changes

**Description:** This PR fixes an issue in the demo notebook of
Databricks Vector Search in "Work with Delta Sync Index" section.

**Issue:** N/A

**Dependencies:** N/A

---------

Co-authored-by: Chengzu Ou <chengzu.ou@databrick.com>
Co-authored-by: Erick Friis <erick@langchain.dev>

.github/scripts/check_diff.py

@@ -1,7 +1,6 @@
 import glob
 import json
 import os
-import re
 import sys
 import tomllib
 from collections import defaultdict
@@ -86,6 +85,11 @@ def add_dependents(dirs_to_eval: Set[str], dependents: dict) -> List[str]:
 
 
 def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
+    if dir_ == "libs/core":
+        return [
+            {"working-directory": dir_, "python-version": f"3.{v}"}
+            for v in range(8, 13)
+        ]
     min_python = "3.8"
     max_python = "3.12"
 
@@ -100,6 +104,10 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
         # even in uv
         max_python = "3.11"
 
+    if dir_ == "libs/community" and job == "compile-integration-tests":
+        # community integration deps are slow in 3.12
+        max_python = "3.11"
+
     return [
         {"working-directory": dir_, "python-version": min_python},
         {"working-directory": dir_, "python-version": max_python},

.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

MIGRATE.md

@@ -52,7 +52,7 @@ Now:
 
 `from langchain_experimental.sql import SQLDatabaseChain`
 
-Alternatively, if you are just interested in using the query generation part of the SQL chain, you can check out [`create_sql_query_chain`](https://github.com/langchain-ai/langchain/blob/master/docs/extras/use_cases/tabular/sql_query.ipynb)
+Alternatively, if you are just interested in using the query generation part of the SQL chain, you can check out this [`SQL question-answering tutorial`](https://python.langchain.com/v0.2/docs/tutorials/sql_qa/#convert-question-to-sql-query)
 
 `from langchain.chains import create_sql_query_chain`
 

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.

README.md

@@ -7,7 +7,6 @@
 [![PyPI - License](https://img.shields.io/pypi/l/langchain-core?style=flat-square)](https://opensource.org/licenses/MIT)
 [![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-core?style=flat-square)](https://pypistats.org/packages/langchain-core)
 [![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=flat-square)](https://star-history.com/#langchain-ai/langchain)
-[![Dependency Status](https://img.shields.io/librariesio/github/langchain-ai/langchain?style=flat-square)](https://libraries.io/github/langchain-ai/langchain)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langchain?style=flat-square)](https://github.com/langchain-ai/langchain/issues)
 [![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
 [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/langchain)

cookbook/Semi_Structured_RAG.ipynb

@@ -39,7 +39,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain langchain-chroma unstructured[all-docs] pydantic lxml langchainhub"
+    "! pip install langchain langchain-chroma \"unstructured[all-docs]\" pydantic lxml langchainhub"
    ]
   },
   {

cookbook/Semi_structured_and_multi_modal_RAG.ipynb

@@ -59,7 +59,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain langchain-chroma unstructured[all-docs] pydantic lxml"
+    "! pip install langchain langchain-chroma \"unstructured[all-docs]\" pydantic lxml"
    ]
   },
   {

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -59,7 +59,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install langchain langchain-chroma unstructured[all-docs] pydantic lxml"
+    "! pip install langchain langchain-chroma \"unstructured[all-docs]\" pydantic lxml"
    ]
   },
   {

cookbook/databricks_sql_db.ipynb

@@ -166,7 +166,7 @@
    "source": [
     "### SQL Database Agent example\n",
     "\n",
-    "This example demonstrates the use of the [SQL Database Agent](/docs/integrations/toolkits/sql_database.html) for answering questions over a Databricks database."
+    "This example demonstrates the use of the [SQL Database Agent](/docs/integrations/tools/sql_database) for answering questions over a Databricks database."
    ]
   },
   {

docs/Makefile

@@ -13,7 +13,12 @@ OUTPUT_NEW_DOCS_DIR = $(OUTPUT_NEW_DIR)/docs
 
 PYTHON = .venv/bin/python
 
-PARTNER_DEPS_LIST := $(shell find ../libs/partners -mindepth 1 -maxdepth 1 -type d -exec test -e "{}/pyproject.toml" \; -print | grep -vE "airbyte|ibm|couchbase" | tr '\n' ' ')
+PARTNER_DEPS_LIST := $(shell find ../libs/partners -mindepth 1 -maxdepth 1 -type d -exec sh -c ' \
+for dir; do \
+    if find "$$dir" -maxdepth 1 -type f \( -name "pyproject.toml" -o -name "setup.py" \) | grep -q .; then \
+        echo "$$dir"; \
+    fi \
+done' sh {} + | grep -vE "airbyte|ibm|couchbase" | tr '\n' ' ')
 
 PORT ?= 3001
 
@@ -36,11 +41,11 @@ 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)
 
 	$(PYTHON) scripts/copy_templates.py $(INTERMEDIATE_DIR)
 
@@ -65,16 +70,23 @@ render:
 md-sync:
 	rsync -avm --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --include="*/_category_.yml" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
 
+append-related:
+	$(PYTHON) scripts/append_related_links.py $(OUTPUT_NEW_DOCS_DIR)
+
 generate-references:
 	$(PYTHON) scripts/generate_api_reference_links.py --docs_dir $(OUTPUT_NEW_DOCS_DIR)
 
-build: install-py-deps generate-files copy-infra render md-sync
+build: install-py-deps generate-files copy-infra render md-sync append-related
 
 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;
+}

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+<defs>
+<clipPath id="clip0_4019_689">
+<rect width="71.0711" height="18.68" fill="white" transform="translate(0 0.360107)"/>
+</clipPath>
+</defs>
+</svg>

docs/api_reference/conf.py

@@ -15,6 +15,8 @@ from pathlib import Path
 
 import toml
 from docutils import nodes
+from docutils.parsers.rst.directives.admonitions import BaseAdmonition
+from docutils.statemachine import StringList
 from sphinx.util.docutils import SphinxDirective
 
 # If extensions (or modules to document with autodoc) are in another directory,
@@ -60,26 +62,41 @@ 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]
 
 
+class Beta(BaseAdmonition):
+    required_arguments = 0
+    node_class = nodes.admonition
+
+    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."
+                )
+            ]
+        )
+        self.arguments = self.arguments or ["Beta"]
+        return super().run()
+
+
 def setup(app):
     app.add_directive("example_links", ExampleLinksDirective)
+    app.add_directive("beta", Beta)
 
 
 # -- 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"
 
 
@@ -95,11 +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
@@ -131,23 +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
@@ -155,8 +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,
@@ -166,9 +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"]
@@ -185,3 +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

@@ -38,6 +38,8 @@ class ClassInfo(TypedDict):
     """The kind of the class."""
     is_public: bool
     """Whether the class is public or not."""
+    is_deprecated: bool
+    """Whether the class is deprecated."""
 
 
 class FunctionInfo(TypedDict):
@@ -49,6 +51,8 @@ class FunctionInfo(TypedDict):
     """The fully qualified name of the function."""
     is_public: bool
     """Whether the function is public or not."""
+    is_deprecated: bool
+    """Whether the function is deprecated."""
 
 
 class ModuleMembers(TypedDict):
@@ -121,6 +125,7 @@ def _load_module_members(module_path: str, namespace: str) -> ModuleMembers:
                     qualified_name=f"{namespace}.{name}",
                     kind=kind,
                     is_public=not name.startswith("_"),
+                    is_deprecated=".. deprecated::" in (type_.__doc__ or ""),
                 )
             )
         elif inspect.isfunction(type_):
@@ -129,6 +134,7 @@ def _load_module_members(module_path: str, namespace: str) -> ModuleMembers:
                     name=name,
                     qualified_name=f"{namespace}.{name}",
                     is_public=not name.startswith("_"),
+                    is_deprecated=".. deprecated::" in (type_.__doc__ or ""),
                 )
             )
         else:
@@ -233,7 +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:
@@ -245,23 +251,62 @@ 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}
+
+.. _{package_namespace}_{module}:
+"""
         _members = members_by_namespace[module]
-        classes = [el for el in _members["classes_"] if el["is_public"]]
-        functions = [el for el in _members["functions"] if el["is_public"]]
+        classes = [
+            el
+            for el in _members["classes_"]
+            if el["is_public"] and not el["is_deprecated"]
+        ]
+        functions = [
+            el
+            for el in _members["functions"]
+            if el["is_public"] and not el["is_deprecated"]
+        ]
+        deprecated_classes = [
+            el for el in _members["classes_"] if el["is_public"] and el["is_deprecated"]
+        ]
+        deprecated_functions = [
+            el
+            for el in _members["functions"]
+            if el["is_public"] and el["is_deprecated"]
+        ]
         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}
 
@@ -269,16 +314,26 @@ def _construct_doc(
     :no-members:
     :no-inherited-members:
 
+"""
+
+        index_autosummary += f"""
+:ref:`{package_namespace}_{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"]):
@@ -295,19 +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::
@@ -317,7 +375,81 @@ Functions
     {fstring}
 
 """
-    return full_doc
+
+            index_autosummary += f"""
+**Functions**
+
+.. autosummary::
+
+    {fstring}
+"""
+        if deprecated_classes:
+            module_doc += f"""\
+**Deprecated classes**
+
+.. currentmodule:: {package_namespace}
+
+.. autosummary::
+    :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"
+                elif class_["kind"] == "enum":
+                    template = "enum.rst"
+                elif class_["kind"] == "Pydantic":
+                    template = "pydantic.rst"
+                elif class_["kind"] == "RunnablePydantic":
+                    template = "runnable_pydantic.rst"
+                elif class_["kind"] == "RunnableNonPydantic":
+                    template = "runnable_non_pydantic.rst"
+                else:
+                    template = "class.rst"
+
+                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))
+            module_doc += f"""\
+**Deprecated functions**
+
+.. currentmodule:: {package_namespace}
+
+.. autosummary::
+    :toctree: {module}
+    :template: function.rst
+
+    {fstring}
+
+"""
+            index_autosummary += f"""
+**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:
@@ -329,13 +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:
@@ -385,12 +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:
@@ -418,6 +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/additional_resources/arxiv_references.mdx

@@ -4,8 +4,11 @@ LangChain implements the latest research in the field of Natural Language Proces
 This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference,
  Templates, and Cookbooks.
 
-From the opposite direction, scientists use LangChain in research and reference LangChain in the research papers. 
-Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype=all&source=header).
+From the opposite direction, scientists use `LangChain` in research and reference it in the research papers. 
+Here you find papers that reference:
+- [LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header)
+- [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header)
+- [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header)
 
 ## Summary
 
@@ -23,32 +26,30 @@ Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype
 | `2305.14283v3` [Query Rewriting for Retrieval-Augmented Large Language Models](http://arxiv.org/abs/2305.14283v3) | Xinbei Ma, Yeyun Gong, Pengcheng He,  et al. | 2023-05-23 | `Template:` [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read), `Cookbook:` [rewrite](https://github.com/langchain-ai/langchain/blob/master/cookbook/rewrite.ipynb)
 | `2305.08291v1` [Large Language Model Guided Tree-of-Thought](http://arxiv.org/abs/2305.08291v1) | Jieyi Long | 2023-05-15 | `API:` [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot), `Cookbook:` [tree_of_thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
 | `2305.04091v3` [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](http://arxiv.org/abs/2305.04091v3) | Lei Wang, Wanyu Xu, Yihuai Lan,  et al. | 2023-05-06 | `Cookbook:` [plan_and_execute_agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
+| `2305.02156v1` [Zero-Shot Listwise Document Reranking with a Large Language Model](http://arxiv.org/abs/2305.02156v1) | Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al. | 2023-05-03 | `API:` [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
 | `2304.08485v2` [Visual Instruction Tuning](http://arxiv.org/abs/2304.08485v2) | Haotian Liu, Chunyuan Li, Qingyang Wu,  et al. | 2023-04-17 | `Cookbook:` [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb), [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb)
 | `2304.03442v2` [Generative Agents: Interactive Simulacra of Human Behavior](http://arxiv.org/abs/2304.03442v2) | Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al. | 2023-04-07 | `Cookbook:` [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb), [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb)
 | `2303.17760v2` [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](http://arxiv.org/abs/2303.17760v2) | Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al. | 2023-03-31 | `Cookbook:` [camel_role_playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
 | `2303.17580v4` [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](http://arxiv.org/abs/2303.17580v4) | Yongliang Shen, Kaitao Song, Xu Tan,  et al. | 2023-03-30 | `API:` [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents), `Cookbook:` [hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
-| `2303.08774v6` [GPT-4 Technical Report](http://arxiv.org/abs/2303.08774v6) | OpenAI, Josh Achiam, Steven Adler,  et al. | 2023-03-15 | `Docs:` [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas)
-| `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al. | 2023-01-24 | `API:` [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+| `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al. | 2023-01-24 | `API:` [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
 | `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin,  et al. | 2022-12-20 | `API:` [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [hypothetical_document_embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
 | `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al. | 2022-12-12 | `API:` [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
 | `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al. | 2022-11-25 | `API:` [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
-| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou,  et al. | 2022-11-18 | `API:` [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), `Cookbook:` [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
-| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al. | 2022-10-06 | `Docs:` [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/chat/huggingface](https://python.langchain.com/docs/integrations/chat/huggingface), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), `API:` [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
+| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou,  et al. | 2022-11-18 | `API:` [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), `Cookbook:` [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
+| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al. | 2022-10-06 | `Docs:` [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), `API:` [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain), [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent)
 | `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al. | 2022-09-22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
+| `2205.13147v4` [Matryoshka Representation Learning](http://arxiv.org/abs/2205.13147v4) | Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al. | 2022-05-26 | `Docs:` [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
 | `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022-05-25 | `API:` [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
-| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022-03-15 | `API:` [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL), [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase)
-| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher,  et al. | 2022-02-01 | `API:` [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022-03-15 | `API:` [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
+| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher,  et al. | 2022-02-01 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
 | `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy,  et al. | 2021-02-26 | `API:` [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
-| `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al. | 2019-09-11 | `API:` [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
-| `1908.10084v1` [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](http://arxiv.org/abs/1908.10084v1) | Nils Reimers, Iryna Gurevych | 2019-08-27 | `Docs:` [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers)
+| `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al. | 2019-09-11 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
 
 ## Self-Discover: Large Language Models Self-Compose Reasoning Structures
 
-- **arXiv id:** 2402.03620v1
+- **arXiv id:** [2402.03620v1](http://arxiv.org/abs/2402.03620v1)  **Published Date:** 2024-02-06
 - **Title:** Self-Discover: Large Language Models Self-Compose Reasoning Structures
 - **Authors:** Pei Zhou, Jay Pujara, Xiang Ren,  et al.
-- **Published Date:** 2024-02-06
-- **URL:** http://arxiv.org/abs/2402.03620v1
 - **LangChain:**
 
    - **Cookbook:** [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
@@ -70,11 +71,9 @@ commonalities with human reasoning patterns.
                 
 ## RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
 
-- **arXiv id:** 2401.18059v1
+- **arXiv id:** [2401.18059v1](http://arxiv.org/abs/2401.18059v1)  **Published Date:** 2024-01-31
 - **Title:** RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
 - **Authors:** Parth Sarthi, Salman Abdullah, Aditi Tuli,  et al.
-- **Published Date:** 2024-01-31
-- **URL:** http://arxiv.org/abs/2401.18059v1
 - **LangChain:**
 
    - **Cookbook:** [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
@@ -96,11 +95,9 @@ benchmark by 20% in absolute accuracy.
                 
 ## Corrective Retrieval Augmented Generation
 
-- **arXiv id:** 2401.15884v2
+- **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2)  **Published Date:** 2024-01-29
 - **Title:** Corrective Retrieval Augmented Generation
 - **Authors:** Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al.
-- **Published Date:** 2024-01-29
-- **URL:** http://arxiv.org/abs/2401.15884v2
 - **LangChain:**
 
    - **Cookbook:** [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
@@ -126,11 +123,9 @@ performance of RAG-based approaches.
                 
 ## Mixtral of Experts
 
-- **arXiv id:** 2401.04088v1
+- **arXiv id:** [2401.04088v1](http://arxiv.org/abs/2401.04088v1)  **Published Date:** 2024-01-08
 - **Title:** Mixtral of Experts
 - **Authors:** Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux,  et al.
-- **Published Date:** 2024-01-08
-- **URL:** http://arxiv.org/abs/2401.04088v1
 - **LangChain:**
 
    - **Cookbook:** [together_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
@@ -152,11 +147,9 @@ the base and instruct models are released under the Apache 2.0 license.
                 
 ## Dense X Retrieval: What Retrieval Granularity Should We Use?
 
-- **arXiv id:** 2312.06648v2
+- **arXiv id:** [2312.06648v2](http://arxiv.org/abs/2312.06648v2)  **Published Date:** 2023-12-11
 - **Title:** Dense X Retrieval: What Retrieval Granularity Should We Use?
 - **Authors:** Tong Chen, Hongwei Wang, Sihao Chen,  et al.
-- **Published Date:** 2023-12-11
-- **URL:** http://arxiv.org/abs/2312.06648v2
 - **LangChain:**
 
    - **Template:** [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
@@ -181,11 +174,9 @@ information.
                 
 ## Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
 
-- **arXiv id:** 2311.09210v1
+- **arXiv id:** [2311.09210v1](http://arxiv.org/abs/2311.09210v1)  **Published Date:** 2023-11-15
 - **Title:** Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
 - **Authors:** Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al.
-- **Published Date:** 2023-11-15
-- **URL:** http://arxiv.org/abs/2311.09210v1
 - **LangChain:**
 
    - **Template:** [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
@@ -215,11 +206,9 @@ outside the pre-training knowledge scope.
                 
 ## Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
 
-- **arXiv id:** 2310.11511v1
+- **arXiv id:** [2310.11511v1](http://arxiv.org/abs/2310.11511v1)  **Published Date:** 2023-10-17
 - **Title:** Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
 - **Authors:** Akari Asai, Zeqiu Wu, Yizhong Wang,  et al.
-- **Published Date:** 2023-10-17
-- **URL:** http://arxiv.org/abs/2310.11511v1
 - **LangChain:**
 
    - **Cookbook:** [langgraph_self_rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
@@ -248,11 +237,9 @@ to these models.
                 
 ## Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
 
-- **arXiv id:** 2310.06117v2
+- **arXiv id:** [2310.06117v2](http://arxiv.org/abs/2310.06117v2)  **Published Date:** 2023-10-09
 - **Title:** Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
 - **Authors:** Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al.
-- **Published Date:** 2023-10-09
-- **URL:** http://arxiv.org/abs/2310.06117v2
 - **LangChain:**
 
    - **Template:** [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting)
@@ -271,11 +258,9 @@ and 11% respectively, TimeQA by 27%, and MuSiQue by 7%.
                 
 ## Llama 2: Open Foundation and Fine-Tuned Chat Models
 
-- **arXiv id:** 2307.09288v2
+- **arXiv id:** [2307.09288v2](http://arxiv.org/abs/2307.09288v2)  **Published Date:** 2023-07-18
 - **Title:** Llama 2: Open Foundation and Fine-Tuned Chat Models
 - **Authors:** Hugo Touvron, Louis Martin, Kevin Stone,  et al.
-- **Published Date:** 2023-07-18
-- **URL:** http://arxiv.org/abs/2307.09288v2
 - **LangChain:**
 
    - **Cookbook:** [Semi_Structured_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
@@ -292,11 +277,9 @@ contribute to the responsible development of LLMs.
                 
 ## Query Rewriting for Retrieval-Augmented Large Language Models
 
-- **arXiv id:** 2305.14283v3
+- **arXiv id:** [2305.14283v3](http://arxiv.org/abs/2305.14283v3)  **Published Date:** 2023-05-23
 - **Title:** Query Rewriting for Retrieval-Augmented Large Language Models
 - **Authors:** Xinbei Ma, Yeyun Gong, Pengcheng He,  et al.
-- **Published Date:** 2023-05-23
-- **URL:** http://arxiv.org/abs/2305.14283v3
 - **LangChain:**
 
    - **Template:** [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read)
@@ -322,11 +305,9 @@ for retrieval-augmented LLM.
                 
 ## Large Language Model Guided Tree-of-Thought
 
-- **arXiv id:** 2305.08291v1
+- **arXiv id:** [2305.08291v1](http://arxiv.org/abs/2305.08291v1)  **Published Date:** 2023-05-15
 - **Title:** Large Language Model Guided Tree-of-Thought
 - **Authors:** Jieyi Long
-- **Published Date:** 2023-05-15
-- **URL:** http://arxiv.org/abs/2305.08291v1
 - **LangChain:**
 
    - **API Reference:** [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot)
@@ -352,11 +333,9 @@ implementation of the ToT-based Sudoku solver is available on GitHub:
                 
 ## Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
 
-- **arXiv id:** 2305.04091v3
+- **arXiv id:** [2305.04091v3](http://arxiv.org/abs/2305.04091v3)  **Published Date:** 2023-05-06
 - **Title:** Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
 - **Authors:** Lei Wang, Wanyu Xu, Yihuai Lan,  et al.
-- **Published Date:** 2023-05-06
-- **URL:** http://arxiv.org/abs/2305.04091v3
 - **LangChain:**
 
    - **Cookbook:** [plan_and_execute_agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
@@ -383,13 +362,35 @@ Prompting, and has comparable performance with 8-shot CoT prompting on the math
 reasoning problem. The code can be found at
 https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.
                 
+## Zero-Shot Listwise Document Reranking with a Large Language Model
+
+- **arXiv id:** [2305.02156v1](http://arxiv.org/abs/2305.02156v1)  **Published Date:** 2023-05-03
+- **Title:** Zero-Shot Listwise Document Reranking with a Large Language Model
+- **Authors:** Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
+
+**Abstract:** Supervised ranking methods based on bi-encoder or cross-encoder architectures
+have shown success in multi-stage text ranking tasks, but they require large
+amounts of relevance judgments as training data. In this work, we propose
+Listwise Reranker with a Large Language Model (LRL), which achieves strong
+reranking effectiveness without using any task-specific training data.
+Different from the existing pointwise ranking methods, where documents are
+scored independently and ranked according to the scores, LRL directly generates
+a reordered list of document identifiers given the candidate documents.
+Experiments on three TREC web search datasets demonstrate that LRL not only
+outperforms zero-shot pointwise methods when reranking first-stage retrieval
+results, but can also act as a final-stage reranker to improve the top-ranked
+results of a pointwise method for improved efficiency. Additionally, we apply
+our approach to subsets of MIRACL, a recent multilingual retrieval dataset,
+with results showing its potential to generalize across different languages.
+                
 ## Visual Instruction Tuning
 
-- **arXiv id:** 2304.08485v2
+- **arXiv id:** [2304.08485v2](http://arxiv.org/abs/2304.08485v2)  **Published Date:** 2023-04-17
 - **Title:** Visual Instruction Tuning
 - **Authors:** Haotian Liu, Chunyuan Li, Qingyang Wu,  et al.
-- **Published Date:** 2023-04-17
-- **URL:** http://arxiv.org/abs/2304.08485v2
 - **LangChain:**
 
    - **Cookbook:** [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb), [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb)
@@ -412,11 +413,9 @@ publicly available.
                 
 ## Generative Agents: Interactive Simulacra of Human Behavior
 
-- **arXiv id:** 2304.03442v2
+- **arXiv id:** [2304.03442v2](http://arxiv.org/abs/2304.03442v2)  **Published Date:** 2023-04-07
 - **Title:** Generative Agents: Interactive Simulacra of Human Behavior
 - **Authors:** Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al.
-- **Published Date:** 2023-04-07
-- **URL:** http://arxiv.org/abs/2304.03442v2
 - **LangChain:**
 
    - **Cookbook:** [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb), [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb)
@@ -448,11 +447,9 @@ interaction patterns for enabling believable simulations of human behavior.
                 
 ## CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
 
-- **arXiv id:** 2303.17760v2
+- **arXiv id:** [2303.17760v2](http://arxiv.org/abs/2303.17760v2)  **Published Date:** 2023-03-31
 - **Title:** CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
 - **Authors:** Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al.
-- **Published Date:** 2023-03-31
-- **URL:** http://arxiv.org/abs/2303.17760v2
 - **LangChain:**
 
    - **Cookbook:** [camel_role_playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
@@ -478,11 +475,9 @@ agents and beyond: https://github.com/camel-ai/camel.
                 
 ## HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
 
-- **arXiv id:** 2303.17580v4
+- **arXiv id:** [2303.17580v4](http://arxiv.org/abs/2303.17580v4)  **Published Date:** 2023-03-30
 - **Title:** HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
 - **Authors:** Yongliang Shen, Kaitao Song, Xu Tan,  et al.
-- **Published Date:** 2023-03-30
-- **URL:** http://arxiv.org/abs/2303.17580v4
 - **LangChain:**
 
    - **API Reference:** [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents)
@@ -508,40 +503,14 @@ modalities and domains and achieve impressive results in language, vision,
 speech, and other challenging tasks, which paves a new way towards the
 realization of artificial general intelligence.
                 
-## GPT-4 Technical Report
-
-- **arXiv id:** 2303.08774v6
-- **Title:** GPT-4 Technical Report
-- **Authors:** OpenAI, Josh Achiam, Steven Adler,  et al.
-- **Published Date:** 2023-03-15
-- **URL:** http://arxiv.org/abs/2303.08774v6
-- **LangChain:**
-
-   - **Documentation:** [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas)
-
-**Abstract:** We report the development of GPT-4, a large-scale, multimodal model which can
-accept image and text inputs and produce text outputs. While less capable than
-humans in many real-world scenarios, GPT-4 exhibits human-level performance on
-various professional and academic benchmarks, including passing a simulated bar
-exam with a score around the top 10% of test takers. GPT-4 is a
-Transformer-based model pre-trained to predict the next token in a document.
-The post-training alignment process results in improved performance on measures
-of factuality and adherence to desired behavior. A core component of this
-project was developing infrastructure and optimization methods that behave
-predictably across a wide range of scales. This allowed us to accurately
-predict some aspects of GPT-4's performance based on models trained with no
-more than 1/1,000th the compute of GPT-4.
-                
 ## A Watermark for Large Language Models
 
-- **arXiv id:** 2301.10226v4
+- **arXiv id:** [2301.10226v4](http://arxiv.org/abs/2301.10226v4)  **Published Date:** 2023-01-24
 - **Title:** A Watermark for Large Language Models
 - **Authors:** John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al.
-- **Published Date:** 2023-01-24
-- **URL:** http://arxiv.org/abs/2301.10226v4
 - **LangChain:**
 
-   - **API Reference:** [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+   - **API Reference:** [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
 
 **Abstract:** Potential harms of large language models can be mitigated by watermarking
 model output, i.e., embedding signals into generated text that are invisible to
@@ -559,11 +528,9 @@ family, and discuss robustness and security.
                 
 ## Precise Zero-Shot Dense Retrieval without Relevance Labels
 
-- **arXiv id:** 2212.10496v1
+- **arXiv id:** [2212.10496v1](http://arxiv.org/abs/2212.10496v1)  **Published Date:** 2022-12-20
 - **Title:** Precise Zero-Shot Dense Retrieval without Relevance Labels
 - **Authors:** Luyu Gao, Xueguang Ma, Jimmy Lin,  et al.
-- **Published Date:** 2022-12-20
-- **URL:** http://arxiv.org/abs/2212.10496v1
 - **LangChain:**
 
    - **API Reference:** [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
@@ -590,11 +557,9 @@ search, QA, fact verification) and languages~(e.g. sw, ko, ja).
                 
 ## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
 
-- **arXiv id:** 2212.07425v3
+- **arXiv id:** [2212.07425v3](http://arxiv.org/abs/2212.07425v3)  **Published Date:** 2022-12-12
 - **Title:** Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
 - **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al.
-- **Published Date:** 2022-12-12
-- **URL:** http://arxiv.org/abs/2212.07425v3
 - **LangChain:**
 
    - **API Reference:** [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
@@ -623,11 +588,9 @@ further work on logical fallacy identification.
                 
 ## Complementary Explanations for Effective In-Context Learning
 
-- **arXiv id:** 2211.13892v2
+- **arXiv id:** [2211.13892v2](http://arxiv.org/abs/2211.13892v2)  **Published Date:** 2022-11-25
 - **Title:** Complementary Explanations for Effective In-Context Learning
 - **Authors:** Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al.
-- **Published Date:** 2022-11-25
-- **URL:** http://arxiv.org/abs/2211.13892v2
 - **LangChain:**
 
    - **API Reference:** [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
@@ -651,14 +614,12 @@ performance across three real-world tasks on multiple LLMs.
                 
 ## PAL: Program-aided Language Models
 
-- **arXiv id:** 2211.10435v2
+- **arXiv id:** [2211.10435v2](http://arxiv.org/abs/2211.10435v2)  **Published Date:** 2022-11-18
 - **Title:** PAL: Program-aided Language Models
 - **Authors:** Luyu Gao, Aman Madaan, Shuyan Zhou,  et al.
-- **Published Date:** 2022-11-18
-- **URL:** http://arxiv.org/abs/2211.10435v2
 - **LangChain:**
 
-   - **API Reference:** [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain)
+   - **API Reference:** [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain)
    - **Cookbook:** [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
 
 **Abstract:** Large language models (LLMs) have recently demonstrated an impressive ability
@@ -686,15 +647,13 @@ publicly available at http://reasonwithpal.com/ .
                 
