core[patch]: Release 0.2.31 (#25388)

can't have a class and function with same name but different
capitalization in same file for api reference building

.gitignore

@@ -172,6 +172,8 @@ docs/api_reference/*/
 !docs/api_reference/_static/
 !docs/api_reference/templates/
 !docs/api_reference/themes/
+!docs/api_reference/_extensions/
+!docs/api_reference/scripts/
 docs/docs/build
 docs/docs/node_modules
 docs/docs/yarn.lock

Makefile

@@ -31,6 +31,7 @@ docs_linkcheck:
 api_docs_build:
 	poetry run python docs/api_reference/create_api_rst.py
 	cd docs/api_reference && poetry run make html
+	poetry run python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/
 
 API_PKG ?= text-splitters
 
@@ -38,12 +39,14 @@ api_docs_quick_preview:
 	poetry run pip install "pydantic<2"
 	poetry run python docs/api_reference/create_api_rst.py $(API_PKG)
 	cd docs/api_reference && poetry run make html
-	open docs/api_reference/_build/html/$(shell echo $(API_PKG) | sed 's/-/_/g')_api_reference.html
+	poetry run python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/
+	open docs/api_reference/_build/html/reference.html
 
 ## api_docs_clean: Clean the API Reference documentation build artifacts.
 api_docs_clean:
 	find ./docs/api_reference -name '*_api_reference.rst' -delete
 	git clean -fdX ./docs/api_reference
+	rm docs/api_reference/index.md
 	
 
 ## api_docs_linkcheck: Run linkchecker on the API Reference documentation.

docs/Makefile

@@ -41,12 +41,8 @@ generate-files:
 	cp -r $(SOURCE_DIR)/* $(INTERMEDIATE_DIR)
 	mkdir -p $(INTERMEDIATE_DIR)/templates
 
-	$(PYTHON) scripts/model_feat_table.py $(INTERMEDIATE_DIR)
-
 	$(PYTHON) scripts/tool_feat_table.py $(INTERMEDIATE_DIR)
 
-	$(PYTHON) scripts/document_loader_feat_table.py $(INTERMEDIATE_DIR)
-
 	$(PYTHON) scripts/kv_store_feat_table.py $(INTERMEDIATE_DIR)
 
 	$(PYTHON) scripts/partner_pkg_table.py $(INTERMEDIATE_DIR)
@@ -86,7 +82,11 @@ vercel-build: install-vercel-deps build generate-references
 	rm -rf docs
 	mv $(OUTPUT_NEW_DOCS_DIR) docs
 	rm -rf build
-	yarn run docusaurus build
+	mkdir static/api_reference
+	git clone --depth=1 https://github.com/baskaryan/langchain-api-docs-build.git
+	mv langchain-api-docs-build/api_reference_build/html/* static/api_reference/
+	rm -rf langchain-api-docs-build
+	NODE_OPTIONS="--max-old-space-size=5000" yarn run docusaurus build
 	mv build v0.2
 	mkdir build
 	mv v0.2 build

docs/api_reference/_extensions/gallery_directive.py

@@ -0,0 +1,144 @@
+"""A directive to generate a gallery of images from structured data.
+
+Generating a gallery of images that are all the same size is a common
+pattern in documentation, and this can be cumbersome if the gallery is
+generated programmatically. This directive wraps this particular use-case
+in a helper-directive to generate it with a single YAML configuration file.
+
+It currently exists for maintainers of the pydata-sphinx-theme,
+but might be abstracted into a standalone package if it proves useful.
+"""
+
+from pathlib import Path
+from typing import Any, ClassVar, Dict, List
+
+from docutils import nodes
+from docutils.parsers.rst import directives
+from sphinx.application import Sphinx
+from sphinx.util import logging
+from sphinx.util.docutils import SphinxDirective
+from yaml import safe_load
+
+logger = logging.getLogger(__name__)
+
+
+TEMPLATE_GRID = """
+`````{{grid}} {columns}
+{options}
+
+{content}
+
+`````
+"""
+
+GRID_CARD = """
+````{{grid-item-card}} {title}
+{options}
+
+{content}
+````
+"""
+
+
+class GalleryGridDirective(SphinxDirective):
+    """A directive to show a gallery of images and links in a Bootstrap grid.
+
+    The grid can be generated from a YAML file that contains a list of items, or
+    from the content of the directive (also formatted in YAML). Use the parameter
+    "class-card" to add an additional CSS class to all cards. When specifying the grid
+    items, you can use all parameters from "grid-item-card" directive to customize
+    individual cards + ["image", "header", "content", "title"].
+
+    Danger:
+        This directive can only be used in the context of a Myst documentation page as
+        the templates use Markdown flavored formatting.
+    """
+
+    name = "gallery-grid"
+    has_content = True
+    required_arguments = 0
+    optional_arguments = 1
+    final_argument_whitespace = True
+    option_spec: ClassVar[dict[str, Any]] = {
+        # A class to be added to the resulting container
+        "grid-columns": directives.unchanged,
+        "class-container": directives.unchanged,
+        "class-card": directives.unchanged,
+    }
+
+    def run(self) -> List[nodes.Node]:
+        """Create the gallery grid."""
+        if self.arguments:
+            # If an argument is given, assume it's a path to a YAML file
+            # Parse it and load it into the directive content
+            path_data_rel = Path(self.arguments[0])
+            path_doc, _ = self.get_source_info()
+            path_doc = Path(path_doc).parent
+            path_data = (path_doc / path_data_rel).resolve()
+            if not path_data.exists():
+                logger.info(f"Could not find grid data at {path_data}.")
+                nodes.text("No grid data found at {path_data}.")
+                return
+            yaml_string = path_data.read_text()
+        else:
+            yaml_string = "\n".join(self.content)
+
+        # Use all the element with an img-bottom key as sites to show
+        # and generate a card item for each of them
+        grid_items = []
+        for item in safe_load(yaml_string):
+            # remove parameters that are not needed for the card options
+            title = item.pop("title", "")
+
+            # build the content of the card using some extra parameters
+            header = f"{item.pop('header')}  \n^^^  \n" if "header" in item else ""
+            image = f"![image]({item.pop('image')})  \n" if "image" in item else ""
+            content = f"{item.pop('content')}  \n" if "content" in item else ""
+
+            # optional parameter that influence all cards
+            if "class-card" in self.options:
+                item["class-card"] = self.options["class-card"]
+
+            loc_options_str = "\n".join(f":{k}: {v}" for k, v in item.items()) + "  \n"
+
+            card = GRID_CARD.format(
+                options=loc_options_str, content=header + image + content, title=title
+            )
+            grid_items.append(card)
+
+        # Parse the template with Sphinx Design to create an output container
+        # Prep the options for the template grid
+        class_ = "gallery-directive" + f' {self.options.get("class-container", "")}'
+        options = {"gutter": 2, "class-container": class_}
+        options_str = "\n".join(f":{k}: {v}" for k, v in options.items())
+
+        # Create the directive string for the grid
+        grid_directive = TEMPLATE_GRID.format(
+            columns=self.options.get("grid-columns", "1 2 3 4"),
+            options=options_str,
+            content="\n".join(grid_items),
+        )
+
+        # Parse content as a directive so Sphinx Design processes it
+        container = nodes.container()
+        self.state.nested_parse([grid_directive], 0, container)
+
+        # Sphinx Design outputs a container too, so just use that
+        return [container.children[0]]
+
+
+def setup(app: Sphinx) -> Dict[str, Any]:
+    """Add custom configuration to sphinx app.
+
+    Args:
+        app: the Sphinx application
+
+    Returns:
+        the 2 parallel parameters set to ``True``.
+    """
+    app.add_directive("gallery-grid", GalleryGridDirective)
+
+    return {
+        "parallel_read_safe": True,
+        "parallel_write_safe": True,
+    }

docs/api_reference/_static/css/custom.css

@@ -1,26 +1,411 @@
-pre {
-  white-space: break-spaces;
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;700&display=swap');
+
+/*******************************************************************************
+* master color map. Only the colors that actually differ between light and dark
+* themes are specified separately.
+*
+* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1
+*/
+/**
+* Function to get items from nested maps
+*/
+/* Assign base colors for the PyData theme */
+:root {
+  --pst-teal-50: #f4fbfc;
+  --pst-teal-100: #e9f6f8;
+  --pst-teal-200: #d0ecf1;
+  --pst-teal-300: #abdde6;
+  --pst-teal-400: #3fb1c5;
+  --pst-teal-500: #0a7d91;
+  --pst-teal-600: #085d6c;
+  --pst-teal-700: #064752;
+  --pst-teal-800: #042c33;
+  --pst-teal-900: #021b1f;
+  --pst-violet-50: #f4eefb;
+  --pst-violet-100: #e0c7ff;
+  --pst-violet-200: #d5b4fd;
+  --pst-violet-300: #b780ff;
+  --pst-violet-400: #9c5ffd;
+  --pst-violet-500: #8045e5;
+  --pst-violet-600: #6432bd;
+  --pst-violet-700: #4b258f;
+  --pst-violet-800: #341a61;
+  --pst-violet-900: #1e0e39;
+  --pst-gray-50: #f9f9fa;
+  --pst-gray-100: #f3f4f5;
+  --pst-gray-200: #e5e7ea;
+  --pst-gray-300: #d1d5da;
+  --pst-gray-400: #9ca4af;
+  --pst-gray-500: #677384;
+  --pst-gray-600: #48566b;
+  --pst-gray-700: #29313d;
+  --pst-gray-800: #222832;
+  --pst-gray-900: #14181e;
+  --pst-pink-50: #fcf8fd;
+  --pst-pink-100: #fcf0fa;
+  --pst-pink-200: #f8dff5;
+  --pst-pink-300: #f3c7ee;
+  --pst-pink-400: #e47fd7;
+  --pst-pink-500: #c132af;
+  --pst-pink-600: #912583;
+  --pst-pink-700: #6e1c64;
+  --pst-pink-800: #46123f;
+  --pst-pink-900: #2b0b27;
+  --pst-foundation-white: #ffffff;
+  --pst-foundation-black: #14181e;
+  --pst-green-10: #f1fdfd;
+  --pst-green-50: #E0F7F6;
+  --pst-green-100: #B3E8E6;
+  --pst-green-200: #80D6D3;
+  --pst-green-300: #4DC4C0;
+  --pst-green-400: #4FB2AD;
+  --pst-green-500: #287977;
+  --pst-green-600: #246161;
+  --pst-green-700: #204F4F;
+  --pst-green-800: #1C3C3C;
+  --pst-green-900: #0D2427;
+  --pst-lilac-50: #f4eefb;
+  --pst-lilac-100: #DAD6FE;
+  --pst-lilac-200: #BCB2FD;
+  --pst-lilac-300: #9F8BFA;
+  --pst-lilac-400: #7F5CF6;
+  --pst-lilac-500: #6F3AED;
+  --pst-lilac-600: #6028D9;
+  --pst-lilac-700: #5021B6;
+  --pst-lilac-800: #431D95;
+  --pst-lilac-900: #1e0e39;
+  --pst-header-height: 2.5rem;
 }
 
-@media (min-width: 1200px) {
-  .container,
-  .container-lg,
-  .container-md,
-  .container-sm,
-  .container-xl {
-    max-width: 2560px !important;
-  }
+html {
+    --pst-font-family-base: 'Inter';
+    --pst-font-family-heading: 'Inter Tight', sans-serif;
 }
 
-#my-component-root *,
-#headlessui-portal-root * {
-  z-index: 10000;
+/*******************************************************************************
+* write the color rules for each theme (light/dark)
+*/
+/* NOTE:
+ * Mixins enable us to reuse the same definitions for the different modes
+ * https://sass-lang.com/documentation/at-rules/mixin
+ * something inserts a variable into a CSS selector or property name
+ * https://sass-lang.com/documentation/interpolation
+ */
+/* Defaults to light mode if data-theme is not set */
+html:not([data-theme]) {
+  --pst-color-primary: #287977;
+  --pst-color-primary-bg: #80D6D3;
+  --pst-color-secondary: #6F3AED;
+  --pst-color-secondary-bg: #DAD6FE;
+  --pst-color-accent: #c132af;
+  --pst-color-accent-bg: #f8dff5;
+  --pst-color-info: #276be9;
+  --pst-color-info-bg: #dce7fc;
+  --pst-color-warning: #f66a0a;
+  --pst-color-warning-bg: #f8e3d0;
+  --pst-color-success: #00843f;
+  --pst-color-success-bg: #d6ece1;
+  --pst-color-attention: var(--pst-color-warning);
+  --pst-color-attention-bg: var(--pst-color-warning-bg);
+  --pst-color-danger: #d72d47;
+  --pst-color-danger-bg: #f9e1e4;
+  --pst-color-text-base: #222832;
+  --pst-color-text-muted: #48566b;
+  --pst-color-heading-color: #ffffff;
+  --pst-color-shadow: rgba(0, 0, 0, 0.1);
+  --pst-color-border: #d1d5da;
+  --pst-color-border-muted: rgba(23, 23, 26, 0.2);
+  --pst-color-inline-code: #912583;
+  --pst-color-inline-code-links: #246161;
+  --pst-color-target: #f3cf95;
+  --pst-color-background: #ffffff;
+  --pst-color-on-background: #F4F9F8;
+  --pst-color-surface: #F4F9F8;
+  --pst-color-on-surface: #222832;
+}
+html:not([data-theme]) {
+  --pst-color-link: var(--pst-color-primary);
+  --pst-color-link-hover: var(--pst-color-secondary);
+}
+html:not([data-theme]) .only-dark,
+html:not([data-theme]) .only-dark ~ figcaption {
+  display: none !important;
 }
 
