+![map_reduce_diagram](/img/map_reduce.jpg)

docs/docs_skeleton/docs/modules/chains/document/map_rerank.mdx

@@ -0,0 +1,5 @@
+# Map re-rank
+
+The map re-rank documents chain runs an initial prompt on each document, that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest scoring response is returned.
+
+![map_rerank_diagram](/img/map_rerank.jpg)

docs/docs_skeleton/docs/modules/chains/document/refine.mdx

@@ -0,0 +1,12 @@
+---
+sidebar_position: 1
+---
+# Refine
+
+The refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.
+
+Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context.
+The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain.
+There are also certain tasks which are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.
+
+![refine_diagram](/img/refine.jpg)

docs/docs_skeleton/docs/modules/chains/document/stuff.mdx

@@ -0,0 +1,12 @@
+---
+sidebar_position: 0
+---
+# Stuff
+
+The stuff documents chain ("stuff" as in "to stuff" or "to fill") is the most straightforward of the document chains. It takes a list of documents, inserts them all into a prompt and passes that prompt to an LLM.
+
+This chain is well-suited for applications where documents are small and only a few are passed in for most calls.
+
+![stuff_diagram](/img/stuff.jpg)
+
+

docs/docs_skeleton/docs/modules/chains/foundational/index.mdx

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+---
+sidebar_position: 1
+---
+# Foundational
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />

docs/docs_skeleton/docs/modules/chains/foundational/llm_chain.mdx

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+# LLM
+
+An LLMChain is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.
+
+An LLMChain consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.
+
+## Get started
+
+import Example from "@snippets/modules/chains/foundational/llm_chain.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/chains/foundational/sequential_chains.mdx

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+# Sequential
+
+
+
+The next step after calling a language model is make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to another.
+
+In this notebook we will walk through some examples for how to do this, using sequential chains. Sequential chains allow you to connect multiple chains and compose them into pipelines that execute some specific scenario.. There are two types of sequential chains:
+
+- `SimpleSequentialChain`: The simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next.
+- `SequentialChain`: A more general form of sequential chains, allowing for multiple inputs/outputs.
+
+import Example from "@snippets/modules/chains/foundational/sequential_chains.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/chains/how_to/debugging.mdx

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+# Debugging chains
+
+It can be hard to debug a `Chain` object solely from its output as most `Chain` objects involve a fair amount of input prompt preprocessing and LLM output post-processing.
+
+import Example from "@snippets/modules/chains/how_to/debugging.mdx"
+
+<Example/>
+

docs/docs_skeleton/docs/modules/chains/how_to/index.mdx

@@ -0,0 +1,8 @@
+---
+sidebar_position: 0
+---
+# How to
+
+import DocCardList from "@theme/DocCardList";
+
+<DocCardList />

docs/docs_skeleton/docs/modules/chains/how_to/memory.mdx

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+# Adding memory (state)
+
+Chains can be initialized with a Memory object, which will persist data across calls to the chain. This makes a Chain stateful.
+
+## Get started
+
+import GetStarted from "@snippets/modules/chains/how_to/memory.mdx"
+
+<GetStarted/>
+

docs/docs_skeleton/docs/modules/chains/index.mdx

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+---
+sidebar_position: 2
+---
+
+# Chains
+
+Using an LLM in isolation is fine for simple applications,
+but more complex applications require chaining LLMs - either with each other or with other components.
+
+LangChain provides the **Chain** interface for such "chained" applications. We define a Chain very generically as a sequence of calls to components, which can include other chains. The base interface is simple:
+
+import BaseClass from "@snippets/modules/chains/base_class.mdx"
+
+<BaseClass/>
+
+This idea of composing components together in a chain is simple but powerful. It drastically simplifies and makes more modular the implementation of complex applications, which in turn makes it much easier to debug, maintain, and improve your applications.
+
+For more specifics check out:
+- [How-to](/docs/modules/chains/how_to/) for walkthroughs of different chain features
+- [Foundational](/docs/modules/chains/foundational/) to get acquainted with core building block chains
+- [Document](/docs/modules/chains/document/) to learn how to incorporate documents into chains
+- [Popular](/docs/modules/chains/popular/) chains for the most common use cases
+- [Additional](/docs/modules/chains/additional/) to see some of the more advanced chains and integrations that you can use out of the box
+
+## Why do we need chains?
+
+Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.
+
+## Get started
+
+import GetStarted from "@snippets/modules/chains/get_started.mdx"
+
+<GetStarted/>

docs/docs_skeleton/docs/modules/data_connection/document_loaders/csv.mdx

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+# CSV
+
+>A [comma-separated values (CSV)](https://en.wikipedia.org/wiki/Comma-separated_values) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas.
+
+Load CSV data with a single row per document.
+
+import Example from "@snippets/modules/data_connection/document_loaders/how_to/csv.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/data_connection/document_loaders/file_directory.mdx