 ## ReAct: Synergizing Reasoning and Acting in Language Models
 
-- **arXiv id:** 2210.03629v3
+- **arXiv id:** [2210.03629v3](http://arxiv.org/abs/2210.03629v3)  **Published Date:** 2022-10-06
 - **Title:** ReAct: Synergizing Reasoning and Acting in Language Models
 - **Authors:** Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al.
-- **Published Date:** 2022-10-06
-- **URL:** http://arxiv.org/abs/2210.03629v3
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/chat/huggingface](https://python.langchain.com/docs/integrations/chat/huggingface), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping)
-   - **API Reference:** [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
+   - **Documentation:** [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping)
+   - **API Reference:** [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain), [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent)
 
 **Abstract:** While large language models (LLMs) have demonstrated impressive capabilities
 across tasks in language understanding and interactive decision making, their
@@ -721,11 +680,9 @@ Project site with code: https://react-lm.github.io
                 
 ## Deep Lake: a Lakehouse for Deep Learning
 
-- **arXiv id:** 2209.10785v2
+- **arXiv id:** [2209.10785v2](http://arxiv.org/abs/2209.10785v2)  **Published Date:** 2022-09-22
 - **Title:** Deep Lake: a Lakehouse for Deep Learning
 - **Authors:** Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al.
-- **Published Date:** 2022-09-22
-- **URL:** http://arxiv.org/abs/2209.10785v2
 - **LangChain:**
 
    - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
@@ -747,13 +704,43 @@ visualization engine, or (c) deep learning frameworks without sacrificing GPU
 utilization. Datasets stored in Deep Lake can be accessed from PyTorch,
 TensorFlow, JAX, and integrate with numerous MLOps tools.
                 
+## Matryoshka Representation Learning
+
+- **arXiv id:** [2205.13147v4](http://arxiv.org/abs/2205.13147v4)  **Published Date:** 2022-05-26
+- **Title:** Matryoshka Representation Learning
+- **Authors:** Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al.
+- **LangChain:**
+
+   - **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
+
+**Abstract:** Learned representations are a central component in modern ML systems, serving
+a multitude of downstream tasks. When training such representations, it is
+often the case that computational and statistical constraints for each
+downstream task are unknown. In this context rigid, fixed capacity
+representations can be either over or under-accommodating to the task at hand.
+This leads us to ask: can we design a flexible representation that can adapt to
+multiple downstream tasks with varying computational resources? Our main
+contribution is Matryoshka Representation Learning (MRL) which encodes
+information at different granularities and allows a single embedding to adapt
+to the computational constraints of downstream tasks. MRL minimally modifies
+existing representation learning pipelines and imposes no additional cost
+during inference and deployment. MRL learns coarse-to-fine representations that
+are at least as accurate and rich as independently trained low-dimensional
+representations. The flexibility within the learned Matryoshka Representations
+offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at
+the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale
+retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for
+long-tail few-shot classification, all while being as robust as the original
+representations. Finally, we show that MRL extends seamlessly to web-scale
+datasets (ImageNet, JFT) across various modalities -- vision (ViT, ResNet),
+vision + language (ALIGN) and language (BERT). MRL code and pretrained models
+are open-sourced at https://github.com/RAIVNLab/MRL.
+                
 ## Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
 
-- **arXiv id:** 2205.12654v1
+- **arXiv id:** [2205.12654v1](http://arxiv.org/abs/2205.12654v1)  **Published Date:** 2022-05-25
 - **Title:** Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
 - **Authors:** Kevin Heffernan, Onur Çelebi, Holger Schwenk
-- **Published Date:** 2022-05-25
-- **URL:** http://arxiv.org/abs/2205.12654v1
 - **LangChain:**
 
    - **API Reference:** [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
@@ -778,14 +765,12 @@ encoders, mine bitexts, and validate the bitexts by training NMT systems.
                 
 ## Evaluating the Text-to-SQL Capabilities of Large Language Models
 
-- **arXiv id:** 2204.00498v1
+- **arXiv id:** [2204.00498v1](http://arxiv.org/abs/2204.00498v1)  **Published Date:** 2022-03-15
 - **Title:** Evaluating the Text-to-SQL Capabilities of Large Language Models
 - **Authors:** Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
-- **Published Date:** 2022-03-15
-- **URL:** http://arxiv.org/abs/2204.00498v1
 - **LangChain:**
 
-   - **API Reference:** [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL), [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase)
+   - **API Reference:** [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
 
 **Abstract:** We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
 language model. We find that, without any finetuning, Codex is a strong
@@ -797,14 +782,12 @@ few-shot examples.
                 
 ## Locally Typical Sampling
 
-- **arXiv id:** 2202.00666v5
+- **arXiv id:** [2202.00666v5](http://arxiv.org/abs/2202.00666v5)  **Published Date:** 2022-02-01
 - **Title:** Locally Typical Sampling
 - **Authors:** Clara Meister, Tiago Pimentel, Gian Wiher,  et al.
-- **Published Date:** 2022-02-01
-- **URL:** http://arxiv.org/abs/2202.00666v5
 - **LangChain:**
 
-   - **API Reference:** [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+   - **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
 
 **Abstract:** Today's probabilistic language generators fall short when it comes to
 producing coherent and fluent text despite the fact that the underlying models
@@ -829,11 +812,9 @@ reducing degenerate repetitions.
                 
 ## Learning Transferable Visual Models From Natural Language Supervision
 
-- **arXiv id:** 2103.00020v1
+- **arXiv id:** [2103.00020v1](http://arxiv.org/abs/2103.00020v1)  **Published Date:** 2021-02-26
 - **Title:** Learning Transferable Visual Models From Natural Language Supervision
 - **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy,  et al.
-- **Published Date:** 2021-02-26
-- **URL:** http://arxiv.org/abs/2103.00020v1
 - **LangChain:**
 
    - **API Reference:** [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
@@ -861,14 +842,12 @@ https://github.com/OpenAI/CLIP.
                 
 ## CTRL: A Conditional Transformer Language Model for Controllable Generation
 
-- **arXiv id:** 1909.05858v2
+- **arXiv id:** [1909.05858v2](http://arxiv.org/abs/1909.05858v2)  **Published Date:** 2019-09-11
 - **Title:** CTRL: A Conditional Transformer Language Model for Controllable Generation
 - **Authors:** Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al.
-- **Published Date:** 2019-09-11
-- **URL:** http://arxiv.org/abs/1909.05858v2
 - **LangChain:**
 
-   - **API Reference:** [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+   - **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
 
 **Abstract:** Large-scale language models show promising text generation capabilities, but
 users cannot easily control particular aspects of the generated text. We
@@ -881,32 +860,4 @@ codes also allow CTRL to predict which parts of the training data are most
 likely given a sequence. This provides a potential method for analyzing large
 amounts of data via model-based source attribution. We have released multiple
 full-sized, pretrained versions of CTRL at https://github.com/salesforce/ctrl.
-                
-## Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
-
-- **arXiv id:** 1908.10084v1
-- **Title:** Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
-- **Authors:** Nils Reimers, Iryna Gurevych
-- **Published Date:** 2019-08-27
-- **URL:** http://arxiv.org/abs/1908.10084v1
-- **LangChain:**
-
-   - **Documentation:** [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers)
-
-**Abstract:** BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new
-state-of-the-art performance on sentence-pair regression tasks like semantic
-textual similarity (STS). However, it requires that both sentences are fed into
-the network, which causes a massive computational overhead: Finding the most
-similar pair in a collection of 10,000 sentences requires about 50 million
-inference computations (~65 hours) with BERT. The construction of BERT makes it
-unsuitable for semantic similarity search as well as for unsupervised tasks
-like clustering.
-  In this publication, we present Sentence-BERT (SBERT), a modification of the
-pretrained BERT network that use siamese and triplet network structures to
-derive semantically meaningful sentence embeddings that can be compared using
-cosine-similarity. This reduces the effort for finding the most similar pair
-from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while
-maintaining the accuracy from BERT.
-  We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning
-tasks, where it outperforms other state-of-the-art sentence embeddings methods.
                 

docs/docs/concepts.mdx

@@ -90,7 +90,7 @@ LCEL aims to provide consistency around behavior and customization over legacy s
 `ConversationalRetrievalChain`. Many of these legacy chains hide important details like prompts, and as a wider variety
 of viable models emerge, customization has become more and more important.
 
-If you are currently using one of these legacy chains, please see [this guide for guidance on how to migrate](/docs/how_to/migrate_chains/).
+If you are currently using one of these legacy chains, please see [this guide for guidance on how to migrate](/docs/versions/migrating_chains).
 
 For guides on how to do specific tasks with LCEL, check out [the relevant how-to guides](/docs/how_to/#langchain-expression-language-lcel).
 
@@ -498,6 +498,30 @@ Retrievers accept a string query as input and return a list of Document's as out
 
 For specifics on how to use retrievers, see the [relevant how-to guides here](/docs/how_to/#retrievers).
 
+### Key-value stores
+
+For some techniques, such as [indexing and retrieval with multiple vectors per document](/docs/how_to/multi_vector/) or
+[caching embeddings](/docs/how_to/caching_embeddings/), having a form of key-value (KV) storage is helpful.
+
+LangChain includes a [`BaseStore`](https://api.python.langchain.com/en/latest/stores/langchain_core.stores.BaseStore.html) interface,
+which allows for storage of arbitrary data. However, LangChain components that require KV-storage accept a
+more specific `BaseStore[str, bytes]` instance that stores binary data (referred to as a `ByteStore`), and internally take care of
+encoding and decoding data for their specific needs.
+
+This means that as a user, you only need to think about one type of store rather than different ones for different types of data.
+
+#### Interface
+
+All [`BaseStores`](https://api.python.langchain.com/en/latest/stores/langchain_core.stores.BaseStore.html) support the following interface. Note that the interface allows
+for modifying **multiple** key-value pairs at once:
+
+- `mget(key: Sequence[str]) -> List[Optional[bytes]]`: get the contents of multiple keys, returning `None` if the key does not exist
+- `mset(key_value_pairs: Sequence[Tuple[str, bytes]]) -> None`: set the contents of multiple keys
+- `mdelete(key: Sequence[str]) -> None`: delete multiple keys
+- `yield_keys(prefix: Optional[str] = None) -> Iterator[str]`: yield all keys in the store, optionally filtering by a prefix
+
+For key-value store implementations, see [this section](/docs/integrations/stores/).
+
 ### Tools
 <span data-heading-keywords="tool,tools"></span>
 
@@ -518,7 +542,8 @@ Typical usage may look like the following:
 ```python
 tools = [...] # Define a list of tools
 llm_with_tools = llm.bind_tools(tools)
-ai_msg = llm_with_tools.invoke("do xyz...")  # AIMessage(tool_calls=[ToolCall(...), ...], ...)
+ai_msg = llm_with_tools.invoke("do xyz...")
+# -> AIMessage(tool_calls=[ToolCall(...), ...], ...)
 ```
 
 The `AIMessage` returned from the model MAY have `tool_calls` associated with it.
@@ -535,9 +560,14 @@ This generally looks like:
 
 ```python
 # You will want to previously check that the LLM returned tool calls
-tool_call = ai_msg.tool_calls[0]  # ToolCall(args={...}, id=..., ...)
+tool_call = ai_msg.tool_calls[0]
+# ToolCall(args={...}, id=..., ...)
 tool_output = tool.invoke(tool_call["args"])
-tool_message = ToolMessage(content=tool_output, tool_call_id=tool_call["id"], name=tool_call["name"])
+tool_message = ToolMessage(
+    content=tool_output,
+    tool_call_id=tool_call["id"],
+    name=tool_call["name"]
+)
 ```
 
 Note that the `content` field will generally be passed back to the model.
@@ -547,7 +577,12 @@ you can transform the tool output but also pass it as an artifact (read more abo
 ```python
 ... # Same code as above
 response_for_llm = transform(response)
-tool_message = ToolMessage(content=response_for_llm, tool_call_id=tool_call["id"], name=tool_call["name"], artifact=tool_output)
+tool_message = ToolMessage(
+    content=response_for_llm,
+    tool_call_id=tool_call["id"],
+    name=tool_call["name"],
+    artifact=tool_output
+)
 ```
 
 #### Invoke with `ToolCall`
@@ -558,9 +593,14 @@ The benefits of this are that you don't have to write the logic yourself to tran
 This generally looks like:
 
 ```python
-tool_call = ai_msg.tool_calls[0]  # ToolCall(args={...}, id=..., ...)
+tool_call = ai_msg.tool_calls[0]
+# -> ToolCall(args={...}, id=..., ...)
 tool_message = tool.invoke(tool_call)
-# -> ToolMessage(content="tool result foobar...", tool_call_id=..., name="tool_name")
+# -> ToolMessage(
+    content="tool result foobar...",
+    tool_call_id=...,
+    name="tool_name"
+)
 ```
 
 If you are invoking the tool this way and want to include an [artifact](/docs/concepts/#toolmessage) for the ToolMessage, you will need to have the tool return two things.

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/code_splitter.ipynb

@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9e4144de-d925-4d4c-91c3-685ef8baa57c",
+   "id": "2bb9c73f-9d00-4a19-a81f-cab2f0fd921a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "a9e37aa1",
    "metadata": {},
    "outputs": [],
@@ -718,8 +718,44 @@
     "php_splitter = RecursiveCharacterTextSplitter.from_language(\n",
     "    language=Language.PHP, chunk_size=50, chunk_overlap=0\n",
     ")\n",
-    "haskell_docs = php_splitter.create_documents([PHP_CODE])\n",
-    "haskell_docs"
+    "php_docs = php_splitter.create_documents([PHP_CODE])\n",
+    "php_docs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9fa62c1",
+   "metadata": {},
+   "source": [
+    "## PowerShell\n",
+    "Here's an example using the PowerShell text splitter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e6893ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "POWERSHELL_CODE = \"\"\"\n",
+    "$directoryPath = Get-Location\n",
+    "\n",
+    "$items = Get-ChildItem -Path $directoryPath\n",
+    "\n",
+    "$files = $items | Where-Object { -not $_.PSIsContainer }\n",
+    "\n",
+    "$sortedFiles = $files | Sort-Object LastWriteTime\n",
+    "\n",
+    "foreach ($file in $sortedFiles) {\n",
+    "    Write-Output (\"Name: \" + $file.Name + \" | Last Write Time: \" + $file.LastWriteTime)\n",
+    "}\n",
+    "\"\"\"\n",
+    "powershell_splitter = RecursiveCharacterTextSplitter.from_language(\n",
+    "    language=Language.POWERSHELL, chunk_size=100, chunk_overlap=0\n",
+    ")\n",
+    "powershell_docs = powershell_splitter.create_documents([POWERSHELL_CODE])\n",
+    "powershell_docs"
    ]
   }
  ],
@@ -739,7 +775,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/configure.ipynb

@@ -409,7 +409,7 @@
     "    # When configuring the end runnable, we can then use this id to configure this field\n",
     "    ConfigurableField(id=\"prompt\"),\n",
     "    # This sets a default_key.\n",
-    "    # If we specify this key, the default LLM (ChatAnthropic initialized above) will be used\n",
+    "    # If we specify this key, the default prompt (asking for a joke, as initialized above) will be used\n",
     "    default_key=\"joke\",\n",
     "    # This adds a new option, with name `poem`\n",
     "    poem=PromptTemplate.from_template(\"Write a short poem about {topic}\"),\n",
@@ -494,7 +494,7 @@
     "    # When configuring the end runnable, we can then use this id to configure this field\n",
     "    ConfigurableField(id=\"prompt\"),\n",
     "    # This sets a default_key.\n",
-    "    # If we specify this key, the default LLM (ChatAnthropic initialized above) will be used\n",
+    "    # If we specify this key, the default prompt (asking for a joke, as initialized above) will be used\n",
     "    default_key=\"joke\",\n",
     "    # This adds a new option, with name `poem`\n",
     "    poem=PromptTemplate.from_template(\"Write a short poem about {topic}\"),\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/document_loader_json.mdx