-table.longtable code {
-  white-space: normal;
+/* NOTE: @each {...} is like a for-loop
+ * https://sass-lang.com/documentation/at-rules/control/each
+ */
+html[data-theme=light] {
+  --pst-color-primary: #287977;
+  --pst-color-primary-bg: #80D6D3;
+  --pst-color-secondary: #6F3AED;
+  --pst-color-secondary-bg: #DAD6FE;
+  --pst-color-accent: #c132af;
+  --pst-color-accent-bg: #f8dff5;
+  --pst-color-info: #276be9;
+  --pst-color-info-bg: #dce7fc;
+  --pst-color-warning: #f66a0a;
+  --pst-color-warning-bg: #f8e3d0;
+  --pst-color-success: #00843f;
+  --pst-color-success-bg: #d6ece1;
+  --pst-color-attention: var(--pst-color-warning);
+  --pst-color-attention-bg: var(--pst-color-warning-bg);
+  --pst-color-danger: #d72d47;
+  --pst-color-danger-bg: #f9e1e4;
+  --pst-color-text-base: #222832;
+  --pst-color-text-muted: #48566b;
+  --pst-color-heading-color: #ffffff;
+  --pst-color-shadow: rgba(0, 0, 0, 0.1);
+  --pst-color-border: #d1d5da;
+  --pst-color-border-muted: rgba(23, 23, 26, 0.2);
+  --pst-color-inline-code: #912583;
+  --pst-color-inline-code-links: #246161;
+  --pst-color-target: #f3cf95;
+  --pst-color-background: #ffffff;
+  --pst-color-on-background: #F4F9F8;
+  --pst-color-surface: #F4F9F8;
+  --pst-color-on-surface: #222832;
+  color-scheme: light;
+}
+html[data-theme=light] {
+  --pst-color-link: var(--pst-color-primary);
+  --pst-color-link-hover: var(--pst-color-secondary);
+}
+html[data-theme=light] .only-dark,
+html[data-theme=light] .only-dark ~ figcaption {
+  display: none !important;
 }
 
-table.longtable td {
-  max-width: 600px;
+html[data-theme=dark] {
+  --pst-color-primary: #4FB2AD;
+  --pst-color-primary-bg: #1C3C3C;
+  --pst-color-secondary: #7F5CF6;
+  --pst-color-secondary-bg: #431D95;
+  --pst-color-accent: #e47fd7;
+  --pst-color-accent-bg: #46123f;
+  --pst-color-info: #79a3f2;
+  --pst-color-info-bg: #06245d;
+  --pst-color-warning: #ff9245;
+  --pst-color-warning-bg: #652a02;
+  --pst-color-success: #5fb488;
+  --pst-color-success-bg: #002f17;
+  --pst-color-attention: var(--pst-color-warning);
+  --pst-color-attention-bg: var(--pst-color-warning-bg);
+  --pst-color-danger: #e78894;
+  --pst-color-danger-bg: #4e111b;
+  --pst-color-text-base: #ced6dd;
+  --pst-color-text-muted: #9ca4af;
+  --pst-color-heading-color: #14181e;
+  --pst-color-shadow: rgba(0, 0, 0, 0.2);
+  --pst-color-border: #48566b;
+  --pst-color-border-muted: #29313d;
+  --pst-color-inline-code: #f3c7ee;
+  --pst-color-inline-code-links: #4FB2AD;
+  --pst-color-target: #675c04;
+  --pst-color-background: #14181e;
+  --pst-color-on-background: #222832;
+  --pst-color-surface: #29313d;
+  --pst-color-on-surface: #f3f4f5;
+  /* Adjust images in dark mode (unless they have class .only-dark or
+   * .dark-light, in which case assume they're already optimized for dark
+   * mode).
+   */
+  /* Give images a light background in dark mode in case they have
+  *  transparency and black text (unless they have class .only-dark or .dark-light, in
+  *  which case assume they're already optimized for dark mode).
+  */
+  color-scheme: dark;
 }
+html[data-theme=dark] {
+  --pst-color-link: var(--pst-color-primary);
+  --pst-color-link-hover: var(--pst-color-secondary);
+}
+html[data-theme=dark] .only-light,
+html[data-theme=dark] .only-light ~ figcaption {
+  display: none !important;
+}
+html[data-theme=dark] img:not(.only-dark):not(.dark-light) {
+  filter: brightness(0.8) contrast(1.2);
+}
+html[data-theme=dark] .bd-content img:not(.only-dark):not(.dark-light) {
+  background: rgb(255, 255, 255);
+  border-radius: 0.25rem;
+}
+html[data-theme=dark] .MathJax_SVG * {
+  fill: var(--pst-color-text-base);
+}
+
+.pst-color-primary {
+  color: var(--pst-color-primary);
+}
+
+.pst-color-secondary {
+  color: var(--pst-color-secondary);
+}
+
+.pst-color-accent {
+  color: var(--pst-color-accent);
+}
+
+.pst-color-info {
+  color: var(--pst-color-info);
+}
+
+.pst-color-warning {
+  color: var(--pst-color-warning);
+}
+
+.pst-color-success {
+  color: var(--pst-color-success);
+}
+
+.pst-color-attention {
+  color: var(--pst-color-attention);
+}
+
+.pst-color-danger {
+  color: var(--pst-color-danger);
+}
+
+.pst-color-text-base {
+  color: var(--pst-color-text-base);
+}
+
+.pst-color-text-muted {
+  color: var(--pst-color-text-muted);
+}
+
+.pst-color-heading-color {
+  color: var(--pst-color-heading-color);
+}
+
+.pst-color-shadow {
+  color: var(--pst-color-shadow);
+}
+
+.pst-color-border {
+  color: var(--pst-color-border);
+}
+
+.pst-color-border-muted {
+  color: var(--pst-color-border-muted);
+}
+
+.pst-color-inline-code {
+  color: var(--pst-color-inline-code);
+}
+
+.pst-color-inline-code-links {
+  color: var(--pst-color-inline-code-links);
+}
+
+.pst-color-target {
+  color: var(--pst-color-target);
+}
+
+.pst-color-background {
+  color: var(--pst-color-background);
+}
+
+.pst-color-on-background {
+  color: var(--pst-color-on-background);
+}
+
+.pst-color-surface {
+  color: var(--pst-color-surface);
+}
+
+.pst-color-on-surface {
+  color: var(--pst-color-on-surface);
+}
+
+
+
+/* Adjust the height of the navbar */
+.bd-header .bd-header__inner{
+    height: 52px; /* Adjust this value as needed */
+}
+
+.navbar-nav > li > a {
+    line-height: 52px; /* Vertically center the navbar links */
+}
+
+/* Make sure the navbar items align properly */
+.navbar-nav {
+    display: flex;
+}
+
+
+.bd-header .navbar-header-items__start{
+  margin-left: 0rem
+}
+
+.bd-header button.primary-toggle {
+  margin-right: 0rem;
+}
+
+.bd-header ul.navbar-nav .dropdown .dropdown-menu {
+  overflow-y: auto; /* Enable vertical scrolling */
+  max-height: 80vh
+}
+
+.bd-sidebar-primary {
+    width: 22%;  /* Adjust this value to your preference */
+    line-height: 1.4;
+}
+
+.bd-sidebar-secondary {
+    line-height: 1.4;
+}
+
+.toc-entry a.nav-link, .toc-entry a>code {  
+  background-color: transparent;
+  border-color: transparent;
+}
+
+.bd-sidebar-primary code{
+  background-color: transparent;
+  border-color: transparent;
+}
+
+
+.toctree-wrapper li[class^=toctree-l1]>a {
+  font-size: 1.3em
+}
+
+.toctree-wrapper li[class^=toctree-l1] {
+  margin-bottom: 2em;
+}
+
+.toctree-wrapper li[class^=toctree-l]>ul {
+  margin-top: 0.5em;
+  font-size: 0.9em;
+}
+
+*, :after, :before {
+  font-style: normal;
+}
+
+div.deprecated {
+  margin-top: 0.5em;
+  margin-bottom: 2em;
+}
+
+.admonition-beta.admonition, div.admonition-beta.admonition {
+  border-color: var(--pst-color-warning);
+  margin-top:0.5em;
+  margin-bottom: 2em;
+}
+
+.admonition-beta>.admonition-title, div.admonition-beta>.admonition-title {
+  background-color: var(--pst-color-warning-bg);
+}
+
+dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) dd {
+  margin-left: 1rem;
+}
+
+p {
+  font-size: 0.9rem;
+  margin-bottom: 0.5rem;
+}

docs/api_reference/_static/wordmark-api-dark.svg

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+</svg>

docs/api_reference/_static/wordmark-api.svg

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+<defs>
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+</defs>
+</svg>

docs/api_reference/conf.py

@@ -62,7 +62,7 @@ class ExampleLinksDirective(SphinxDirective):
             item_node.append(para_node)
             list_node.append(item_node)
         if list_node.children:
-            title_node = nodes.title()
+            title_node = nodes.rubric()
             title_node.append(nodes.Text(f"Examples using {class_or_func_name}"))
             return [title_node, list_node]
         return [list_node]
@@ -75,7 +75,10 @@ class Beta(BaseAdmonition):
     def run(self):
         self.content = self.content or StringList(
             [
-                "This feature is in beta. It is actively being worked on, so the API may change."
+                (
+                    "This feature is in beta. It is actively being worked on, so the "
+                    "API may change."
+                )
             ]
         )
         self.arguments = self.arguments or ["Beta"]
@@ -90,13 +93,10 @@ def setup(app):
 # -- Project information -----------------------------------------------------
 
 project = "🦜🔗 LangChain"
-copyright = "2023, LangChain, Inc."
-author = "LangChain, Inc."
+copyright = "2023, LangChain Inc"
+author = "LangChain, Inc"
 
-version = data["tool"]["poetry"]["version"]
-release = version
-
-html_title = project + " " + version
+html_favicon = "_static/img/brand/favicon.png"
 html_last_updated_fmt = "%b %d, %Y"
 
 
@@ -112,11 +112,13 @@ extensions = [
     "sphinx.ext.napoleon",
     "sphinx.ext.viewcode",
     "sphinxcontrib.autodoc_pydantic",
-    "sphinx_copybutton",
-    "sphinx_panels",
     "IPython.sphinxext.ipython_console_highlighting",
+    "myst_parser",
+    "_extensions.gallery_directive",
+    "sphinx_design",
+    "sphinx_copybutton",
 ]
-source_suffix = [".rst"]
+source_suffix = [".rst", ".md"]
 
 # some autodoc pydantic options are repeated in the actual template.
 # potentially user error, but there may be bugs in the sphinx extension
@@ -148,23 +150,84 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = "scikit-learn-modern"
-html_theme_path = ["themes"]
+# The theme to use for HTML and HTML Help pages.
+html_theme = "pydata_sphinx_theme"
 
-# redirects dictionary maps from old links to new links
-html_additional_pages = {}
-redirects = {
-    "index": "langchain_api_reference",
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+html_theme_options = {
+    #     # -- General configuration ------------------------------------------------
+    "sidebar_includehidden": True,
+    "use_edit_page_button": False,
+    #     # "analytics": {
+    #     #     "plausible_analytics_domain": "scikit-learn.org",
+    #     #     "plausible_analytics_url": "https://views.scientific-python.org/js/script.js",
+    #     # },
+    #     # If "prev-next" is included in article_footer_items, then setting show_prev_next
+    #     # to True would repeat prev and next links. See
+    #     # https://github.com/pydata/pydata-sphinx-theme/blob/b731dc230bc26a3d1d1bb039c56c977a9b3d25d8/src/pydata_sphinx_theme/theme/pydata_sphinx_theme/layout.html#L118-L129
+    "show_prev_next": False,
+    "search_bar_text": "Search",
+    "navigation_with_keys": True,
+    "collapse_navigation": True,
+    "navigation_depth": 3,
+    "show_nav_level": 1,
+    "show_toc_level": 3,
+    "navbar_align": "left",
+    "header_links_before_dropdown": 5,
+    "header_dropdown_text": "Integrations",
+    "logo": {
+        "image_light": "_static/wordmark-api.svg",
+        "image_dark": "_static/wordmark-api-dark.svg",
+    },
+    "surface_warnings": True,
+    #     # -- Template placement in theme layouts ----------------------------------
+    "navbar_start": ["navbar-logo"],
+    #     # Note that the alignment of navbar_center is controlled by navbar_align
+    "navbar_center": ["navbar-nav"],
+    "navbar_end": ["langchain_docs", "theme-switcher", "navbar-icon-links"],
+    #     # navbar_persistent is persistent right (even when on mobiles)
+    "navbar_persistent": ["search-field"],
+    "article_header_start": ["breadcrumbs"],
+    "article_header_end": [],
+    "article_footer_items": [],
+    "content_footer_items": [],
+    #     # Use html_sidebars that map page patterns to list of sidebar templates
+    #     "primary_sidebar_end": [],
+    "footer_start": ["copyright"],
+    "footer_center": [],
+    "footer_end": [],
+    #     # When specified as a dictionary, the keys should follow glob-style patterns, as in
+    #     # https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-exclude_patterns
+    #     # In particular, "**" specifies the default for all pages
+    #     # Use :html_theme.sidebar_secondary.remove: for file-wide removal
+    #     "secondary_sidebar_items": {"**": ["page-toc", "sourcelink"]},
+    #     "show_version_warning_banner": True,
+    #     "announcement": None,
+    "icon_links": [
+        {
+            # Label for this link
+            "name": "GitHub",
+            # URL where the link will redirect
+            "url": "https://github.com/langchain-ai/langchain",  # required
+            # Icon class (if "type": "fontawesome"), or path to local image (if "type": "local")
+            "icon": "fa-brands fa-square-github",
+            # The type of image to be used (see below for details)
+            "type": "fontawesome",
+        },
+        {
+            "name": "X / Twitter",
+            "url": "https://twitter.com/langchainai",
+            "icon": "fab fa-twitter-square",
+        },
+    ],
+    "icon_links_label": "Quick Links",
+    "external_links": [
+        {"name": "Legacy reference", "url": "https://api.python.langchain.com/"},
+    ],
 }
-for old_link in redirects:
-    html_additional_pages[old_link] = "redirects.html"
 
-partners_dir = Path(__file__).parent.parent.parent / "libs/partners"
-partners = [
-    (p.name, p.name.replace("-", "_") + "_api_reference")
-    for p in partners_dir.iterdir()
-]
-partners = sorted(partners)
 
 html_context = {
     "display_github": True,  # Integrate GitHub
@@ -172,8 +235,6 @@ html_context = {
     "github_repo": "langchain",  # Repo name
     "github_version": "master",  # Version
     "conf_py_path": "/docs/api_reference",  # Path in the checkout to the docs root
-    "redirects": redirects,
-    "partners": partners,
 }
 
 # Add any paths that contain custom static files (such as style sheets) here,
@@ -183,9 +244,7 @@ html_static_path = ["_static"]
 
 # These paths are either relative to html_static_path
 # or fully qualified paths (e.g. https://...)
-html_css_files = [
-    "css/custom.css",
-]
+html_css_files = ["css/custom.css"]
 html_use_index = False
 
 myst_enable_extensions = ["colon_fence"]
@@ -202,3 +261,5 @@ html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "")
 # Tell Jinja2 templates the build is running on Read the Docs
 if os.environ.get("READTHEDOCS", "") == "True":
     html_context["READTHEDOCS"] = True
+
+master_doc = "index"

docs/api_reference/create_api_rst.py

@@ -239,7 +239,7 @@ def _construct_doc(
     package_namespace: str,
     members_by_namespace: Dict[str, ModuleMembers],
     package_version: str,
-) -> str:
+) -> List[typing.Tuple[str, str]]:
     """Construct the contents of the reference.rst file for the given package.
 