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+# File Directory
+
+This covers how to load all documents in a directory.
+
+import Example from "@snippets/modules/data_connection/document_loaders/how_to/file_directory.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/data_connection/document_loaders/html.mdx

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+# HTML
+
+>[The HyperText Markup Language or HTML](https://en.wikipedia.org/wiki/HTML) is the standard markup language for documents designed to be displayed in a web browser.
+
+This covers how to load `HTML` documents into a document format that we can use downstream.
+
+import Example from "@snippets/modules/data_connection/document_loaders/how_to/html.mdx"
+
+<Example/>

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

@@ -0,0 +1,21 @@
+---
+sidebar_position: 0
+---
+# Document loaders
+
+:::info
+Head to [Integrations](/docs/integrations/document_loaders/) for documentation on built-in document loader integrations with 3rd-party tools.
+:::
+
+Use document loaders to load data from a source as `Document`'s. A `Document` is a piece of text
+and associated metadata. For example, there are document loaders for loading a simple `.txt` file, for loading the text
+contents of any web page, or even for loading a transcript of a YouTube video.
+
+Document loaders expose a "load" method for loading data as documents from a configured source. They optionally
+implement a "lazy load" as well for lazily loading data into memory.
+
+## Get started
+
+import GetStarted from "@snippets/modules/data_connection/document_loaders/get_started.mdx"
+
+<GetStarted/>

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

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+# JSON
+
+>[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/document_loaders/markdown.mdx

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+# Markdown
+
+>[Markdown](https://en.wikipedia.org/wiki/Markdown) is a lightweight markup language for creating formatted text using a plain-text editor.
+
+This covers how to load `Markdown` documents into a document format that we can use downstream.
+
+import Example from "@snippets/modules/data_connection/document_loaders/how_to/markdown.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/data_connection/document_loaders/pdf.mdx

@@ -0,0 +1,9 @@
+# PDF
+
+>[Portable Document Format (PDF)](https://en.wikipedia.org/wiki/PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems.
+
+This covers how to load `PDF` documents into the Document format that we use downstream.
+
+import Example from "@snippets/modules/data_connection/document_loaders/how_to/pdf.mdx"
+
+<Example/>

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

@@ -0,0 +1,35 @@
+---
+sidebar_position: 1
+---
+# Document transformers
+
+:::info
+Head to [Integrations](/docs/integrations/document_transformers/) for documentation on built-in document transformer integrations with 3rd-party tools.
+:::
+
+Once you've loaded documents, you'll often want to transform them to better suit your application. The simplest example
+is you may want to split a long document into smaller chunks that can fit into your model's context window. LangChain
+has a number of built-in document transformers that make it easy to split, combine, filter, and otherwise manipulate documents.
+
+## Text splitters
+
+When you want to deal with long pieces of text, it is necessary to split up that text into chunks.
+As simple as this sounds, there is a lot of potential complexity here. Ideally, you want to keep the semantically related pieces of text together. What "semantically related" means could depend on the type of text.
+This notebook showcases several ways to do that.
+
+At a high level, text splitters work as following:
+
+1. Split the text up into small, semantically meaningful chunks (often sentences).
+2. Start combining these small chunks into a larger chunk until you reach a certain size (as measured by some function).
+3. Once you reach that size, make that chunk its own piece of text and then start creating a new chunk of text with some overlap (to keep context between chunks).
+
+That means there are two different axes along which you can customize your text splitter:
+
+1. How the text is split
+2. How the chunk size is measured
+
+### Get started with text splitters
+
+import GetStarted from "@snippets/modules/data_connection/document_transformers/get_started.mdx"
+
+<GetStarted/>

docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/_category_.yml

@@ -0,0 +1,2 @@
+label: 'Text splitters'
+position: 0

docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter.mdx

@@ -0,0 +1,10 @@
+# Split by character
+
+This is the simplest method. This splits based on characters (by default "\n\n") and measure chunk length by number of characters.
+
+1. How the text is split: by single character
+2. How the chunk size is measured: by number of characters
+
+import Example from "@snippets/modules/data_connection/document_transformers/text_splitters/character_text_splitter.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx

@@ -0,0 +1,7 @@
+# Split code
+
+CodeTextSplitter allows you to split your code with multiple language support. Import enum `Language` and specify the language. 
+
+import Example from "@snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter.mdx

@@ -0,0 +1,10 @@
+# Recursively split by character
+
+This text splitter is the recommended one for generic text. It is parameterized by a list of characters. It tries to split on them in order until the chunks are small enough. The default list is `["\n\n", "\n", " ", ""]`. This has the effect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text.
+
+1. How the text is split: by list of characters
+2. How the chunk size is measured: by number of characters
+
+import Example from "@snippets/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter.mdx"
+
+<Example/>