@@ -182,7 +182,7 @@ pprint(data)
 </CodeOutputBlock>
 
 
-Another option is set `jq_schema='.'` and provide `content_key`:
+Another option is to set `jq_schema='.'` and provide `content_key`:
 
 ```python
 loader = JSONLoader(

docs/docs/how_to/functions.ipynb

@@ -28,7 +28,7 @@
     "\n",
     "You can use arbitrary functions as [Runnables](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable). This is useful for formatting or when you need functionality not provided by other LangChain components, and custom functions used as Runnables are called [`RunnableLambdas`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableLambda.html).\n",
     "\n",
-    "Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single dict input and unpacks it into multiple argument.\n",
+    "Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single dict input and unpacks it into multiple arguments.\n",
     "\n",
     "This guide will cover:\n",
     "\n",

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/index.mdx

@@ -31,6 +31,8 @@ This highlights functionality that is core to using LangChain.
 
 [**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives.
 
+[**Migration guide**](/docs/versions/migrating_chains): For migrating legacy chain abstractions to LCEL.
+
 - [How to: chain runnables](/docs/how_to/sequence)
 - [How to: stream runnables](/docs/how_to/streaming)
 - [How to: invoke runnables in parallel](/docs/how_to/parallel/)
@@ -43,7 +45,6 @@ This highlights functionality that is core to using LangChain.
 - [How to: create a dynamic (self-constructing) chain](/docs/how_to/dynamic_chain/)
 - [How to: inspect runnables](/docs/how_to/inspect)
 - [How to: add fallbacks to a runnable](/docs/how_to/fallbacks)
-- [How to: migrate chains to LCEL](/docs/how_to/migrate_chains)
 - [How to: pass runtime secrets to a runnable](/docs/how_to/runnable_runtime_secrets)
 
 ## Components
@@ -87,6 +88,7 @@ These are the core building blocks you can use when building applications.
 - [How to: few shot prompt tool behavior](/docs/how_to/tools_few_shot)
 - [How to: bind model-specific formatted tools](/docs/how_to/tools_model_specific)
 - [How to: force a specific tool call](/docs/how_to/tool_choice)
+- [How to: work with local models](/docs/how_to/local_llms)
 - [How to: init any model in one line](/docs/how_to/chat_models_universal_init/)
 
 ### Messages
@@ -105,7 +107,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
 - [How to: create a custom LLM class](/docs/how_to/custom_llm)
 - [How to: stream a response back](/docs/how_to/streaming_llm)
 - [How to: track token usage](/docs/how_to/llm_token_usage_tracking)
-- [How to: work with local LLMs](/docs/how_to/local_llms)
+- [How to: work with local models](/docs/how_to/local_llms)
 
 ### Output parsers
 

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/local_llms.ipynb

@@ -5,11 +5,11 @@
    "id": "b8982428",
    "metadata": {},
    "source": [
-    "# Run LLMs locally\n",
+    "# Run models locally\n",
     "\n",
     "## Use case\n",
     "\n",
-    "The popularity of projects like [PrivateGPT](https://github.com/imartinez/privateGPT), [llama.cpp](https://github.com/ggerganov/llama.cpp), [Ollama](https://github.com/ollama/ollama), [GPT4All](https://github.com/nomic-ai/gpt4all), [llamafile](https://github.com/Mozilla-Ocho/llamafile), and others underscore the demand to run LLMs locally (on your own device).\n",
+    "The popularity of projects like [llama.cpp](https://github.com/ggerganov/llama.cpp), [Ollama](https://github.com/ollama/ollama), [GPT4All](https://github.com/nomic-ai/gpt4all), [llamafile](https://github.com/Mozilla-Ocho/llamafile), and others underscore the demand to run LLMs locally (on your own device).\n",
     "\n",
     "This has at least two important benefits:\n",
     "\n",
@@ -66,6 +66,12 @@
     "\n",
     "![Image description](../../static/img/llama_t_put.png)\n",
     "\n",
+    "### Formatting prompts\n",
+    "\n",
+    "Some providers have [chat model](/docs/concepts/#chat-models) wrappers that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models with a [text-in/text-out LLM](/docs/concepts/#llms) wrapper, you may need to use a prompt tailed for your specific model.\n",
+    "\n",
+    "This can [require the inclusion of special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). [Here's an example for LLaMA 2](https://smith.langchain.com/hub/rlm/rag-prompt-llama).\n",
+    "\n",
     "## Quickstart\n",
     "\n",
     "[`Ollama`](https://ollama.ai/) is one way to easily run inference on macOS.\n",
@@ -73,10 +79,20 @@
     "The instructions [here](https://github.com/jmorganca/ollama?tab=readme-ov-file#ollama) provide details, which we summarize:\n",
     " \n",
     "* [Download and run](https://ollama.ai/download) the app\n",
-    "* From command line, fetch a model from this [list of options](https://github.com/jmorganca/ollama): e.g., `ollama pull llama2`\n",
+    "* From command line, fetch a model from this [list of options](https://github.com/jmorganca/ollama): e.g., `ollama pull llama3.1:8b`\n",
     "* When the app is running, all models are automatically served on `localhost:11434`\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29450fc9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_ollama"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -86,7 +102,7 @@
     {
      "data": {
       "text/plain": [
-       "' The first man on the moon was Neil Armstrong, who landed on the moon on July 20, 1969 as part of the Apollo 11 mission. obviously.'"
+       "'...Neil Armstrong!\\n\\nOn July 20, 1969, Neil Armstrong became the first person to set foot on the lunar surface, famously declaring \"That\\'s one small step for man, one giant leap for mankind\" as he stepped off the lunar module Eagle onto the Moon\\'s surface.\\n\\nWould you like to know more about the Apollo 11 mission or Neil Armstrong\\'s achievements?'"
       ]
      },
      "execution_count": 2,
@@ -95,51 +111,78 @@
     }
    ],
    "source": [
-    "from langchain_community.llms import Ollama\n",
+    "from langchain_ollama import OllamaLLM\n",
+    "\n",
+    "llm = OllamaLLM(model=\"llama3.1:8b\")\n",
     "\n",
-    "llm = Ollama(model=\"llama2\")\n",
     "llm.invoke(\"The first man on the moon was ...\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "343ab645",
+   "id": "674cc672",
    "metadata": {},
    "source": [
-    "Stream tokens as they are being generated."
+    "Stream tokens as they are being generated:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "9cd83603",
+   "execution_count": 3,
+   "id": "1386a852",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " The first man to walk on the moon was Neil Armstrong, an American astronaut who was part of the Apollo 11 mission in 1969. февруари 20, 1969, Armstrong stepped out of the lunar module Eagle and onto the moon's surface, famously declaring \"That's one small step for man, one giant leap for mankind\" as he took his first steps. He was followed by fellow astronaut Edwin \"Buzz\" Aldrin, who also walked on the moon during the mission."
+      "...|"
      ]
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neil| Armstrong|,| an| American| astronaut|.| He| stepped| out| of| the| lunar| module| Eagle| and| onto| the| surface| of| the| Moon| on| July| |20|,| |196|9|,| famously| declaring|:| \"|That|'s| one| small| step| for| man|,| one| giant| leap| for| mankind|.\"||"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in llm.stream(\"The first man on the moon was ...\"):\n",
+    "    print(chunk, end=\"|\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5731060",
+   "metadata": {},
+   "source": [
+    "Ollama also includes a chat model wrapper that handles formatting conversation turns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f14a778a",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/plain": [
-       "' The first man to walk on the moon was Neil Armstrong, an American astronaut who was part of the Apollo 11 mission in 1969. февруари 20, 1969, Armstrong stepped out of the lunar module Eagle and onto the moon\\'s surface, famously declaring \"That\\'s one small step for man, one giant leap for mankind\" as he took his first steps. He was followed by fellow astronaut Edwin \"Buzz\" Aldrin, who also walked on the moon during the mission.'"
+       "AIMessage(content='The answer is a historic one!\\n\\nThe first man to walk on the Moon was Neil Armstrong, an American astronaut and commander of the Apollo 11 mission. On July 20, 1969, Armstrong stepped out of the lunar module Eagle onto the surface of the Moon, famously declaring:\\n\\n\"That\\'s one small step for man, one giant leap for mankind.\"\\n\\nArmstrong was followed by fellow astronaut Edwin \"Buzz\" Aldrin, who also walked on the Moon during the mission. Michael Collins remained in orbit around the Moon in the command module Columbia.\\n\\nNeil Armstrong passed away on August 25, 2012, but his legacy as a pioneering astronaut and engineer continues to inspire people around the world!', response_metadata={'model': 'llama3.1:8b', 'created_at': '2024-08-01T00:38:29.176717Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 10681861417, 'load_duration': 34270292, 'prompt_eval_count': 19, 'prompt_eval_duration': 6209448000, 'eval_count': 141, 'eval_duration': 4432022000}, id='run-7bed57c5-7f54-4092-912c-ae49073dcd48-0', usage_metadata={'input_tokens': 19, 'output_tokens': 141, 'total_tokens': 160})"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler\n",
+    "from langchain_ollama import ChatOllama\n",
     "\n",
-    "llm = Ollama(\n",
-    "    model=\"llama2\", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])\n",
-    ")\n",
-    "llm.invoke(\"The first man on the moon was ...\")"
+    "chat_model = ChatOllama(model=\"llama3.1:8b\")\n",
+    "\n",
+    "chat_model.invoke(\"Who was the first man on the moon?\")"
    ]
   },
   {
@@ -199,7 +242,7 @@
     "\n",
     "With [Ollama](https://github.com/jmorganca/ollama), fetch a model via `ollama pull <model family>:<tag>`:\n",
     "\n",
-    "* E.g., for Llama-7b: `ollama pull llama2` will download the most basic version of the model (e.g., smallest # parameters and 4 bit quantization)\n",
+    "* E.g., for Llama 2 7b: `ollama pull llama2` will download the most basic version of the model (e.g., smallest # parameters and 4 bit quantization)\n",
     "* We can also specify a particular version from the [model list](https://github.com/jmorganca/ollama?tab=readme-ov-file#model-library), e.g., `ollama pull llama2:13b`\n",
     "* See the full set of parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.ollama.Ollama.html)"
    ]
@@ -222,9 +265,7 @@
     }
    ],
    "source": [
-    "from langchain_community.llms import Ollama\n",
-    "\n",
-    "llm = Ollama(model=\"llama2:13b\")\n",
+    "llm = OllamaLLM(model=\"llama2:13b\")\n",
     "llm.invoke(\"The first man on the moon was ... think step by step\")"
    ]
   },
@@ -268,11 +309,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "5eba38dc",
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%env CMAKE_ARGS=\"-DLLAMA_METAL=on\"\n",
@@ -542,7 +579,6 @@
     }
    ],
    "source": [
-    "from langchain.chains import LLMChain\n",
     "from langchain.chains.prompt_selector import ConditionalPromptSelector\n",
     "from langchain_core.prompts import PromptTemplate\n",
     "\n",
@@ -613,9 +649,9 @@
    ],
    "source": [
     "# Chain\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "chain = prompt | llm\n",
     "question = \"What NFL team won the Super Bowl in the year that Justin Bieber was born?\"\n",
-    "llm_chain.run({\"question\": question})"
+    "chain.invoke({\"question\": question})"
    ]
   },
   {
@@ -666,7 +702,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

docs/docs/how_to/migrate_agent.ipynb

@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "662fac50",
    "metadata": {},
    "outputs": [],
@@ -50,6 +50,26 @@
     "%pip install -U langgraph langchain langchain-openai"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6f8ec38f",
+   "metadata": {},
+   "source": [
+    "Then, set your OpenAI API key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5fca87ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8e50635c-1671-46e6-be65-ce95f8167c2f",
@@ -62,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "1e425fea-2796-4b99-bee6-9a6ffe73f756",
    "metadata": {},
    "outputs": [],
@@ -95,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "03ea357c-9c36-4464-b2cc-27bd150e1554",
    "metadata": {},
    "outputs": [
@@ -106,7 +126,7 @@
        " 'output': 'The value of `magic_function(3)` is 5.'}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -142,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "53a3737a-d167-4255-89bf-20ac37f89a3e",
    "metadata": {},
    "outputs": [
@@ -153,7 +173,7 @@
        " 'output': 'The value of `magic_function(3)` is 5.'}"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -173,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "74ecebe3-512e-409c-a661-bdd5b0a2b782",
    "metadata": {},
    "outputs": [
@@ -181,10 +201,10 @@
      "data": {
       "text/plain": [
        "{'input': 'Pardon?',\n",
-       " 'output': 'The result of applying `magic_function` to the input 3 is 5.'}"
+       " 'output': 'The value you get when you apply `magic_function` to the input 3 is 5.'}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "a9a11ccd-75e2-4c11-844d-a34870b0ff91",
    "metadata": {},
    "outputs": [
@@ -234,7 +254,7 @@
        " 'output': 'El valor de `magic_function(3)` es 5.'}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,19 +283,19 @@
    "source": [
     "Now, let's pass a custom system message to [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent).\n",
     "\n",
-    "LangGraph's prebuilt `create_react_agent` does not take a prompt template directly as a parameter, but instead takes a [`messages_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) parameter. This modifies messages before they are passed into the model, and can be one of four values:\n",
+    "LangGraph's prebuilt `create_react_agent` does not take a prompt template directly as a parameter, but instead takes a [`state_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) parameter. This modifies the graph state before the llm is called, and can be one of four values:\n",
     "\n",
     "- A `SystemMessage`, which is added to the beginning of the list of messages.\n",
     "- A `string`, which is converted to a `SystemMessage` and added to the beginning of the list of messages.\n",
-    "- A `Callable`, which should take in a list of messages. The output is then passed to the language model.\n",
-    "- Or a [`Runnable`](/docs/concepts/#langchain-expression-language-lcel), which should should take in a list of messages. The output is then passed to the language model.\n",
+    "- A `Callable`, which should take in full graph state. The output is then passed to the language model.\n",
+    "- Or a [`Runnable`](/docs/concepts/#langchain-expression-language-lcel), which should take in full graph state. The output is then passed to the language model.\n",
     "\n",
     "Here's how it looks in action:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "a9486805-676a-4d19-a5c4-08b41b172989",
    "metadata": {},
    "outputs": [],
@@ -287,7 +307,7 @@
     "# This could also be a SystemMessage object\n",
     "# system_message = SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")\n",
     "\n",
-    "app = create_react_agent(model, tools, messages_modifier=system_message)\n",
+    "app = create_react_agent(model, tools, state_modifier=system_message)\n",
     "\n",
     "\n",
     "messages = app.invoke({\"messages\": [(\"user\", query)]})"
@@ -304,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "d369ab45-0c82-45f4-9d3e-8efb8dd47e2c",
    "metadata": {},
    "outputs": [
@@ -317,8 +337,8 @@
     }
    ],
    "source": [
-    "from langchain_core.messages import AnyMessage\n",
     "from langgraph.prebuilt import create_react_agent\n",
+    "from langgraph.prebuilt.chat_agent_executor import AgentState\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
@@ -328,13 +348,13 @@
     ")\n",
     "\n",
     "\n",
-    "def _modify_messages(messages: list[AnyMessage]):\n",
-    "    return prompt.invoke({\"messages\": messages}).to_messages() + [\n",
+    "def _modify_state_messages(state: AgentState):\n",
+    "    return prompt.invoke({\"messages\": state[\"messages\"]}).to_messages() + [\n",
     "        (\"user\", \"Also say 'Pandamonium!' after the answer.\")\n",
     "    ]\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
+    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
     "\n",
     "\n",
     "messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
@@ -366,8 +386,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "1fb52a2c",
+   "execution_count": 9,
+   "id": "b97beba5-8f74-430c-9399-91b77c8fa15c",
    "metadata": {},
    "outputs": [
     {
@@ -376,7 +396,7 @@
      "text": [
       "Hi Polly! The output of the magic function for the input 3 is 5.\n",
       "---\n",
-      "Yes, I remember your name, Polly! How can I assist you further?\n",
+      "Yes, your name is Polly!\n",
       "---\n",
       "The output of the magic function for the input 3 is 5.\n"
      ]
@@ -384,14 +404,14 @@
    ],
    "source": [
     "from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
-    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
+    "from langchain_core.chat_history import InMemoryChatMessageHistory\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
     "from langchain_core.tools import tool\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(model=\"gpt-4o\")\n",
-    "memory = ChatMessageHistory(session_id=\"test-session\")\n",
+    "memory = InMemoryChatMessageHistory(session_id=\"test-session\")\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
     "        (\"system\", \"You are a helpful assistant.\"),\n",
@@ -456,24 +476,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "035e1253",
+   "execution_count": 10,
+   "id": "baca3dc6-678b-4509-9275-2fd653102898",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hi Polly! The output of the magic_function for the input 3 is 5.\n",
+      "Hi Polly! The output of the magic_function for the input of 3 is 5.\n",
       "---\n",
       "Yes, your name is Polly!\n",
       "---\n",
-      "The output of the magic_function for the input 3 was 5.\n"
+      "The output of the magic_function for the input of 3 was 5.\n"
      ]
     }
    ],
    "source": [
-    "from langchain_core.messages import SystemMessage\n",
     "from langgraph.checkpoint import MemorySaver  # an in-memory checkpointer\n",
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
@@ -483,7 +502,7 @@
     "\n",
     "memory = MemorySaver()\n",
     "app = create_react_agent(\n",
-    "    model, tools, messages_modifier=system_message, checkpointer=memory\n",
+    "    model, tools, state_modifier=system_message, checkpointer=memory\n",
     ")\n",
     "\n",
     "config = {\"configurable\": {\"thread_id\": \"test-thread\"}}\n",
@@ -525,16 +544,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "d640feb3",
+   "execution_count": 11,
+   "id": "e62843c4-1107-41f0-a50b-aea256e28053",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'actions': [ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])], tool_call_id='call_q9MgGFjqJbV2xSUX93WqxmOt')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])]}\n",
-      "{'steps': [AgentStep(action=ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])], tool_call_id='call_q9MgGFjqJbV2xSUX93WqxmOt'), observation=5)], 'messages': [FunctionMessage(content='5', name='magic_function')]}\n",
+      "{'actions': [ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-5664e138-7085-4da7-a49e-5656a87b8d78', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_1exy0rScfPmo4fy27FbQ5qJ2')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-5664e138-7085-4da7-a49e-5656a87b8d78', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'index': 0, 'type': 'tool_call_chunk'}])]}\n",
+      "{'steps': [AgentStep(action=ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-5664e138-7085-4da7-a49e-5656a87b8d78', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_1exy0rScfPmo4fy27FbQ5qJ2'), observation=5)], 'messages': [FunctionMessage(content='5', name='magic_function')]}\n",
       "{'output': 'The value of `magic_function(3)` is 5.', 'messages': [AIMessage(content='The value of `magic_function(3)` is 5.')]}\n"
      ]
     }
@@ -585,23 +604,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "86abbe07",
+   "execution_count": 12,
+   "id": "076ebc85-f804-4093-a25a-a16334c9898e",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yTjXXibj76tyFyPRa1soLo0S', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 70, 'total_tokens': 84}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b275f314-c42e-4e77-9dec-5c23f7dbd53b-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_yTjXXibj76tyFyPRa1soLo0S'}])]}}\n",
-      "{'tools': {'messages': [ToolMessage(content='5', name='magic_function', id='41c5f227-528d-4483-a313-b03b23b1d327', tool_call_id='call_yTjXXibj76tyFyPRa1soLo0S')]}}\n",
-      "{'agent': {'messages': [AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 93, 'total_tokens': 107}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None}, id='run-0ef12b6e-415d-4758-9b62-5e5e1b350072-0')]}}\n"
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_my9rzFSKR4T1yYKwCsfbZB8A', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 61, 'total_tokens': 75}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_bc2a86f5f5', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-dd705555-8fae-4fb1-a033-5d99a23e3c22-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_my9rzFSKR4T1yYKwCsfbZB8A', 'type': 'tool_call'}], usage_metadata={'input_tokens': 61, 'output_tokens': 14, 'total_tokens': 75})]}}\n",
+      "{'tools': {'messages': [ToolMessage(content='5', name='magic_function', tool_call_id='call_my9rzFSKR4T1yYKwCsfbZB8A')]}}\n",
+      "{'agent': {'messages': [AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 84, 'total_tokens': 98}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None}, id='run-698cad05-8cb2-4d08-8c2a-881e354f6cc7-0', usage_metadata={'input_tokens': 84, 'output_tokens': 14, 'total_tokens': 98})]}}\n"
      ]
     }
    ],
    "source": [
-    "from langchain_core.messages import AnyMessage\n",
     "from langgraph.prebuilt import create_react_agent\n",
+    "from langgraph.prebuilt.chat_agent_executor import AgentState\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
@@ -611,12 +630,11 @@
     ")\n",
     "\n",
     "\n",
-    "def _modify_messages(messages: list[AnyMessage]):\n",
-    "    return prompt.invoke({\"messages\": messages}).to_messages()\n",
+    "def _modify_state_messages(state: AgentState):\n",
+    "    return prompt.invoke({\"messages\": state[\"messages\"]}).to_messages()\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
-    "\n",
+    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
     "\n",
     "for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
     "    print(step)"
@@ -637,14 +655,14 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "4eff44bc-a620-4c8a-97b1-268692a842bb",
+   "id": "a2f720f3-c121-4be2-b498-92c16bb44b0a",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-837e794f-cfd8-40e0-8abc-4d98ced11b75', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca', 'index': 0}])], tool_call_id='call_ABI4hftfEdnVgKyfF6OzZbca'), 5)]\n"
+      "[(ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_uPZ2D1Bo5mdED3gwgaeWURrf', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-a792db4a-278d-4090-82ae-904a30eada93', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_uPZ2D1Bo5mdED3gwgaeWURrf', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_uPZ2D1Bo5mdED3gwgaeWURrf', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_uPZ2D1Bo5mdED3gwgaeWURrf'), 5)]\n"
      ]
     }
    ],
@@ -667,16 +685,16 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "4f4364ea-dffe-4d25-bdce-ef7d0020b880",
+   "id": "ef23117a-5ccb-42ce-80c3-ea49a9d3a942",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'messages': [HumanMessage(content='what is the value of magic_function(3)?', id='0f63e437-c4d8-4da9-b6f5-b293ebfe4a64'),\n",
-       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_S96v28LlI6hNkQrNnIio0JPh', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-ffef7898-14b1-4537-ad90-7c000a8a5d25-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_S96v28LlI6hNkQrNnIio0JPh'}]),\n",
-       "  ToolMessage(content='5', name='magic_function', id='fbd9df4e-1dda-4d3e-9044-b001f7875476', tool_call_id='call_S96v28LlI6hNkQrNnIio0JPh'),\n",
-       "  AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 87, 'total_tokens': 101}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None}, id='run-e5d94c54-d9f4-45cd-be8e-a9101a8d88d6-0')]}"
+       "{'messages': [HumanMessage(content='what is the value of magic_function(3)?', id='cd7d0f49-a0e0-425a-b2b0-603a716058ed'),\n",
+       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_VfZ9287DuybOSrBsQH5X12xf', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a1e965cd-bf61-44f9-aec1-8aaecb80955f-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_VfZ9287DuybOSrBsQH5X12xf', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}),\n",
+       "  ToolMessage(content='5', name='magic_function', id='20d5c2fe-a5d8-47fa-9e04-5282642e2039', tool_call_id='call_VfZ9287DuybOSrBsQH5X12xf'),\n",
+       "  AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 78, 'total_tokens': 92}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None}, id='run-abf9341c-ef41-4157-935d-a3be5dfa2f41-0', usage_metadata={'input_tokens': 78, 'output_tokens': 14, 'total_tokens': 92})]}"
       ]
      },
      "execution_count": 13,
@@ -708,7 +726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "id": "16f189a7-fc78-4cb5-aa16-a94ca06401a6",
    "metadata": {},
    "outputs": [],
@@ -724,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "c96aefd7-6f6e-4670-aca6-1ac3d4e7871f",
    "metadata": {},
    "outputs": [
@@ -739,11 +757,7 @@
       "Invoking: `magic_function` with `{'input': '3'}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `magic_function` with `{'input': '3'}`\n",
-      "responded: Parece que hubo un error al intentar obtener el valor de `magic_function(3)`. Permíteme intentarlo de nuevo.\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3mAún no puedo obtener el valor de `magic_function(3)`. ¿Hay algo más en lo que pueda ayudarte?\u001b[0m\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3mParece que hubo un error al intentar calcular el valor de la función mágica. ¿Te gustaría que lo intente de nuevo?\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -752,10 +766,10 @@
      "data": {
       "text/plain": [
        "{'input': 'what is the value of magic_function(3)?',\n",
-       " 'output': 'Aún no puedo obtener el valor de `magic_function(3)`. ¿Hay algo más en lo que pueda ayudarte?'}"
+       " 'output': 'Parece que hubo un error al intentar calcular el valor de la función mágica. ¿Te gustaría que lo intente de nuevo?'}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -797,7 +811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "id": "b974a91f-6ae8-4644-83d9-73666258a6db",
    "metadata": {},
    "outputs": [
@@ -805,12 +819,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "('human', 'what is the value of magic_function(3)?')\n",
-      "content='' additional_kwargs={'tool_calls': [{'id': 'call_pFdKcCu5taDTtOOfX14vEDRp', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-25836468-ba7e-43be-a7cf-76bba06a2a08-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_pFdKcCu5taDTtOOfX14vEDRp'}]\n",
-      "content='Sorry, there was an error. Please try again.' name='magic_function' id='1a08b883-9c7b-4969-9e9b-67ce64cdcb5f' tool_call_id='call_pFdKcCu5taDTtOOfX14vEDRp'\n",
-      "content='It seems there was an error when trying to apply the magic function. Let me try again.' additional_kwargs={'tool_calls': [{'id': 'call_DA0lpDIkBFg2GHy4WsEcZG4K', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 34, 'prompt_tokens': 97, 'total_tokens': 131}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-d571b774-0ea3-4e35-8b7d-f32932c3f3cc-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_DA0lpDIkBFg2GHy4WsEcZG4K'}]\n",
-      "content='Sorry, there was an error. Please try again.' name='magic_function' id='0b45787b-c82a-487f-9a5a-de129c30460f' tool_call_id='call_DA0lpDIkBFg2GHy4WsEcZG4K'\n",
-      "content='It appears that there is a consistent issue when trying to apply the magic function to the input \"3.\" This could be due to various reasons, such as the input not being in the correct format or an internal error.\\n\\nIf you have any other questions or if there\\'s something else you\\'d like to try, please let me know!' response_metadata={'token_usage': {'completion_tokens': 66, 'prompt_tokens': 153, 'total_tokens': 219}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None} id='run-50a962e6-21b7-4327-8dea-8e2304062627-0'\n"
+      "content='what is the value of magic_function(3)?' id='74e2d5e8-2b59-4820-979c-8d11ecfc14c2'\n",
+      "content='' additional_kwargs={'tool_calls': [{'id': 'call_ihtrH6IG95pDXpKluIwAgi3J', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-5a35e465-8a08-43dd-ac8b-4a76dcace305-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_ihtrH6IG95pDXpKluIwAgi3J', 'type': 'tool_call'}] usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}\n",
+      "content='Sorry, there was an error. Please try again.' name='magic_function' id='8c37c19b-3586-46b1-aab9-a045786801a2' tool_call_id='call_ihtrH6IG95pDXpKluIwAgi3J'\n",
+      "content='It seems there was an error in processing the request. Let me try again.' additional_kwargs={'tool_calls': [{'id': 'call_iF0vYWAd6rfely0cXSqdMOnF', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 88, 'total_tokens': 119}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-eb88ec77-d492-43a5-a5dd-4cefef9a6920-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_iF0vYWAd6rfely0cXSqdMOnF', 'type': 'tool_call'}] usage_metadata={'input_tokens': 88, 'output_tokens': 31, 'total_tokens': 119}\n",
+      "content='Sorry, there was an error. Please try again.' name='magic_function' id='c9ff261f-a0f1-4c92-a9f2-cd749f62d911' tool_call_id='call_iF0vYWAd6rfely0cXSqdMOnF'\n",
+      "content='I am currently unable to process the request with the input \"3\" for the `magic_function`. If you have any other questions or need assistance with something else, please let me know!' response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 141, 'total_tokens': 180}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None} id='run-d42508aa-f286-4b57-80fb-f8a76736d470-0' usage_metadata={'input_tokens': 141, 'output_tokens': 39, 'total_tokens': 180}\n"
      ]
     }
    ],
@@ -847,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "id": "4b8498fc-a7af-4164-a401-d8714f082306",
    "metadata": {},
    "outputs": [
@@ -874,7 +888,7 @@
        " 'output': 'Agent stopped due to max iterations.'}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -917,7 +931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "id": "a2b29113-e6be-4f91-aa4c-5c63dea3e423",
    "metadata": {},
    "outputs": [
@@ -925,7 +939,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_HaQkeCwD5QskzJzFixCBacZ4', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-596c9200-771f-436d-8576-72fcb81620f1-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_HaQkeCwD5QskzJzFixCBacZ4'}])]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_FKiTkTd0Ffd4rkYSzERprf1M', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b842f7b6-ec10-40f8-8c0e-baa220b77e91-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_FKiTkTd0Ffd4rkYSzERprf1M', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69})]}}\n",
       "------\n",
       "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
      ]
@@ -956,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "id": "e9eb55f4-a321-4bac-b52d-9e43b411cf92",
    "metadata": {},
    "outputs": [
@@ -964,7 +978,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_4agJXUHtmHrOOMogjF6ZuzAv', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a1c77db7-405f-43d9-8d57-751f2ca1a58c-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_4agJXUHtmHrOOMogjF6ZuzAv'}])]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_WoOB8juagB08xrP38twYlYKR', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73dee47e-30ab-42c9-bb0c-6f227cac96cd-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_WoOB8juagB08xrP38twYlYKR', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69})]}}\n",
       "------\n",
       "Task Cancelled.\n"
      ]
@@ -1005,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "id": "3f6e2cf2",
    "metadata": {},
    "outputs": [
@@ -1067,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "id": "73cabbc4",
    "metadata": {},
    "outputs": [
@@ -1075,10 +1089,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "('human', 'what is the value of magic_function(3)?')\n",
-      "content='' additional_kwargs={'tool_calls': [{'id': 'call_bTURmOn9C8zslmn0kMFeykIn', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-0844a504-7e6b-4ea6-a069-7017e38121ee-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_bTURmOn9C8zslmn0kMFeykIn'}]\n",
-      "content='Sorry there was an error, please try again.' name='magic_function' id='00d5386f-eb23-4628-9a29-d9ce6a7098cc' tool_call_id='call_bTURmOn9C8zslmn0kMFeykIn'\n",
-      "content='' additional_kwargs={'tool_calls': [{'id': 'call_JYqvvvWmXow2u012DuPoDHFV', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 96, 'total_tokens': 110}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-b73b1b1c-c829-4348-98cd-60b315c85448-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_JYqvvvWmXow2u012DuPoDHFV'}]\n",
+      "content='what is the value of magic_function(3)?' id='4fa7fbe5-758c-47a3-9268-717665d10680'\n",