     Args:
@@ -251,15 +251,38 @@ def _construct_doc(
     Returns:
         The contents of the reference.rst file.
     """
-    full_doc = f"""\
-=======================
-``{package_namespace}`` {package_version}
-=======================
+    docs = []
+    index_doc = f"""\
+:html_theme.sidebar_secondary.remove:
 
+.. currentmodule:: {package_namespace}
+
+.. _{package_namespace}:
+
+======================================
+{package_namespace.replace('_', '-')}: {package_version}
+======================================
+
+.. automodule:: {package_namespace}
+    :no-members:
+    :no-inherited-members:
+
+.. toctree::
+    :hidden:
+    :maxdepth: 2
+    
+"""
+    index_autosummary = """
 """
     namespaces = sorted(members_by_namespace)
 
     for module in namespaces:
+        index_doc += f"    {module}\n"
+        module_doc = f"""\
+.. currentmodule:: {package_namespace}
+
+.. _{module}:
+"""
         _members = members_by_namespace[module]
         classes = [
             el
@@ -281,9 +304,9 @@ def _construct_doc(
         ]
         if not (classes or functions):
             continue
-        section = f":mod:`{package_namespace}.{module}`"
+        section = f":mod:`{module}`"
         underline = "=" * (len(section) + 1)
-        full_doc += f"""\
+        module_doc += f"""
 {section}
 {underline}
 
@@ -291,16 +314,26 @@ def _construct_doc(
     :no-members:
     :no-inherited-members:
 
+"""
+
+        index_autosummary += f"""
+:ref:`{module}`
+{'^' * (len(module) + 5)}
 """
 
         if classes:
-            full_doc += f"""\
-Classes
---------------
+            module_doc += f"""\
+**Classes**
+
 .. currentmodule:: {package_namespace}
 
 .. autosummary::
     :toctree: {module}
+"""
+            index_autosummary += """
+**Classes**
+
+.. autosummary::
 """
 
             for class_ in sorted(classes, key=lambda c: c["qualified_name"]):
@@ -317,19 +350,22 @@ Classes
                 else:
                     template = "class.rst"
 
-                full_doc += f"""\
+                module_doc += f"""\
     :template: {template}
     
     {class_["qualified_name"]}
     
+"""
+                index_autosummary += f"""
+    {class_['qualified_name']}
 """
 
         if functions:
             _functions = [f["qualified_name"] for f in functions]
             fstring = "\n    ".join(sorted(_functions))
-            full_doc += f"""\
-Functions
---------------
+            module_doc += f"""\
+**Functions**
+
 .. currentmodule:: {package_namespace}
 
 .. autosummary::
@@ -338,11 +374,18 @@ Functions
 
     {fstring}
 
+"""
+
+            index_autosummary += f"""
+**Functions**
+
+.. autosummary::
+
+    {fstring}
 """
         if deprecated_classes:
-            full_doc += f"""\
-Deprecated classes
---------------
+            module_doc += f"""\
+**Deprecated classes**
 
 .. currentmodule:: {package_namespace}
 
@@ -350,6 +393,12 @@ Deprecated classes
     :toctree: {module}
 """
 
+            index_autosummary += """
+**Deprecated classes*
+
+.. autosummary::
+"""
+
             for class_ in sorted(deprecated_classes, key=lambda c: c["qualified_name"]):
                 if class_["kind"] == "TypedDict":
                     template = "typeddict.rst"
@@ -364,19 +413,21 @@ Deprecated classes
                 else:
                     template = "class.rst"
 
-                full_doc += f"""\
+                module_doc += f"""\
     :template: {template}
 
     {class_["qualified_name"]}
 
+"""
+                index_autosummary += f"""
+    {class_['qualified_name']}
 """
 
         if deprecated_functions:
             _functions = [f["qualified_name"] for f in deprecated_functions]
             fstring = "\n    ".join(sorted(_functions))
-            full_doc += f"""\
-Deprecated functions
---------------
+            module_doc += f"""\
+**Deprecated functions**
 
 .. currentmodule:: {package_namespace}
 
@@ -387,7 +438,18 @@ Deprecated functions
     {fstring}
 
 """
-    return full_doc
+            index_autosummary += """
+**Deprecated functions**
+
+.. autosummary::
+
+    {fstring}
+
+"""
+        docs.append((f"{module}.rst", module_doc))
+    docs.append(("index.rst", index_doc + index_autosummary))
+
+    return docs
 
 
 def _build_rst_file(package_name: str = "langchain") -> None:
@@ -399,13 +461,14 @@ def _build_rst_file(package_name: str = "langchain") -> None:
     package_dir = _package_dir(package_name)
     package_members = _load_package_modules(package_dir)
     package_version = _get_package_version(package_dir)
-    with open(_out_file_path(package_name), "w") as f:
-        f.write(
-            _doc_first_line(package_name)
-            + _construct_doc(
-                _package_namespace(package_name), package_members, package_version
-            )
-        )
+    output_dir = _out_file_path(package_name)
+    os.mkdir(output_dir)
+    rsts = _construct_doc(
+        _package_namespace(package_name), package_members, package_version
+    )
+    for name, rst in rsts:
+        with open(output_dir / name, "w") as f:
+            f.write(rst)
 
 
 def _package_namespace(package_name: str) -> str:
@@ -455,12 +518,117 @@ def _get_package_version(package_dir: Path) -> str:
 
 def _out_file_path(package_name: str) -> Path:
     """Return the path to the file containing the documentation."""
-    return HERE / f"{package_name.replace('-', '_')}_api_reference.rst"
+    return HERE / f"{package_name.replace('-', '_')}"
 
 
-def _doc_first_line(package_name: str) -> str:
-    """Return the path to the file containing the documentation."""
-    return f".. {package_name.replace('-', '_')}_api_reference:\n\n"
+def _build_index(dirs: List[str]) -> None:
+    custom_names = {
+        "airbyte": "Airbyte",
+        "aws": "AWS",
+        "ai21": "AI21",
+    }
+    ordered = ["core", "langchain", "text-splitters", "community", "experimental"]
+    main_ = [dir_ for dir_ in ordered if dir_ in dirs]
+    integrations = sorted(dir_ for dir_ in dirs if dir_ not in main_)
+    main_headers = [
+        " ".join(custom_names.get(x, x.title()) for x in dir_.split("-"))
+        for dir_ in main_
+    ]
+    integration_headers = [
+        " ".join(
+            custom_names.get(x, x.title().replace("ai", "AI").replace("db", "DB"))
+            for x in dir_.split("-")
+        )
+        for dir_ in integrations
+    ]
+    main_tree = "\n".join(
+        f"{header_name}<{dir_.replace('-', '_')}/index>"
+        for header_name, dir_ in zip(main_headers, main_)
+    )
+    main_grid = "\n".join(
+        f'- header: "**{header_name}**"\n  content: "{_package_namespace(dir_).replace("_", "-")}: {_get_package_version(_package_dir(dir_))}"\n  link: {dir_.replace("-", "_")}/index.html'
+        for header_name, dir_ in zip(main_headers, main_)
+    )
+    integration_tree = "\n".join(
+        f"{header_name}<{dir_.replace('-', '_')}/index>"
+        for header_name, dir_ in zip(integration_headers, integrations)
+    )
+
+    integration_grid = ""
+    integrations_to_show = [
+        "openai",
+        "anthropic",
+        "google-vertexai",
+        "aws",
+        "huggingface",
+        "mistralai",
+    ]
+    for header_name, dir_ in sorted(
+        zip(integration_headers, integrations),
+        key=lambda h_d: integrations_to_show.index(h_d[1])
+        if h_d[1] in integrations_to_show
+        else len(integrations_to_show),
+    )[: len(integrations_to_show)]:
+        integration_grid += f'\n- header: "**{header_name}**"\n  content: {_package_namespace(dir_).replace("_", "-")} {_get_package_version(_package_dir(dir_))}\n  link: {dir_.replace("-", "_")}/index.html'
+    doc = f"""# LangChain Python API Reference
+
+Welcome to the LangChain Python API reference. This is a reference for all 
+`langchain-x` packages. 
+
+For user guides see [https://python.langchain.com](https://python.langchain.com).
+
+For the legacy API reference hosted on ReadTheDocs see [https://api.python.langchain.com/](https://api.python.langchain.com/).
+
+## Base packages
+
+```{{gallery-grid}}
+:grid-columns: "1 2 2 3"
+
+{main_grid}
+```
+
+```{{toctree}}
+:maxdepth: 2
+:hidden:
+:caption: Base packages
+
+{main_tree}
+```
+
+## Integrations
+
+```{{gallery-grid}}
+:grid-columns: "1 2 2 3"
+
+{integration_grid}
+```
+
+See the full list of integrations in the Section Navigation.
+
+```{{toctree}}
+:maxdepth: 2
+:hidden:
+:caption: Integrations
+
+{integration_tree}
+```
+
+"""
+    with open(HERE / "reference.md", "w") as f:
+        f.write(doc)
+
+    dummy_index = """\
+# API reference
+
+```{toctree}
+:maxdepth: 3
+:hidden:
+
+Reference<reference>
+```
+"""
+    with open(HERE / "index.md", "w") as f:
+        f.write(dummy_index)
 
 
 def main(dirs: Optional[list] = None) -> None:
@@ -488,6 +656,8 @@ def main(dirs: Optional[list] = None) -> None:
         else:
             print("Building package:", dir_)
             _build_rst_file(package_name=dir_)
+
+    _build_index(dirs)
     print("API reference files built.")
 
 

docs/api_reference/index.rst

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

docs/api_reference/requirements.txt

@@ -1,17 +1,11 @@
--e libs/experimental
--e libs/langchain
--e libs/core
--e libs/community
-pydantic<2
-autodoc_pydantic==1.8.0
-myst_parser
-nbsphinx==0.8.9
-sphinx>=5
-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
+autodoc_pydantic>=1,<2
+sphinx<=7
+myst-parser>=3
+sphinx-autobuild>=2024
+pydata-sphinx-theme>=0.15
+toml>=0.10.2
+myst-nb>=1.1.1
+pyyaml
+sphinx-design
+sphinx-copybutton
+beautifulsoup4

docs/api_reference/scripts/custom_formatter.py

@@ -0,0 +1,41 @@
+import sys
+from glob import glob
+from pathlib import Path
+
+from bs4 import BeautifulSoup
+
+CUR_DIR = Path(__file__).parents[1]
+
+
+def process_toc_h3_elements(html_content: str) -> str:
+    """Update Class.method() TOC headers to just method()."""
+    # Create a BeautifulSoup object
+    soup = BeautifulSoup(html_content, "html.parser")
+
+    # Find all <li> elements with class "toc-h3"
+    toc_h3_elements = soup.find_all("li", class_="toc-h3")
+
+    # Process each element
+    for element in toc_h3_elements:
+        element = element.a.code.span
+        # Get the text content of the element
+        content = element.get_text()
+
+        # Apply the regex substitution
+        modified_content = content.split(".")[-1]
+
+        # Update the element's content
+        element.string = modified_content
+
+    # Return the modified HTML
+    return str(soup)
+
+
+if __name__ == "__main__":
+    dir = sys.argv[1]
+    for fn in glob(str(f"{dir.rstrip('/')}/**/*.html"), recursive=True):
+        with open(fn, "r") as f:
+            html = f.read()
+        processed_html = process_toc_h3_elements(html)
+        with open(fn, "w") as f:
+            f.write(processed_html)

docs/api_reference/templates/class.rst

@@ -1,4 +1,4 @@
-:mod:`{{module}}`.{{objname}}
+{{ objname }}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}
@@ -11,7 +11,7 @@
 
    .. autosummary::
    {% for item in attributes %}
-      ~{{ name }}.{{ item }}
+      ~{{ item }}
    {%- endfor %}
    {% endif %}
    {% endblock %}
@@ -22,11 +22,11 @@
 
    .. autosummary::
    {% for item in methods %}
-      ~{{ name }}.{{ item }}
+      ~{{ item }}
    {%- endfor %}
 
    {% for item in methods %}
-   .. automethod:: {{ name }}.{{ item }}
+   .. automethod:: {{ item }}
    {%- endfor %}
 
    {% endif %}

docs/api_reference/templates/enum.rst

@@ -1,4 +1,4 @@
-:mod:`{{module}}`.{{objname}}
+{{ objname }}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}

docs/api_reference/templates/function.rst

@@ -1,4 +1,4 @@
-:mod:`{{module}}`.{{objname}}
+{{ objname }}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}

docs/api_reference/templates/langchain_docs.html

@@ -0,0 +1,12 @@
+<!-- This will display a link to LangChain docs -->
+<head>
+    <style>
+        .text-link {
+            text-decoration: none; /* Remove underline */
+            color: inherit;        /* Inherit color from parent element */
+        }
+    </style>
+</head>
+<body>
+<a href="https://python.langchain.com/" class='text-link'>Docs</a>
+</body>

docs/api_reference/templates/pydantic.rst

@@ -1,4 +1,4 @@
-:mod:`{{module}}`.{{objname}}
+{{ objname }}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}

docs/api_reference/templates/runnable_non_pydantic.rst

@@ -1,21 +1,21 @@
-:mod:`{{module}}`.{{objname}}
+{{ objname }}
 {{ underline }}==============
 
-.. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
-
-    The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
-
 .. currentmodule:: {{ module }}
 