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

@@ -0,0 +1,16 @@
+---
+sidebar_position: 1
+---
+
+# Data connection
+
+Many LLM applications require user-specific data that is not part of the model's training set. LangChain gives you the 
+building blocks to load, transform, store and query your data via:
+
+- [Document loaders](/docs/modules/data_connection/document_loaders/): Load documents from many different sources
+- [Document transformers](/docs/modules/data_connection/document_transformers/): Split documents, convert documents into Q&A format, drop redundant documents, and more
+- [Text embedding models](/docs/modules/data_connection/text_embedding/): Take unstructured text and turn it into a list of floating point numbers
+- [Vector stores](/docs/modules/data_connection/vectorstores/): Store and search over embedded data
+- [Retrievers](/docs/modules/data_connection/retrievers/): Query your data
+
+![data_connection_diagram](/img/data_connection.jpg)

docs/docs_skeleton/docs/modules/data_connection/retrievers/contextual_compression/index.mdx

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+# Contextual compression
+
+One challenge with retrieval is that usually you don't know the specific queries your document storage system will face when you ingest data into the system. This means that the information most relevant to a query may be buried in a document with a lot of irrelevant text. Passing that full document through your application can lead to more expensive LLM calls and poorer responses.
+
+Contextual compression is meant to fix this. The idea is simple: instead of immediately returning retrieved documents as-is, you can compress them using the context of the given query, so that only the relevant information is returned. “Compressing” here refers to both compressing the contents of an individual document and filtering out documents wholesale.
+
+To use the Contextual Compression Retriever, you'll need:
+- a base Retriever
+- a Document Compressor
+
+The Contextual Compression Retriever passes queries to the base Retriever, takes the initial documents and passes them through the Document Compressor. The Document Compressor takes a list of Documents and shortens it by reducing the contents of Documents or dropping Documents altogether.
+
+![](https://drive.google.com/uc?id=1CtNgWODXZudxAWSRiWgSGEoTNrUFT98v)
+
+## Get started
+
+import Example from "@snippets/modules/data_connection/retrievers/contextual_compression/get_started.mdx"
+
+<Example/>

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

@@ -0,0 +1,19 @@
+---
+sidebar_position: 4
+---
+# Retrievers
+
+:::info
+Head to [Integrations](/docs/integrations/retrievers/) for documentation on built-in retriever integrations with 3rd-party tools.
+:::
+
+A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store.
+A retriever does not need to be able to store documents, only to return (or retrieve) it. Vector stores can be used
+as the backbone of a retriever, but there are other types of retrievers as well.
+
+## Get started
+
+import GetStarted from "@snippets/modules/data_connection/retrievers/get_started.mdx"
+
+<GetStarted/>
+

docs/docs_skeleton/docs/modules/data_connection/retrievers/self_query/index.mdx

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+# Self-querying
+
+A self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to it's underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documented, but to also extract filters from the user query on the metadata of stored documents and to execute those filters.
+
+![](https://drive.google.com/uc?id=1OQUN-0MJcDUxmPXofgS7MqReEs720pqS)
+
+import Example from "@snippets/modules/data_connection/retrievers/self_query/get_started.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/data_connection/retrievers/time_weighted_vectorstore.mdx

@@ -0,0 +1,15 @@
+# Time-weighted vector store retriever
+
+This retriever uses a combination of semantic similarity and a time decay.
+
+The algorithm for scoring them is:
+
+```
+semantic_similarity + (1.0 - decay_rate) ^ hours_passed
+```
+
+Notably, `hours_passed` refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain "fresh."
+
+import Example from "@snippets/modules/data_connection/retrievers/how_to/time_weighted_vectorstore.mdx"
+
+<Example/>

docs/docs_skeleton/docs/modules/data_connection/retrievers/vectorstore.mdx

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+# Vector store-backed retriever
+
+A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the Vector Store class to make it conform to the Retriever interface.
+It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store.
+
+Once you construct a Vector store, it's very easy to construct a retriever. Let's walk through an example.
+
+import Example from "@snippets/modules/data_connection/retrievers/how_to/vectorstore.mdx"
+
+<Example/>

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

@@ -0,0 +1,20 @@
+---
+sidebar_position: 2
+---
+# Text embedding models
+
+:::info
+Head to [Integrations](/docs/integrations/text_embedding/) for documentation on built-in integrations with text embedding model providers.
+:::
+
+The Embeddings class is a class designed for interfacing with text embedding models. There are lots of embedding model providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them.
+
+Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.
+
+The base Embeddings class in LangChain exposes two methods: one for embedding documents and one for embedding a query. The former takes as input multiple texts, while the latter takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).
+
+## Get started
+
+import GetStarted from "@snippets/modules/data_connection/text_embedding/get_started.mdx"
+
+<GetStarted/>