+      "content='' additional_kwargs={'tool_calls': [{'id': 'call_ujE0IQBbIQnxcF9gsZXQfdhF', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-65d689aa-baee-4342-a5d2-048feefab418-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_ujE0IQBbIQnxcF9gsZXQfdhF', 'type': 'tool_call'}] usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}\n",
+      "content='Sorry there was an error, please try again.' name='magic_function' id='ef8ddf1d-9ad7-4ac0-b784-b673c4d94bbd' tool_call_id='call_ujE0IQBbIQnxcF9gsZXQfdhF'\n",
+      "content='It seems there was an issue with the previous attempt. Let me try that again.' additional_kwargs={'tool_calls': [{'id': 'call_GcsAfCFUHJ50BN2IOWnwTbQ7', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 32, 'prompt_tokens': 87, 'total_tokens': 119}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-54527c4b-8ff0-4ee8-8abf-224886bd222e-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_GcsAfCFUHJ50BN2IOWnwTbQ7', 'type': 'tool_call'}] usage_metadata={'input_tokens': 87, 'output_tokens': 32, 'total_tokens': 119}\n",
       "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
      ]
     }
@@ -1118,7 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "id": "b94bb169",
    "metadata": {},
    "outputs": [
@@ -1216,12 +1230,12 @@
    "source": [
     "### In LangGraph\n",
     "\n",
-    "We can use the [`messages_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) just as before when passing in [prompt templates](#prompt-templates)."
+    "We can use the [`state_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) just as before when passing in [prompt templates](#prompt-templates)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "id": "b309ba9a",
    "metadata": {},
    "outputs": [
@@ -1246,9 +1260,9 @@
     }
    ],
    "source": [
-    "from langchain_core.messages import AnyMessage\n",
     "from langgraph.errors import GraphRecursionError\n",
     "from langgraph.prebuilt import create_react_agent\n",
+    "from langgraph.prebuilt.chat_agent_executor import AgentState\n",
     "\n",
     "magic_step_num = 1\n",
     "\n",
@@ -1265,12 +1279,12 @@
     "tools = [magic_function]\n",
     "\n",
     "\n",
-    "def _modify_messages(messages: list[AnyMessage]):\n",
+    "def _modify_state_messages(state: AgentState):\n",
     "    # Give the agent amnesia, only keeping the original user query\n",
-    "    return [(\"system\", \"You are a helpful assistant\"), messages[0]]\n",
+    "    return [(\"system\", \"You are a helpful assistant\"), state[\"messages\"][0]]\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
+    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
     "\n",
     "try:\n",
     "    for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
@@ -1308,7 +1322,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/migrate_chains.ipynb

@@ -1,811 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f331037f-be3f-4782-856f-d55dab952488",
-   "metadata": {},
-   "source": [
-    "# How to migrate chains to LCEL\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language](/docs/concepts#langchain-expression-language-lcel)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "LCEL is designed to streamline the process of building useful apps with LLMs and combining related components. It does this by providing:\n",
-    "\n",
-    "1. **A unified interface**: Every LCEL object implements the `Runnable` interface, which defines a common set of invocation methods (`invoke`, `batch`, `stream`, `ainvoke`, ...). This makes it possible to also automatically and consistently support useful operations like streaming of intermediate steps and batching, since every chain composed of LCEL objects is itself an LCEL object.\n",
-    "2. **Composition primitives**: LCEL provides a number of primitives that make it easy to compose chains, parallelize components, add fallbacks, dynamically configure chain internals, and more.\n",
-    "\n",
-    "LangChain maintains a number of legacy abstractions. Many of these can be reimplemented via short combinations of LCEL primitives. Doing so confers some general advantages:\n",
-    "\n",
-    "- The resulting chains typically implement the full `Runnable` interface, including streaming and asynchronous support where appropriate;\n",
-    "- The chains may be more easily extended or modified;\n",
-    "- The parameters of the chain are typically surfaced for easier customization (e.g., prompts) over previous versions, which tended to be subclasses and had opaque parameters and internals.\n",
-    "\n",
-    "The LCEL implementations can be slightly more verbose, but there are significant benefits in transparency and customizability.\n",
-    "\n",
-    "In this guide we review LCEL implementations of common legacy abstractions. Where appropriate, we link out to separate guides with more detail."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b99b47ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet langchain-community langchain langchain-openai faiss-cpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "717c8673",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3621b62-a037-42b8-8faa-59575608bb8b",
-   "metadata": {},
-   "source": [
-    "## `LLMChain`\n",
-    "<span data-heading-keywords=\"llmchain\"></span>\n",
-    "\n",
-    "[`LLMChain`](https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html) combined a prompt template, LLM, and output parser into a class.\n",
-    "\n",
-    "Some advantages of switching to the LCEL implementation are:\n",
-    "\n",
-    "- Clarity around contents and parameters. The legacy `LLMChain` contains a default output parser and other options.\n",
-    "- Easier streaming. `LLMChain` only supports streaming via callbacks.\n",
-    "- Easier access to raw message outputs if desired. `LLMChain` only exposes these via a parameter or via callback.\n",
-    "\n",
-    "import { ColumnContainer, Column } from \"@theme/Columns\";\n",
-    "\n",
-    "<ColumnContainer>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### Legacy\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "e628905c-430e-4e4a-9d7c-c91d2f42052e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'adjective': 'funny',\n",
-       " 'text': \"Why couldn't the bicycle find its way home?\\n\\nBecause it lost its bearings!\"}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import LLMChain\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"user\", \"Tell me a {adjective} joke\")],\n",
-    ")\n",
-    "\n",
-    "chain = LLMChain(llm=ChatOpenAI(), prompt=prompt)\n",
-    "\n",
-    "chain({\"adjective\": \"funny\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cdc3b527-c09e-4c77-9711-c3cc4506cd95",
-   "metadata": {},
-   "source": [
-    "\n",
-    "</Column>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### LCEL\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0d2a7cf8-1bc7-405c-bb0d-f2ab2ba3b6ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Why couldn't the bicycle stand up by itself?\\n\\nBecause it was two tired!\""
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"user\", \"Tell me a {adjective} joke\")],\n",
-    ")\n",
-    "\n",
-    "chain = prompt | ChatOpenAI() | StrOutputParser()\n",
-    "\n",
-    "chain.invoke({\"adjective\": \"funny\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c0b0513-77b8-4371-a20e-3e487cec7e7f",
-   "metadata": {},
-   "source": [
-    "\n",
-    "</Column>\n",
-    "</ColumnContainer>\n",
-    "\n",
-    "Note that `LLMChain` by default returns a `dict` containing both the input and the output. If this behavior is desired, we can replicate it using another LCEL primitive, [`RunnablePassthrough`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "529206c5-abbe-4213-9e6c-3b8586c8000d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'adjective': 'funny',\n",
-       " 'text': \"Why couldn't the bicycle stand up by itself?\\n\\nBecause it was two tired!\"}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "\n",
-    "outer_chain = RunnablePassthrough().assign(text=chain)\n",
-    "\n",
-    "outer_chain.invoke({\"adjective\": \"funny\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "29d2e26c-2854-4971-9c2b-613450993921",
-   "metadata": {},
-   "source": [
-    "See [this tutorial](/docs/tutorials/llm_chain) for more detail on building with prompt templates, LLMs, and output parsers."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00df631d-5121-4918-94aa-b88acce9b769",
-   "metadata": {},
-   "source": [
-    "## `ConversationChain`\n",
-    "<span data-heading-keywords=\"conversationchain\"></span>\n",
-    "\n",
-    "[`ConversationChain`](https://api.python.langchain.com/en/latest/chains/langchain.chains.conversation.base.ConversationChain.html) incorporates a memory of previous messages to sustain a stateful conversation.\n",
-    "\n",
-    "Some advantages of switching to the LCEL implementation are:\n",
-    "\n",
-    "- Innate support for threads/separate sessions. To make this work with `ConversationChain`, you'd need to instantiate a separate memory class outside the chain.\n",
-    "- More explicit parameters. `ConversationChain` contains a hidden default prompt, which can cause confusion.\n",
-    "- Streaming support. `ConversationChain` only supports streaming via callbacks.\n",
-    "\n",
-    "`RunnableWithMessageHistory` implements sessions via configuration parameters. It should be instantiated with a callable that returns a [chat message history](https://api.python.langchain.com/en/latest/chat_history/langchain_core.chat_history.BaseChatMessageHistory.html). By default, it expects this function to take a single argument `session_id`.\n",
-    "\n",
-    "<ColumnContainer>\n",
-    "<Column>\n",
-    "\n",
-    "#### Legacy\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "4f2cc6dc-d70a-4c13-9258-452f14290da6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input': 'how are you?',\n",
-       " 'history': '',\n",
-       " 'response': \"Arrr, I be doin' well, me matey! Just sailin' the high seas in search of treasure and adventure. How can I assist ye today?\"}"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import ConversationChain\n",
-    "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "template = \"\"\"\n",
-    "You are a pirate. Answer the following questions as best you can.\n",
-    "Chat history: {history}\n",
-    "Question: {input}\n",
-    "\"\"\"\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(template)\n",
-    "\n",
-    "memory = ConversationBufferMemory()\n",
-    "\n",
-    "chain = ConversationChain(\n",
-    "    llm=ChatOpenAI(),\n",
-    "    memory=memory,\n",
-    "    prompt=prompt,\n",
-    ")\n",
-    "\n",
-    "chain({\"input\": \"how are you?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8e36b0e-c7dc-4130-a51b-189d4b756c7f",
-   "metadata": {},
-   "source": [
-    "</Column>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### LCEL\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "173e1a9c-2a18-4669-b0de-136f39197786",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Arrr, I be doin' well, me heartie! Just sailin' the high seas in search of treasure and adventure. How be ye?\""
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.chat_history import InMemoryChatMessageHistory\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a pirate. Answer the following questions as best you can.\"),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "history = InMemoryChatMessageHistory()\n",
-    "\n",
-    "\n",
-    "def get_history():\n",
-    "    return history\n",
-    "\n",
-    "\n",
-    "chain = prompt | ChatOpenAI() | StrOutputParser()\n",
-    "\n",
-    "wrapped_chain = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    get_history,\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")\n",
-    "\n",
-    "wrapped_chain.invoke({\"input\": \"how are you?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b386ce6-895e-442c-88f3-7bec0ab9f401",
-   "metadata": {},
-   "source": [
-    "\n",
-    "</Column>\n",
-    "</ColumnContainer>\n",
-    "\n",
-    "The above example uses the same `history` for all sessions. The example below shows how to use a different chat history for each session."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4e05994f-1fbc-4699-bf2e-62cb0e4deeb8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Ahoy matey! What can this old pirate do for ye today?'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.chat_history import BaseChatMessageHistory\n",
-    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
-    "\n",
-    "store = {}\n",
-    "\n",
-    "\n",
-    "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
-    "    if session_id not in store:\n",
-    "        store[session_id] = InMemoryChatMessageHistory()\n",
-    "    return store[session_id]\n",
-    "\n",
-    "\n",
-    "chain = prompt | ChatOpenAI() | StrOutputParser()\n",
-    "\n",
-    "wrapped_chain = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    get_session_history,\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")\n",
-    "\n",
-    "wrapped_chain.invoke(\n",
-    "    {\"input\": \"Hello!\"},\n",
-    "    config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c36ebecb",
-   "metadata": {},
-   "source": [
-    "See [this tutorial](/docs/tutorials/chatbot) for a more end-to-end guide on building with [`RunnableWithMessageHistory`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html).\n",
-    "\n",
-    "## `RetrievalQA`\n",
-    "<span data-heading-keywords=\"retrievalqa\"></span>\n",
-    "\n",
-    "The [`RetrievalQA`](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html) chain performed natural-language question answering over a data source using retrieval-augmented generation.\n",
-    "\n",
-    "Some advantages of switching to the LCEL implementation are:\n",
-    "\n",
-    "- Easier customizability. Details such as the prompt and how documents are formatted are only configurable via specific parameters in the `RetrievalQA` chain.\n",
-    "- More easily return source documents.\n",
-    "- Support for runnable methods like streaming and async operations.\n",
-    "\n",
-    "Now let's look at them side-by-side. We'll use the same ingestion code to load a [blog post by Lilian Weng](https://lilianweng.github.io/posts/2023-06-23-agent/) on autonomous agents into a local vector store:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "1efbe16e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load docs\n",
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain_community.document_loaders import WebBaseLoader\n",
-    "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_openai.chat_models import ChatOpenAI\n",
-    "from langchain_openai.embeddings import OpenAIEmbeddings\n",
-    "\n",
-    "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
-    "data = loader.load()\n",
-    "\n",
-    "# Split\n",
-    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
-    "all_splits = text_splitter.split_documents(data)\n",
-    "\n",
-    "# Store splits\n",
-    "vectorstore = FAISS.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())\n",
-    "\n",
-    "# LLM\n",
-    "llm = ChatOpenAI()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7e16438",
-   "metadata": {},
-   "source": [
-    "<ColumnContainer>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### Legacy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "43bf55a0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'query': 'What are autonomous agents?',\n",
-       " 'result': 'Autonomous agents are LLM-empowered agents that handle autonomous design, planning, and performance of complex tasks, such as scientific experiments. These agents can browse the Internet, read documentation, execute code, call robotics experimentation APIs, and leverage other LLMs. They are capable of reasoning and planning ahead for complicated tasks by breaking them down into smaller steps.'}"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain import hub\n",
-    "from langchain.chains import RetrievalQA\n",
-    "\n",
-    "# See full prompt at https://smith.langchain.com/hub/rlm/rag-prompt\n",
-    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "\n",
-    "qa_chain = RetrievalQA.from_llm(\n",
-    "    llm, retriever=vectorstore.as_retriever(), prompt=prompt\n",
-    ")\n",
-    "\n",
-    "qa_chain(\"What are autonomous agents?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "081948e5",
-   "metadata": {},
-   "source": [
-    "</Column>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### LCEL\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "9efcc931",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Autonomous agents are agents that can handle autonomous design, planning, and performance of complex tasks, such as scientific experiments. They can browse the Internet, read documentation, execute code, call robotics experimentation APIs, and leverage other language model models. These agents use reasoning steps to develop solutions to specific tasks, like creating a novel anticancer drug.'"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain import hub\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "\n",
-    "# See full prompt at https://smith.langchain.com/hub/rlm/rag-prompt\n",
-    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "\n",
-    "\n",
-    "def format_docs(docs):\n",
-    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
-    "\n",
-    "\n",
-    "qa_chain = (\n",
-    "    {\n",
-    "        \"context\": vectorstore.as_retriever() | format_docs,\n",
-    "        \"question\": RunnablePassthrough(),\n",
-    "    }\n",
-    "    | prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "qa_chain.invoke(\"What are autonomous agents?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6f44fe8",
-   "metadata": {},
-   "source": [
-    "</Column>\n",
-    "</ColumnContainer>\n",
-    "\n",
-    "The LCEL implementation exposes the internals of what's happening around retrieving, formatting documents, and passing them through a prompt to the LLM, but it is more verbose. You can customize and wrap this composition logic in a helper function, or use the higher-level [`create_retrieval_chain`](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval.create_retrieval_chain.html) and [`create_stuff_documents_chain`](https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) helper method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "5fe42761",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input': 'What are autonomous agents?',\n",
-       " 'context': [Document(page_content='Boiko et al. (2023) also looked into LLM-empowered agents for scientific discovery, to handle autonomous design, planning, and performance of complex scientific experiments. This agent can use tools to browse the Internet, read documentation, execute code, call robotics experimentation APIs and leverage other LLMs.\\nFor example, when requested to \"develop a novel anticancer drug\", the model came up with the following reasoning steps:', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}),\n",
-       "  Document(page_content='Weng, Lilian. (Jun 2023). “LLM-powered Autonomous Agents”. Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}),\n",
-       "  Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}),\n",
-       "  Document(page_content=\"LLM Powered Autonomous Agents | Lil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nPosts\\n\\n\\n\\n\\nArchive\\n\\n\\n\\n\\nSearch\\n\\n\\n\\n\\nTags\\n\\n\\n\\n\\nFAQ\\n\\n\\n\\n\\nemojisearch.app\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n      LLM Powered Autonomous Agents\\n    \\nDate: June 23, 2023  |  Estimated Reading Time: 31 min  |  Author: Lilian Weng\\n\\n\\n \\n\\n\\nTable of Contents\\n\\n\\n\\nAgent System Overview\\n\\nComponent One: Planning\\n\\nTask Decomposition\\n\\nSelf-Reflection\\n\\n\\nComponent Two: Memory\\n\\nTypes of Memory\\n\\nMaximum Inner Product Search (MIPS)\", metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'})],\n",
-       " 'answer': 'Autonomous agents are entities that can operate independently, making decisions and taking actions without direct human intervention. These agents can perform tasks such as planning, executing complex experiments, and leveraging various tools and resources to achieve objectives. In the context provided, LLM-powered autonomous agents are specifically designed for scientific discovery, capable of handling tasks like designing novel anticancer drugs through reasoning steps.'}"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain import hub\n",
-    "from langchain.chains import create_retrieval_chain\n",
-    "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
-    "\n",
-    "# See full prompt at https://smith.langchain.com/hub/langchain-ai/retrieval-qa-chat\n",
-    "retrieval_qa_chat_prompt = hub.pull(\"langchain-ai/retrieval-qa-chat\")\n",
-    "\n",
-    "combine_docs_chain = create_stuff_documents_chain(llm, retrieval_qa_chat_prompt)\n",
-    "rag_chain = create_retrieval_chain(vectorstore.as_retriever(), combine_docs_chain)\n",
-    "\n",
-    "rag_chain.invoke({\"input\": \"What are autonomous agents?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2772f4e9",
-   "metadata": {},
-   "source": [
-    "## `ConversationalRetrievalChain`\n",
-    "<span data-heading-keywords=\"conversationalretrievalchain\"></span>\n",
-    "\n",
-    "The [`ConversationalRetrievalChain`](https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html) was an all-in one way that combined retrieval-augmented generation with chat history, allowing you to \"chat with\" your documents.\n",
-    "\n",
-    "Advantages of switching to the LCEL implementation are similar to the `RetrievalQA` section above:\n",
-    "\n",
-    "- Clearer internals. The `ConversationalRetrievalChain` chain hides an entire question rephrasing step which dereferences the initial query against the chat history.\n",
-    "  - This means the class contains two sets of configurable prompts, LLMs, etc.\n",
-    "- More easily return source documents.\n",
-    "- Support for runnable methods like streaming and async operations.\n",
-    "\n",
-    "Here are side-by-side implementations with custom prompts. We'll reuse the loaded documents and vector store from the previous section:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bc06416",
-   "metadata": {},
-   "source": [
-    "<ColumnContainer>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### Legacy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "54eb9576",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'question': 'What are autonomous agents?',\n",
-       " 'chat_history': '',\n",
-       " 'answer': 'Autonomous agents are powered by Large Language Models (LLMs) to handle tasks like scientific discovery and complex experiments autonomously. These agents can browse the internet, read documentation, execute code, and leverage other LLMs to perform tasks. They can reason and plan ahead to decompose complicated tasks into manageable steps.'}"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import ConversationalRetrievalChain\n",
-    "\n",
-    "condense_question_template = \"\"\"\n",
-    "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n",
-    "\n",
-    "Chat History:\n",
-    "{chat_history}\n",
-    "Follow Up Input: {question}\n",
-    "Standalone question:\"\"\"\n",
-    "\n",
-    "condense_question_prompt = ChatPromptTemplate.from_template(condense_question_template)\n",
-    "\n",
-    "qa_template = \"\"\"\n",
-    "You are an assistant for question-answering tasks.\n",
-    "Use the following pieces of retrieved context to answer\n",
-    "the question. If you don't know the answer, say that you\n",
-    "don't know. Use three sentences maximum and keep the\n",
-    "answer concise.\n",
-    "\n",
-    "Chat History:\n",
-    "{chat_history}\n",
-    "\n",
-    "Other context:\n",
-    "{context}\n",
-    "\n",
-    "Question: {question}\n",
-    "\"\"\"\n",
-    "\n",
-    "qa_prompt = ChatPromptTemplate.from_template(qa_template)\n",
-    "\n",
-    "convo_qa_chain = ConversationalRetrievalChain.from_llm(\n",
-    "    llm,\n",
-    "    vectorstore.as_retriever(),\n",
-    "    condense_question_prompt=condense_question_prompt,\n",
-    "    combine_docs_chain_kwargs={\n",
-    "        \"prompt\": qa_prompt,\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "convo_qa_chain(\n",
-    "    {\n",
-    "        \"question\": \"What are autonomous agents?\",\n",
-    "        \"chat_history\": \"\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43a8a23c",
-   "metadata": {},
-   "source": [
-    "</Column>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### LCEL\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "c884b138",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input': 'What are autonomous agents?',\n",
-       " 'chat_history': [],\n",
-       " 'context': [Document(page_content='Boiko et al. (2023) also looked into LLM-empowered agents for scientific discovery, to handle autonomous design, planning, and performance of complex scientific experiments. This agent can use tools to browse the Internet, read documentation, execute code, call robotics experimentation APIs and leverage other LLMs.\\nFor example, when requested to \"develop a novel anticancer drug\", the model came up with the following reasoning steps:', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}),\n",
-       "  Document(page_content='Weng, Lilian. (Jun 2023). “LLM-powered Autonomous Agents”. Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}),\n",
-       "  Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}),\n",
-       "  Document(page_content='Or\\n@article{weng2023agent,\\n  title   = \"LLM-powered Autonomous Agents\",\\n  author  = \"Weng, Lilian\",\\n  journal = \"lilianweng.github.io\",\\n  year    = \"2023\",\\n  month   = \"Jun\",\\n  url     = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\\n}\\nReferences#\\n[1] Wei et al. “Chain of thought prompting elicits reasoning in large language models.” NeurIPS 2022\\n[2] Yao et al. “Tree of Thoughts: Dliberate Problem Solving with Large Language Models.” arXiv preprint arXiv:2305.10601 (2023).', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'})],\n",
-       " 'answer': 'Autonomous agents are entities capable of acting independently, making decisions, and performing tasks without direct human intervention. These agents can interact with their environment, perceive information, and take actions based on their goals or objectives. They often use artificial intelligence techniques to navigate and accomplish tasks in complex or dynamic environments.'}"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import create_history_aware_retriever, create_retrieval_chain\n",
-    "\n",
-    "condense_question_system_template = (\n",
-    "    \"Given a chat history and the latest user question \"\n",
-    "    \"which might reference context in the chat history, \"\n",
-    "    \"formulate a standalone question which can be understood \"\n",
-    "    \"without the chat history. Do NOT answer the question, \"\n",
-    "    \"just reformulate it if needed and otherwise return it as is.\"\n",
-    ")\n",
-    "\n",
-    "condense_question_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", condense_question_system_template),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "history_aware_retriever = create_history_aware_retriever(\n",
-    "    llm, vectorstore.as_retriever(), condense_question_prompt\n",
-    ")\n",
-    "\n",
-    "system_prompt = (\n",
-    "    \"You are an assistant for question-answering tasks. \"\n",
-    "    \"Use the following pieces of retrieved context to answer \"\n",
-    "    \"the question. If you don't know the answer, say that you \"\n",
-    "    \"don't know. Use three sentences maximum and keep the \"\n",
-    "    \"answer concise.\"\n",
-    "    \"\\n\\n\"\n",
-    "    \"{context}\"\n",
-    ")\n",
-    "\n",
-    "qa_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", system_prompt),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "qa_chain = create_stuff_documents_chain(llm, qa_prompt)\n",
-    "\n",
-    "convo_qa_chain = create_retrieval_chain(history_aware_retriever, qa_chain)\n",
-    "\n",
-    "convo_qa_chain.invoke(\n",
-    "    {\n",
-    "        \"input\": \"What are autonomous agents?\",\n",
-    "        \"chat_history\": [],\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2717810",
-   "metadata": {},
-   "source": [
-    "</Column>\n",
-    "\n",
-    "</ColumnContainer>\n",
-    "\n",
-    "## Next steps\n",
-    "\n",
-    "You've now seen how to migrate existing usage of some legacy chains to LCEL.\n",
-    "\n",
-    "Next, check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/how_to/pydantic_compatibility.md

@@ -1,27 +1,103 @@
 # How to use LangChain with different Pydantic versions
 
-- Pydantic v2 was released in June, 2023 (https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/)
-- v2 contains has a number of breaking changes (https://docs.pydantic.dev/2.0/migration/)
-- Pydantic v2 and v1 are under the same package name, so both versions cannot be installed at the same time
+- Pydantic v2 was released in June, 2023 (https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/).
+- v2 contains has a number of breaking changes (https://docs.pydantic.dev/2.0/migration/).
+- Pydantic 1 End of Life was in June 2024. LangChain will be dropping support for Pydantic 1 in the near future,
+and likely migrating internally to Pydantic 2. The timeline is tentatively September. This change will be accompanied by a minor version bump in the main langchain packages to version 0.3.x.
 
-## LangChain Pydantic migration plan
+As of `langchain>=0.0.267`, LangChain allows users to install either Pydantic V1 or V2.
 
-As of `langchain>=0.0.267`, LangChain will allow users to install either Pydantic V1 or V2. 
-   * Internally LangChain will continue to [use V1](https://docs.pydantic.dev/latest/migration/#continue-using-pydantic-v1-features).
-   * During this time, users can pin their pydantic version to v1 to avoid breaking changes, or start a partial
-   migration using pydantic v2 throughout their code, but avoiding mixing v1 and v2 code for LangChain (see below).
+Internally, LangChain continues to use the [Pydantic V1](https://docs.pydantic.dev/latest/migration/#continue-using-pydantic-v1-features) via
+the v1 namespace of Pydantic 2.
 
-User can either pin to pydantic v1, and upgrade their code in one go once LangChain has migrated to v2 internally, or they can start a partial migration to v2, but must avoid mixing v1 and v2 code for LangChain.
+Because Pydantic does not support mixing .v1 and .v2 objects, users should be aware of a number of issues
+when using LangChain with Pydantic.
+
+:::caution
+While LangChain supports Pydantic V2 objects in some APIs (listed below), it's suggested that users keep using Pydantic V1 objects until LangChain 0.3 is released.
+:::
+
+
+## 1. Passing Pydantic objects to LangChain APIs
+
+Most LangChain APIs for *tool usage* (see list below) have been updated to accept either Pydantic v1 or v2 objects.
+
+* Pydantic v1 objects correspond to subclasses of `pydantic.BaseModel` if `pydantic 1` is installed or subclasses of `pydantic.v1.BaseModel` if `pydantic 2` is installed.
+* Pydantic v2 objects correspond to subclasses of `pydantic.BaseModel` if `pydantic 2` is installed.
+
+
+| API                                    | Pydantic 1 | Pydantic 2                                                     |
+|----------------------------------------|------------|----------------------------------------------------------------|
+| `BaseChatModel.bind_tools`             | Yes        | langchain-core>=0.2.23, appropriate version of partner package |
+| `BaseChatModel.with_structured_output` | Yes        | langchain-core>=0.2.23, appropriate version of partner package |
+| `Tool.from_function`                   | Yes        | langchain-core>=0.2.23                                         |
+| `StructuredTool.from_function`         | Yes        | langchain-core>=0.2.23                                         |
+
+
+Partner packages that accept pydantic v2 objects via `bind_tools` or `with_structured_output` APIs:
+
+| Package Name        | pydantic v1 | pydantic v2 |
+|---------------------|-------------|-------------|
+| langchain-mistralai | Yes         | >=0.1.11    |
+| langchain-anthropic | Yes         | >=0.1.21    |
+| langchain-robocorp  | Yes         | >=0.0.10    |
+| langchain-openai    | Yes         | >=0.1.19    |
+| langchain-fireworks | Yes         | >=0.1.5     |
+| langchain-aws       | Yes         | >=0.1.15    |
+
+Additional partner packages will be updated to accept Pydantic v2 objects in the future.
+
+If you are still seeing issues with these APIs or other APIs that accept Pydantic objects, please open an issue, and we'll
+address it.
+
+Example:
+
+Prior to `langchain-core<0.2.23`, use Pydantic v1 objects when passing to LangChain APIs.
+
+
+```python
+from langchain_openai import ChatOpenAI
+from pydantic.v1 import BaseModel # <-- Note v1 namespace
+
+class Person(BaseModel):
+    """Personal information"""
+    name: str
+    
+model = ChatOpenAI()
+model = model.with_structured_output(Person)
+
+model.invoke('Bob is a person.')
+```
+
+After `langchain-core>=0.2.23`, use either Pydantic v1 or v2 objects when passing to LangChain APIs.
+
+```python
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel
+
+class Person(BaseModel):
+    """Personal information"""
+    name: str
+    
+    
+model = ChatOpenAI()
+model = model.with_structured_output(Person)
+
+model.invoke('Bob is a person.')
+```
+
+## 2. Sub-classing LangChain models
+
+Because LangChain internally uses Pydantic v1, if you are sub-classing LangChain models, you should use Pydantic v1
+primitives.
 
-Below are two examples of showing how to avoid mixing pydantic v1 and v2 code in
-the case of inheritance and in the case of passing objects to LangChain.
 
 **Example 1: Extending via inheritance**
 
 **YES** 
 