 .. autoclass:: {{ objname }}
 
+.. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
+
+    The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
+
    {% block attributes %}
    {% if attributes %}
    .. rubric:: {{ _('Attributes') }}
 
    .. autosummary::
    {% for item in attributes %}
-      ~{{ name }}.{{ item }}
+      ~{{ item }}
    {%- endfor %}
    {% endif %}
    {% endblock %}
@@ -26,11 +26,11 @@
 
    .. autosummary::
    {% for item in methods %}
-      ~{{ name }}.{{ item }}
+      ~{{ item }}
    {%- endfor %}
 
    {% for item in methods %}
-   .. automethod:: {{ name }}.{{ item }}
+   .. automethod:: {{ item }}
    {%- endfor %}
 
    {% endif %}

docs/api_reference/templates/runnable_pydantic.rst

@@ -1,10 +1,6 @@
-:mod:`{{module}}`.{{objname}}
+{{ objname }}
 {{ underline }}==============
 
-.. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
-
-    The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
-
 .. currentmodule:: {{ module }}
 
 .. autopydantic_model:: {{ objname }}
@@ -19,6 +15,10 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign, as_tool
+
+    .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
+
+        The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
 
 .. example_links:: {{ objname }}

docs/api_reference/templates/typeddict.rst

@@ -1,4 +1,4 @@
-:mod:`{{module}}`.{{objname}}
+{{ objname }}
 {{ underline }}==============
 
 .. currentmodule:: {{ module }}

docs/api_reference/themes/COPYRIGHT.txt

@@ -1,27 +0,0 @@
-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

@@ -1,67 +0,0 @@
-<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

@@ -1,132 +0,0 @@
-{# 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="https://api.python.langchain.com/en/latest/{{ 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">
-                {%- 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

@@ -1,78 +0,0 @@
-{%- 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 %}
-
-<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('langchain_api_reference') }}">LangChain</a>
-        </li>
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" href="{{ pathto('core_api_reference') }}">Core</a>
-        </li>
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" href="{{ pathto('community_api_reference') }}">Community</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" href="{{ pathto('text_splitters_api_reference') }}">Text splitters</a>
-        </li>
-        {%- for title, pathname in partners %}
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="{{ pathto(pathname) }}">{{ title }}</a>
-        </li>
-        {%- endfor %}
-        <li class="nav-item dropdown nav-more-item-dropdown">
-          <a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Partner libs</a>
-          <div class="dropdown-menu" aria-labelledby="navbarDropdown">
-            {%- for title, pathname in partners %}
-              <a class="sk-nav-dropdown-item dropdown-item" href="{{ pathto(pathname) }}">{{ title }}</a>
-            {%- endfor %}
-          </div>
-        </li>
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" target="_blank" rel="noopener noreferrer" href="https://python.langchain.com/">Docs</a>
-        </li>
-      </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

@@ -1,16 +0,0 @@
-{%- 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

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

docs/docs/how_to/chat_model_caching.ipynb

@@ -63,7 +63,7 @@
    "outputs": [],
    "source": [
     "# <!-- ruff: noqa: F821 -->\n",
-    "from langchain.globals import set_llm_cache"
+    "from langchain_core.globals import set_llm_cache"
    ]
   },
   {
@@ -103,7 +103,7 @@
    ],
    "source": [
     "%%time\n",
-    "from langchain.cache import InMemoryCache\n",
+    "from langchain_core.caches import InMemoryCache\n",
     "\n",
     "set_llm_cache(InMemoryCache())\n",
     "\n",

docs/docs/how_to/document_loader_custom.ipynb

@@ -63,7 +63,7 @@
     "* The `load` methods is a convenience method meant solely for prototyping work -- it just invokes `list(self.lazy_load())`.\n",
     "* The `alazy_load` has a default implementation that will delegate to `lazy_load`. If you're using async, we recommend overriding the default implementation and providing a native async implementation.\n",
     "\n",
-    "::: {.callout-important}\n",
+    ":::{.callout-important}\n",
     "When implementing a document loader do **NOT** provide parameters via the `lazy_load` or `alazy_load` methods.\n",
     "\n",
     "All configuration is expected to be passed through the initializer (__init__). This was a design choice made by LangChain to make sure that once a document loader has been instantiated it has all the information needed to load documents.\n",
@@ -235,7 +235,7 @@
    "id": "56cb443e-f987-4386-b4ec-975ee129adb2",
    "metadata": {},
    "source": [
-    "::: {.callout-tip}\n",
+    ":::{.callout-tip}\n",
     "\n",
     "`load()` can be helpful in an interactive environment such as a jupyter notebook.\n",
     "\n",
@@ -276,7 +276,7 @@
    "source": [
     "## Working with Files\n",
     "\n",
-    "Many document loaders invovle parsing files. The difference between such loaders usually stems from how the file is parsed rather than how the file is loaded. For example, you can use `open` to read the binary content of either a PDF or a markdown file, but you need different parsing logic to convert that binary data into text.\n",
+    "Many document loaders involve parsing files. The difference between such loaders usually stems from how the file is parsed, rather than how the file is loaded. For example, you can use `open` to read the binary content of either a PDF or a markdown file, but you need different parsing logic to convert that binary data into text.\n",
     "\n",
     "As a result, it can be helpful to decouple the parsing logic from the loading logic, which makes it easier to re-use a given parser regardless of how the data was loaded.\n",
     "\n",
@@ -355,7 +355,7 @@
    "id": "433bfb7c-7767-43bc-b71e-42413d7494a8",
    "metadata": {},
    "source": [
-    "Using the **blob** API also allows one to load content direclty from memory without having to read it from a file!"
+    "Using the **blob** API also allows one to load content directly from memory without having to read it from a file!"
    ]
   },
   {

docs/docs/how_to/graph_mapping.ipynb

@@ -364,7 +364,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema\n",
+    "from langchain_community.chains.graph_qa.cypher_utils import (\n",
+    "    CypherQueryCorrector,\n",
+    "    Schema,\n",
+    ")\n",
     "\n",
     "# Cypher validation tool for relationship directions\n",
     "corrector_schema = [\n",

docs/docs/how_to/llm_caching.ipynb

@@ -36,7 +36,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.globals import set_llm_cache\n",
+    "from langchain_core.globals import set_llm_cache\n",
     "from langchain_openai import OpenAI\n",
     "\n",
     "# To make the caching really obvious, lets use a slower and older model.\n",
@@ -71,7 +71,7 @@
    ],
    "source": [
     "%%time\n",
-    "from langchain.cache import InMemoryCache\n",
+    "from langchain_core.caches import InMemoryCache\n",
     "\n",
     "set_llm_cache(InMemoryCache())\n",
     "\n",

docs/docs/how_to/query_constructing_filters.ipynb

@@ -38,8 +38,8 @@
     "    Operator,\n",
     "    StructuredQuery,\n",
     ")\n",
-    "from langchain.retrievers.self_query.chroma import ChromaTranslator\n",
-    "from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator\n",
+    "from langchain_community.query_constructors.chroma import ChromaTranslator\n",
+    "from langchain_community.query_constructors.elasticsearch import ElasticsearchTranslator\n",
     "from langchain_core.pydantic_v1 import BaseModel"
    ]
   },

docs/docs/how_to/self_query.ipynb

@@ -512,7 +512,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.retrievers.self_query.chroma import ChromaTranslator\n",
+    "from langchain_community.query_constructors.chroma import ChromaTranslator\n",
     "\n",
     "retriever = SelfQueryRetriever(\n",
     "    query_constructor=query_constructor,\n",

docs/docs/how_to/semantic-chunker.ipynb

@@ -299,16 +299,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "423c6e099e94ca69",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "### Gradient\n",
     "\n",
     "In this method, the gradient of distance is used to split chunks along with the percentile method.\n",
     "This method is useful when chunks are highly correlated with each other or specific to a domain e.g. legal or medical. The idea is to apply anomaly detection on gradient array so that the distribution become wider and easy to identify boundaries in highly semantic data."
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "423c6e099e94ca69"
+   ]
   },
   {
    "cell_type": "code",
@@ -325,6 +325,8 @@
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "e9f393d316ce1f6c",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -337,13 +339,13 @@
    "source": [
     "docs = text_splitter.create_documents([state_of_the_union])\n",
     "print(docs[0].page_content)"
-   ],
-   "metadata": {},
-   "id": "e9f393d316ce1f6c"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "a407cd57f02a0db4",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -355,9 +357,7 @@
    ],
    "source": [
     "print(len(docs))"
-   ],
-   "metadata": {},
-   "id": "a407cd57f02a0db4"
+   ]
   }
  ],
  "metadata": {

docs/docs/integrations/chat/index.mdx

@@ -0,0 +1,32 @@
+---
+sidebar_position: 0
+sidebar_class_name: hidden
+keywords: [compatibility]
+---
+
+# Chat models
+
+[Chat models](/docs/concepts/#chat-models) are language models that use a sequence of [messages](/docs/concepts/#messages) as inputs and return messages as outputs (as opposed to using plain text). These are generally newer models.
+
+:::info
+
+If you'd like to write your own chat model, see [this how-to](/docs/how_to/custom_chat_model/).
+If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
+
+:::
+
+## Featured Providers
+
+:::info
+While all these LangChain classes support the indicated advanced feature, you may have
+to open the provider-specific documentation to learn which hosted models or backends support
+the feature.
+:::
+
+import { CategoryTable, IndexTable } from "@theme/FeatureTables";
+
+<CategoryTable category="chat" />
+
+## All chat models
+
+<IndexTable />

docs/docs/integrations/chat/llamacpp.ipynb

@@ -4,9 +4,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# ChatLlamaCpp\n",
+    "# Llama.cpp\n",
     "\n",
-    "This notebook provides a quick overview for getting started with chat model intergrated with [llama cpp python](https://github.com/abetlen/llama-cpp-python)."
+    ">[llama.cpp python](https://github.com/abetlen/llama-cpp-python) library is a simple Python bindings for `@ggerganov`\n",
+    ">[llama.cpp](https://github.com/ggerganov/llama.cpp).\n",
+    ">\n",
+    ">This package provides:\n",
+    ">\n",
+    "> - Low-level access to C API via ctypes interface.\n",
+    "> - High-level Python API for text completion\n",
+    ">   - `OpenAI`-like API\n",
+    ">   - `LangChain` compatibility\n",
+    ">   - `LlamaIndex` compatibility\n",
+    "> - OpenAI compatible web server\n",
+    ">   - Local Copilot replacement\n",
+    ">   - Function Calling support\n",
+    ">   - Vision API support\n",
+    ">   - Multiple Models\n"
    ]
   },
   {
@@ -212,8 +226,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.tools import tool\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_core.tools import tool\n",
     "\n",
     "\n",
     "class WeatherInput(BaseModel):\n",
@@ -410,7 +424,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/octoai.ipynb

@@ -99,7 +99,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/docs/integrations/chat/perplexity.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# ChatPerplexity\n",
     "\n",
-    "This notebook covers how to get started with Perplexity chat models."
+    "This notebook covers how to get started with `Perplexity` chat models."
    ]
   },
   {
@@ -37,17 +37,31 @@
     "from langchain_core.prompts import ChatPromptTemplate"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b26e2035-2f81-4451-ba44-fa2e2d5aeb62",
+   "metadata": {},
+   "source": [
+    "The code provided assumes that your PPLX_API_KEY is set in your environment variables. If you would like to manually specify your API key and also choose a different model, you can use the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d986aac6-1bae-4608-8514-d3ba5b35b10e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat = ChatPerplexity(\n",
+    "    temperature=0, pplx_api_key=\"YOUR_API_KEY\", model=\"llama-3-sonar-small-32k-online\"\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "97a8ce3a",
    "metadata": {},
    "source": [
-    "The code provided assumes that your PPLX_API_KEY is set in your environment variables. If you would like to manually specify your API key and also choose a different model, you can use the following code:\n",
-    "\n",
-    "```python\n",
-    "chat = ChatPerplexity(temperature=0, pplx_api_key=\"YOUR_API_KEY\", model=\"llama-3-sonar-small-32k-online\")\n",
-    "```\n",
-    "\n",
     "You can check a list of available models [here](https://docs.perplexity.ai/docs/model-cards). For reproducibility, we can set the API key dynamically by taking it as an input in this notebook."
    ]
   },
@@ -221,7 +235,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/github.ipynb

@@ -164,7 +164,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.document_loaders import GithubFileLoader"
+    "from langchain_community.document_loaders import GithubFileLoader"
    ]
   },
   {

docs/docs/integrations/document_loaders/index.mdx

@@ -0,0 +1,45 @@
+---
+sidebar_position: 0
+sidebar_class_name: hidden
+---
+
+# Document loaders
+
+import { CategoryTable, IndexTable } from "@theme/FeatureTables";
+
+DocumentLoaders load data into the standard LangChain Document format.
+
+Each DocumentLoader has its own specific parameters, but they can all be invoked in the same way with the .load method.
+An example use case is as follows:
+
+```python
+from langchain_community.document_loaders.csv_loader import CSVLoader
+
+loader = CSVLoader(
+    ...  # <-- Integration specific parameters here
+)
+data = loader.load()
+```
+
+## Common File Types
+
+The below document loaders allow you to load data from common data formats.
+
+<CategoryTable category="common_loaders" />
+
+## PDFs
+
+The below document loaders allow you to load documents.
+
+<CategoryTable category="pdf_loaders" />
+
+## Webpages
+
+The below document loaders allow you to load webpages.
+
+<CategoryTable category="webpage_loaders" />
+
+
+## All document loaders
+
+<IndexTable />

docs/docs/integrations/document_loaders/pypdfloader.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# PyPDFLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with `PyPDF` [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html).\n",
+    "This notebook provides a quick overview for getting started with `PyPDF` [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). For detailed documentation of all DocumentLoader features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html).\n",
     "\n",
     "\n",
     "## Overview\n",
@@ -43,7 +43,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU langchain_community"
+    "%pip install -qU langchain_community pypdf"
    ]
   },
   {
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -131,21 +131,41 @@
        "6"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "page = []\n",
+    "pages = []\n",
     "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
+    "    pages.append(doc)\n",
+    "    if len(pages) >= 10:\n",
     "        # do some paged operation, e.g.\n",
     "        # index.upsert(page)\n",
     "\n",
-    "        page = []\n",
-    "len(page)"
+    "        pages = []\n",
+    "len(pages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LayoutParser : A Unified Toolkit for DL-Based DIA 11\n",
+      "focuses on precision, efficiency, and robustness. \n",
+      "{'source': './example_data/layout-parser-paper.pdf', 'page': 10}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(pages[0].page_content[:100])\n",
+    "print(pages[0].metadata)"
    ]
   },
   {
@@ -160,7 +180,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -174,9 +194,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/integrations/document_loaders/recursive_url.ipynb