 ```python
-from pydantic.v1 import root_validator, validator
+from pydantic.v1 import validator
 from langchain_core.tools import BaseTool
 
 class CustomTool(BaseTool): # BaseTool is v1 code
@@ -70,38 +146,33 @@ CustomTool(
 )
 ```
 
-**Example 2: Passing objects to LangChain**
 
-**YES**
+## 3. Disable run-time validation for LangChain objects used inside Pydantic v2 models
+
+e.g.,
 
 ```python
-from langchain_core.tools import Tool
-from pydantic.v1 import BaseModel, Field # <-- Uses v1 namespace
+from typing import Annotated
 
-class CalculatorInput(BaseModel):
-    question: str = Field()
+from langchain_openai import ChatOpenAI # <-- ChatOpenAI uses pydantic v1
+from pydantic import BaseModel, SkipValidation
 
-Tool.from_function( # <-- tool uses v1 namespace
-    func=lambda question: 'hello',
-    name="Calculator",
-    description="useful for when you need to answer questions about math",
-    args_schema=CalculatorInput
-)
+
+class Foo(BaseModel): # <-- BaseModel is from Pydantic v2
+    model: Annotated[ChatOpenAI, SkipValidation()]
+
+Foo(model=ChatOpenAI(api_key="hello"))
 ```
 
-**NO**
+## 4: LangServe cannot generate OpenAPI docs if running Pydantic 2
 
-```python
-from langchain_core.tools import Tool
-from pydantic import BaseModel, Field # <-- Uses v2 namespace
+If you are using Pydantic 2, you will not be able to generate OpenAPI docs using LangServe.
 
-class CalculatorInput(BaseModel):
-    question: str = Field()
+If you need OpenAPI docs, your options are to either install Pydantic 1:
 
-Tool.from_function( # <-- tool uses v1 namespace
-    func=lambda question: 'hello',
-    name="Calculator",
-    description="useful for when you need to answer questions about math",
-    args_schema=CalculatorInput
-)
-```
+`pip install pydantic==1.10.17`
+
+or else to use the `APIHandler` object in LangChain to manually create the
+routes for your API.
+
+See: https://python.langchain.com/v0.2/docs/langserve/#pydantic

docs/docs/how_to/qa_chat_history_how_to.ipynb

@@ -721,9 +721,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "\n",
     "agent_executor = create_react_agent(llm, tools, checkpointer=memory)"
    ]
@@ -890,9 +890,9 @@
     "from langchain_community.document_loaders import WebBaseLoader\n",
     "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
-    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
     "\n",
-    "memory = SqliteSaver.from_conn_string(\":memory:\")\n",
+    "memory = MemorySaver()\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",
     "\n",

docs/docs/how_to/qa_sources.ipynb

@@ -14,7 +14,9 @@
     "We will cover two approaches:\n",
     "\n",
     "1. Using the built-in [create_retrieval_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval.create_retrieval_chain.html), which returns sources by default;\n",
-    "2. Using a simple [LCEL](/docs/concepts#langchain-expression-language-lcel) implementation, to show the operating principle."
+    "2. Using a simple [LCEL](/docs/concepts#langchain-expression-language-lcel) implementation, to show the operating principle.\n",
+    "\n",
+    "We will also show how to structure sources into the model response, such that a model can report what specific sources it used in generating its answer."
    ]
   },
   {
@@ -130,8 +132,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "820244ae-74b4-4593-b392-822979dd91b8",
+   "execution_count": null,
+   "id": "24a69b8c-024e-4e34-b827-9c9de46512a3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,11 +213,11 @@
      "data": {
       "text/plain": [
        "{'input': 'What is Task Decomposition?',\n",
-       " 'context': [Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
-       "  Document(page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
-       "  Document(page_content='Resources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
-       "  Document(page_content=\"(3) Task execution: Expert models execute on the specific tasks and log results.\\nInstruction:\\n\\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.\", metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'})],\n",
-       " 'answer': 'Task decomposition involves breaking down a complex task into smaller and simpler steps. This process helps agents or models handle challenging tasks by dividing them into more manageable subtasks. Techniques like Chain of Thought and Tree of Thoughts are used to decompose tasks into multiple steps for better problem-solving.'}"
+       " 'context': [Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.'),\n",
+       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.'),\n",
+       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Resources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.'),\n",
+       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content=\"(3) Task execution: Expert models execute on the specific tasks and log results.\\nInstruction:\\n\\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.\")],\n",
+       " 'answer': 'Task decomposition involves breaking down a complex task into smaller and more manageable steps. This process helps agents or models tackle difficult tasks by dividing them into simpler subtasks or components. Task decomposition can be achieved through techniques like Chain of Thought or Tree of Thoughts, which guide the agent in breaking down tasks into sequential or branching steps.'}"
       ]
      },
      "execution_count": 5,
@@ -251,18 +253,18 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "22ea137c-1a7a-44dd-ac73-281213979957",
+   "id": "1950953a-e6f1-439d-b7b9-c3bd456e388d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'input': 'What is Task Decomposition',\n",
-       " 'context': [Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
-       "  Document(page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
-       "  Document(page_content='The AI assistant can parse user input to several tasks: [{\"task\": task, \"id\", task_id, \"dep\": dependency_task_ids, \"args\": {\"text\": text, \"image\": URL, \"audio\": URL, \"video\": URL}}]. The \"dep\" field denotes the id of the previous task which generates a new resource that the current task relies on. A special tag \"-task_id\" refers to the generated text image, audio and video in the dependency task with id as task_id. The task MUST be selected from the following options: {{ Available Task List }}. There is a logical relationship between tasks, please note their order. If the user input can\\'t be parsed, you need to reply empty JSON. Here are several cases for your reference: {{ Demonstrations }}. The chat history is recorded as {{ Chat History }}. From this chat history, you can find the path of the user-mentioned resources for your task planning.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
-       "  Document(page_content='Fig. 11. Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)\\nThe system comprises of 4 stages:\\n(1) Task planning: LLM works as the brain and parses the user requests into multiple tasks. There are four attributes associated with each task: task type, ID, dependencies, and arguments. They use few-shot examples to guide LLM to do task parsing and planning.\\nInstruction:', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'})],\n",
-       " 'answer': 'Task decomposition involves breaking down complex tasks into smaller and simpler steps to make them more manageable for autonomous agents or models. This process can be achieved by techniques like Chain of Thought (CoT) or Tree of Thoughts, which guide the model to think step by step or explore multiple reasoning possibilities at each step. Task decomposition can be done through simple prompting with language models, task-specific instructions, or human inputs.'}"
+       " 'context': [Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.'),\n",
+       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.'),\n",
+       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='The AI assistant can parse user input to several tasks: [{\"task\": task, \"id\", task_id, \"dep\": dependency_task_ids, \"args\": {\"text\": text, \"image\": URL, \"audio\": URL, \"video\": URL}}]. The \"dep\" field denotes the id of the previous task which generates a new resource that the current task relies on. A special tag \"-task_id\" refers to the generated text image, audio and video in the dependency task with id as task_id. The task MUST be selected from the following options: {{ Available Task List }}. There is a logical relationship between tasks, please note their order. If the user input can\\'t be parsed, you need to reply empty JSON. Here are several cases for your reference: {{ Demonstrations }}. The chat history is recorded as {{ Chat History }}. From this chat history, you can find the path of the user-mentioned resources for your task planning.'),\n",
+       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Fig. 11. Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)\\nThe system comprises of 4 stages:\\n(1) Task planning: LLM works as the brain and parses the user requests into multiple tasks. There are four attributes associated with each task: task type, ID, dependencies, and arguments. They use few-shot examples to guide LLM to do task parsing and planning.\\nInstruction:')],\n",
+       " 'answer': 'Task decomposition is a technique used in artificial intelligence to break down complex tasks into smaller and more manageable subtasks. This approach helps agents or models to tackle difficult problems by dividing them into simpler steps, improving performance and interpretability. Different methods like Chain of Thought and Tree of Thoughts have been developed to enhance task decomposition in AI systems.'}"
       ]
      },
      "execution_count": 6,
@@ -279,15 +281,25 @@
     "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
     "\n",
     "\n",
+    "# This Runnable takes a dict with keys 'input' and 'context',\n",
+    "# formats them into a prompt, and generates a response.\n",
     "rag_chain_from_docs = (\n",
-    "    RunnablePassthrough.assign(context=(lambda x: format_docs(x[\"context\"])))\n",
-    "    | prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
+    "    {\n",
+    "        \"input\": lambda x: x[\"input\"],  # input query\n",
+    "        \"context\": lambda x: format_docs(x[\"context\"]),  # context\n",
+    "    }\n",
+    "    | prompt  # format query and context into prompt\n",
+    "    | llm  # generate response\n",
+    "    | StrOutputParser()  # coerce to string\n",
     ")\n",
     "\n",
+    "# Pass input query to retriever\n",
     "retrieve_docs = (lambda x: x[\"input\"]) | retriever\n",
     "\n",
+    "# Below, we chain `.assign` calls. This takes a dict and successively\n",
+    "# adds keys-- \"context\" and \"answer\"-- where the value for each key\n",
+    "# is determined by a Runnable. The Runnable operates on all existing\n",
+    "# keys in the dict.\n",
     "chain = RunnablePassthrough.assign(context=retrieve_docs).assign(\n",
     "    answer=rag_chain_from_docs\n",
     ")\n",
@@ -302,7 +314,105 @@
    "source": [
     ":::{.callout-tip}\n",
     "\n",
-    "Check out the [LangSmith trace](https://smith.langchain.com/public/0cb42685-e29e-4280-a503-bef2014d7ba2/r)\n",
+    "Check out the [LangSmith trace](https://smith.langchain.com/public/1c055a3b-0236-4670-a3fb-023d418ba796/r)\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1c17797-d965-4fd2-b8d4-d386f25dd352",
+   "metadata": {},
+   "source": [
+    "## Structure sources in model response\n",
+    "\n",
+    "Up to this point, we've simply propagated the documents returned from the retrieval step through to the final response. But this may not illustrate what subset of information the model relied on when generating its answer. Below, we show how to structure sources into the model response, allowing the model to report what specific context it relied on for its answer.\n",
+    "\n",
+    "Because the above LCEL implementation is composed of [Runnable](/docs/concepts/#runnable-interface) primitives, it is straightforward to extend. Below, we make a simple change:\n",
+    "\n",
+    "- We use the model's tool-calling features to generate [structured output](/docs/how_to/structured_output/), consisting of an answer and list of sources. The schema for the response is represented in the `AnswerWithSources` TypedDict, below.\n",
+    "- We remove the `StrOutputParser()`, as we expect `dict` output in this scenario."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8f916b14-1b0a-4975-a62f-52f1353bde15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from typing_extensions import Annotated, TypedDict\n",
+    "\n",
+    "\n",
+    "# Desired schema for response\n",
+    "class AnswerWithSources(TypedDict):\n",
+    "    \"\"\"An answer to the question, with sources.\"\"\"\n",
+    "\n",
+    "    answer: str\n",
+    "    sources: Annotated[\n",
+    "        List[str],\n",
+    "        ...,\n",
+    "        \"List of sources (author + year) used to answer the question\",\n",
+    "    ]\n",
+    "\n",
+    "\n",
+    "# Our rag_chain_from_docs has the following changes:\n",
+    "# - add `.with_structured_output` to the LLM;\n",
+    "# - remove the output parser\n",
+    "rag_chain_from_docs = (\n",
+    "    {\n",
+    "        \"input\": lambda x: x[\"input\"],\n",
+    "        \"context\": lambda x: format_docs(x[\"context\"]),\n",
+    "    }\n",
+    "    | prompt\n",
+    "    | llm.with_structured_output(AnswerWithSources)\n",
+    ")\n",
+    "\n",
+    "retrieve_docs = (lambda x: x[\"input\"]) | retriever\n",
+    "\n",
+    "chain = RunnablePassthrough.assign(context=retrieve_docs).assign(\n",
+    "    answer=rag_chain_from_docs\n",
+    ")\n",
+    "\n",
+    "response = chain.invoke({\"input\": \"What is Chain of Thought?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7a8fc0c5-afb3-4012-a467-3951996a6850",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"answer\": \"Chain of Thought (CoT) is a prompting technique that enhances model performance on complex tasks by instructing the model to \\\"think step by step\\\" to decompose hard tasks into smaller and simpler steps. It transforms big tasks into multiple manageable tasks and sheds light on the interpretation of the model's thinking process.\",\n",
+      "  \"sources\": [\n",
+      "    \"Wei et al. 2022\"\n",
+      "  ]\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "print(json.dumps(response[\"answer\"], indent=2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7440f785-29c5-4c6b-9656-0d9d5efbac05",
+   "metadata": {},
+   "source": [
+    ":::{.callout-tip}\n",
+    "\n",
+    "View [LangSmith trace](https://smith.langchain.com/public/0eeddf06-3a7b-4f27-974c-310ca8160f60/r)\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/how_to/sql_csv.ipynb

@@ -761,7 +761,7 @@
     "* [SQL tutorial](/docs/tutorials/sql_qa): Many of the challenges of working with SQL db's and CSV's are generic to any structured data type, so it's useful to read the SQL techniques even if you're using Pandas for CSV data analysis.\n",
     "* [Tool use](/docs/how_to/tool_calling): Guides on general best practices when working with chains and agents that invoke tools\n",
     "* [Agents](/docs/tutorials/agents): Understand the fundamentals of building LLM agents.\n",
-    "* Integrations: Sandboxed envs like [E2B](/docs/integrations/tools/e2b_data_analysis) and [Bearly](/docs/integrations/tools/bearly), utilities like [SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), related agents like [Spark DataFrame agent](/docs/integrations/toolkits/spark)."
+    "* Integrations: Sandboxed envs like [E2B](/docs/integrations/tools/e2b_data_analysis) and [Bearly](/docs/integrations/tools/bearly), utilities like [SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), related agents like [Spark DataFrame agent](/docs/integrations/tools/spark_sql)."
    ]
   }
  ],

docs/docs/how_to/structured_output.ipynb

@@ -43,7 +43,7 @@
     "\n",
     "This is the easiest and most reliable way to get structured outputs. `with_structured_output()` is implemented for models that provide native APIs for structuring outputs, like tool/function calling or JSON mode, and makes use of these capabilities under the hood.\n",
     "\n",
-    "This method takes a schema as input which specifies the names, types, and descriptions of the desired output attributes. The method returns a model-like Runnable, except that instead of outputting strings or Messages it outputs objects corresponding to the given schema. The schema can be specified as a [JSON Schema](https://json-schema.org/) or a Pydantic class. If JSON Schema is used then a dictionary will be returned by the Runnable, and if a Pydantic class is used then Pydantic objects will be returned.\n",
+    "This method takes a schema as input which specifies the names, types, and descriptions of the desired output attributes. The method returns a model-like Runnable, except that instead of outputting strings or Messages it outputs objects corresponding to the given schema. The schema can be specified as a TypedDict class, [JSON Schema](https://json-schema.org/) or a Pydantic class. If TypedDict or JSON Schema are used then a dictionary will be returned by the Runnable, and if a Pydantic class is used then a Pydantic object will be returned.\n",
     "\n",
     "As an example, let's get a model to generate a joke and separate the setup from the punchline:\n",
     "\n",
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "6d55008f",
    "metadata": {},
    "outputs": [],
@@ -68,7 +68,7 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4-0125-preview\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-4o\", temperature=0)"
    ]
   },
   {
@@ -76,22 +76,24 @@
    "id": "a808a401-be1f-49f9-ad13-58dd68f7db5f",
    "metadata": {},
    "source": [
-    "If we want the model to return a Pydantic object, we just need to pass in the desired Pydantic class:"
+    "### Pydantic class\n",
+    "\n",
+    "If we want the model to return a Pydantic object, we just need to pass in the desired Pydantic class. The key advantage of using Pydantic is that the model-generated output will be validated. Pydantic will raise an error if any required fields are missing or if any fields are of the wrong type."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "070bf702",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=8)"
+       "Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=7)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -102,12 +104,15 @@
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
+    "# Pydantic\n",
     "class Joke(BaseModel):\n",
     "    \"\"\"Joke to tell user.\"\"\"\n",
     "\n",
     "    setup: str = Field(description=\"The setup of the joke\")\n",
     "    punchline: str = Field(description=\"The punchline to the joke\")\n",
-    "    rating: Optional[int] = Field(description=\"How funny the joke is, from 1 to 10\")\n",
+    "    rating: Optional[int] = Field(\n",
+    "        default=None, description=\"How funny the joke is, from 1 to 10\"\n",
+    "    )\n",
     "\n",
     "\n",
     "structured_llm = llm.with_structured_output(Joke)\n",
@@ -130,12 +135,73 @@
    "id": "deddb6d3",
    "metadata": {},
    "source": [
-    "We can also pass in a [JSON Schema](https://json-schema.org/) dict if you prefer not to use Pydantic. In this case, the response is also a dict:"
+    "### TypedDict or JSON Schema\n",
+    "\n",
+    "If you don't want to use Pydantic, explicitly don't want validation of the arguments, or want to be able to stream the model outputs, you can define your schema using a TypedDict class. We can optionally use a special `Annotated` syntax supported by LangChain that allows you to specify the default value and description of a field. Note, the default value is *not* filled in automatically if the model doesn't generate it, it is only used in defining the schema that is passed to the model.\n",
+    "\n",
+    ":::info Requirements\n",
+    "\n",
+    "- Core: `langchain-core>=0.2.26`\n",
+    "- Typing extensions: It is highly recommended to import `Annotated` and `TypedDict` from `typing_extensions` instead of `typing` to ensure consistent behavior across Python versions.\n",
+    "\n",
+    ":::"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "70d82891-42e8-424a-919e-07d83bcfec61",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'setup': 'Why was the cat sitting on the computer?',\n",
+       " 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
+       " 'rating': 7}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing_extensions import Annotated, TypedDict\n",
+    "\n",
+    "\n",
+    "# TypedDict\n",
+    "class Joke(TypedDict):\n",
+    "    \"\"\"Joke to tell user.\"\"\"\n",
+    "\n",
+    "    setup: Annotated[str, ..., \"The setup of the joke\"]\n",
+    "\n",
+    "    # Alternatively, we could have specified setup as:\n",
+    "\n",
+    "    # setup: str                    # no default, no description\n",
+    "    # setup: Annotated[str, ...]    # no default, no description\n",
+    "    # setup: Annotated[str, \"foo\"]  # default, no description\n",
+    "\n",
+    "    punchline: Annotated[str, ..., \"The punchline of the joke\"]\n",
+    "    rating: Annotated[Optional[int], None, \"How funny the joke is, from 1 to 10\"]\n",
+    "\n",
+    "\n",
+    "structured_llm = llm.with_structured_output(Joke)\n",
+    "\n",
+    "structured_llm.invoke(\"Tell me a joke about cats\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4d7b4dc-f617-4ea8-aa58-847c228791b4",
+   "metadata": {},
+   "source": [
+    "Equivalently, we can pass in a [JSON Schema](https://json-schema.org/) dict. This requires no imports or classes and makes it very clear exactly how each parameter is documented, at the cost of being a bit more verbose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "id": "6700994a",
    "metadata": {},
    "outputs": [
@@ -144,10 +210,10 @@
       "text/plain": [
        "{'setup': 'Why was the cat sitting on the computer?',\n",
        " 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
-       " 'rating': 8}"
+       " 'rating': 7}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -169,6 +235,7 @@
     "        \"rating\": {\n",
     "            \"type\": \"integer\",\n",
     "            \"description\": \"How funny the joke is, from 1 to 10\",\n",
+    "            \"default\": None,\n",
     "        },\n",
     "    },\n",
     "    \"required\": [\"setup\", \"punchline\"],\n",
@@ -185,7 +252,7 @@
    "source": [
     "### Choosing between multiple schemas\n",
     "\n",
-    "The simplest way to let the model choose from multiple schemas is to create a parent Pydantic class that has a Union-typed attribute:"
+    "The simplest way to let the model choose from multiple schemas is to create a parent schema that has a Union-typed attribute:"
    ]
   },
   {
@@ -209,6 +276,17 @@
     "from typing import Union\n",
     "\n",
     "\n",
+    "# Pydantic\n",
+    "class Joke(BaseModel):\n",
+    "    \"\"\"Joke to tell user.\"\"\"\n",
+    "\n",
+    "    setup: str = Field(description=\"The setup of the joke\")\n",
+    "    punchline: str = Field(description=\"The punchline to the joke\")\n",
+    "    rating: Optional[int] = Field(\n",
+    "        default=None, description=\"How funny the joke is, from 1 to 10\"\n",
+    "    )\n",
+    "\n",
+    "\n",
     "class ConversationalResponse(BaseModel):\n",
     "    \"\"\"Respond in a conversational manner. Be kind and helpful.\"\"\"\n",
     "\n",
@@ -260,7 +338,7 @@
    "source": [
     "### Streaming\n",
     "\n",
-    "We can stream outputs from our structured model when the output type is a dict (i.e., when the schema is specified as a JSON Schema dict). \n",
+    "We can stream outputs from our structured model when the output type is a dict (i.e., when the schema is specified as a TypedDict class or  JSON Schema dict). \n",
     "\n",
     ":::info\n",
     "\n",
@@ -271,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 9,
    "id": "aff89877-28a3-472f-a1aa-eff893fe7736",
    "metadata": {},
    "outputs": [
@@ -302,12 +380,24 @@
       "{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the'}\n",
       "{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the mouse'}\n",
       "{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the mouse!'}\n",
-      "{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the mouse!', 'rating': 8}\n"
+      "{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the mouse!', 'rating': 7}\n"
      ]
     }
    ],
    "source": [
-    "structured_llm = llm.with_structured_output(json_schema)\n",
+    "from typing_extensions import Annotated, TypedDict\n",
+    "\n",
+    "\n",
+    "# TypedDict\n",
+    "class Joke(TypedDict):\n",
+    "    \"\"\"Joke to tell user.\"\"\"\n",
+    "\n",
+    "    setup: Annotated[str, ..., \"The setup of the joke\"]\n",
+    "    punchline: Annotated[str, ..., \"The punchline of the joke\"]\n",
+    "    rating: Annotated[Optional[int], None, \"How funny the joke is, from 1 to 10\"]\n",
+    "\n",
+    "\n",
+    "structured_llm = llm.with_structured_output(Joke)\n",
     "\n",
     "for chunk in structured_llm.stream(\"Tell me a joke about cats\"):\n",
     "    print(chunk)"
@@ -327,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 11,
    "id": "283ba784-2072-47ee-9b2c-1119e3c69e8e",
    "metadata": {},
    "outputs": [
@@ -335,11 +425,11 @@
      "data": {
       "text/plain": [
        "{'setup': 'Woodpecker',\n",
-       " 'punchline': \"Woodpecker goes 'knock knock', but don't worry, they never expect you to answer the door!\",\n",
-       " 'rating': 8}"
+       " 'punchline': \"Woodpecker who? Woodpecker who can't find a tree is just a bird with a headache!\",\n",
+       " 'rating': 7}"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -377,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 12,
    "id": "d7381cb0-b2c3-4302-a319-ed72d0b9e43f",
    "metadata": {},
    "outputs": [
@@ -385,11 +475,11 @@
      "data": {
       "text/plain": [
        "{'setup': 'Crocodile',\n",
-       " 'punchline': \"Crocodile 'see you later', but in a while, it becomes an alligator!\",\n",
+       " 'punchline': 'Crocodile be seeing you later, alligator!',\n",
        " 'rating': 7}"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -491,23 +581,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "id": "df0370e3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=None)"
+       "{'setup': 'Why was the cat sitting on the computer?',\n",
+       " 'punchline': 'Because it wanted to keep an eye on the mouse!'}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "structured_llm = llm.with_structured_output(Joke, method=\"json_mode\")\n",
+    "structured_llm = llm.with_structured_output(None, method=\"json_mode\")\n",
     "\n",
     "structured_llm.invoke(\n",
     "    \"Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys\"\n",
@@ -526,19 +617,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 17,
    "id": "10ed2842",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_ASK4EmZeZ69Fi3p554Mb4rWy', 'function': {'arguments': '{\"setup\":\"Why was the cat sitting on the computer?\",\"punchline\":\"Because it wanted to keep an eye on the mouse!\"}', 'name': 'Joke'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 36, 'prompt_tokens': 107, 'total_tokens': 143}, 'model_name': 'gpt-4-0125-preview', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-6491d35b-9164-4656-b75c-d7882cfb76cb-0', tool_calls=[{'name': 'Joke', 'args': {'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the mouse!'}, 'id': 'call_ASK4EmZeZ69Fi3p554Mb4rWy'}], usage_metadata={'input_tokens': 107, 'output_tokens': 36, 'total_tokens': 143}),\n",
-       " 'parsed': Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=None),\n",
+       "{'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_f25ZRmh8u5vHlOWfTUw8sJFZ', 'function': {'arguments': '{\"setup\":\"Why was the cat sitting on the computer?\",\"punchline\":\"Because it wanted to keep an eye on the mouse!\",\"rating\":7}', 'name': 'Joke'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 33, 'prompt_tokens': 93, 'total_tokens': 126}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None}, id='run-d880d7e2-df08-4e9e-ad92-dfc29f2fd52f-0', tool_calls=[{'name': 'Joke', 'args': {'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the mouse!', 'rating': 7}, 'id': 'call_f25ZRmh8u5vHlOWfTUw8sJFZ', 'type': 'tool_call'}], usage_metadata={'input_tokens': 93, 'output_tokens': 33, 'total_tokens': 126}),\n",
+       " 'parsed': {'setup': 'Why was the cat sitting on the computer?',\n",
+       "  'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
+       "  'rating': 7},\n",
        " 'parsing_error': None}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -546,9 +639,7 @@
    "source": [
     "structured_llm = llm.with_structured_output(Joke, include_raw=True)\n",
     "\n",
-    "structured_llm.invoke(\n",
-    "    \"Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys\"\n",
-    ")"
+    "structured_llm.invoke(\"Tell me a joke about cats\")"
    ]
   },
   {
@@ -824,7 +915,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -838,7 +929,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tool_calling.ipynb

@@ -24,10 +24,9 @@
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
     "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
     "- [Tool calling](/docs/concepts/#functiontool-calling)\n",
+    "- [Tools](/docs/concepts/#tools)\n",
     "- [Output parsers](/docs/concepts/#output-parsers)\n",
-    "\n",
     ":::\n",
     "\n",
     "[Tool calling](/docs/concepts/#functiontool-calling) allows a chat model to respond to a given prompt by \"calling a tool\".\n",
@@ -38,15 +37,11 @@
     "\n",
     "![Diagram of calling a tool](/img/tool_call.png)\n",
     "\n",
-    "If you want to see how to use the model-generated tool call to actually run a tool function [check out this guide](/docs/how_to/tool_results_pass_to_model/).\n",
+    "If you want to see how to use the model-generated tool call to actually run a tool [check out this guide](/docs/how_to/tool_results_pass_to_model/).\n",
     "\n",
     ":::note Supported models\n",
     "\n",
-    "Tool calling is not universal, but is supported by many popular LLM providers, including [Anthropic](/docs/integrations/chat/anthropic/), \n",
-    "[Cohere](/docs/integrations/chat/cohere/), [Google](/docs/integrations/chat/google_vertex_ai_palm/), \n",
-    "[Mistral](/docs/integrations/chat/mistralai/), [OpenAI](/docs/integrations/chat/openai/), and even for locally-running models via [Ollama](/docs/integrations/chat/ollama/).\n",
-    "\n",
-    "You can find a [list of all models that support tool calling here](/docs/integrations/chat/).\n",
+    "Tool calling is not universal, but is supported by many popular LLM providers. You can find a [list of all models that support tool calling here](/docs/integrations/chat/).\n",
     "\n",
     ":::\n",
     "\n",
@@ -58,14 +53,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Passing tools to chat models\n",
+    "## Defining tool schemas\n",
     "\n",
-    "Chat models that support tool calling features implement a `.bind_tools` method, which \n",
-    "receives a list of functions, Pydantic models, or LangChain [tool objects](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.BaseTool.html#langchain_core.tools.BaseTool) \n",
-    "and binds them to the chat model in its expected format. Subsequent invocations of the \n",
-    "chat model will include tool schemas in its calls to the LLM.\n",
+    "For a model to be able to call tools, we need to pass in tool schemas that describe what the tool does and what it's arguments are. Chat models that support tool calling features implement a `.bind_tools()` method for passing tool schemas to the model. Tool schemas can be passed in as Python functions (with typehints and docstrings), Pydantic models, TypedDict classes, or LangChain [Tool objects](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.BaseTool.html#langchain_core.tools.BaseTool). Subsequent invocations of the model will pass in these tool schemas along with the prompt.\n",
     "\n",
-    "For example, below we implement simple tools for arithmetic:"
+    "### Python functions\n",
+    "Our tool schemas can be Python functions:"
    ]
   },
   {
@@ -74,26 +67,41 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# The function name, type hints, and docstring are all part of the tool\n",
+    "# schema that's passed to the model. Defining good, descriptive schemas\n",
+    "# is an extension of prompt engineering and is an important part of\n",
+    "# getting models to perform well.\n",
     "def add(a: int, b: int) -> int:\n",
-    "    \"\"\"Adds a and b.\"\"\"\n",
+    "    \"\"\"Add two integers.\n",
+    "\n",
+    "    Args:\n",
+    "        a: First integer\n",
+    "        b: Second integer\n",
+    "    \"\"\"\n",
     "    return a + b\n",
     "\n",
     "\n",
     "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiplies a and b.\"\"\"\n",
-    "    return a * b\n",
+    "    \"\"\"Multiply two integers.\n",
     "\n",
-    "\n",
-    "tools = [add, multiply]"
+    "    Args:\n",
+    "        a: First integer\n",
+    "        b: Second integer\n",
+    "    \"\"\"\n",
+    "    return a * b"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### LangChain Tool\n",
+    "\n",
     "LangChain also implements a `@tool` decorator that allows for further control of the tool schema, such as tool names and argument descriptions. See the how-to guide [here](/docs/how_to/custom_tools/#creating-tools-from-functions) for details.\n",
     "\n",
-    "We can also define the schemas without the accompanying functions using [Pydantic](https://docs.pydantic.dev):"
+    "### Pydantic class\n",
+    "\n",
+    "You can equivalently define the schemas without the accompanying functions using [Pydantic](https://docs.pydantic.dev):"
    ]
   },
   {
@@ -105,23 +113,57 @@
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
-    "# Note that the docstrings here are crucial, as they will be passed along\n",
-    "# to the model along with the class name.\n",
-    "class Add(BaseModel):\n",
-    "    \"\"\"Add two integers together.\"\"\"\n",
+    "class add(BaseModel):\n",
+    "    \"\"\"Add two integers.\"\"\"\n",
     "\n",
     "    a: int = Field(..., description=\"First integer\")\n",
     "    b: int = Field(..., description=\"Second integer\")\n",
     "\n",
     "\n",
-    "class Multiply(BaseModel):\n",
-    "    \"\"\"Multiply two integers together.\"\"\"\n",
+    "class multiply(BaseModel):\n",
+    "    \"\"\"Multiply two integers.\"\"\"\n",
     "\n",
     "    a: int = Field(..., description=\"First integer\")\n",
-    "    b: int = Field(..., description=\"Second integer\")\n",
+    "    b: int = Field(..., description=\"Second integer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TypedDict class\n",
+    "\n",
+    ":::info Requires `langchain-core>=0.2.25`\n",
+    ":::\n",
+    "\n",
+    "Or using TypedDicts and annotations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing_extensions import Annotated, TypedDict\n",
     "\n",
     "\n",
-    "tools = [Add, Multiply]"
+    "class add(TypedDict):\n",
+    "    \"\"\"Add two integers.\"\"\"\n",
+    "\n",
+    "    # Annotations must have the type and can optionally include a default value and description (in that order).\n",
+    "    a: Annotated[int, ..., \"First integer\"]\n",
+    "    b: Annotated[int, ..., \"Second integer\"]\n",
+    "\n",
+    "\n",
+    "class multiply(BaseModel):\n",
+    "    \"\"\"Multiply two integers.\"\"\"\n",
+    "\n",
+    "    a: Annotated[int, ..., \"First integer\"]\n",
+    "    b: Annotated[int, ..., \"Second integer\"]\n",
+    "\n",
+    "\n",
+    "tools = [add, multiply]"
    ]
   },
   {
@@ -129,7 +171,7 @@
    "metadata": {},
    "source": [
     "To actually bind those schemas to a chat model, we'll use the `.bind_tools()` method. This handles converting\n",
-    "the `Add` and `Multiply` schemas to the proper format for the model. The tool schema will then be passed it in each time the model is invoked.\n",
+    "the `add` and `multiply` schemas to the proper format for the model. The tool schema will then be passed it in each time the model is invoked.\n",
     "\n",
     "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
@@ -164,16 +206,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_wLTBasMppAwpdiA5CD92l9x7', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 89, 'total_tokens': 107}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_0f03d4f0ee', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d3f36cca-f225-416f-ac16-0217046f0b38-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_wLTBasMppAwpdiA5CD92l9x7', 'type': 'tool_call'}], usage_metadata={'input_tokens': 89, 'output_tokens': 18, 'total_tokens': 107})"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_BwYJ4UgU5pRVCBOUmiu7NhF9', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 80, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_ba606877f9', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7f05e19e-4561-40e2-a2d0-8f4e28e9a00f-0', tool_calls=[{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_BwYJ4UgU5pRVCBOUmiu7NhF9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 80, 'output_tokens': 17, 'total_tokens': 97})"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,23 +256,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'name': 'Multiply',\n",
+       "[{'name': 'multiply',\n",
        "  'args': {'a': 3, 'b': 12},\n",
-       "  'id': 'call_uqJsNrDJ8ZZnFa1BHHYAllEv',\n",
+       "  'id': 'call_rcdMie7E89Xx06lEKKxJyB5N',\n",
        "  'type': 'tool_call'},\n",
-       " {'name': 'Add',\n",
+       " {'name': 'add',\n",
        "  'args': {'a': 11, 'b': 49},\n",
-       "  'id': 'call_ud1uHAaYsdpWuxugwoJ63BDs',\n",
+       "  'id': 'call_nheGN8yfvSJsnIuGZaXihou3',\n",
        "  'type': 'tool_call'}]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -252,31 +294,49 @@
     "are populated in the `.invalid_tool_calls` attribute. An `InvalidToolCall` can have \n",
     "a name, string arguments, identifier, and error message.\n",
     "\n",
-    "If desired, [output parsers](/docs/how_to#output-parsers) can further \n",
-    "process the output. For example, we can convert existing values populated on the `.tool_calls` attribute back to the original Pydantic class using the\n",
+    "\n",
+    "## Parsing\n",
+    "\n",
+    "If desired, [output parsers](/docs/how_to#output-parsers) can further process the output. For example, we can convert existing values populated on the `.tool_calls` to Pydantic objects using the\n",
     "[PydanticToolsParser](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.openai_tools.PydanticToolsParser.html):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Multiply(a=3, b=12), Add(a=11, b=49)]"
+       "[multiply(a=3, b=12), add(a=11, b=49)]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from langchain_core.output_parsers import PydanticToolsParser\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
-    "chain = llm_with_tools | PydanticToolsParser(tools=[Multiply, Add])\n",
+    "\n",
+    "class add(BaseModel):\n",
+    "    \"\"\"Add two integers.\"\"\"\n",
+    "\n",
+    "    a: int = Field(..., description=\"First integer\")\n",
+    "    b: int = Field(..., description=\"Second integer\")\n",
+    "\n",
+    "\n",
+    "class multiply(BaseModel):\n",
+    "    \"\"\"Multiply two integers.\"\"\"\n",
+    "\n",
+    "    a: int = Field(..., description=\"First integer\")\n",
+    "    b: int = Field(..., description=\"Second integer\")\n",