@@ -229,14 +229,14 @@
     }
    ],
    "source": [
-    "page = []\n",
+    "pages = []\n",
     "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
+    "    pages.append(doc)\n",
+    "    if len(pages) >= 10:\n",
     "        # do some paged operation, e.g.\n",
     "        # index.upsert(page)\n",
     "\n",
-    "        page = []"
+    "        pages = []"
    ]
   },
   {

docs/docs/integrations/document_loaders/url.ipynb

@@ -138,6 +138,8 @@
    "source": [
     "## Playwright URL Loader\n",
     "\n",
+    ">[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool developed by `Microsoft` that allows you to programmatically control and automate web browsers. It is designed for end-to-end testing, scraping, and automating tasks across various web browsers such as `Chromium`, `Firefox`, and `WebKit`.\n",
+    "\n",
     "This covers how to load HTML documents from a list of URLs using the `PlaywrightURLLoader`.\n",
     "\n",
     "[Playwright](https://playwright.dev/) enables reliable end-to-end testing for modern web apps.\n",
@@ -224,7 +226,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/youtube_transcript.ipynb

@@ -15,45 +15,47 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "427d5745",
    "metadata": {},
-   "source": "from langchain_community.document_loaders import YoutubeLoader",
    "outputs": [],
-   "execution_count": null
+   "source": [
+    "from langchain_community.document_loaders import YoutubeLoader"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "34a25b57",
    "metadata": {
     "scrolled": true
    },
+   "outputs": [],
    "source": [
     "%pip install --upgrade --quiet  youtube-transcript-api"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "bc8b308a",
    "metadata": {},
+   "outputs": [],
    "source": [
     "loader = YoutubeLoader.from_youtube_url(\n",
     "    \"https://www.youtube.com/watch?v=QsYGlZkevEg\", add_video_info=False\n",
     ")"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "d073dd36",
    "metadata": {},
+   "outputs": [],
    "source": [
     "loader.load()"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "attachments": {},
@@ -66,26 +68,26 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "ba28af69",
    "metadata": {},
+   "outputs": [],
    "source": [
     "%pip install --upgrade --quiet  pytube"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "9b8ea390",
    "metadata": {},
+   "outputs": [],
    "source": [
     "loader = YoutubeLoader.from_youtube_url(\n",
     "    \"https://www.youtube.com/watch?v=QsYGlZkevEg\", add_video_info=True\n",
     ")\n",
     "loader.load()"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "attachments": {},
@@ -102,8 +104,10 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "08510625",
    "metadata": {},
+   "outputs": [],
    "source": [
     "loader = YoutubeLoader.from_youtube_url(\n",
     "    \"https://www.youtube.com/watch?v=QsYGlZkevEg\",\n",
@@ -112,13 +116,12 @@
     "    translation=\"en\",\n",
     ")\n",
     "loader.load()"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "69f4e399a9764d73",
+   "metadata": {},
    "source": [
     "### Get transcripts as timestamped chunks\n",
     "\n",
@@ -127,12 +130,14 @@
     "`transcript_format` param:  One of the `langchain_community.document_loaders.youtube.TranscriptFormat` values.  In this case, `TranscriptFormat.CHUNKS`.\n",
     "\n",
     "`chunk_size_seconds` param:  An integer number of video seconds to be represented by each chunk of transcript data.  Default is 120 seconds."
-   ],
-   "id": "69f4e399a9764d73"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "540bbf19182f38bc",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from langchain_community.document_loaders.youtube import TranscriptFormat\n",
     "\n",
@@ -143,10 +148,7 @@
     "    chunk_size_seconds=30,\n",
     ")\n",
     "print(\"\\n\\n\".join(map(repr, loader.load())))"
-   ],
-   "id": "540bbf19182f38bc",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "attachments": {},
@@ -172,8 +174,10 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "c345bc43",
    "metadata": {},
+   "outputs": [],
    "source": [
     "# Init the GoogleApiClient\n",
     "from pathlib import Path\n",
@@ -198,9 +202,7 @@
     "\n",
     "# returns a list of Documents\n",
     "youtube_loader_channel.load()"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   }
  ],
  "metadata": {

docs/docs/integrations/document_transformers/openvino_rerank.ipynb

@@ -331,8 +331,8 @@
     }
    ],
    "source": [
-    "from langchain.embeddings import OpenVINOEmbeddings\n",
     "from langchain_community.document_loaders import TextLoader\n",
+    "from langchain_community.embeddings import OpenVINOEmbeddings\n",
     "from langchain_community.vectorstores import FAISS\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",

docs/docs/integrations/graphs/amazon_neptune_sparql.ipynb

@@ -245,8 +245,8 @@
    "outputs": [],
    "source": [
     "import boto3\n",
-    "from langchain.chains.graph_qa.neptune_sparql import NeptuneSparqlQAChain\n",
     "from langchain_aws import ChatBedrock\n",
+    "from langchain_community.chains.graph_qa.neptune_sparql import NeptuneSparqlQAChain\n",
     "from langchain_community.graphs import NeptuneRdfGraph\n",
     "\n",
     "host = \"<your host>\"\n",

docs/docs/integrations/graphs/azure_cosmosdb_gremlin.ipynb

@@ -65,7 +65,7 @@
    "outputs": [],
    "source": [
     "import nest_asyncio\n",
-    "from langchain.chains.graph_qa.gremlin import GremlinQAChain\n",
+    "from langchain_community.chains.graph_qa.gremlin import GremlinQAChain\n",
     "from langchain_community.graphs import GremlinGraph\n",
     "from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship\n",
     "from langchain_core.documents import Document\n",

docs/docs/integrations/graphs/networkx.ipynb

@@ -49,7 +49,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.indexes import GraphIndexCreator\n",
+    "from langchain_community.graphs.index_creator import GraphIndexCreator\n",
     "from langchain_openai import OpenAI"
    ]
   },
@@ -252,7 +252,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.indexes.graph import NetworkxEntityGraph"
+    "from langchain_community.graphs import NetworkxEntityGraph"
    ]
   },
   {

docs/docs/integrations/llms/anthropic.ipynb

@@ -23,7 +23,7 @@
     "# AnthropicLLM\n",
     "\n",
     ":::caution\n",
-    "You are currently on a page documenting the use of Anthropic legacy Claude 2 models as [text completion models](/docs/concepts/#llms). The latest and most popular Anthropic models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of Anthropic legacy Claude 2 models as [text completion models](/docs/concepts/#llms). The latest and most popular Anthropic models are [chat completion models](/docs/concepts/#chat-models), and the text completion models have been deprecated.\n",
     "\n",
     "You are probably looking for [this page instead](/docs/integrations/chat/anthropic/).\n",
     ":::\n",
@@ -115,14 +115,6 @@
     "\n",
     "chain.invoke({\"question\": \"What is LangChain?\"})"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a52f765c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

docs/docs/integrations/llms/clarifai.ipynb

@@ -226,7 +226,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Intialize the parameters as dict.\n",
+    "# Initialize the parameters as dict.\n",
     "params = dict(temperature=str(0.3), max_tokens=100)"
    ]
   },

docs/docs/integrations/llms/cohere.ipynb

@@ -15,7 +15,14 @@
     "\n",
     ">[Cohere](https://cohere.ai/about) is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.\n",
     "\n",
-    "Head to the [API reference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.cohere.Cohere.html) for detailed documentation of all attributes and methods."
+    "Head to the [API reference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.cohere.Cohere.html) for detailed documentation of all attributes and methods.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/llms/cohere/) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [Cohere](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.cohere.Cohere.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n"
    ]
   },
   {
@@ -29,34 +36,43 @@
     "\n",
     "The integration lives in the `langchain-community` package. We also need to install the `cohere` package itself. We can install these with:\n",
     "\n",
-    "```bash\n",
-    "pip install -U langchain-community langchain-cohere\n",
-    "```\n",
+    "### Credentials\n",
     "\n",
-    "We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
+    "We'll need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "3f5dc9d7-65e3-4b5b-9086-3327d016cfe0",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"COHERE_API_KEY\"] = getpass.getpass()"
+    "if \"COHERE_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"COHERE_API_KEY\"] = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff211537",
+   "metadata": {},
+   "source": [
+    "### Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "318454f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install -U langchain-community langchain-cohere"
    ]
   },
   {
@@ -83,7 +99,7 @@
    "id": "0b4e02bf-5beb-48af-a2a2-52cbcd8ebed6",
    "metadata": {},
    "source": [
-    "## Usage\n",
+    "## Invocation\n",
     "\n",
     "Cohere supports all [LLM](/docs/how_to#llms) functionality:"
    ]
@@ -199,6 +215,8 @@
    "id": "39198f7d-6fc8-4662-954a-37ad38c4bec4",
    "metadata": {},
    "source": [
+    "## Chaining\n",
+    "\n",
     "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts#langchain-expression-language-lcel)"
    ]
   },
@@ -237,12 +255,14 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4797d719",
+   "cell_type": "markdown",
+   "id": "ac5fcbed",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `Cohere` llm features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/langchain_community.llms.cohere.Cohere.html"
+   ]
   }
  ],
  "metadata": {

docs/docs/integrations/llms/fireworks.ipynb

@@ -15,7 +15,14 @@
     "\n",
     ">[Fireworks](https://app.fireworks.ai/) accelerates product development on generative AI by creating an innovative AI experiment and production platform. \n",
     "\n",
-    "This example goes over how to use LangChain to interact with `Fireworks` models."
+    "This example goes over how to use LangChain to interact with `Fireworks` models.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.1/docs/integrations/llms/fireworks/) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [Fireworks](https://api.python.langchain.com/en/latest/llms/langchain_fireworks.llms.Fireworks.html#langchain_fireworks.llms.Fireworks) | [langchain_fireworks](https://api.python.langchain.com/en/latest/fireworks_api_reference.html) | ❌ | ❌ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_fireworks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_fireworks?style=flat-square&label=%20) |"
    ]
   },
   {
@@ -24,29 +31,18 @@
    "id": "fb345268",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install -qU langchain-fireworks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "60b6dbb2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_fireworks import Fireworks"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
    "id": "ccff689e",
    "metadata": {},
    "source": [
-    "# Setup\n",
+    "## Setup\n",
     "\n",
-    "1. Make sure the `langchain-fireworks` package is installed in your environment.\n",
-    "2. Sign in to [Fireworks AI](http://fireworks.ai) for the an API Key to access our models, and make sure it is set as the `FIREWORKS_API_KEY` environment variable.\n",
+    "### Credentials \n",
+    "\n",
+    "Sign in to [Fireworks AI](http://fireworks.ai) for the an API Key to access our models, and make sure it is set as the `FIREWORKS_API_KEY` environment variable.\n",
     "3. Set up your model using a model id. If the model is not set, the default model is fireworks-llama-v2-7b-chat. See the full, most up-to-date model list on [fireworks.ai](https://fireworks.ai)."
    ]
   },
@@ -60,10 +56,46 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "from langchain_fireworks import Fireworks\n",
-    "\n",
     "if \"FIREWORKS_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Fireworks API Key:\")\n",
+    "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Fireworks API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e42ced7e",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "You need to install the `langchain_fireworks` python package for the rest of the notebook to work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca824723",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-fireworks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acc24d0c",
+   "metadata": {},
+   "source": [
+    "## Instantiation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d285fd7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_fireworks import Fireworks\n",
     "\n",
     "# Initialize a Fireworks model\n",
     "llm = Fireworks(\n",
@@ -74,10 +106,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acc24d0c",
+   "id": "a4c29f7b",
    "metadata": {},
    "source": [
-    "# Calling the Model Directly\n",
+    "## Invocation\n",
     "\n",
     "You can call the model directly with string prompts to get completions."
    ]
@@ -98,11 +130,18 @@
     }
    ],
    "source": [
-    "# Single prompt\n",
     "output = llm.invoke(\"Who's the best quarterback in the NFL?\")\n",
     "print(output)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b0283343",
+   "metadata": {},
+   "source": [
+    "### Invoking with multiple prompts"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -128,6 +167,14 @@
     "print(output.generations)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f18f5717",
+   "metadata": {},
+   "source": [
+    "### Invoking with additional parameters"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -158,7 +205,7 @@
    "id": "137662a6",
    "metadata": {},
    "source": [
-    "# Simple Chain with Non-Chat Model"
+    "## Chaining"
    ]
   },
   {
@@ -206,6 +253,8 @@
    "id": "d0a29826",
    "metadata": {},
    "source": [
+    "## Streaming\n",
+    "\n",
     "You can stream the output, if you want."
    ]
   },
@@ -233,12 +282,14 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fcc0eecb",
+   "cell_type": "markdown",
+   "id": "692c5e76",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `Fireworks` LLM features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/langchain_fireworks.llms.Fireworks.html#langchain_fireworks.llms.Fireworks"
+   ]
   }
  ],
  "metadata": {

docs/docs/integrations/llms/index.mdx

@@ -0,0 +1,30 @@
+---
+sidebar_position: 0
+sidebar_class_name: hidden
+keywords: [compatibility]
+---
+
+# LLMs
+
+:::caution
+You are currently on a page documenting the use of [text completion models](/docs/concepts/#llms). Many of the latest and most popular models are [chat completion models](/docs/concepts/#chat-models).
+
+Unless you are specifically using more advanced prompting techniques, you are probably looking for [this page instead](/docs/integrations/chat/).
+:::
+
+[LLMs](docs/concepts/#llms) are language models that take a string as input and return a string as output.
+
+:::info
+
+If you'd like to write your own LLM, see [this how-to](/docs/how_to/custom_llm/).
+If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
+
+:::
+
+import { CategoryTable, IndexTable } from "@theme/FeatureTables";
+
+<CategoryTable category="llms" />
+
+## All LLMs
+
+<IndexTable />