+    "\n",
+    "\n",
+    "chain = llm_with_tools | PydanticToolsParser(tools=[add, multiply])\n",
     "chain.invoke(query)"
    ]
   },
@@ -294,18 +354,18 @@
     "\n",
     "You can also check out some more specific uses of tool calling:\n",
     "\n",
+    "- Getting [structured outputs](/docs/how_to/structured_output/) from models\n",
     "- Few shot prompting [with tools](/docs/how_to/tools_few_shot/)\n",
     "- Stream [tool calls](/docs/how_to/tool_streaming/)\n",
-    "- Pass [runtime values to tools](/docs/how_to/tool_runtime)\n",
-    "- Getting [structured outputs](/docs/how_to/structured_output/) from models"
+    "- Pass [runtime values to tools](/docs/how_to/tool_runtime)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-311",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -317,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tool_runtime.ipynb

@@ -6,26 +6,20 @@
    "source": [
     "# How to pass run time values to tools\n",
     "\n",
-    ":::info Prerequisites\n",
+    "import Prerequisites from \"@theme/Prerequisites\";\n",
+    "import Compatibility from \"@theme/Compatibility\";\n",
     "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
-    "- [How to create tools](/docs/how_to/custom_tools)\n",
-    "- [How to use a model to call tools](/docs/how_to/tool_calling)\n",
-    ":::\n",
+    "<Prerequisites titlesAndLinks={[\n",
+    "  [\"Chat models\", \"/docs/concepts/#chat-models\"],\n",
+    "  [\"LangChain Tools\", \"/docs/concepts/#tools\"],\n",
+    "  [\"How to create tools\", \"/docs/how_to/custom_tools\"],\n",
+    "  [\"How to use a model to call tools\", \"/docs/how_to/tool_calling\"],\n",
+    "]} />\n",
     "\n",
-    ":::info Using with LangGraph\n",
     "\n",
-    "If you're using LangGraph, please refer to [this how-to guide](https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/)\n",
-    "which shows how to create an agent that keeps track of a given user's favorite pets.\n",
-    ":::\n",
-    "\n",
-    ":::caution Added in `langchain-core==0.2.21`\n",
-    "\n",
-    "Must have `langchain-core>=0.2.21` to use this functionality.\n",
-    "\n",
-    ":::\n",
+    "<Compatibility packagesAndVersions={[\n",
+    "  [\"langchain-core\", \"0.2.21\"],\n",
+    "]} />\n",
     "\n",
     "You may need to bind values to a tool that are only known at runtime. For example, the tool logic may require using the ID of the user who made the request.\n",
     "\n",
@@ -33,7 +27,13 @@
     "\n",
     "Instead, the LLM should only control the parameters of the tool that are meant to be controlled by the LLM, while other parameters (such as user ID) should be fixed by the application logic.\n",
     "\n",
-    "This how-to guide shows you how to prevent the model from generating certain tool arguments and injecting them in directly at runtime."
+    "This how-to guide shows you how to prevent the model from generating certain tool arguments and injecting them in directly at runtime.\n",
+    "\n",
+    ":::info Using with LangGraph\n",
+    "\n",
+    "If you're using LangGraph, please refer to [this how-to guide](https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/)\n",
+    "which shows how to create an agent that keeps track of a given user's favorite pets.\n",
+    ":::"
    ]
   },
   {
@@ -597,9 +597,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv-311",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "poetry-venv-311"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -611,7 +611,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

docs/docs/how_to/toolkits.mdx

@@ -5,7 +5,6 @@ sidebar_position: 3
 
 
 Toolkits are collections of tools that are designed to be used together for specific tasks. They have convenient loading methods.
-For a complete list of available ready-made toolkits, visit [Integrations](/docs/integrations/toolkits/).
 
 All Toolkits expose a `get_tools` method which returns a list of tools.
 You can therefore do:

docs/docs/how_to/tools_builtin.ipynb

@@ -196,8 +196,6 @@
     "\n",
     "Toolkits are collections of tools that are designed to be used together for specific tasks. They have convenient loading methods.\n",
     "\n",
-    "For a complete list of available ready-made toolkits, visit [Integrations](/docs/integrations/toolkits/).\n",
-    "\n",
     "All Toolkits expose a `get_tools` method which returns a list of tools.\n",
     "\n",
     "You're usually meant to use them this way:\n",

docs/docs/integrations/chat/ai21.ipynb

@@ -17,26 +17,25 @@
    "source": [
     "# ChatAI21\n",
     "\n",
+    "## Overview\n",
+    "\n",
     "This notebook covers how to get started with AI21 chat models.\n",
-    "Note that different chat models support different parameters. See the ",
-    "[AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n",
+    "Note that different chat models support different parameters. See the [AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n",
     "[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/) \n",
-    "## Installation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4c3bef91",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-02-15T06:50:44.929635Z",
-     "start_time": "2024-02-15T06:50:41.209704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install -qU langchain-ai21"
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatAI21](https://api.python.langchain.com/en/latest/chat_models/langchain_ai21.chat_models.ChatAI21.html#langchain_ai21.chat_models.ChatAI21) | [langchain-ai21](https://api.python.langchain.com/en/latest/ai21_api_reference.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ai21?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ai21?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
+    "\n",
+    "\n",
+    "## Setup"
    ]
   },
   {
@@ -44,10 +43,9 @@
    "id": "2b4f3e15",
    "metadata": {},
    "source": [
-    "## Environment Setup\n",
+    "### Credentials\n",
     "\n",
-    "We'll need to get an [AI21 API key](https://docs.ai21.com/) and set the ",
-    "`AI21_API_KEY` environment variable:\n"
+    "We'll need to get an [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
    ]
   },
   {
@@ -67,48 +65,166 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4828829d3da430ce",
-   "metadata": {
-    "collapsed": false
-   },
+   "id": "f6844fff-3702-4489-ab74-732f69f3b9d7",
+   "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": 1,
-   "id": "39353473fce5dd2e",
+   "execution_count": null,
+   "id": "7c2e19d3-7c58-4470-9e1a-718b27a32056",
+   "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": "98e22f31-8acc-42d6-916d-415d1263c56e",
+   "metadata": {},
+   "source": [
+    "### Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9699cd9-58f2-450e-aa64-799e66906c0f",
+   "metadata": {},
+   "source": [
+    "!pip install -qU langchain-ai21"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4828829d3da430ce",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c40756fb-cbf8-4d44-a293-3989d707237e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_ai21 import ChatAI21\n",
+    "\n",
+    "llm = ChatAI21(model=\"jamba-instruct\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bdc5d68-2a19-495e-8c04-d11adc86d3ae",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "46b982dc-5d8a-46da-a711-81c03ccd6adc",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Bonjour, comment vas-tu?')"
+       "AIMessage(content=\"J'adore programmer.\", id='run-2e8d16d6-a06e-45cb-8d0c-1c8208645033-0')"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10a30f84-b531-4fd5-8b5b-91512fbdc75b",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "39353473fce5dd2e",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe das Programmieren.', id='run-e1bd82dc-1a7e-4b2e-bde9-ac995929ac0f-0')"
+      ]
+     },
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_ai21 import ChatAI21\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
-    "chat = ChatAI21(model=\"jamba-instruct\")\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
+    "prompt = ChatPromptTemplate(\n",
     "    [\n",
-    "        (\"system\", \"You are a helpful assistant that translates English to French.\"),\n",
-    "        (\"human\", \"Translate this sentence from English to French. {english_text}.\"),\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
     "    ]\n",
     ")\n",
     "\n",
-    "chain = prompt | chat\n",
-    "chain.invoke({\"english_text\": \"Hello, how are you?\"})"
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e79de691-9dd6-4697-b57e-59a4a3cc073a",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatAI21 features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_ai21.chat_models.ChatAI21.html"
    ]
   }
  ],
@@ -128,7 +244,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/azure_chat_openai.ipynb

@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
    "metadata": {},
    "outputs": [],
@@ -123,8 +123,8 @@
     "from langchain_openai import AzureChatOpenAI\n",
     "\n",
     "llm = AzureChatOpenAI(\n",
-    "    azure_deployment=\"YOUR-DEPLOYMENT\",\n",
-    "    api_version=\"2024-05-01-preview\",\n",
+    "    azure_deployment=\"gpt-35-turbo\",  # or your deployment\n",
+    "    api_version=\"2023-06-01-preview\",  # or your api version\n",
     "    temperature=0,\n",
     "    max_tokens=None,\n",
     "    timeout=None,\n",
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "62e0dbc3",
    "metadata": {
     "tags": []
@@ -152,10 +152,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-a6a732c2-cb02-4e50-9a9c-ab30eab034fc-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39})"
+       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-bea4b46c-e3e1-4495-9d3a-698370ad963d-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39})"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
    "metadata": {},
    "outputs": [
@@ -202,17 +202,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 26, 'total_tokens': 32}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-084967d7-06f2-441f-b5c1-477e2a9e9d03-0', usage_metadata={'input_tokens': 26, 'output_tokens': 6, 'total_tokens': 32})"
+       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 26, 'total_tokens': 32}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-cbc44038-09d3-40d4-9da2-c5910ee636ca-0', usage_metadata={'input_tokens': 26, 'output_tokens': 6, 'total_tokens': 32})"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -264,8 +264,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "84c411b0-1790-4798-8bb7-47d8ece4c2dc",
+   "execution_count": 6,
+   "id": "2ca02d23-60d0-43eb-8d04-070f61f8fefd",
    "metadata": {},
    "outputs": [
     {
@@ -288,22 +288,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "21234693-d92b-4d69-8a7f-55aa062084bf",
+   "execution_count": 7,
+   "id": "e1b07ae2-3de7-44bd-bfdc-b76f4ba45a35",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total Cost (USD): $0.000078\n"
+      "Total Cost (USD): $0.000074\n"
      ]
     }
    ],
    "source": [
     "llm_0301 = AzureChatOpenAI(\n",
-    "    azure_deployment=\"YOUR-DEPLOYMENT\",\n",
-    "    api_version=\"2024-05-01-preview\",\n",
+    "    azure_deployment=\"gpt-35-turbo\",  # or your deployment\n",
+    "    api_version=\"2023-06-01-preview\",  # or your api version\n",
     "    model_version=\"0301\",\n",
     ")\n",
     "with get_openai_callback() as cb:\n",
@@ -338,7 +338,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/cohere.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "raw",
-   "id": "afaf8039",
+   "id": "53fbf15f",
    "metadata": {},
    "source": [
     "---\n",
@@ -12,129 +12,103 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e49f1e0d",
+   "id": "bf733a38-db84-4363-89e2-de6735c37230",
    "metadata": {},
    "source": [
-    "# ChatCohere\n",
+    "# Cohere\n",
     "\n",
-    "This doc will help you get started with Cohere [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatCohere features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_cohere.chat_models.ChatCohere.html).\n",
-    "\n",
-    "For an overview of all Cohere models head to the [Cohere docs](https://docs.cohere.com/docs/models).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/cohere) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatCohere](https://api.python.langchain.com/en/latest/chat_models/langchain_cohere.chat_models.ChatCohere.html) | [langchain-cohere](https://api.python.langchain.com/en/latest/cohere_api_reference.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-cohere?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-cohere?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | \n",
+    "This notebook covers how to get started with [Cohere chat models](https://cohere.com/chat).\n",
     "\n",
+    "Head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.cohere.ChatCohere.html) for detailed documentation of all attributes and methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3607d67e-e56c-4102-bbba-df2edc0e109e",
+   "metadata": {},
+   "source": [
     "## Setup\n",
     "\n",
-    "To access Cohere models you'll need to create a Cohere account, get an API key, and install the `langchain-cohere` integration package.\n",
+    "The integration lives in the `langchain-cohere` package. We can install these with:\n",
     "\n",
-    "### Credentials\n",
+    "```bash\n",
+    "pip install -U langchain-cohere\n",
+    "```\n",
     "\n",
-    "Head to https://dashboard.cohere.com/welcome/login to sign up to Cohere and generate an API key. Once you've done this set the COHERE_API_KEY environment variable:"
+    "We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "execution_count": 11,
+   "id": "2108b517-1e8d-473d-92fa-4f930e8072a7",
    "metadata": {},
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"COHERE_API_KEY\"] = getpass.getpass(\"Enter your Cohere API key: \")"
+    "os.environ[\"COHERE_API_KEY\"] = getpass.getpass()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "id": "cf690fbb",
    "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:"
+    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "execution_count": 12,
+   "id": "7f11de02",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "id": "4c26754b-b3c9-4d93-8f36-43049bd943bf",
    "metadata": {},
    "source": [
-    "### Installation\n",
+    "## Usage\n",
     "\n",
-    "The LangChain Cohere integration lives in the `langchain-cohere` package:"
+    "ChatCohere supports all [ChatModel](/docs/how_to#chat-models) functionality:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-cohere"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
+   "execution_count": 5,
+   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from langchain_cohere import ChatCohere\n",
-    "\n",
-    "llm = ChatCohere(\n",
-    "    model=\"command-r-plus\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "62e0dbc3",
+   "execution_count": 13,
+   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat = ChatCohere()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
    "metadata": {
     "tags": []
    },
@@ -142,110 +116,223 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'd84f80f3-4611-46e6-aed0-9d8665a20a11', 'token_count': {'input_tokens': 89, 'output_tokens': 5}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'd84f80f3-4611-46e6-aed0-9d8665a20a11', 'token_count': {'input_tokens': 89, 'output_tokens': 5}}, id='run-514ab516-ed7e-48ac-b132-2598fb80ebef-0')"
+       "AIMessage(content='4 && 5 \\n6 || 7 \\n\\nWould you like to play a game of odds and evens?', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, id='run-3475e0c8-c89b-4937-9300-e07d652455e1-0')"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
+    "messages = [HumanMessage(content=\"1\"), HumanMessage(content=\"2 3\")]\n",
+    "chat.invoke(messages)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
+   "execution_count": 16,
+   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-1635e63e-2994-4e7f-986e-152ddfc95777-0')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await chat.ainvoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "J'adore programmer.\n"
+      "4 && 5"
      ]
     }
    ],
    "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+    "for chunk in chat.stream(messages):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "execution_count": 18,
+   "id": "064288e4-f184-4496-9427-bcf148fa055e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Ich liebe Programmierung.', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '053bebde-4e1d-4d06-8ee6-3446e7afa25e', 'token_count': {'input_tokens': 84, 'output_tokens': 6}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '053bebde-4e1d-4d06-8ee6-3446e7afa25e', 'token_count': {'input_tokens': 84, 'output_tokens': 6}}, id='run-53700708-b7fb-417b-af36-1a6fcde38e7d-0')"
+       "[AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-8d6fade2-1b39-4e31-ab23-4be622dd0027-0')]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
+    "chat.batch([messages])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "id": "f1c56460",
    "metadata": {},
    "source": [
-    "## API reference\n",
+    "## Chaining\n",
     "\n",
-    "For detailed documentation of all ChatCohere features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_cohere.chat_models.ChatCohere.html"
+    "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0851b103",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
+    "chain = prompt | chat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ae950c0f-1691-47f1-b609-273033cae707",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='What color socks do bears wear?\\n\\nThey don’t wear socks, they have bear feet. \\n\\nHope you laughed! If not, maybe this will help: laughter is the best medicine, and a good sense of humor is infectious!', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, id='run-ef7f9789-0d4d-43bf-a4f7-f2a0e27a5320-0')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"topic\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12db8d69",
+   "metadata": {},
+   "source": [
+    "## Tool calling\n",
+    "\n",
+    "Cohere supports tool calling functionalities!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "337e24af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import (\n",
+    "    HumanMessage,\n",
+    "    ToolMessage,\n",
+    ")\n",
+    "from langchain_core.tools import tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "74d292e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tool\n",
+    "def magic_function(number: int) -> int:\n",
+    "    \"\"\"Applies a magic operation to an integer\n",
+    "    Args:\n",
+    "        number: Number to have magic operation performed on\n",
+    "    \"\"\"\n",
+    "    return number + 10\n",
+    "\n",
+    "\n",
+    "def invoke_tools(tool_calls, messages):\n",
+    "    for tool_call in tool_calls:\n",
+    "        selected_tool = {\"magic_function\": magic_function}[tool_call[\"name\"].lower()]\n",
+    "        tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
+    "        messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
+    "    return messages\n",
+    "\n",
+    "\n",
+    "tools = [magic_function]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ecafcbc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_with_tools = chat.bind_tools(tools=tools)\n",
+    "messages = [HumanMessage(content=\"What is the value of magic_function(2)?\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "aa34fc39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='The value of magic_function(2) is 12.', additional_kwargs={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, response_metadata={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, id='run-f318a9cf-55c8-44f4-91d1-27cf46c6a465-0')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = llm_with_tools.invoke(messages)\n",
+    "while res.tool_calls:\n",
+    "    messages.append(res)\n",
+    "    messages = invoke_tools(res.tool_calls, messages)\n",
+    "    res = llm_with_tools.invoke(messages)\n",
+    "\n",
+    "res"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv-2",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv-2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -257,7 +344,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/databricks.ipynb

@@ -36,7 +36,7 @@
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
     "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |  ✅ | ✅ | ✅ | ❌ | \n",
+    "| ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |  ✅ | ✅ | ✅ | ❌ | \n",
     "\n",
     "### Supported Methods\n",
     "\n",
@@ -395,6 +395,66 @@
     "chat_model_external.invoke(\"How to use Databricks?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Function calling on Databricks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Databricks Function Calling is OpenAI-compatible and is only available during model serving as part of Foundation Model APIs.\n",
+    "\n",
+    "See [Databricks function calling introduction](https://docs.databricks.com/en/machine-learning/model-serving/function-calling.html#supported-models) for supported models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models.databricks import ChatDatabricks\n",
+    "\n",
+    "llm = ChatDatabricks(endpoint=\"databricks-meta-llama-3-70b-instruct\")\n",
+    "tools = [\n",
+    "    {\n",
+    "        \"type\": \"function\",\n",
+    "        \"function\": {\n",
+    "            \"name\": \"get_current_weather\",\n",
+    "            \"description\": \"Get the current weather in a given location\",\n",
+    "            \"parameters\": {\n",
+    "                \"type\": \"object\",\n",
+    "                \"properties\": {\n",
+    "                    \"location\": {\n",
+    "                        \"type\": \"string\",\n",
+    "                        \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
+    "                    },\n",
+    "                    \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
+    "                },\n",
+    "            },\n",
+    "        },\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "# supported tool_choice values: \"auto\", \"required\", \"none\", function name in string format,\n",
+    "# or a dictionary as {\"type\": \"function\", \"function\": {\"name\": <<tool_name>>}}\n",
+    "model = llm.bind_tools(tools, tool_choice=\"auto\")\n",
+    "\n",
+    "messages = [{\"role\": \"user\", \"content\": \"What is the current temperature of Chicago?\"}]\n",
+    "print(model.invoke(messages))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See [Databricks Unity Catalog](docs/integrations/tools/databricks.ipynb) about how to use UC functions in chains."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/docs/integrations/chat/huggingface.ipynb

@@ -4,18 +4,68 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Hugging Face\n",
+    "# ChatHuggingFace\n",
     "\n",
-    "This notebook shows how to get started using `Hugging Face` LLM's as chat models.\n",
+    "This will help you getting started with `langchain_huggingface` [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
     "\n",
-    "In particular, we will:\n",
-    "1. Utilize the [HuggingFaceEndpoint](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_endpoint.py) integrations to instantiate an `LLM`.\n",
-    "2. Utilize the `ChatHuggingFace` class to enable any of these LLMs to interface with LangChain's [Chat Messages](/docs/concepts/#message-types) abstraction.\n",
-    "3. Explore tool calling with the `ChatHuggingFace`.\n",
-    "4. Demonstrate how to use an open-source LLM to power an `ChatAgent` pipeline\n",
+    "## Overview\n",
+    "### Integration details\n",
     "\n",
+    "### Integration details\n",
     "\n",
-    "> Note: To get started, you'll need to have a [Hugging Face Access Token](https://huggingface.co/docs/hub/security-tokens) saved as an environment variable: `HUGGINGFACEHUB_API_TOKEN`."
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatHuggingFace](https://api.python.langchain.com/en/latest/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html) | [langchain-huggingface](https://api.python.langchain.com/en/latest/huggingface_api_reference.html) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_huggingface?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_huggingface?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Hugging Face models you'll need to create a Hugging Face account, get an API key, and install the `langchain-huggingface` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Generate a [Hugging Face Access Token](https://huggingface.co/docs/hub/security-tokens) and store it as an environment variable: `HUGGINGFACEHUB_API_TOKEN`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"HUGGINGFACEHUB_API_TOKEN\"):\n",
+    "    os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = getpass.getpass(\"Enter your token: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatHuggingFace](https://api.python.langchain.com/en/latest/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html) | [langchain_huggingface](https://api.python.langchain.com/en/latest/huggingface_api_reference.html) | ✅ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_huggingface?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_huggingface?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access `langchain_huggingface` models you'll need to create a/an `Hugging Face` account, get an API key, and install the `langchain_huggingface` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "You'll need to have a [Hugging Face Access Token](https://huggingface.co/docs/hub/security-tokens) saved as an environment variable: `HUGGINGFACEHUB_API_TOKEN`."
    ]
   },
   {
@@ -24,14 +74,41 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain-huggingface text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2"
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = getpass.getpass(\n",
+    "    \"Enter your Hugging Face API key: \"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install --upgrade --quiet  langchain-huggingface text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2 bitsandbytes accelerate"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. Instantiate an LLM"
+    "## Instantiation\n",
+    "\n",
+    "You can instantiate a `ChatHuggingFace` model in two different ways, either from a `HuggingFaceEndpoint` or from a `HuggingFacePipeline`."
    ]
   },
   {
@@ -43,19 +120,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
+      "Token is valid (permission: fineGrained).\n",
+      "Your token has been saved to /Users/isaachershenson/.cache/huggingface/token\n",
+      "Login successful\n"
+     ]
+    }
+   ],
    "source": [
-    "from langchain_huggingface import HuggingFaceEndpoint\n",
+    "from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint\n",
     "\n",
     "llm = HuggingFaceEndpoint(\n",
-    "    repo_id=\"meta-llama/Meta-Llama-3-70B-Instruct\",\n",
+    "    repo_id=\"HuggingFaceH4/zephyr-7b-beta\",\n",
     "    task=\"text-generation\",\n",
     "    max_new_tokens=512,\n",
     "    do_sample=False,\n",
     "    repetition_penalty=1.03,\n",
-    ")"
+    ")\n",
+    "\n",
+    "chat_model = ChatHuggingFace(llm=llm)"
    ]
   },
   {
@@ -67,11 +157,194 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da32ae8ec8864ccfb480044fe2eec065",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/638 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee1891b7e5f64fba88ba35f444e598fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ff1ec7f575b42adb608c15955de7888",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading shards:   0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5214696698814b919f561647a684d1e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00001-of-00008.safetensors:   0%|          | 0.00/1.89G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ac334c69a2048a0a77340cca44d8c80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00002-of-00008.safetensors:   0%|          | 0.00/1.95G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "465ad1a51d414e0daf1cd9308455be94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00003-of-00008.safetensors:   0%|          | 0.00/1.98G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a329c43c3d574df0afd38c7457cc639c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00004-of-00008.safetensors:   0%|          | 0.00/1.95G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a736a6c4023542af8c6ecc232b823d18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00005-of-00008.safetensors:   0%|          | 0.00/1.98G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bdee70b843d433e8236fff83ecda022",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00006-of-00008.safetensors:   0%|          | 0.00/1.95G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ecb6103e0304ae188a14d598119a361",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00007-of-00008.safetensors:   0%|          | 0.00/1.98G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "174e3cb487bd453c9c70d7614254a35e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00008-of-00008.safetensors:   0%|          | 0.00/816M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28f8c233b04b45d7800e12c785a8c4bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "449dfa023dc8430fbcde94544ba01c4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "from langchain_huggingface import HuggingFacePipeline\n",
+    "from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline\n",
     "\n",
     "llm = HuggingFacePipeline.from_model_id(\n",
     "    model_id=\"HuggingFaceH4/zephyr-7b-beta\",\n",
@@ -81,81 +354,7 @@
     "        do_sample=False,\n",
     "        repetition_penalty=1.03,\n",
     "    ),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To run a quantized version, you might specify a `bitsandbytes` quantization config as follows:\n",
-    "\n",
-    "```python\n",
-    "from transformers import BitsAndBytesConfig\n",
-    "\n",
-    "quantization_config = BitsAndBytesConfig(\n",
-    "    load_in_4bit=True,\n",
-    "    bnb_4bit_quant_type=\"nf4\",\n",
-    "    bnb_4bit_compute_dtype=\"float16\",\n",
-    "    bnb_4bit_use_double_quant=True\n",
     ")\n",
-    "```\n",
-    "\n",
-    "and pass it to the `HuggingFacePipeline` as a part of its `model_kwargs`:\n",
-    "\n",
-    "```python\n",
-    "pipeline = HuggingFacePipeline(\n",
-    "    ...\n",
-    "\n",
-    "    model_kwargs={\"quantization_config\": quantization_config},\n",
-    "    \n",
-    "    ...\n",
-    ")\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Instantiate the `ChatHuggingFace` to apply chat templates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instantiate the chat model and some messages to pass. \n",
-    "\n",
-    "**Note**: you need to pass the `model_id` explicitly if you are using self-hosted `text-generation-inference`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import (\n",
-    "    HumanMessage,\n",
-    "    SystemMessage,\n",
-    ")\n",
-    "from langchain_huggingface import ChatHuggingFace\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(content=\"You're a helpful assistant\"),\n",
-    "    HumanMessage(\n",
-    "        content=\"What happens when an unstoppable force meets an immovable object?\"\n",
-    "    ),\n",
-    "]\n",
     "\n",
     "chat_model = ChatHuggingFace(llm=llm)"
    ]
@@ -164,284 +363,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Check the `model_id`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'meta-llama/Meta-Llama-3-70B-Instruct'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat_model.model_id"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Inspect how the chat messages are formatted for the LLM call."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nYou're a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nWhat happens when an unstoppable force meets an immovable object?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n\""
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat_model._to_chat_prompt(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call the model."
+    "### Instatiating with Quantization\n",
+    "\n",
+    "To run a quantized version of your model, you can specify a `bitsandbytes` quantization config as follows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "One of the classic thought experiments in physics!\n",
-      "\n",
-      "The concept of an unstoppable force meeting an immovable object is a paradox that has puzzled philosophers and physicists for centuries. It's a mind-bending scenario that challenges our understanding of the fundamental laws of physics.\n",
-      "\n",
-      "In essence, an unstoppable force is something that cannot be halted or slowed down, while an immovable object is something that cannot be moved or displaced. If we assume that both entities exist in the same universe, we run into a logical contradiction.\n",
-      "\n",
-      "Here\n"
-     ]
-    }
-   ],
-   "source": [
-    "res = chat_model.invoke(messages)\n",
-    "print(res.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Explore the tool calling with `ChatHuggingFace`\n",
-    "\n",
-    "`text-generation-inference` supports tool with open source LLMs starting from v2.0.1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a basic tool (`Calculator`):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from transformers import BitsAndBytesConfig\n",
     "\n",
-    "\n",
-    "class Calculator(BaseModel):\n",
-    "    \"\"\"Multiply two integers together.\"\"\"\n",
-    "\n",
-    "    a: int = Field(..., description=\"First integer\")\n",
-    "    b: int = Field(..., description=\"Second integer\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Bind the tool to the `chat_model` and give it a try:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Calculator(a=3, b=12)]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
-    "\n",
-    "llm_with_multiply = chat_model.bind_tools([Calculator], tool_choice=\"auto\")\n",
-    "parser = PydanticToolsParser(tools=[Calculator])\n",
-    "tool_chain = llm_with_multiply | parser\n",
-    "tool_chain.invoke(\"How much is 3 multiplied by 12?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Take it for a spin as an agent!\n",
-    "\n",
-    "Here we'll test out `Zephyr-7B-beta` as a zero-shot `ReAct` Agent. \n",
-    "\n",
-    "The agent is based on the paper [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629)\n",
-    "\n",
-    "The example below is taken from [here](https://python.langchain.com/v0.1/docs/modules/agents/agent_types/react/#using-chat-models).\n",
-    "\n",
-    "> Note: To run this section, you'll need to have a [SerpAPI Token](https://serpapi.com/) saved as an environment variable: `SERPAPI_API_KEY`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import hub\n",
-    "from langchain.agents import AgentExecutor, load_tools\n",
-    "from langchain.agents.format_scratchpad import format_log_to_str\n",
-    "from langchain.agents.output_parsers import (\n",
-    "    ReActJsonSingleInputOutputParser,\n",
-    ")\n",
-    "from langchain.tools.render import render_text_description\n",
-    "from langchain_community.utilities import SerpAPIWrapper"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Configure the agent with a `react-json` style prompt and access to a search engine and calculator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# setup tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
-    "\n",
-    "# setup ReAct style prompt\n",
-    "prompt = hub.pull(\"hwchase17/react-json\")\n",
-    "prompt = prompt.partial(\n",
-    "    tools=render_text_description(tools),\n",
-    "    tool_names=\", \".join([t.name for t in tools]),\n",
-    ")\n",
-    "\n",
-    "# define the agent\n",
-    "chat_model_with_stop = chat_model.bind(stop=[\"\\nObservation\"])\n",
-    "agent = (\n",
-    "    {\n",
-    "        \"input\": lambda x: x[\"input\"],\n",
-    "        \"agent_scratchpad\": lambda x: format_log_to_str(x[\"intermediate_steps\"]),\n",
-    "    }\n",
-    "    | prompt\n",
-    "    | chat_model_with_stop\n",
-    "    | ReActJsonSingleInputOutputParser()\n",
-    ")\n",
-    "\n",
-    "# instantiate AgentExecutor\n",
-    "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\n",
-      "\n",
-      "Thought: I need to use the Search tool to find out who Leo DiCaprio's current girlfriend is. Then, I can use the Calculator tool to raise her current age to the power of 0.43.\n",
-      "\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"leo dicaprio girlfriend\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mLeonardo DiCaprio may have found The One in Vittoria Ceretti. “They are in love,” a source exclusively reveals in the latest issue of Us Weekly. “Leo was clearly very proud to be showing Vittoria off and letting everyone see how happy they are together.”\u001b[0m\u001b[32;1m\u001b[1;3mNow that we know Leo DiCaprio's current girlfriend is Vittoria Ceretti, let's find out her current age.\n",
-      "\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"vittoria ceretti age\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m25 years\u001b[0m\u001b[32;1m\u001b[1;3mNow that we know Vittoria Ceretti's current age is 25, let's use the Calculator tool to raise it to the power of 0.43.\n",
-      "\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Calculator\",\n",
-      "  \"action_input\": \"25^0.43\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mFinal Answer: Vittoria Ceretti, Leo DiCaprio's current girlfriend, when raised to the power of 0.43 is approximately 4.0 rounded to two decimal places. Her current age is 25 years old.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'input': \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\",\n",
-       " 'output': \"Vittoria Ceretti, Leo DiCaprio's current girlfriend, when raised to the power of 0.43 is approximately 4.0 rounded to two decimal places. Her current age is 25 years old.\"}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_executor.invoke(\n",
-    "    {\n",
-    "        \"input\": \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    "    }\n",
+    "quantization_config = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,\n",
+    "    bnb_4bit_quant_type=\"nf4\",\n",
+    "    bnb_4bit_compute_dtype=\"float16\",\n",
+    "    bnb_4bit_use_double_quant=True,\n",
     ")"
    ]
   },
@@ -449,14 +388,92 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Wahoo! Our open-source 7b parameter Zephyr model was able to:\n",
+    "and pass it to the `HuggingFacePipeline` as a part of its `model_kwargs`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = HuggingFacePipeline.from_model_id(\n",
+    "    model_id=\"HuggingFaceH4/zephyr-7b-beta\",\n",
+    "    task=\"text-generation\",\n",
+    "    pipeline_kwargs=dict(\n",
+    "        max_new_tokens=512,\n",
+    "        do_sample=False,\n",
+    "        repetition_penalty=1.03,\n",
+    "    ),\n",
+    "    model_kwargs={\"quantization_config\": quantization_config},\n",
+    ")\n",
     "\n",
-    "1. Plan out a series of actions: `I need to use the Search tool to find out who Leo DiCaprio's current girlfriend is. Then, I can use the Calculator tool to raise her current age to the power of 0.43.`\n",
-    "2. Then execute a search using the SerpAPI tool to find who Leo DiCaprio's current girlfriend is\n",
-    "3. Execute another search to find her age\n",
-    "4. And finally use a calculator tool to calculate her age raised to the power of 0.43\n",
+    "chat_model = ChatHuggingFace(llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import (\n",