docs/docs/integrations/llms/konko.ipynb

@@ -74,7 +74,7 @@
     }
    ],
    "source": [
-    "from langchain.llms import Konko\n",
+    "from langchain_community.llms import Konko\n",
     "\n",
     "llm = Konko(model=\"mistralai/mistral-7b-v0.1\", temperature=0.1, max_tokens=128)\n",
     "\n",

docs/docs/integrations/llms/openai.ipynb

@@ -19,33 +19,86 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5d71df86-8a17-4283-83d7-4e46e7c06c44",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "id": "74312161",
+   "metadata": {},
    "source": [
-    "# get a token: https://platform.openai.com/account/api-keys\n",
+    "## Overview\n",
     "\n",
-    "from getpass import getpass\n",
+    "### Integration details\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatOpenAI](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain-openai](https://api.python.langchain.com/en/latest/openai_api_reference.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
     "\n",
-    "OPENAI_API_KEY = getpass()"
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access OpenAI models you'll need to create an OpenAI account, get an API key, and install the `langchain-openai` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to https://platform.openai.com to sign up to OpenAI and generate an API key. Once you've done this set the OPENAI_API_KEY environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "5472a7cd-af26-48ca-ae9b-5f6ae73c74d2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "execution_count": null,
+   "id": "efcdb2b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter your OpenAI API key:  ········\n"
+     ]
+    }
+   ],
    "source": [
+    "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5d528fa",
+   "metadata": {},
+   "source": [
+    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52fa46e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fad78d8",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain OpenAI integration lives in the `langchain-openai` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e300149",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-openai"
    ]
   },
   {
@@ -60,7 +113,11 @@
     "OPENAI_ORGANIZATION = getpass()\n",
     "\n",
     "os.environ[\"OPENAI_ORGANIZATION\"] = OPENAI_ORGANIZATION\n",
-    "```"
+    "```\n",
+    "\n",
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
    ]
   },
   {
@@ -72,74 +129,29 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_openai import OpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "035dea0f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Question: {question}\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate.from_template(template)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3f3458d9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
     "llm = OpenAI()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4fc152cd",
+   "id": "464003c1",
    "metadata": {},
    "source": [
-    "If you manually want to specify your OpenAI API key and/or organization ID, you can use the following:\n",
-    "```python\n",
-    "llm = OpenAI(openai_api_key=\"YOUR_API_KEY\", openai_organization=\"YOUR_ORGANIZATION_ID\")\n",
-    "```\n",
-    "Remove the openai_organization parameter should it not apply to you."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a641dbd9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm_chain = prompt | llm"
+    "## Invocation"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "9f844993",
-   "metadata": {
-    "tags": []
-   },
+   "id": "85b49da0",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "' Justin Bieber was born on March 1, 1994. The Super Bowl is typically played in late January or early February. So, we need to look at the Super Bowl from 1994. In 1994, the Super Bowl was Super Bowl XXVIII, played on January 30, 1994. The winning team of that Super Bowl was the Dallas Cowboys.'"
+       "\"\\n\\nI'm an AI language model created by OpenAI, so I don't have feelings or emotions. But thank you for asking! How can I assist you today?\""
       ]
      },
      "execution_count": 5,
@@ -148,9 +160,37 @@
     }
    ],
    "source": [
-    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "llm.invoke(\"Hello how are you?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b7e0dfc",
+   "metadata": {},
+   "source": [
+    "## Chaining"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a641dbd9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
-    "llm_chain.invoke(question)"
+    "prompt = PromptTemplate(\"How to say {input} in {output_language}:\\n\")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -158,6 +198,8 @@
    "id": "58a9ddb1",
    "metadata": {},
    "source": [
+    "## Using a proxy\n",
+    "\n",
     "If you are behind an explicit proxy, you can specify the http_client to pass through"
    ]
   },
@@ -168,11 +210,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pip install httpx\n",
+    "%pip install httpx\n",
     "\n",
     "import httpx\n",
     "\n",
-    "openai = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", http_client=httpx.Client(proxies=\"http://proxy.yourcompany.com:8080\"))"
+    "openai = OpenAI(\n",
+    "    model_name=\"gpt-3.5-turbo-instruct\",\n",
+    "    http_client=httpx.Client(proxies=\"http://proxy.yourcompany.com:8080\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73e207dd",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `OpenAI` llm features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/langchain_openai.llms.base.OpenAI.html"
    ]
   }
  ],
@@ -192,7 +247,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.11.9"
   },
   "vscode": {
    "interpreter": {

docs/docs/integrations/llms/runhouse.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Runhouse\n",
     "\n",
-    "The [Runhouse](https://github.com/run-house/runhouse) allows remote compute and data across environments and users. See the [Runhouse docs](https://runhouse-docs.readthedocs-hosted.com/en/latest/).\n",
+    "[Runhouse](https://github.com/run-house/runhouse) allows remote compute and data across environments and users. See the [Runhouse docs](https://www.run.house/docs).\n",
     "\n",
     "This example goes over how to use LangChain and [Runhouse](https://github.com/run-house/runhouse) to interact with models hosted on your own GPU, or on-demand GPUs on AWS, GCP, AWS, or Lambda.\n",
     "\n",

docs/docs/integrations/memory/motorhead_memory.ipynb

@@ -19,7 +19,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.memory.motorhead_memory import MotorheadMemory"
+    "from langchain_community.memory.motorhead_memory import MotorheadMemory"
    ]
   },
   {

docs/docs/integrations/memory/zep_memory.ipynb

@@ -48,7 +48,7 @@
     "from uuid import uuid4\n",
     "\n",
     "from langchain.agents import AgentType, initialize_agent\n",
-    "from langchain.memory import ZepMemory\n",
+    "from langchain_community.memory.zep_memory import ZepMemory\n",
     "from langchain_community.retrievers import ZepRetriever\n",
     "from langchain_community.utilities import WikipediaAPIWrapper\n",
     "from langchain_core.messages import AIMessage, HumanMessage\n",

docs/docs/integrations/memory/zep_memory_cloud.ipynb

@@ -69,11 +69,12 @@
    "source": [
     "from uuid import uuid4\n",
     "\n",
-    "from langchain.agents import AgentType, Tool, initialize_agent\n",
+    "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain_community.memory.zep_cloud_memory import ZepCloudMemory\n",
     "from langchain_community.retrievers import ZepCloudRetriever\n",
     "from langchain_community.utilities import WikipediaAPIWrapper\n",
     "from langchain_core.messages import AIMessage, HumanMessage\n",
+    "from langchain_core.tools import Tool\n",
     "from langchain_openai import OpenAI\n",
     "\n",
     "session_id = str(uuid4())  # This is a unique identifier for the session"

docs/docs/integrations/platforms/aws.mdx

@@ -308,7 +308,7 @@ See a [usage example](/docs/integrations/graphs/amazon_neptune_open_cypher).
 ```python
 from langchain_community.graphs import NeptuneGraph
 from langchain_community.graphs import NeptuneAnalyticsGraph
-from langchain.chains import NeptuneOpenCypherQAChain
+from langchain_community.chains.graph_qa.neptune_cypher import NeptuneOpenCypherQAChain
 ```
 
 ### Amazon Neptune with SPARQL
@@ -317,7 +317,7 @@ See a [usage example](/docs/integrations/graphs/amazon_neptune_sparql).
 
 ```python
 from langchain_community.graphs import NeptuneRdfGraph
-from langchain.chains.graph_qa.neptune_sparql import NeptuneSparqlQAChain
+from langchain_community.chains.graph_qa.neptune_sparql import NeptuneSparqlQAChain
 ```
 
 

docs/docs/integrations/platforms/huggingface.mdx

@@ -122,5 +122,5 @@ pip install transformers huggingface_hub
 See a [usage example](/docs/integrations/tools/huggingface_tools).
 
 ```python
-from langchain.agents import load_huggingface_tool
+from langchain_community.agent_toolkits.load_tools import load_huggingface_tool
 ```

docs/docs/integrations/platforms/microsoft.mdx

@@ -237,6 +237,26 @@ See a [usage example](/docs/integrations/document_loaders/microsoft_onenote).
 from langchain_community.document_loaders.onenote import OneNoteLoader
 ```
 
+### Playwright URL Loader
+
+>[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool 
+> developed by `Microsoft` that allows you to programmatically control and automate 
+> web browsers. It is designed for end-to-end testing, scraping, and automating 
+> tasks across various web browsers such as `Chromium`, `Firefox`, and `WebKit`.
+
+
+First, let's install dependencies:
+
+```bash
+pip install playwright unstructured
+```
+
+See a [usage example](/docs/integrations/document_loaders/url/#playwright-url-loader).
+
+```python
+from langchain_community.document_loaders.onenote import OneNoteLoader
+```
+
 ## AI Agent Memory System
 
 [AI agent](https://learn.microsoft.com/en-us/azure/cosmos-db/ai-agents) needs robust memory systems that support multi-modality, offer strong operational performance, and enable agent memory sharing as well as separation.
@@ -406,6 +426,24 @@ from langchain_community.agent_toolkits import PowerBIToolkit
 from langchain_community.utilities.powerbi import PowerBIDataset
 ```
 
+### PlayWright Browser Toolkit
+
+>[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool 
+> developed by `Microsoft` that allows you to programmatically control and automate 
+> web browsers. It is designed for end-to-end testing, scraping, and automating 
+> tasks across various web browsers such as `Chromium`, `Firefox`, and `WebKit`.
+
+We need to install several python packages.
+
+```bash
+pip install playwright lxml
+```
+
+See a [usage example](/docs/integrations/tools/playwright).
+
+```python
+from langchain_community.agent_toolkits import PlayWrightBrowserToolkit
+```
 
 ## Graphs
 

docs/docs/integrations/providers/github.mdx

@@ -18,6 +18,5 @@ There are two document loaders available for GitHub.
 See a [usage example](/docs/integrations/document_loaders/github).
 
 ```python
-from langchain_community.document_loaders import GitHubIssuesLoader
-from langchain.document_loaders import GithubFileLoader
+from langchain_community.document_loaders import GitHubIssuesLoader, GithubFileLoader
 ```

docs/docs/integrations/providers/ieit_systems.mdx

@@ -0,0 +1,31 @@
+# IEIT Systems
+
+>[IEIT Systems](https://en.ieisystem.com/) is a Chinese information technology company 
+> established in 1999. It provides the IT infrastructure products, solutions, 
+> and services, innovative IT products and solutions across cloud computing, 
+> big data, and artificial intelligence.
+
+
+## LLMs
+
+See a [usage example](/docs/integrations/llms/yuan2).
+
+```python
+from langchain_community.llms.yuan2 import Yuan2
+```
+
+## Chat models
+
+See the [installation instructions](/docs/integrations/chat/yuan2/#setting-up-your-api-server).
+
+Yuan2.0 provided an OpenAI compatible API, and ChatYuan2 is integrated into langchain by using `OpenAI client`. 
+Therefore, ensure the `openai` package is installed.
+
+```bash
+pip install openai
+```
+See a [usage example](/docs/integrations/chat/yuan2).
+
+```python
+from langchain_community.chat_models import ChatYuan2
+```

docs/docs/integrations/providers/iflytek.mdx

@@ -0,0 +1,38 @@
+# iFlytek
+
+>[iFlytek](https://www.iflytek.com) is a Chinese information technology company 
+> established in 1999. It creates voice recognition software and 
+> voice-based internet/mobile products covering education, communication, 
+> music, intelligent toys industries.
+
+
+## Installation and Setup
+
+- Get `SparkLLM` app_id, api_key and api_secret from [iFlyTek SparkLLM API Console](https://console.xfyun.cn/services/bm3) (for more info, see [iFlyTek SparkLLM Intro](https://xinghuo.xfyun.cn/sparkapi)).
+- Install the Python package (not for the embedding models):
+
+```bash
+pip install websocket-client
+```
+
+## LLMs
+
+See a [usage example](/docs/integrations/llms/sparkllm).
+
+```python
+from langchain_community.llms import SparkLLM
+```
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/sparkllm).
+
+```python
+from langchain_community.chat_models import ChatSparkLLM
+```
+
+## Embedding models
+
+```python
+from langchain_community.embeddings import SparkLLMTextEmbeddings
+```

docs/docs/integrations/providers/konko.mdx

@@ -41,7 +41,7 @@ See a usage [example](/docs/integrations/llms/konko).
 - **Completion with mistralai/Mistral-7B-v0.1:**
 