+    "    HumanMessage,\n",
+    "    SystemMessage,\n",
+    ")\n",
     "\n",
-    "It's exciting to see how far open-source LLM's can go as general purpose reasoning agents. Give it a try yourself!"
+    "messages = [\n",
+    "    SystemMessage(content=\"You're a helpful assistant\"),\n",
+    "    HumanMessage(\n",
+    "        content=\"What happens when an unstoppable force meets an immovable object?\"\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "ai_msg = chat_model.invoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "According to the popular phrase and hypothetical scenario, when an unstoppable force meets an immovable object, a paradoxical situation arises as both forces are seemingly contradictory. On one hand, an unstoppable force is an entity that cannot be stopped or prevented from moving forward, while on the other hand, an immovable object is something that cannot be moved or displaced from its position. \n",
+      "\n",
+      "In this scenario, it is un\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `ChatHuggingFace` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatHuggingFace features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html"
    ]
   }
  ],
@@ -476,7 +493,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

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/kinetica.ipynb

@@ -13,7 +13,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Kinetica SqlAssist LLM Demo\n",
+    "# Kinetica Language To SQL Chat Model\n",
     "\n",
     "This notebook demonstrates how to use Kinetica to transform natural language into SQL\n",
     "and simplify the process of data retrieval. This demo is intended to show the mechanics\n",

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/mistralai.ipynb

@@ -12,254 +12,228 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
+   "id": "d295c2a2",
    "metadata": {},
    "source": [
-    "# MistralAI\n",
+    "# ChatMistralAI\n",
     "\n",
-    "This notebook covers how to get started with MistralAI chat models, via their [API](https://docs.mistral.ai/api/).\n",
+    "This will help you getting started with Mistral [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
     "\n",
-    "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API.\n",
+    "## Overview\n",
+    "### Integration details\n",
     "\n",
-    "Head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods."
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/mistral) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatMistralAI](https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) | [langchain_mistralai](https://api.python.langchain.com/en/latest/mistralai_api_reference.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_mistralai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_mistralai?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "\n",
+    "To access `ChatMistralAI` models you'll need to create a Mistral account, get an API key, and install the `langchain_mistralai` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "\n",
+    "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API. Once you've done this set the MISTRAL_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2461605e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"MISTRAL_API_KEY\"] = getpass.getpass(\"Enter your Mistral API key: \")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cc686b8f",
+   "id": "788f37ac",
    "metadata": {},
    "source": [
-    "## Setup\n",
+    "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": null,
+   "id": "007209d5",
+   "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": "0f5c74f9",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
     "\n",
-    "You will need the `langchain-core` and `langchain-mistralai` package to use the API. You can install these with:\n",
+    "The LangChain Mistral integration lives in the `langchain_mistralai` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ab11a65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_mistralai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb1a335e",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
     "\n",
-    "```bash\n",
-    "pip install -U langchain-core langchain-mistralai\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e6c38580",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_mistralai import ChatMistralAI\n",
     "\n",
-    "We'll also need to get a [Mistral API key](https://console.mistral.ai/users/api-keys/)"
+    "llm = ChatMistralAI(\n",
+    "    model=\"mistral-large-latest\",\n",
+    "    temperature=0,\n",
+    "    max_retries=2,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aec79099",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8838c3cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Sure, I\\'d be happy to help you translate that sentence into French! The English sentence \"I love programming\" translates to \"J\\'aime programmer\" in French. Let me know if you have any other questions or need further assistance!', response_metadata={'token_usage': {'prompt_tokens': 32, 'total_tokens': 84, 'completion_tokens': 52}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-64bac156-7160-4b68-b67e-4161f63e021f-0', usage_metadata={'input_tokens': 32, 'output_tokens': 52, 'total_tokens': 84})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "c3fd4184",
+   "id": "bbf6a048",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "\n",
-    "api_key = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "502127fd",
-   "metadata": {},
-   "source": [
-    "## Usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_core.messages import HumanMessage\n",
-    "from langchain_mistralai.chat_models import ChatMistralAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# If api_key is not passed, default behavior is to use the `MISTRAL_API_KEY` environment variable.\n",
-    "chat = ChatMistralAI(api_key=api_key)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"Who's there? I was just about to ask the same thing! How can I assist you today?\")"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [HumanMessage(content=\"knock knock\")]\n",
-    "chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
-   "metadata": {},
-   "source": [
-    "### Async"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Who\\'s there?\\n\\n(You can then continue the \"knock knock\" joke by saying the name of the person or character who should be responding. For example, if I say \"Banana,\" you could respond with \"Banana who?\" and I would say \"Banana bunch! Get it? Because a group of bananas is called a \\'bunch\\'!\" and then we would both laugh and have a great time. But really, you can put anything you want in the spot where I put \"Banana\" and it will still technically be a \"knock knock\" joke. The possibilities are endless!)')"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await chat.ainvoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "86ccef97",
-   "metadata": {},
-   "source": [
-    "### Streaming\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
-   "metadata": {
-    "tags": []
-   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Who's there?\n",
-      "\n",
-      "(After this, the conversation can continue as a call and response \"who's there\" joke. Here is an example of how it could go:\n",
-      "\n",
-      "You say: Orange.\n",
-      "I say: Orange who?\n",
-      "You say: Orange you glad I didn't say banana!?)\n",
-      "\n",
-      "But since you asked for a knock knock joke specifically, here's one for you:\n",
-      "\n",
-      "Knock knock.\n",
-      "\n",
-      "Me: Who's there?\n",
-      "\n",
-      "You: Lettuce.\n",
-      "\n",
-      "Me: Lettuce who?\n",
-      "\n",
-      "You: Lettuce in, it's too cold out here!\n",
-      "\n",
-      "I hope this brings a smile to your face! Do you have a favorite knock knock joke you'd like to share? I'd love to hear it."
+      "Sure, I'd be happy to help you translate that sentence into French! The English sentence \"I love programming\" translates to \"J'aime programmer\" in French. Let me know if you have any other questions or need further assistance!\n"
      ]
     }
    ],
    "source": [
-    "for chunk in chat.stream(messages):\n",
-    "    print(chunk.content, end=\"\")"
+    "print(ai_msg.content)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f6189577",
-   "metadata": {},
-   "source": [
-    "### Batch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "e63aebcb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[AIMessage(content=\"Who's there? I was just about to ask the same thing! Go ahead and tell me who's there. I love a good knock-knock joke.\")]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat.batch([messages])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38e39e71",
+   "id": "32b87f87",
    "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)"
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "ee43a1ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
-    "chain = prompt | chat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "0dc49212",
+   "execution_count": 8,
+   "id": "24e2c51c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Why do bears hate shoes so much? They like to run around in their bear feet.')"
+       "AIMessage(content='Ich liebe Programmierung. (German translation)', response_metadata={'token_usage': {'prompt_tokens': 26, 'total_tokens': 38, 'completion_tokens': 12}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-dfd4094f-e347-47b0-9056-8ebd7ea35fe7-0', usage_metadata={'input_tokens': 26, 'output_tokens': 12, 'total_tokens': 38})"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain.invoke({\"topic\": \"bears\"})"
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb9b5834",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "Head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods."
    ]
   }
  ],
@@ -279,7 +253,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb

@@ -2,13 +2,24 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cc6caafa",
-   "metadata": {
-    "id": "cc6caafa"
-   },
+   "id": "1f666798-8635-4bc0-a515-04d318588d67",
+   "metadata": {},
    "source": [
-    "# NVIDIA NIMs\n",
+    "---\n",
+    "sidebar_label: NVIDIA AI Endpoints\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa8eb20e-4db8-45e3-9e79-c595f4f274da",
+   "metadata": {},
+   "source": [
+    "# ChatNVIDIA\n",
     "\n",
+    "This will help you getting started with NVIDIA [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
+    "\n",
+    "## Overview\n",
     "The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on \n",
     "NVIDIA NIM inference microservice. NIM supports models across domains like chat, embedding, and re-ranking models \n",
     "from the community as well as NVIDIA. These models are optimized by NVIDIA to deliver the best performance on NVIDIA \n",
@@ -24,7 +35,66 @@
     "\n",
     "This example goes over how to use LangChain to interact with NVIDIA supported via the `ChatNVIDIA` class.\n",
     "\n",
-    "For more information on accessing the chat models through this api, check out the [ChatNVIDIA](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/) documentation."
+    "For more information on accessing the chat models through this api, check out the [ChatNVIDIA](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/) documentation.\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatNVIDIA](https://api.python.langchain.com/en/latest/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html) | [langchain_nvidia_ai_endpoints](https://api.python.langchain.com/en/latest/nvidia_ai_endpoints_api_reference.html) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_nvidia_ai_endpoints?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_nvidia_ai_endpoints?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "**To get started:**\n",
+    "\n",
+    "1. Create a free account with [NVIDIA](https://build.nvidia.com/), which hosts NVIDIA AI Foundation models.\n",
+    "\n",
+    "2. Click on your model of choice.\n",
+    "\n",
+    "3. Under `Input` select the `Python` tab, and click `Get API Key`. Then click `Generate Key`.\n",
+    "\n",
+    "4. Copy and save the generated key as `NVIDIA_API_KEY`. From there, you should have access to the endpoints.\n",
+    "\n",
+    "### Credentials\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "208b72da-1535-4249-bbd3-2500028e25e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"NVIDIA_API_KEY\"):\n",
+    "    # Note: the API key should start with \"nvapi-\"\n",
+    "    os.environ[\"NVIDIA_API_KEY\"] = getpass.getpass(\"Enter your NVIDIA API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52dc8dcb-0a48-4a4e-9947-764116d2ffd4",
+   "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": null,
+   "id": "2cd9cb12-6ca5-432a-9e42-8a57da073c7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
    ]
   },
   {
@@ -32,7 +102,9 @@
    "id": "f2be90a9",
    "metadata": {},
    "source": [
-    "## Installation"
+    "### Installation\n",
+    "\n",
+    "The LangChain NVIDIA AI Endpoints integration lives in the `langchain_nvidia_ai_endpoints` package:"
    ]
   },
   {
@@ -45,51 +117,14 @@
     "%pip install --upgrade --quiet langchain-nvidia-ai-endpoints"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ccff689e",
-   "metadata": {
-    "id": "ccff689e"
-   },
-   "source": [
-    "## Setup\n",
-    "\n",
-    "**To get started:**\n",
-    "\n",
-    "1. Create a free account with [NVIDIA](https://build.nvidia.com/), which hosts NVIDIA AI Foundation models.\n",
-    "\n",
-    "2. Click on your model of choice.\n",
-    "\n",
-    "3. Under `Input` select the `Python` tab, and click `Get API Key`. Then click `Generate Key`.\n",
-    "\n",
-    "4. Copy and save the generated key as `NVIDIA_API_KEY`. From there, you should have access to the endpoints."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "686c4d2f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "# del os.environ['NVIDIA_API_KEY']  ## delete key and reset\n",
-    "if os.environ.get(\"NVIDIA_API_KEY\", \"\").startswith(\"nvapi-\"):\n",
-    "    print(\"Valid NVIDIA_API_KEY already in environment. Delete to reset\")\n",
-    "else:\n",
-    "    nvapi_key = getpass.getpass(\"NVAPI Key (starts with nvapi-): \")\n",
-    "    assert nvapi_key.startswith(\"nvapi-\"), f\"{nvapi_key[:5]}... is not a valid key\"\n",
-    "    os.environ[\"NVIDIA_API_KEY\"] = nvapi_key"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "af0ce26b",
    "metadata": {},
    "source": [
-    "## Working with NVIDIA API Catalog"
+    "## Instantiation\n",
+    "\n",
+    "Now we can access models in the NVIDIA API Catalog:"
    ]
   },
   {
@@ -108,7 +143,24 @@
     "## Core LC Chat Interface\n",
     "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n",
     "\n",
-    "llm = ChatNVIDIA(model=\"mistralai/mixtral-8x7b-instruct-v0.1\")\n",
+    "llm = ChatNVIDIA(model=\"mistralai/mixtral-8x7b-instruct-v0.1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "469c8c7f-de62-457f-a30f-674763a8b717",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9512c81b-1f3a-4eca-9470-f52cedff5c74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "result = llm.invoke(\"Write a ballad about LangChain.\")\n",
     "print(result.content)"
    ]
@@ -630,6 +682,55 @@
    "source": [
     "See [How to use chat models to call tools](https://python.langchain.com/v0.2/docs/how_to/tool_calling/) for additional examples."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9a3c438-121d-46eb-8fb5-b8d5a13cd4a4",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af585c6b-fe0a-4833-9860-a4209a71b3c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2f25dd3-0b4a-465f-a53e-95521cdc253c",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `ChatNVIDIA` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html"
+   ]
   }
  ],
  "metadata": {
@@ -651,7 +752,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.4"
   }
  },
  "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/openai.ipynb

@@ -56,23 +56,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "e817fe2e-4f1d-4533-b19e-2400b1cf6ce8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "Enter your OpenAI API key:  ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
+    "if not os.environ.get(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
    ]
   },
   {
@@ -126,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "522686de",
    "metadata": {
     "tags": []
@@ -158,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
    "metadata": {
     "tags": []
@@ -167,10 +160,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 31, 'total_tokens': 36}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_43dfabdef1', 'finish_reason': 'stop', 'logprobs': None}, id='run-012cffe2-5d3d-424d-83b5-51c6d4a593d1-0', usage_metadata={'input_tokens': 31, 'output_tokens': 5, 'total_tokens': 36})"
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 31, 'total_tokens': 36}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'stop', 'logprobs': None}, id='run-63219b22-03e3-4561-8cc4-78b7c7c3a3ca-0', usage_metadata={'input_tokens': 31, 'output_tokens': 5, 'total_tokens': 36})"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -189,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "2cd224b8-4499-41fb-a604-d53a7ff17b2e",
    "metadata": {},
    "outputs": [
@@ -217,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "fbb043e6",
    "metadata": {
     "tags": []
@@ -226,10 +219,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren.', response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 26, 'total_tokens': 31}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_b28b39ffa8', 'finish_reason': 'stop', 'logprobs': None}, id='run-94fa6741-c99b-4513-afce-c3f562631c79-0')"
+       "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 26, 'total_tokens': 32}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'stop', 'logprobs': None}, id='run-350585e1-16ca-4dad-9460-3d9e7e49aaf1-0', usage_metadata={'input_tokens': 26, 'output_tokens': 6, 'total_tokens': 32})"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -281,12 +274,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "b7ea7690-ec7a-4337-b392-e87d1f39a6ec",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetWeather(BaseModel):\n",
@@ -300,17 +293,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "1d1ab955-6a68-42f8-bb5d-86eb1111478a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_H7fABDuzEau48T10Qn0Lsh0D', 'function': {'arguments': '{\"location\":\"San Francisco\"}', 'name': 'GetWeather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 70, 'total_tokens': 85}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_b28b39ffa8', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b469135e-2718-446a-8164-eef37e672ba2-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco'}, 'id': 'call_H7fABDuzEau48T10Qn0Lsh0D'}])"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_o9udf3EVOWiV4Iupktpbpofk', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 68, 'total_tokens': 85}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-1617c9b2-dda5-4120-996b-0333ed5992e2-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_o9udf3EVOWiV4Iupktpbpofk', 'type': 'tool_call'}], usage_metadata={'input_tokens': 68, 'output_tokens': 17, 'total_tokens': 85})"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -322,6 +315,47 @@
     "ai_msg"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "67b0f63d-15e6-45e0-9e86-2852ddcff54f",
+   "metadata": {},
+   "source": [
+    "### ``strict=True``\n",
+    "\n",
+    ":::info Requires ``langchain-openai>=0.1.21rc1``\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "As of Aug 6, 2024, OpenAI supports a `strict` argument when calling tools that will enforce that the tool argument schema is respected by the model. See more here: https://platform.openai.com/docs/guides/function-calling\n",
+    "\n",
+    "**Note**: If ``strict=True`` the tool definition will also be validated, and a subset of JSON schema are accepted. Crucially, schema cannot have optional args (those with default values). Read the full docs on what types of schema are supported here: https://platform.openai.com/docs/guides/structured-outputs/supported-schemas. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dc8ac4f1-4039-4392-90c1-2d8331cd6910",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_jUqhd8wzAIzInTJl72Rla8ht', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 68, 'total_tokens': 85}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-5e3356a9-132d-4623-8e73-dd5a898cf4a6-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_jUqhd8wzAIzInTJl72Rla8ht', 'type': 'tool_call'}], usage_metadata={'input_tokens': 68, 'output_tokens': 17, 'total_tokens': 85})"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_with_tools = llm.bind_tools([GetWeather], strict=True)\n",
+    "ai_msg = llm_with_tools.invoke(\n",
+    "    \"what is the weather like in San Francisco\",\n",
+    ")\n",
+    "ai_msg"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "768d1ae4-4b1a-48eb-a329-c8d5051067a3",
@@ -333,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "166cb7ce-831d-4a7c-9721-abc107f11084",
    "metadata": {},
    "outputs": [
@@ -341,11 +375,12 @@
      "data": {
       "text/plain": [
        "[{'name': 'GetWeather',\n",
-       "  'args': {'location': 'San Francisco'},\n",
-       "  'id': 'call_H7fABDuzEau48T10Qn0Lsh0D'}]"
+       "  'args': {'location': 'San Francisco, CA'},\n",
+       "  'id': 'call_jUqhd8wzAIzInTJl72Rla8ht',\n",
+       "  'type': 'tool_call'}]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,17 +411,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "id": "33c4a8b0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, example=False)"
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0f39b30e-c56e-4f3b-af99-5c948c984146-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39})"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,7 +431,7 @@
     "    temperature=0, model_name=\"ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR\"\n",
     ")\n",
     "\n",
-    "fine_tuned_model(messages)"
+    "fine_tuned_model.invoke(messages)"
    ]
   },
   {
@@ -412,9 +447,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv-2",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv-2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -426,7 +461,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

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/chat/together.ipynb

@@ -53,7 +53,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
+    "if \"TOGETHER_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
    ]
   },
   {
@@ -87,21 +88,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install -qU langchain-together"
    ]
@@ -113,14 +103,12 @@
    "source": [
     "## Instantiation\n",
     "\n",
-    "Now we can instantiate our model object and generate chat completions:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
+    "Now we can instantiate our model object and generate chat completions:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
    "metadata": {},
    "outputs": [],
@@ -147,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "62e0dbc3",
    "metadata": {
     "tags": []
@@ -156,10 +144,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 35, 'total_tokens': 44}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-79efa49b-dbaf-4ef8-9dce-958533823ef6-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
+       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 35, 'total_tokens': 44}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-eabcbe33-cdd8-45b8-ab0b-f90b6e7dfad8-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -178,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
    "metadata": {},
    "outputs": [
@@ -206,17 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 30, 'total_tokens': 37}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-80bba5fa-1723-4242-8d5a-c09b76b8350b-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
+       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 30, 'total_tokens': 37}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-a249aa24-ee31-46ba-9bf9-f4eb135b0a95-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,7 +259,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/vllm.ipynb

@@ -12,14 +12,83 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb7e5679-aa06-47e4-a1a3-b6b70e604017",
+   "id": "8f82e243-f4ee-44e2-b417-099b6401ae3e",
    "metadata": {},
    "source": [
     "# vLLM Chat\n",
     "\n",
     "vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.\n",
     "\n",
-    "This notebook covers how to get started with vLLM chat models using langchain's `ChatOpenAI` **as it is**."
+    "## Overview\n",
+    "This will help you getting started with vLLM [chat models](/docs/concepts/#chat-models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | 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/langchain_openai.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",
+    "### Model features\n",
+    "Specific model features-- such as tool calling, support for multi-modal inputs, support for token-level streaming, etc.-- will depend on the hosted model.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "See the vLLM docs [here](https://docs.vllm.ai/en/latest/).\n",
+    "\n",
+    "To access vLLM models through LangChain, you'll need to install the `langchain-openai` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Authentication will depend on specifics of the inference server."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3b1707a-cf2c-4367-94e3-436c43402503",
+   "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": null,
+   "id": "1e40bd5e-cbaa-41ef-aaf9-0858eb207184",
+   "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": "0739b647-609b-46d3-bdd3-e86fe4463288",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain vLLM integration can be accessed via the `langchain-openai` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7afcfbdc-56aa-4529-825a-8acbe7aa5241",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cf576d6-7b67-4937-bf99-39071e85720c",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
    ]
   },
   {
@@ -51,7 +120,7 @@
    "source": [
     "inference_server_url = \"http://localhost:8000/v1\"\n",
     "\n",
-    "chat = ChatOpenAI(\n",
+    "llm = ChatOpenAI(\n",
     "    model=\"mosaicml/mpt-7b\",\n",
     "    openai_api_key=\"EMPTY\",\n",
     "    openai_api_base=inference_server_url,\n",
@@ -60,6 +129,14 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "34b18328-5e8b-4ff2-9b89-6fbb76b5c7f0",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -88,82 +165,66 @@
     "        content=\"Translate the following sentence from English to Italian: I love programming.\"\n",
     "    ),\n",
     "]\n",
-    "chat(messages)"
+    "llm.invoke(messages)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "55fc7046-a6dc-4720-8c0c-24a6db76a4f4",
+   "id": "a580a1e4-11a3-4277-bfba-bfb414ac7201",
    "metadata": {},
    "source": [
-    "You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use ChatPromptTemplate's format_prompt -- this returns a `PromptValue`, which you can convert to a string or `Message` object, depending on whether you want to use the formatted value as input to an llm or chat model.\n",
+    "## Chaining\n",
     "\n",
-    "For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "123980e9-0dee-4ce5-bde6-d964dd90129c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "template = (\n",
-    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
-    ")\n",
-    "system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
-    "human_template = \"{text}\"\n",
-    "human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "b2fb8c59-8892-4270-85a2-4f8ab276b75d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=' I love programming too.', additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [system_message_prompt, human_message_prompt]\n",
-    ")\n",
-    "\n",
-    "# get a chat completion from the formatted messages\n",
-    "chat(\n",
-    "    chat_prompt.format_prompt(\n",
-    "        input_language=\"English\", output_language=\"Italian\", text=\"I love programming.\"\n",
-    "    ).to_messages()\n",
-    ")"
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0bbd9861-2b94-4920-8708-b690004f4c4d",
+   "id": "dd0f4043-48bd-4245-8bdb-e7669666a277",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "265f5d51-0a76-4808-8d13-ef598ee6e366",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all features and configurations exposed via `langchain-openai`, head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html\n",
+    "\n",
+    "Refer to the vLLM [documentation](https://docs.vllm.ai/en/latest/) as well."
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "conda_pytorch_p310",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda_pytorch_p310"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -175,7 +236,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/apify_dataset.ipynb

@@ -13,7 +13,7 @@
     "\n",
     "## Prerequisites\n",
     "\n",
-    "You need to have an existing dataset on the Apify platform. If you don't have one, please first check out [this notebook](/docs/integrations/tools/apify) on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs. This example shows how to load a dataset produced by the [Website Content Crawler](https://apify.com/apify/website-content-crawler)."
+    "You need to have an existing dataset on the Apify platform. This example shows how to load a dataset produced by the [Website Content Crawler](https://apify.com/apify/website-content-crawler)."
    ]
   },
   {

docs/docs/integrations/document_loaders/bshtml.ipynb

@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BSHTMLLoader\n",
+    "\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with BeautifulSoup4 [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.html_bs.BSHTMLLoader.html).\n",
+    "\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [BSHTMLLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ❌ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| BSHTMLLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access BSHTMLLoader document loader you'll need to install the `langchain-community` integration package and the `bs4` python package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are needed to use the `BSHTMLLoader` class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community** and **bs4**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_community bs4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:\n",
+    "\n",
+    "- TODO: Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import BSHTMLLoader\n",
+    "\n",
+    "loader = BSHTMLLoader(\n",
+    "    file_path=\"./example_data/fake-content.html\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}, page_content='\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': './example_data/fake-content.html', 'title': 'Test Title'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}, page_content='\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "page = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    page.append(doc)\n",
+    "    if len(page) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        page = []\n",
+    "page[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding separator to BS4\n",
+    "\n",
+    "We can also pass a separator to use when calling get_text on the soup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='\n",
+      ", Test Title, \n",
+      ", \n",
+      ", \n",
+      ", My First Heading, \n",
+      ", My first paragraph., \n",
+      ", \n",
+      ", \n",
+      "' metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = BSHTMLLoader(\n",
+    "    file_path=\"./example_data/fake-content.html\", get_text_separator=\", \"\n",
+    ")\n",
+    "\n",
+    "docs = loader.load()\n",
+    "print(docs[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all BSHTMLLoader features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/document_loaders/example_data/example.md

@@ -0,0 +1,55 @@
+# Sample Markdown Document
+
+## Introduction
+
+Welcome to this sample Markdown document. Markdown is a lightweight markup language used for formatting text. It's widely used for documentation, readme files, and more.
+
+## Features
+
+### Headers
+
+Markdown supports multiple levels of headers:
+
+- **Header 1**: `# Header 1`
+- **Header 2**: `## Header 2`
+- **Header 3**: `### Header 3`
+
+### Lists
+
+#### Unordered List
+
+- Item 1
+- Item 2
+  - Subitem 2.1
+  - Subitem 2.2
+
+#### Ordered List
+
+1. First item
+2. Second item
+3. Third item
+
+### Links
+
+[OpenAI](https://www.openai.com) is an AI research organization.
+
+### Images
+
+Here's an example image:
+
+![Sample Image](https://via.placeholder.com/150)
+
+### Code
+
+#### Inline Code
+
+Use `code` for inline code snippets.
+
+#### Code Block
+
+```python
+def greet(name):
+    return f"Hello, {name}!"
+
+print(greet("World"))
+```

docs/docs/integrations/document_loaders/example_data/facebook_chat.json

@@ -30,6 +30,7 @@
         {
             "sender_name": "User 2",
             "timestamp_ms": 1675595060730,
+            "content": "",
             "photos": [
                 {"uri": "url_of_some_picture.jpg", "creation_timestamp": 1675595059}
             ]

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()
+```
+
+## Webpages
+
+The below document loaders allow you to load webpages.
+
+<CategoryTable category="webpage_loaders" />
+
+## PDFs
+
+The below document loaders allow you to load PDF documents.
+
+<CategoryTable category="pdf_loaders" />
+
+## Common File Types
+
+The below document loaders allow you to load data from common data formats.
+
+<CategoryTable category="common_loaders" />
+
+
+## All document loaders
+
+<IndexTable />

docs/docs/integrations/document_loaders/json.ipynb

@@ -0,0 +1,348 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# JSONLoader\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
+    "\n",
+    "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/file_loaders/json/)|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [JSONLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ✅ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| JSONLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access JSON document loader you'll need to install the `langchain-community` integration package as well as the ``jq`` python package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are required to use the `JSONLoader` class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community** and **jq**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_community jq "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:\n",
+    "\n",
+    "- TODO: Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import JSONLoader\n",
+    "\n",
+    "loader = JSONLoader(\n",
+    "    file_path=\"./example_data/facebook_chat.json\",\n",
+    "    jq_schema=\".messages[].content\",\n",
+    "    text_content=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}, page_content='Bye!')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pages = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    pages.append(doc)\n",
+    "    if len(pages) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(pages)\n",
+    "\n",
+    "        pages = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read from JSON Lines file\n",
+    "\n",
+    "If you want to load documents from a JSON Lines file, you pass `json_lines=True`\n",
+    "and specify `jq_schema` to extract `page_content` from a single JSON object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = JSONLoader(\n",
+    "    file_path=\"./example_data/facebook_chat_messages.jsonl\",\n",
+    "    jq_schema=\".content\",\n",
+    "    text_content=False,\n",
+    "    json_lines=True,\n",
+    ")\n",
+    "\n",
+    "docs = loader.load()\n",
+    "print(docs[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read specific content keys\n",
+    "\n",
+    "Another option is to set `jq_schema='.'` and provide a `content_key` in order to only load specific content:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='User 2' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = JSONLoader(\n",
+    "    file_path=\"./example_data/facebook_chat_messages.jsonl\",\n",
+    "    jq_schema=\".\",\n",
+    "    content_key=\"sender_name\",\n",
+    "    json_lines=True,\n",
+    ")\n",
+    "\n",
+    "docs = loader.load()\n",
+    "print(docs[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JSON file with jq schema `content_key`\n",
+    "\n",
+    "To load documents from a JSON file using the `content_key` within the jq schema, set `is_content_key_jq_parsable=True`. Ensure that `content_key` is compatible and can be parsed using the jq schema."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = JSONLoader(\n",
+    "    file_path=\"./example_data/facebook_chat.json\",\n",
+    "    jq_schema=\".messages[]\",\n",
+    "    content_key=\".content\",\n",
+    "    is_content_key_jq_parsable=True,\n",
+    ")\n",
+    "\n",
+    "docs = loader.load()\n",
+    "print(docs[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extracting metadata\n",
+    "\n",
+    "Generally, we want to include metadata available in the JSON file into the documents that we create from the content.\n",
+    "\n",
+    "The following demonstrates how metadata can be extracted using the `JSONLoader`.\n",
+    "\n",
+    "There are some key changes to be noted. In the previous example where we didn't collect the metadata, we managed to directly specify in the schema where the value for the `page_content` can be extracted from.\n",
+    "\n",
+    "In this example, we have to tell the loader to iterate over the records in the `messages` field. The jq_schema then has to be `.messages[]`\n",
+    "\n",
+    "This allows us to pass the records (dict) into the `metadata_func` that has to be implemented. The `metadata_func` is responsible for identifying which pieces of information in the record should be included in the metadata stored in the final `Document` object.\n",
+    "\n",
+    "Additionally, we now have to explicitly specify in the loader, via the `content_key` argument, the key from the record where the value for the `page_content` needs to be extracted from."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define the metadata extraction function.\n",
+    "def metadata_func(record: dict, metadata: dict) -> dict:\n",
+    "    metadata[\"sender_name\"] = record.get(\"sender_name\")\n",
+    "    metadata[\"timestamp_ms\"] = record.get(\"timestamp_ms\")\n",
+    "\n",
+    "    return metadata\n",
+    "\n",
+    "\n",
+    "loader = JSONLoader(\n",
+    "    file_path=\"./example_data/facebook_chat.json\",\n",
+    "    jq_schema=\".messages[]\",\n",
+    "    content_key=\"content\",\n",
+    "    metadata_func=metadata_func,\n",
+    ")\n",
+    "\n",
+    "docs = loader.load()\n",
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all JSONLoader features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/document_loaders/mathpix.ipynb

@@ -0,0 +1,178 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MathPixPDFLoader\n",
+    "\n",
+    "Inspired by Daniel Gross's snippet here: [https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21](https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21)\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [MathPixPDFLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ❌ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| MathPixPDFLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Sign up for Mathpix and [create an API key](https://mathpix.com/docs/ocr/creating-an-api-key) to set the `MATHPIX_API_KEY` variables in your environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if \"MATHPIX_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"MATHPIX_API_KEY\"] = getpass.getpass(\"Enter your Mathpix API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we are ready to initialize our loader:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import MathpixPDFLoader\n",
+    "\n",
+    "file_path = \"./example_data/layout-parser-paper.pdf\"\n",
+    "loader = MathpixPDFLoader(file_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "page = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    page.append(doc)\n",
+    "    if len(page) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        page = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all MathpixPDFLoader features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/document_loaders/pdfplumber.ipynb

@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PDFPlumber\n",
+    "\n",
+    "Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [PDFPlumberLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ❌ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| PDFPlumberLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are needed to use this loader."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import PDFPlumberLoader\n",
+    "\n",
+    "loader = PDFPlumberLoader(\"./example_data/layout-parser-paper.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}, page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportantinnovationsbyawideaudience.Thoughtherehavebeenon-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: DocumentImageAnalysis·DeepLearning·LayoutAnalysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,\\n1202\\nnuJ\\n12\\n]VC.sc[\\n2v84351.3012:viXra\\n')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "page = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    page.append(doc)\n",
+    "    if len(page) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        page = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all PDFPlumberLoader features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/document_loaders/pymupdf.ipynb

@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyMuPDF\n",
+    "\n",
+    "`PyMuPDF` is optimized for speed, and contains detailed metadata about the PDF and its pages. It returns one document per page.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [PyMuPDFLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PyMuPDFLoader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ❌ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| PyMuPDFLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are needed to use the `PyMuPDFLoader`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community** and **pymupdf**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-community pymupdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can initialize our loader and start loading documents. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import PyMuPDFLoader\n",
+    "\n",
+    "loader = PyMuPDFLoader(\"./example_data/layout-parser-paper.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load\n",
+    "\n",
+    "You can pass along any of the options from the [PyMuPDF documentation](https://pymupdf.readthedocs.io/en/latest/app1.html#plain-text/) as keyword arguments in the `load` call, and it will be pass along to the `get_text()` call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': ''}, page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (\\x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\\n· Character Recognition · Open Source library · Toolkit.\\n1\\nIntroduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [11,\\narXiv:2103.15348v2  [cs.CV]  21 Jun 2021\\n')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': ''}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "page = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    page.append(doc)\n",
+    "    if len(page) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        page = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all PyMuPDFLoader features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PyMuPDFLoader.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/document_loaders/pypdfdirectory.ipynb

@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyPDFDirectoryLoader\n",
+    "\n",
+    "This loader loads all PDF files from a specific directory.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [PyPDFDirectoryLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ❌ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| PyPDFDirectoryLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are needed for this loader."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import PyPDFDirectoryLoader\n",
+    "\n",
+    "directory_path = (\n",
+    "    \"../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf\"\n",
+    ")\n",
+    "loader = PyPDFDirectoryLoader(\"example_data/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': 0}, page_content='LayoutParser : A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1( \\x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1Allen Institute for AI\\nshannons@allenai.org\\n2Brown University\\nruochen zhang@brown.edu\\n3Harvard University\\n{melissadell,jacob carlson }@fas.harvard.edu\\n4University of Washington\\nbcgl@cs.washington.edu\\n5University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser , an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io .\\nKeywords: Document Image Analysis ·Deep Learning ·Layout Analysis\\n·Character Recognition ·Open Source library ·Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [ 11,arXiv:2103.15348v2  [cs.CV]  21 Jun 2021')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': 'example_data/layout-parser-paper.pdf', 'page': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "page = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    page.append(doc)\n",
+    "    if len(page) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        page = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all PyPDFDirectoryLoader features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/document_loaders/pypdfium2.ipynb

@@ -0,0 +1,188 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyPDFium2Loader\n",
+    "\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with PyPDFium2 [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.PyPDFium2Loader.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [PyPDFium2Loader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ❌ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| PyPDFium2Loader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "\n",
+    "To access PyPDFium2 document loader you'll need to install the `langchain-community` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import PyPDFium2Loader\n",
+    "\n",
+    "file_path = \"./example_data/layout-parser-paper.pdf\"\n",
+    "loader = PyPDFium2Loader(file_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'page': 0}, page_content='LayoutParser: A Unified Toolkit for Deep\\r\\nLearning Based Document Image Analysis\\r\\nZejiang Shen\\r\\n1\\r\\n(), Ruochen Zhang\\r\\n2\\r\\n, Melissa Dell\\r\\n3\\r\\n, Benjamin Charles Germain\\r\\nLee\\r\\n4\\r\\n, Jacob Carlson\\r\\n3\\r\\n, and Weining Li\\r\\n5\\r\\n1 Allen Institute for AI\\r\\nshannons@allenai.org 2 Brown University\\r\\nruochen zhang@brown.edu 3 Harvard University\\r\\n{melissadell,jacob carlson}@fas.harvard.edu\\r\\n4 University of Washington\\r\\nbcgl@cs.washington.edu 5 University of Waterloo\\r\\nw422li@uwaterloo.ca\\r\\nAbstract. Recent advances in document image analysis (DIA) have been\\r\\nprimarily driven by the application of neural networks. Ideally, research\\r\\noutcomes could be easily deployed in production and extended for further\\r\\ninvestigation. However, various factors like loosely organized codebases\\r\\nand sophisticated model configurations complicate the easy reuse of im\\x02portant innovations by a wide audience. Though there have been on-going\\r\\nefforts to improve reusability and simplify deep learning (DL) model\\r\\ndevelopment in disciplines like natural language processing and computer\\r\\nvision, none of them are optimized for challenges in the domain of DIA.\\r\\nThis represents a major gap in the existing toolkit, as DIA is central to\\r\\nacademic research across a wide range of disciplines in the social sciences\\r\\nand humanities. This paper introduces LayoutParser, an open-source\\r\\nlibrary for streamlining the usage of DL in DIA research and applica\\x02tions. The core LayoutParser library comes with a set of simple and\\r\\nintuitive interfaces for applying and customizing DL models for layout de\\x02tection, character recognition, and many other document processing tasks.\\r\\nTo promote extensibility, LayoutParser also incorporates a community\\r\\nplatform for sharing both pre-trained models and full document digiti\\x02zation pipelines. We demonstrate that LayoutParser is helpful for both\\r\\nlightweight and large-scale digitization pipelines in real-word use cases.\\r\\nThe library is publicly available at https://layout-parser.github.io.\\r\\nKeywords: Document Image Analysis· Deep Learning· Layout Analysis\\r\\n· Character Recognition· Open Source library· Toolkit.\\r\\n1 Introduction\\r\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\r\\ndocument image analysis (DIA) tasks including document image classification [11,\\r\\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\\n')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': './example_data/layout-parser-paper.pdf', 'page': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "page = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    page.append(doc)\n",
+    "    if len(page) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        page = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all PyPDFium2Loader features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/document_loaders/pypdfloader.ipynb

@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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 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",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [PyPDFLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ❌ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| PyPDFLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are required to use `PyPDFLoader`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "To use `PyPDFLoader` you need to have the `langchain-community` python package downloaded:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_community pypdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import PyPDFLoader\n",
+    "\n",
+    "loader = PyPDFLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'page': 0}, page_content='LayoutParser : A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1( \\x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1Allen Institute for AI\\nshannons@allenai.org\\n2Brown University\\nruochen zhang@brown.edu\\n3Harvard University\\n{melissadell,jacob carlson }@fas.harvard.edu\\n4University of Washington\\nbcgl@cs.washington.edu\\n5University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser , an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io .\\nKeywords: Document Image Analysis ·Deep Learning ·Layout Analysis\\n·Character Recognition ·Open Source library ·Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [ 11,arXiv:2103.15348v2  [cs.CV]  21 Jun 2021')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': './example_data/layout-parser-paper.pdf', 'page': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pages = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    pages.append(doc)\n",
+    "    if len(pages) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `PyPDFLoader` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/document_loaders/recursive_url.ipynb

@@ -7,7 +7,18 @@
    "source": [
     "# Recursive URL\n",
     "\n",
-    "The `RecursiveUrlLoader` lets you recursively scrape all child links from a root URL and parse them into Documents."
+    "The `RecursiveUrlLoader` lets you recursively scrape all child links from a root URL and parse them into Documents.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/web_loaders/recursive_url_loader/)|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [RecursiveUrlLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ✅ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| RecursiveUrlLoader | ✅ | ❌ | \n"
    ]
   },
   {
@@ -17,6 +28,12 @@
    "source": [
     "## Setup\n",
     "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are required to use the `RecursiveUrlLoader`.\n",
+    "\n",
+    "### Installation\n",
+    "\n",
     "The `RecursiveUrlLoader` lives in the `langchain-community` package. There's no other required packages, though you will get richer default Document metadata if you have ``beautifulsoup4` installed as well."
    ]
   },
@@ -186,6 +203,50 @@
     "That certainly looks like HTML that comes from the url https://docs.python.org/3.9/, which is what we expected. Let's now look at some variations we can make to our basic example that can be helpful in different situations. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b17b7202",
+   "metadata": {},
+   "source": [
+    "## Lazy loading\n",
+    "\n",
+    "If we're loading a  large number of Documents and our downstream operations can be done over subsets of all loaded Documents, we can lazily load our Documents one at a time to minimize our memory footprint:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b13e4d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/4j/2rz3865x6qg07tx43146py8h0000gn/T/ipykernel_73962/2110507528.py:6: XMLParsedAsHTMLWarning: It looks like you're parsing an XML document using an HTML parser. If this really is an HTML document (maybe it's XHTML?), you can ignore or filter this warning. If it's XML, you should know that using an XML parser will be more reliable. To parse this document as XML, make sure you have the lxml package installed, and pass the keyword argument `features=\"xml\"` into the BeautifulSoup constructor.\n",
+      "  soup = BeautifulSoup(html, \"lxml\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "pages = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    pages.append(doc)\n",
+    "    if len(pages) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        pages = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb039682",
+   "metadata": {},
+   "source": [
+    "In this example we never have more than 10 Documents loaded into memory at a time."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8f41cc89",
@@ -256,50 +317,6 @@
     "You can similarly pass in a `metadata_extractor` to customize how Document metadata is extracted from the HTTP response. See the [API reference](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader.html) for more on this."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "1dddbc94",
-   "metadata": {},
-   "source": [
-    "## Lazy loading\n",
-    "\n",
-    "If we're loading a  large number of Documents and our downstream operations can be done over subsets of all loaded Documents, we can lazily load our Documents one at a time to minimize our memory footprint:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "7d0114fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/4j/2rz3865x6qg07tx43146py8h0000gn/T/ipykernel_73962/2110507528.py:6: XMLParsedAsHTMLWarning: It looks like you're parsing an XML document using an HTML parser. If this really is an HTML document (maybe it's XHTML?), you can ignore or filter this warning. If it's XML, you should know that using an XML parser will be more reliable. To parse this document as XML, make sure you have the lxml package installed, and pass the keyword argument `features=\"xml\"` into the BeautifulSoup constructor.\n",
-      "  soup = BeautifulSoup(html, \"lxml\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f88a7c2f-35df-4c3a-b238-f91be2674b96",
-   "metadata": {},
-   "source": [
-    "In this example we never have more than 10 Documents loaded into memory at a time."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "3e4d1c8f",

docs/docs/integrations/document_loaders/sitemap.ipynb

@@ -6,9 +6,35 @@
    "source": [
     "# Sitemap\n",
     "\n",
-    "Extends from the `WebBaseLoader`, `SitemapLoader` loads a sitemap from a given URL, and then scrape and load all pages in the sitemap, returning each page as a Document.\n",
+    "Extends from the `WebBaseLoader`, `SitemapLoader` loads a sitemap from a given URL, and then scrapes and loads all pages in the sitemap, returning each page as a Document.\n",
     "\n",
-    "The scraping is done concurrently.  There are reasonable limits to concurrent requests, defaulting to 2 per second.  If you aren't concerned about being a good citizen, or you control the scrapped server, or don't care about load. Note, while this will speed up the scraping process, but it may cause the server to block you.  Be careful!"
+    "The scraping is done concurrently. There are reasonable limits to concurrent requests, defaulting to 2 per second.  If you aren't concerned about being a good citizen, or you control the scrapped server, or don't care about load you can increase this limit. Note, while this will speed up the scraping process, it may cause the server to block you. Be careful!\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/web_loaders/sitemap/)|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [SiteMapLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ✅ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| SiteMapLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access SiteMap document loader you'll need to install the `langchain-community` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are needed to run this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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:"
    ]
   },
   {
@@ -17,21 +43,55 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  nest_asyncio"
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fix notebook asyncio bug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# fixes a bug with asyncio and jupyter\n",
     "import nest_asyncio\n",
     "\n",
     "nest_asyncio.apply()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -43,13 +103,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sitemap_loader = SitemapLoader(web_path=\"https://api.python.langchain.com/sitemap.xml\")\n",
-    "\n",
-    "docs = sitemap_loader.load()"
+    "sitemap_loader = SitemapLoader(web_path=\"https://api.python.langchain.com/sitemap.xml\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fetching pages: 100%|##########| 28/28 [00:04<00:00,  6.83it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-05-15T00:29:42.163001+00:00', 'changefreq': 'weekly', 'priority': '1'}, page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = sitemap_loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-05-15T00:29:42.163001+00:00', 'changefreq': 'weekly', 'priority': '1'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].metadata)"
    ]
   },
   {
@@ -71,24 +181,37 @@
     "sitemap_loader.requests_kwargs = {\"verify\": False}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load\n",
+    "\n",
+    "You can also load the pages lazily in order to minimize the memory load."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-02-09T01:10:49.422114+00:00', 'changefreq': 'weekly', 'priority': '1'})"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fetching pages: 100%|##########| 28/28 [00:01<00:00, 19.06it/s]\n"
+     ]
     }
    ],
    "source": [
-    "docs[0]"
+    "page = []\n",
+    "for doc in sitemap_loader.lazy_load():\n",
+    "    page.append(doc)\n",
+    "    if len(page) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        page = []"
    ]
   },
   {
@@ -224,11 +347,13 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all SiteMapLoader features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader"
+   ]
   }
  ],
  "metadata": {
@@ -247,7 +372,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/unstructured_file.ipynb

@@ -7,20 +7,41 @@
    "source": [
     "# Unstructured\n",
     "\n",
-    "This notebook covers how to use `Unstructured` package to load files of many types. `Unstructured` currently supports loading of text files, powerpoints, html, pdfs, images, and more.\n",
+    "This notebook covers how to use `Unstructured` [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders) to load files of many types. `Unstructured` currently supports loading of text files, powerpoints, html, pdfs, images, and more.\n",
     "\n",
-    "Please see [this guide](/docs/integrations/providers/unstructured/) for more instructions on setting up Unstructured locally, including setting up required system dependencies."
+    "Please see [this guide](../../integrations/providers/unstructured.mdx) for more instructions on setting up Unstructured locally, including setting up required system dependencies.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [UnstructuredLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html) | [langchain_community](https://api.python.langchain.com/en/latest/unstructured_api_reference.html) | ✅ | ❌ | ✅ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| UnstructuredLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "By default, `langchain-unstructured` installs a smaller footprint that requires offloading of the partitioning logic to the Unstructured API, which requires an API key. If you use the local installation, you do not need an API key. To get your API key, head over to [this site](https://unstructured.io) and get an API key, and then set it in the cell below:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "2886982e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install package, compatible with API partitioning\n",
-    "%pip install --upgrade --quiet \"langchain-unstructured\""
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"UNSTRUCTURED_API_KEY\"] = getpass.getpass(\n",
+    "    \"Enter your Unstructured API key: \"\n",
+    ")"
    ]
   },
   {
@@ -28,14 +49,32 @@
    "id": "e75e2a6d",
    "metadata": {},
    "source": [
-    "### Local Partitioning (Optional)\n",
+    "### Installation\n",
     "\n",
-    "By default, `langchain-unstructured` installs a smaller footprint that requires\n",
-    "offloading of the partitioning logic to the Unstructured API.\n",
+    "#### Normal Installation\n",
     "\n",
-    "If you would like to run the partitioning logic locally, you will need to install\n",
-    "a combination of system dependencies, as outlined in the \n",
-    "[Unstructured documentation here](https://docs.unstructured.io/open-source/installation/full-installation).\n",
+    "The following packages are required to run the rest of this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9de83b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install package, compatible with API partitioning\n",
+    "%pip install --upgrade --quiet langchain-unstructured unstructured-client unstructured \"unstructured[pdf]\" python-magic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "637eda35",
+   "metadata": {},
+   "source": [
+    "#### Installation for Local\n",
+    "\n",
+    "If you would like to run the partitioning logic locally, you will need to install a combination of system dependencies, as outlined in the [Unstructured documentation here](https://docs.unstructured.io/open-source/installation/full-installation).\n",
     "\n",
     "For example, on Macs you can install the required dependencies with:\n",
     "\n",
@@ -47,7 +86,7 @@
     "brew install libxml2 libxslt\n",
     "```\n",
     "\n",
-    "You can install the required `pip` dependencies with:\n",
+    "You can install the required `pip` dependencies needed for local with:\n",
     "\n",
     "```bash\n",
     "pip install \"langchain-unstructured[local]\"\n",
@@ -59,120 +98,117 @@
    "id": "a9c1c775",
    "metadata": {},
    "source": [
-    "### Quickstart\n",
+    "## Initialization\n",
     "\n",
-    "To simply load a file as a document, you can use the LangChain `DocumentLoader.load` \n",
-    "interface:"
+    "The `UnstructuredLoader` allows loading from a variety of different file types. To read all about the `unstructured` package please refer to their [documentation](https://docs.unstructured.io/open-source/introduction/overview)/. In this example, we show loading from both a text file and a PDF file."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "79d3e549",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_unstructured import UnstructuredLoader\n",
     "\n",
-    "loader = UnstructuredLoader(\"./example_data/state_of_the_union.txt\")\n",
+    "file_paths = [\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    \"./example_data/state_of_the_union.txt\",\n",
+    "]\n",
     "\n",
-    "docs = loader.load()"
+    "\n",
+    "loader = UnstructuredLoader(file_paths)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b4ab0a79",
+   "id": "8b68dcab",
    "metadata": {},
    "source": [
-    "### Load list of files"
+    "## Load"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "092d9a0b",
+   "execution_count": 2,
+   "id": "8da59ef8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: NumExpr defaulting to 12 threads.\n",
+      "INFO: pikepdf C++ to Python logger bridge initialized\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((16.34, 213.36), (16.34, 253.36), (36.34, 253.36), (36.34, 213.36)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-07-25T21:28:58', 'page_number': 1, 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': 'd3ce55f220dfb75891b4394a18bcb973'}, page_content='1 2 0 2')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "97f7aa1f",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "whatsapp_chat.txt :  1/22/23, 6:30 PM - User 1: Hi! Im interested in your bag. Im offering $50. Let me know if you are in\n",
-      "state_of_the_union.txt :  May God bless you all. May God protect our troops.\n"
+      "{'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((16.34, 213.36), (16.34, 253.36), (36.34, 253.36), (36.34, 213.36)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-07-25T21:28:58', 'page_number': 1, 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': 'd3ce55f220dfb75891b4394a18bcb973'}\n"
      ]
     }
    ],
    "source": [
-    "file_paths = [\n",
-    "    \"./example_data/whatsapp_chat.txt\",\n",
-    "    \"./example_data/state_of_the_union.txt\",\n",
-    "]\n",
-    "\n",
-    "loader = UnstructuredLoader(file_paths)\n",
-    "\n",
-    "docs = loader.load()\n",
-    "\n",
-    "print(docs[0].metadata.get(\"filename\"), \": \", docs[0].page_content[:100])\n",
-    "print(docs[-1].metadata.get(\"filename\"), \": \", docs[-1].page_content[:100])"
+    "print(docs[0].metadata)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8de9ef16",
+   "id": "0d7f991b",
    "metadata": {},
    "source": [
-    "## PDF Example\n",
-    "\n",
-    "Processing PDF documents works exactly the same way. Unstructured detects the file type and extracts the same types of elements."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "672733fd",
-   "metadata": {},
-   "source": [
-    "### Define a Partitioning Strategy\n",
-    "\n",
-    "Unstructured document loader allow users to pass in a `strategy` parameter that lets Unstructured\n",
-    "know how to partition pdf and other OCR'd documents. Currently supported strategies are `\"auto\"`,\n",
-    "`\"hi_res\"`, `\"ocr_only\"`, and `\"fast\"`. Learn more about the different strategies\n",
-    "[here](https://docs.unstructured.io/open-source/core-functionality/partitioning#partition-pdf). \n",
-    "\n",
-    "Not all document types have separate hi res and fast partitioning strategies. For those document types, the `strategy` kwarg is\n",
-    "ignored. In some cases, the high res strategy will fallback to fast if there is a dependency missing\n",
-    "(i.e. a model for document partitioning). You can see how to apply a strategy to an\n",
-    "`UnstructuredLoader` below."
+    "## Lazy Load"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "60685353",
+   "execution_count": 4,
+   "id": "b05604d2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((16.34, 393.9), (16.34, 560.0), (36.34, 560.0), (36.34, 393.9)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'parent_id': '89565df026a24279aaea20dc08cedbec', 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': 'e9fa370aef7ee5c05744eb7bb7d9981b'}, page_content='2 v 8 4 3 5 1 . 3 0 1 2 : v i X r a'),\n",
-       " Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((157.62199999999999, 114.23496279999995), (157.62199999999999, 146.5141628), (457.7358962799999, 146.5141628), (457.7358962799999, 114.23496279999995)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'filetype': 'application/pdf', 'category': 'Title', 'element_id': 'bde0b230a1aa488e3ce837d33015181b'}, page_content='LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis'),\n",
-       " Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((134.809, 168.64029940800003), (134.809, 192.2517444), (480.5464199080001, 192.2517444), (480.5464199080001, 168.64029940800003)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'parent_id': 'bde0b230a1aa488e3ce837d33015181b', 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': '54700f902899f0c8c90488fa8d825bce'}, page_content='Zejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain Lee4, Jacob Carlson3, and Weining Li5'),\n",
-       " Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((207.23000000000002, 202.57205439999996), (207.23000000000002, 311.8195408), (408.12676, 311.8195408), (408.12676, 202.57205439999996)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'parent_id': 'bde0b230a1aa488e3ce837d33015181b', 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': 'b650f5867bad9bb4e30384282c79bcfe'}, page_content='1 Allen Institute for AI shannons@allenai.org 2 Brown University ruochen zhang@brown.edu 3 Harvard University {melissadell,jacob carlson}@fas.harvard.edu 4 University of Washington bcgl@cs.washington.edu 5 University of Waterloo w422li@uwaterloo.ca'),\n",
-       " Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((162.779, 338.45008160000003), (162.779, 566.8455408), (454.0372021523199, 566.8455408), (454.0372021523199, 338.45008160000003)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'links': [{'text': ':// layout - parser . github . io', 'url': 'https://layout-parser.github.io', 'start_index': 1477}], 'page_number': 1, 'parent_id': 'bde0b230a1aa488e3ce837d33015181b', 'filetype': 'application/pdf', 'category': 'NarrativeText', 'element_id': 'cfc957c94fe63c8fd7c7f4bcb56e75a7'}, page_content='Abstract. Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks. Ideally, research outcomes could be easily deployed in production and extended for further investigation. However, various factors like loosely organized codebases and sophisticated model configurations complicate the easy reuse of im- portant innovations by a wide audience. Though there have been on-going efforts to improve reusability and simplify deep learning (DL) model development in disciplines like natural language processing and computer vision, none of them are optimized for challenges in the domain of DIA. This represents a major gap in the existing toolkit, as DIA is central to academic research across a wide range of disciplines in the social sciences and humanities. This paper introduces LayoutParser, an open-source library for streamlining the usage of DL in DIA research and applica- tions. The core LayoutParser library comes with a set of simple and intuitive interfaces for applying and customizing DL models for layout de- tection, character recognition, and many other document processing tasks. To promote extensibility, LayoutParser also incorporates a community platform for sharing both pre-trained models and full document digiti- zation pipelines. We demonstrate that LayoutParser is helpful for both lightweight and large-scale digitization pipelines in real-word use cases. The library is publicly available at https://layout-parser.github.io.')]"
+       "Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((16.34, 213.36), (16.34, 253.36), (36.34, 253.36), (36.34, 213.36)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-07-25T21:28:58', 'page_number': 1, 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': 'd3ce55f220dfb75891b4394a18bcb973'}, page_content='1 2 0 2')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_unstructured import UnstructuredLoader\n",
+    "pages = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    pages.append(doc)\n",
     "\n",
-    "loader = UnstructuredLoader(\"./example_data/layout-parser-paper.pdf\", strategy=\"fast\")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "\n",
-    "docs[5:10]"
+    "pages[0]"
    ]
   },
   {
@@ -241,23 +277,6 @@
     "if you’d like to self-host the Unstructured API or run it locally."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e5fde16",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install package\n",
-    "%pip install \"langchain-unstructured\"\n",
-    "%pip install \"unstructured-client\"\n",
-    "\n",
-    "# Set API key\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"UNSTRUCTURED_API_KEY\"] = \"FAKE_API_KEY\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -358,8 +377,9 @@
     "Partitioning with the Unstructured API relies on the [Unstructured SDK\n",
     "Client](https://docs.unstructured.io/api-reference/api-services/sdk).\n",
     "\n",
-    "Below is an example showing how you can customize some features of the client and use your own\n",
-    "`requests.Session()`, pass in an alternative `server_url`, or customize the `RetryConfig` object for more control over how failed requests are handled."
+    "Below is an example showing how you can customize some features of the client and use your own `requests.Session()`, pass in an alternative `server_url`, or customize the `RetryConfig` object for more control over how failed requests are handled.\n",
+    "\n",
+    "Note that the example below may not use the latest version of the UnstructuredClient and there could be breaking changes in future releases. For the latest examples, refer to the [Unstructured Python SDK](https://docs.unstructured.io/api-reference/api-services/sdk-python) docs."
    ]
   },
   {
@@ -494,6 +514,16 @@
     "print(\"Number of LangChain documents:\", len(docs))\n",
     "print(\"Length of text in the document:\", len(docs[0].page_content))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce01aa40",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `UnstructuredLoader` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html"
+   ]
   }
  ],
  "metadata": {
@@ -512,7 +542,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/unstructured_markdown.ipynb

@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# UnstructuredMarkdownLoader\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with UnstructuredMarkdown [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.markdown.UnstructuredMarkdownLoader.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [UnstructuredMarkdownLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ❌ | ❌ | ✅ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| UnstructuredMarkdownLoader | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access UnstructuredMarkdownLoader document loader you'll need to install the `langchain-community` integration package and the `unstructured` python package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are needed to use this loader."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community** and **unstructured**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_community unstructured"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents. \n",
+    "\n",
+    "You can run the loader in one of two modes: \"single\" and \"elements\". If you use \"single\" mode, the document will be returned as a single `Document` object. If you use \"elements\" mode, the unstructured library will split the document into elements such as `Title` and `NarrativeText`. You can pass in additional `unstructured` kwargs after mode to apply different `unstructured` settings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import UnstructuredMarkdownLoader\n",
+    "\n",
+    "loader = UnstructuredMarkdownLoader(\n",
+    "    \"./example_data/example.md\",\n",
+    "    mode=\"single\",\n",
+    "    strategy=\"fast\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': './example_data/example.md'}, page_content='Sample Markdown Document\\n\\nIntroduction\\n\\nWelcome to this sample Markdown document. Markdown is a lightweight markup language used for formatting text. It\\'s widely used for documentation, readme files, and more.\\n\\nFeatures\\n\\nHeaders\\n\\nMarkdown supports multiple levels of headers:\\n\\nHeader 1: # Header 1\\n\\nHeader 2: ## Header 2\\n\\nHeader 3: ### Header 3\\n\\nLists\\n\\nUnordered List\\n\\nItem 1\\n\\nItem 2\\n\\nSubitem 2.1\\n\\nSubitem 2.2\\n\\nOrdered List\\n\\nFirst item\\n\\nSecond item\\n\\nThird item\\n\\nLinks\\n\\nOpenAI is an AI research organization.\\n\\nImages\\n\\nHere\\'s an example image:\\n\\nCode\\n\\nInline Code\\n\\nUse code for inline code snippets.\\n\\nCode Block\\n\\n```python def greet(name): return f\"Hello, {name}!\"\\n\\nprint(greet(\"World\")) ```')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': './example_data/example.md'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'source': './example_data/example.md', 'link_texts': ['OpenAI'], 'link_urls': ['https://www.openai.com'], 'last_modified': '2024-08-14T15:04:18', 'languages': ['eng'], 'parent_id': 'de1f74bf226224377ab4d8b54f215bb9', 'filetype': 'text/markdown', 'file_directory': './example_data', 'filename': 'example.md', 'category': 'NarrativeText', 'element_id': '898a542a261f7dc65e0072d1e847d535'}, page_content='OpenAI is an AI research organization.')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "page = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    page.append(doc)\n",
+    "    if len(page) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        page = []\n",
+    "page[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Elements\n",
+    "\n",
+    "In this example we will load in the `elements` mode, which will return a list of the different elements in the markdown document:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "29"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_community.document_loaders import UnstructuredMarkdownLoader\n",
+    "\n",
+    "loader = UnstructuredMarkdownLoader(\n",
+    "    \"./example_data/example.md\",\n",
+    "    mode=\"elements\",\n",
+    "    strategy=\"fast\",\n",
+    ")\n",
+    "\n",
+    "docs = loader.load()\n",
+    "len(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you see there are 29 elements that were pulled from the `example.md` file. The first element is the title of the document as expected:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Sample Markdown Document'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all UnstructuredMarkdownLoader features and configurations head to the API reference: https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}