   ```python
-  from langchain.llms import Konko
+  from langchain_community.llms import Konko
   llm = Konko(max_tokens=800, model='mistralai/Mistral-7B-v0.1')
   prompt = "Generate a Product Description for Apple Iphone 15"
   response = llm.invoke(prompt)

docs/docs/integrations/providers/llamacpp.mdx

@@ -1,26 +1,50 @@
 # Llama.cpp
 
-This page covers how to use [llama.cpp](https://github.com/ggerganov/llama.cpp) within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Llama-cpp wrappers.
+>[llama.cpp python](https://github.com/abetlen/llama-cpp-python) library is a simple Python bindings for `@ggerganov`
+>[llama.cpp](https://github.com/ggerganov/llama.cpp).
+>
+>This package provides:
+>
+> - Low-level access to C API via ctypes interface.
+> - High-level Python API for text completion
+>   - `OpenAI`-like API
+>   - `LangChain` compatibility
+>   - `LlamaIndex` compatibility
+> - OpenAI compatible web server
+>   - Local Copilot replacement
+>   - Function Calling support
+>   - Vision API support
+>   - Multiple Models
 
 ## Installation and Setup
-- Install the Python package with `pip install llama-cpp-python`
+
+- Install the Python package
+  ```bash
+  pip install llama-cpp-python
+  ````
 - Download one of the [supported models](https://github.com/ggerganov/llama.cpp#description) and convert them to the llama.cpp format per the [instructions](https://github.com/ggerganov/llama.cpp)
 
-## Wrappers
 
-### LLM
+## Chat models
+
+See a [usage example](/docs/integrations/chat/llamacpp).
+
+```python
+from langchain_community.chat_models import ChatLlamaCpp
+```
+
+## LLMs
+
+See a [usage example](/docs/integrations/llms/llamacpp).
 
-There exists a LlamaCpp LLM wrapper, which you can access with 
 ```python
 from langchain_community.llms import LlamaCpp
 ```
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/llamacpp)
 
-### Embeddings
+## Embedding models
+
+See a [usage example](/docs/integrations/text_embedding/llamacpp).
 
-There exists a LlamaCpp Embeddings wrapper, which you can access with 
 ```python
 from langchain_community.embeddings import LlamaCppEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/llamacpp)

docs/docs/integrations/providers/maritalk.mdx

@@ -0,0 +1,21 @@
+# MariTalk
+
+>[MariTalk](https://www.maritaca.ai/en) is an LLM-based chatbot trained to meet the needs of Brazil.
+
+## Installation and Setup
+
+You have to get the MariTalk API key.
+
+You also need to install the `httpx` Python package.
+
+```bash
+pip install httpx
+```
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/maritalk).
+
+```python
+from langchain_community.chat_models import ChatMaritalk
+```

docs/docs/integrations/providers/mlflow_tracking.ipynb

@@ -167,7 +167,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",

docs/docs/integrations/providers/mlx.mdx

@@ -0,0 +1,34 @@
+# MLX
+
+>[MLX](https://ml-explore.github.io/mlx/build/html/index.html) is a `NumPy`-like array framework 
+> designed for efficient and flexible machine learning on `Apple` silicon, 
+> brought to you by `Apple machine learning research`.
+
+
+## Installation and Setup
+
+Install several Python packages:
+
+```bash
+pip install mlx-lm transformers huggingface_hub
+````
+
+
+## Chat models
+
+
+See a [usage example](/docs/integrations/chat/mlx).
+
+```python
+from langchain_community.chat_models.mlx import ChatMLX
+```
+
+## LLMs
+
+### MLX Local Pipelines
+
+See a [usage example](/docs/integrations/llms/mlx_pipelines).
+
+```python
+from langchain_community.llms.mlx_pipeline import MLXPipeline
+```

docs/docs/integrations/providers/motorhead.mdx

@@ -12,5 +12,5 @@ See instructions at [Motörhead](https://github.com/getmetal/motorhead) for runn
 See a [usage example](/docs/integrations/memory/motorhead_memory).
 
 ```python
-from langchain.memory import MotorheadMemory
+from langchain_community.memory import MotorheadMemory
 ```

docs/docs/integrations/providers/octoai.mdx

@@ -0,0 +1,37 @@
+# OctoAI
+
+>[OctoAI](https://docs.octoai.cloud/docs) offers easy access to efficient compute 
+> and enables users to integrate their choice of AI models into applications. 
+> The `OctoAI` compute service helps you run, tune, and scale AI applications easily.
+
+
+## Installation and Setup
+
+- Install the `openai` Python package:
+  ```bash
+  pip install openai
+  ````
+- Register on `OctoAI` and get an API Token from [your OctoAI account page](https://octoai.cloud/settings).
+
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/octoai).
+
+```python
+from langchain_community.chat_models import ChatOctoAI
+```
+
+## LLMs
+
+See a [usage example](/docs/integrations/llms/octoai).
+
+```python
+from langchain_community.llms.octoai_endpoint import OctoAIEndpoint
+```
+
+## Embedding models
+
+```python
+from langchain_community.embeddings.octoai_embeddings import OctoAIEmbeddings
+```

docs/docs/integrations/providers/perplexity.mdx

@@ -0,0 +1,25 @@
+# Perplexity
+
+>[Perplexity](https://www.perplexity.ai/pro) is the most powerful way to search 
+> the internet with unlimited Pro Search, upgraded AI models, unlimited file upload, 
+> image generation, and API credits.
+>
+> You can check a [list of available models](https://docs.perplexity.ai/docs/model-cards).
+
+## Installation and Setup
+
+Install a Python package:
+
+```bash
+pip install openai
+````
+
+Get your API key from [here](https://docs.perplexity.ai/docs/getting-started).
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/perplexity).
+
+```python
+from langchain_community.chat_models import ChatPerplexity
+```

docs/docs/integrations/providers/vectara/vectara_chat.ipynb

@@ -108,7 +108,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.document_loaders import TextLoader\n",
+    "from langchain_community.document_loaders import TextLoader\n",
     "\n",
     "loader = TextLoader(\"state_of_the_union.txt\")\n",
     "documents = loader.load()\n",

docs/docs/integrations/providers/zep.mdx

@@ -86,7 +86,7 @@ See a [Perpetual Memory Example here](/docs/integrations/memory/zep_cloud_chat_m
 
 You can use `ZepCloudMemory` together with agents that support Memory.
 ```python
-from langchain.memory import ZepCloudMemory
+from langchain_community.memory import ZepCloudMemory
 ```
 
 See a [Memory RAG Example here](/docs/integrations/memory/zep_memory_cloud).
@@ -117,4 +117,4 @@ MMR search is useful for ensuring that the retrieved documents are diverse and n
 from langchain_community.vectorstores import ZepCloudVectorStore
 ```
 
-See a [usage example](/docs/integrations/vectorstores/zep_cloud).
+See a [usage example](/docs/integrations/vectorstores/zep_cloud).

docs/docs/integrations/providers/zhipuai.mdx

@@ -0,0 +1,18 @@
+# Zhipu AI
+
+>[Zhipu AI](https://www.zhipuai.cn/en/aboutus), originating from the technological 
+> advancements of `Tsinghua University's Computer Science Department`, 
+> is an artificial intelligence company with the mission of teaching machines 
+> to think like humans. Its world-leading AI team has developed the cutting-edge 
+> large language and multimodal models and built the high-precision billion-scale 
+> knowledge graphs, the combination of which uniquely empowers us to create a powerful 
+> data- and knowledge-driven cognitive engine towards artificial general intelligence.
+
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/zhipuai).
+
+```python
+from langchain_community.chat_models import ChatZhipuAI
+```

docs/docs/integrations/retrievers/arxiv.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "id": "00a924a0-57e2-43fa-95dc-3ea48a56d3a5",
    "metadata": {},
    "source": [
@@ -17,8 +17,6 @@
    "source": [
     "# ArxivRetriever\n",
     "\n",
-    "## Overview\n",
-    "\n",
     ">[arXiv](https://arxiv.org/) is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.\n",
     "\n",
     "This notebook shows how to retrieve scientific articles from Arxiv.org into the [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) format that is used downstream.\n",
@@ -27,9 +25,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Retriever | Source | Package |\n",
-    "| :--- | :--- | :---: |\n",
-    "[ArxivRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html) | Scholarly articles on [arxiv.org](https://arxiv.org/) | langchain_community |\n",
+    "import {ItemTable} from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"external_retrievers\" item=\"ArxivRetriever\" />\n",
     "\n",
     "## Setup\n",
     "\n",

docs/docs/integrations/retrievers/azure_ai_search.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "id": "f9a62e19-b00b-4f6c-a700-1e500e4c290a",
    "metadata": {},
    "source": [
@@ -17,7 +17,6 @@
    "source": [
     "# AzureAISearchRetriever\n",
     "\n",
-    "## Overview\n",
     "[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) (formerly known as `Azure Cognitive Search`) is a Microsoft cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale.\n",
     "\n",
     "`AzureAISearchRetriever` is an integration module that returns documents from an unstructured query. It's based on the BaseRetriever class and it targets the 2023-11-01 stable REST API version of Azure AI Search, which means it supports vector indexing and queries.\n",
@@ -28,9 +27,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Retriever | Self-host | Cloud offering | Package |\n",
-    "| :--- | :--- | :---: | :---: |\n",
-    "[AzureAISearchRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html) | ❌ | ✅ | langchain_community |\n",
+    "import {ItemTable} from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"document_retrievers\" item=\"AzureAISearchRetriever\" />\n",
     "\n",
     "\n",
     "## Setup\n",

docs/docs/integrations/retrievers/bedrock.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "id": "b0872249-1af5-4d54-b816-1babad7a8c9e",
    "metadata": {},
    "source": [
@@ -17,8 +17,6 @@
    "source": [
     "# Bedrock (Knowledge Bases) Retriever\n",
     "\n",
-    "## Overview\n",
-    "\n",
     "This guide will help you getting started with the AWS Knowledge Bases [retriever](/docs/concepts/#retrievers).\n",
     "\n",
     "[Knowledge Bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.\n",
@@ -29,9 +27,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Retriever | Self-host | Cloud offering | Package |\n",
-    "| :--- | :--- | :---: | :---: |\n",
-    "[AmazonKnowledgeBasesRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_aws.retrievers.bedrock.AmazonKnowledgeBasesRetriever.html) | ❌ | ✅ | langchain_aws |\n"
+    "import {ItemTable} from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"document_retrievers\" item=\"AmazonKnowledgeBasesRetriever\" />\n"
    ]
   },
   {

docs/docs/integrations/retrievers/dria_index.ipynb

@@ -76,7 +76,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.retrievers import DriaRetriever\n",
+    "from langchain_community.retrievers import DriaRetriever\n",
     "\n",
     "api_key = os.getenv(\"DRIA_API_KEY\")\n",
     "retriever = DriaRetriever(api_key=api_key)"

docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "id": "41ccce84-f6d9-4ba0-8281-22cbf29f20d3",
    "metadata": {},
    "source": [
@@ -17,7 +17,6 @@
    "source": [
     "# ElasticsearchRetriever\n",
     "\n",
-    "## Overview\n",
     ">[Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. It supports keyword search, vector search, hybrid search and complex filtering.\n",
     "\n",
     "The `ElasticsearchRetriever` is a generic wrapper to enable flexible access to all `Elasticsearch` features through the [Query DSL](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html).  For most use cases the other classes (`ElasticsearchStore`, `ElasticsearchEmbeddings`, etc.) should suffice, but if they don't you can use `ElasticsearchRetriever`.\n",
@@ -26,9 +25,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Retriever | Self-host | Cloud offering | Package |\n",
-    "| :--- | :--- | :---: | :---: |\n",
-    "[ElasticsearchRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html) | ✅ | ✅ | langchain_elasticsearch |\n",
+    "import {ItemTable} from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"document_retrievers\" item=\"ElasticsearchRetriever\" />\n",
     "\n",
     "\n",
     "## Setup\n",

docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "metadata": {},
    "source": [
     "---\n",
@@ -15,8 +15,6 @@
    "source": [
     "# Google Vertex AI Search\n",
     "\n",
-    "## Overview\n",
-    "\n",
     ">[Google Vertex AI Search](https://cloud.google.com/enterprise-search) (formerly known as `Enterprise Search` on `Generative AI App Builder`) is a part of the [Vertex AI](https://cloud.google.com/vertex-ai) machine learning platform offered by `Google Cloud`.\n",
     ">\n",
     ">`Vertex AI Search` lets organizations quickly build generative AI-powered search engines for customers and employees. It's underpinned by a variety of `Google Search` technologies, including semantic search, which helps deliver more relevant results than traditional keyword-based search techniques by using natural language processing and machine learning techniques to infer relationships within the content and intent from the user’s query input. Vertex AI Search also benefits from Google’s expertise in understanding how users search and factors in content relevance to order displayed results.\n",
@@ -29,9 +27,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Retriever | Self-host | Cloud offering | Package |\n",
-    "| :--- | :--- | :---: | :---: |\n",
-    "[VertexAISearchRetriever](https://api.python.langchain.com/en/latest/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html) | ❌ | ✅ | langchain_google_community |\n",
+    "import {ItemTable} from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"document_retrievers\" item=\"VertexAISearchRetriever\" />\n",
     "\n",
     "\n",
     "## Setup\n",

docs/docs/integrations/retrievers/index.mdx

@@ -3,6 +3,8 @@ sidebar_position: 0
 sidebar_class_name: hidden
 ---
 
+import {CategoryTable, IndexTable} from '@theme/FeatureTables'
+
 # Retrievers
 
 A [retriever](/docs/concepts/#retrievers) is an interface that returns documents given an unstructured query.
@@ -22,20 +24,14 @@ This page lists custom retrievers, implemented via subclassing [BaseRetriever](/
 
 The below retrievers allow you to index and search a custom corpus of documents.
 
-| Retriever | Self-host | Cloud offering | Package |
-|-----------|-----------|----------------|---------|
-| [AmazonKnowledgeBasesRetriever](/docs/integrations/retrievers/bedrock) | ❌ | ✅ | [langchain_aws](https://api.python.langchain.com/en/latest/retrievers/langchain_aws.retrievers.bedrock.AmazonKnowledgeBasesRetriever.html) |
-| [AzureAISearchRetriever](/docs/integrations/retrievers/azure_ai_search) | ❌ | ✅ | [langchain_community](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html) |
-| [ElasticsearchRetriever](/docs/integrations/retrievers/elasticsearch_retriever) | ✅ | ✅ | [langchain_elasticsearch](https://api.python.langchain.com/en/latest/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html) |
-| [MilvusCollectionHybridSearchRetriever](/docs/integrations/retrievers/milvus_hybrid_search) | ✅ | ❌ | [langchain_milvus](https://api.python.langchain.com/en/latest/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html) |
-| [VertexAISearchRetriever](/docs/integrations/retrievers/google_vertex_ai_search) | ❌ | ✅ | [langchain_google_community](https://api.python.langchain.com/en/latest/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html) |
+<CategoryTable category="document_retrievers" />
 
 ## External index
 
 The below retrievers will search over an external index (e.g., constructed from Internet data or similar).
 
-| Retriever | Source | Package |
-|-----------|--------|---------|
-| [ArxivRetriever](/docs/integrations/retrievers/arxiv) | Scholarly articles on [arxiv.org](https://arxiv.org/) | [langchain_community](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html) |
-| [TavilySearchAPIRetriever](/docs/integrations/retrievers/tavily) | Internet search | [langchain_community](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html) |
-| [WikipediaRetriever](/docs/integrations/retrievers/wikipedia) | [Wikipedia](https://www.wikipedia.org/) articles | [langchain_community](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html) |
+<CategoryTable category="external_retrievers" />
+
+## All retrievers
+
+<IndexTable />

docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "metadata": {},
    "source": [
     "---\n",
@@ -15,8 +15,6 @@
    "source": [
     "# Milvus Hybrid Search Retriever\n",
     "\n",
-    "## Overview\n",
-    "\n",
     "> [Milvus](https://milvus.io/docs) is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.\n",
     "\n",
     "This will help you getting started with the Milvus Hybrid Search [retriever](/docs/concepts/#retrievers), which combines the strengths of both dense and sparse vector search. For detailed documentation of all `MilvusCollectionHybridSearchRetriever` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html).\n",
@@ -25,11 +23,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Retriever | Self-host | Cloud offering | Package |\n",
-    "| :--- | :--- | :---: | :---: |\n",
-    "[MilvusCollectionHybridSearchRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html) | ✅ | ❌ | langchain_milvus |\n",
-    "\n",
+    "import {ItemTable} from \"@theme/FeatureTables\";\n",
     "\n",
+    "<ItemTable category=\"document_retrievers\" item=\"MilvusCollectionHybridSearchRetriever\" />\n",
     "\n",
     "## Setup\n",
     "\n",

docs/docs/integrations/retrievers/self_query/milvus_self_query.ipynb

@@ -379,4 +379,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/docs/integrations/retrievers/self_query/vectara_self_query.ipynb

@@ -85,13 +85,14 @@
    "source": [
     "import os\n",
     "\n",
+    "from langchain_core.documents import Document\n",
+    "\n",
     "os.environ[\"VECTARA_API_KEY\"] = \"<YOUR_VECTARA_API_KEY>\"\n",
     "os.environ[\"VECTARA_CORPUS_ID\"] = \"<YOUR_VECTARA_CORPUS_ID>\"\n",
     "os.environ[\"VECTARA_CUSTOMER_ID\"] = \"<YOUR_VECTARA_CUSTOMER_ID>\"\n",
     "\n",
     "from langchain.chains.query_constructor.base import AttributeInfo\n",
     "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
-    "from langchain.schema import Document\n",
     "from langchain_community.vectorstores import Vectara\n",
     "from langchain_openai.chat_models import ChatOpenAI"
    ]

docs/docs/integrations/retrievers/tavily.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "metadata": {},
    "source": [
     "---\n",
@@ -15,16 +15,15 @@
    "source": [
     "# TavilySearchAPIRetriever\n",
     "\n",
-    "## Overview\n",
     ">[Tavily's Search API](https://tavily.com) is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.\n",
     "\n",
     "We can use this as a [retriever](/docs/how_to#retrievers). It will show functionality specific to this integration. After going through, it may be useful to explore [relevant use-case pages](/docs/how_to#qa-with-rag) to learn how to use this vectorstore as part of a larger chain.\n",
     "\n",
     "### Integration details\n",
     "\n",
-    "| Retriever | Source | Package |\n",
-    "| :--- | :--- | :---: |\n",
-    "[TavilySearchAPIRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html) | Internet search | langchain_community |\n",
+    "import {ItemTable} from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"external_retrievers\" item=\"TavilySearchAPIRetriever\" />\n",
     "\n",
     "## Setup"
    ]

docs/docs/integrations/retrievers/thirdai_neuraldb.ipynb

@@ -23,7 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.retrievers import NeuralDBRetriever\n",
+    "from langchain_community.retrievers import NeuralDBRetriever\n",
     "\n",
     "# From scratch\n",
     "retriever = NeuralDBRetriever.from_scratch(thirdai_key=\"your-thirdai-key\")\n",

docs/docs/integrations/retrievers/wikipedia.ipynb

@@ -1,7 +1,7 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
    "id": "62727aaa-bcff-4087-891c-e539f824ee1f",
    "metadata": {},
    "source": [
@@ -24,9 +24,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Retriever | Source | Package |\n",
-    "| :--- | :--- | :---: |\n",
-    "[WikipediaRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html) | [Wikipedia](https://www.wikipedia.org/) articles | langchain_community |"
+    "import {ItemTable} from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"external_retrievers\" item=\"WikipediaRetriever\" />"
    ]
   },
   {

docs/docs/integrations/retrievers/zep_memorystore.ipynb

@@ -60,7 +60,7 @@
     "import time\n",
     "from uuid import uuid4\n",
     "\n",
-    "from langchain.memory import ZepMemory\n",
+    "from langchain_community.memory.zep_memory import ZepMemory\n",
     "from langchain_core.messages import AIMessage, HumanMessage\n",
     "\n",
     "# Set this to your Zep server URL\n",

docs/docs/integrations/retrievers/zilliz_cloud_pipeline.ipynb

@@ -219,4 +219,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/docs/integrations/text_embedding/ai21.ipynb

@@ -2,116 +2,248 @@
  "cells": [
   {
    "cell_type": "raw",
-   "id": "c2923bd1",
+   "id": "afaf8039",
    "metadata": {},
    "source": [
     "---\n",
-    "sidebar_label: AI21 Labs\n",
+    "sidebar_label: AI21\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cc3c6ef6bbd57ce9",
-   "metadata": {
-    "collapsed": false
-   },
+   "id": "9a3d6f34",
+   "metadata": {},
    "source": [
     "# AI21Embeddings\n",
     "\n",
-    "This notebook covers how to get started with AI21 embedding models.\n",
+    "This will help you get started with AI21 embedding models using LangChain. For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n",
     "\n",
-    "## Installation"
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "import { ItemTable } from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"text_embedding\" item=\"AI21\" />\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access AI21 embedding models you'll need to create an AI21 account, get an API key, and install the `langchain-ai21` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [https://docs.ai21.com/](https://docs.ai21.com/) to sign up to AI21 and generate an API key. Once you've done this set the `AI21_API_KEY` environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "4c3bef91",
+   "execution_count": 2,
+   "id": "36521c2a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -qU langchain-ai21"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Environment Setup\n",
-    "\n",
-    "We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
+    "import getpass\n",
     "import os\n",
-    "from getpass import getpass\n",
     "\n",
-    "os.environ[\"AI21_API_KEY\"] = getpass()"
+    "if not os.getenv(\"AI21_API_KEY\"):\n",
+    "    os.environ[\"AI21_API_KEY\"] = getpass.getpass(\"Enter your AI21 API key: \")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "74ef9d8b40a1319e",
-   "metadata": {
-    "collapsed": false
-   },
+   "id": "c84fb993",
+   "metadata": {},
    "source": [
-    "## Usage"
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "12fcfb4b",
+   "execution_count": 3,
+   "id": "39a4953b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9664366",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain AI21 integration lives in the `langchain-ai21` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64853226",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-ai21"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45dd1724",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ea7a09b",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_ai21 import AI21Embeddings\n",
     "\n",
-    "embeddings = AI21Embeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1f2e6104",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings.embed_query(\"My query to look up\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a3465d7e63bfb3d1",
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "embeddings.embed_documents(\n",
-    "    [\"This is a content of the document\", \"This is another document\"]\n",
+    "embeddings = AI21Embeddings(\n",
+    "    # Can optionally increase or decrease the batch_size\n",
+    "    # to improve latency.\n",
+    "    # Use larger batch sizes with smaller documents, and\n",
+    "    # smaller batch sizes with larger documents.\n",
+    "    # batch_size=256,\n",
     ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9d60af6d",
+   "cell_type": "markdown",
+   "id": "77d271b6",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d817716b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'LangChain is the framework for building context-aware reasoning applications'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02b9855",
+   "metadata": {},
+   "source": [
+    "## Direct Usage\n",
+    "\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+    "\n",
+    "You can directly call these methods to get embeddings for your own use cases.\n",
+    "\n",
+    "### Embed single texts\n",
+    "\n",
+    "You can embed single texts or documents with `embed_query`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0d2befcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.01913362182676792, 0.004960147198289633, -0.01582135073840618, -0.042474791407585144, 0.040200788\n"
+     ]
+    }
+   ],
+   "source": [
+    "single_vector = embeddings.embed_query(text)\n",
+    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5a7d03",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2f4d6e97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.03029559925198555, 0.002908500377088785, -0.02700909972190857, -0.04616579785943031, 0.0382771529\n",
+      "[0.018214847892522812, 0.011460083536803722, -0.03329407051205635, -0.04951060563325882, 0.032756105\n"
+     ]
+    }
+   ],
+   "source": [
+    "text2 = (\n",
+    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+    ")\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
+    "for vector in two_vectors:\n",
+    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98785c12",
+   "metadata": {},
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n"
+   ]
   }
  ],
  "metadata": {
@@ -130,7 +262,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

docs/docs/integrations/text_embedding/azureopenai.ipynb

@@ -2,195 +2,261 @@
  "cells": [
   {
    "cell_type": "raw",
-   "id": "0aed0743",
+   "id": "afaf8039",
    "metadata": {},
    "source": [
     "---\n",
-    "keywords: [AzureOpenAIEmbeddings]\n",
+    "sidebar_label: AzureOpenAI\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c3852491",
+   "id": "9a3d6f34",
    "metadata": {},
    "source": [
-    "# Azure OpenAI\n",
+    "# AzureOpenAIEmbeddings\n",
     "\n",
-    "Let's load the Azure OpenAI Embedding class with environment variables set to indicate to use Azure endpoints."
+    "This will help you get started with AzureOpenAI embedding models using LangChain. For detailed documentation on `AzureOpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "import { ItemTable } from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"text_embedding\" item=\"AzureOpenAI\" />\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access AzureOpenAI embedding models you'll need to create an Azure account, get an API key, and install the `langchain-openai` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "You’ll need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following this [guide](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).\n",
+    "\n",
+    "Once you have your instance running, make sure you have the name of your instance and key. You can find the key in the Azure Portal, under the “Keys and Endpoint” section of your instance.\n",
+    "\n",
+    "```bash\n",
+    "AZURE_OPENAI_ENDPOINT=<YOUR API ENDPOINT>\n",
+    "AZURE_OPENAI_API_KEY=<YOUR_KEY>\n",
+    "AZURE_OPENAI_API_VERSION=\"2024-02-01\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "36521c2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your AzureOpenAI API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c84fb993",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "39a4953b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9664366",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain AzureOpenAI integration lives in the `langchain-openai` package:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "228faf0c",
+   "id": "64853226",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain-openai"
+    "%pip install -qU langchain-openai"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "8a6ed30d-806f-4800-b5fd-d04126be9060",
+   "cell_type": "markdown",
+   "id": "45dd1724",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "import os\n",
+    "## Instantiation\n",
     "\n",
-    "os.environ[\"AZURE_OPENAI_API_KEY\"] = \"...\"\n",
-    "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://<your-endpoint>.openai.azure.com/\""
+    "Now we can instantiate our model object and generate chat completions:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "20179bc7-3f71-4909-be12-d38bce009b18",
+   "execution_count": 11,
+   "id": "9ea7a09b",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_openai import AzureOpenAIEmbeddings\n",
     "\n",
     "embeddings = AzureOpenAIEmbeddings(\n",
-    "    azure_deployment=\"<your-embeddings-deployment-name>\",\n",
-    "    openai_api_version=\"2023-05-15\",\n",
+    "    model=\"text-embedding-3-large\",\n",
+    "    # dimensions: Optional[int] = None, # Can specify dimensions with new text-embedding-3 models\n",
+    "    # azure_endpoint=\"https://<your-endpoint>.openai.azure.com/\", If not provided, will read env variable AZURE_OPENAI_ENDPOINT\n",
+    "    # api_key=... # Can provide an API key directly. If missing read env variable AZURE_OPENAI_API_KEY\n",
+    "    # openai_api_version=..., # If not provided, will read env variable AZURE_OPENAI_API_VERSION\n",
     ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f8cb9dca-738b-450f-9986-5c3efd3c6eb3",
+   "cell_type": "markdown",
+   "id": "77d271b6",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "text = \"this is a test document\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "0fae0295-b117-4a5a-8b98-500c79306551",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query_result = embeddings.embed_query(text)"
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "65a01ddd-0bbf-444f-a87f-93af25ef902c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "doc_result = embeddings.embed_documents([text])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "45771052-68ca-4e03-9c4f-a0c7796d9442",
+   "id": "d817716b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[-0.012222584727053133,\n",
-       " 0.0072103982392216145,\n",
-       " -0.014818063280923775,\n",
-       " -0.026444746872933557,\n",
-       " -0.0034330499700826883]"
+       "'LangChain is the framework for building context-aware reasoning applications'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "doc_result[0][:5]"
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e66ec1f2-6768-4ee5-84bf-a2d76adc20c8",
+   "id": "e02b9855",
    "metadata": {},
    "source": [
-    "## [Legacy] When using `openai<1`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1b40f827",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set the environment variables needed for openai package to know to reach out to azure\n",
-    "import os\n",
+    "## Direct Usage\n",
     "\n",
-    "os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
-    "os.environ[\"OPENAI_API_BASE\"] = \"https://<your-endpoint.openai.azure.com/\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"your AzureOpenAI key\"\n",
-    "os.environ[\"OPENAI_API_VERSION\"] = \"2023-05-15\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bb36d16c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import OpenAIEmbeddings\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
     "\n",
-    "embeddings = OpenAIEmbeddings(deployment=\"your-embeddings-deployment-name\")"
+    "You can directly call these methods to get embeddings for your own use cases.\n",
+    "\n",
+    "### Embed single texts\n",
+    "\n",
+    "You can embed single texts or documents with `embed_query`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "228abcbb",
+   "execution_count": 6,
+   "id": "0d2befcd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.0011676070280373096, 0.007125577889382839, -0.014674457721412182, -0.034061674028635025, 0.01128\n"
+     ]
+    }
+   ],
    "source": [
-    "text = \"This is a test document.\""
+    "single_vector = embeddings.embed_query(text)\n",
+    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "60dd7fad",
+   "cell_type": "markdown",
+   "id": "1b5a7d03",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "query_result = embeddings.embed_query(text)"
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "83bc1a72",
+   "execution_count": 7,
+   "id": "2f4d6e97",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.0011966148158535361, 0.007160289213061333, -0.014659193344414234, -0.03403077274560928, 0.011280\n",
+      "[-0.005595256108790636, 0.016757294535636902, -0.011055258102715015, -0.031094247475266457, -0.00363\n"
+     ]
+    }
+   ],
    "source": [
-    "doc_result = embeddings.embed_documents([text])"
+    "text2 = (\n",
+    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+    ")\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
+    "for vector in two_vectors:\n",
+    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aaad49f8",
+   "cell_type": "markdown",
+   "id": "98785c12",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `AzureOpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html).\n"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -204,7 +270,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,