bump 263 (#9156)

This is Part 2. See #9139 (Part 1).

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,28 +1,20 @@
 <!-- Thank you for contributing to LangChain!
 
-Replace this comment with:
+Replace this entire comment with:
   - Description: a description of the change, 
   - Issue: the issue # it fixes (if applicable),
   - Dependencies: any dependencies required for this change,
   - Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
   - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
 
-Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
+Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
+
+See contribution guidelines for more information on how to write/run tests, lint, etc: 
+https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
 
 If you're adding a new integration, please include:
   1. a test for the integration, preferably unit tests that do not rely on network access,
-  2. an example notebook showing its use.
+  2. an example notebook showing its use. These live is docs/extras directory.
 
-Maintainer responsibilities:
-  - General / Misc / if you don't know who to tag: @baskaryan
-  - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
-  - Models / Prompts: @hwchase17, @baskaryan
-  - Memory: @hwchase17
-  - Agents / Tools / Toolkits: @hinthornw
-  - Tracing / Callbacks: @agola11
-  - Async: @agola11
-
-If no one reviews your PR within a few days, feel free to @-mention the same people again.
-
-See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
+If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
  -->

.github/workflows/langchain_experimental_ci.yml

@@ -1,5 +1,5 @@
 ---
-name: libs/langchain-experimental CI
+name: libs/experimental CI
 
 on:
   push:

.github/workflows/langchain_experimental_release.yml

@@ -1,5 +1,5 @@
 ---
-name: libs/langchain-experimental Release
+name: libs/experimental Release
 
 on:
   pull_request:

.github/workflows/scheduled_test.yml

@@ -0,0 +1,42 @@
+name: Scheduled tests
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+  schedule:
+    - cron:  '0 13 * * *'
+
+env:
+  POETRY_VERSION: "1.4.2"
+
+jobs:
+  build:
+    defaults:
+      run:
+        working-directory: libs/langchain
+    runs-on: ubuntu-latest
+    environment: Scheduled testing
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    name: Python ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: "1.4.2"
+          working-directory: libs/langchain
+          install-command: |
+            echo "Running scheduled tests, installing dependencies with poetry..."
+            poetry install --with=test_integration
+      - name: Run tests
+        env:
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        run: |
+          make scheduled_tests
+        shell: bash

Makefile

@@ -43,7 +43,12 @@ spell_fix:
 
 help:
 	@echo '----'
-	@echo 'coverage                     - run unit tests and generate coverage report'
+	@echo 'clean                        - run docs_clean and api_docs_clean'
 	@echo 'docs_build                   - build the documentation'
 	@echo 'docs_clean                   - clean the documentation build artifacts'
 	@echo 'docs_linkcheck               - run linkchecker on the documentation'
+	@echo 'api_docs_build               - build the API Reference documentation'
+	@echo 'api_docs_clean               - clean the API Reference documentation build artifacts'
+	@echo 'api_docs_linkcheck           - run linkchecker on the API Reference documentation'
+	@echo 'spell_check               	- run codespell on the project'
+	@echo 'spell_fix               		- run codespell on the project and fix the errors'

README.md

@@ -18,10 +18,10 @@
 
 Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwchase17/langchainjs).
 
-**Production Support:** As you move your LangChains into production, we'd love to offer more comprehensive support.
-Please fill out [this form](https://6w1pwbss0py.typeform.com/to/rrbrdTH2) and we'll set up a dedicated support Slack channel.
+**Production Support:** As you move your LangChains into production, we'd love to offer more hands-on support.
+Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to share more about what you're building, and our team will get in touch.
 
-## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28
+## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
 
 In an effort to make `langchain` leaner and safer, we are moving select chains to `langchain_experimental`.
 This migration has already started, but we are remaining backwards compatible until 7/28.

docs/api_reference/conf.py

@@ -100,6 +100,9 @@ extensions = [
 ]
 source_suffix = [".rst"]
 
+# some autodoc pydantic options are repeated in the actual template.
+# potentially user error, but there may be bugs in the sphinx extension
+# with options not being passed through correctly (from either the location in the code)
 autodoc_pydantic_model_show_json = False
 autodoc_pydantic_field_list_validators = False
 autodoc_pydantic_config_members = False
@@ -112,13 +115,6 @@ autodoc_member_order = "groupwise"
 autoclass_content = "both"
 autodoc_typehints_format = "short"
 
-autodoc_default_options = {
-    "members": True,
-    "show-inheritance": True,
-    "inherited-members": "BaseModel",
-    "undoc-members": True,
-    "special-members": "__call__",
-}
 # autodoc_typehints = "description"
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["templates"]

docs/api_reference/create_api_rst.py

@@ -1,49 +1,215 @@
-"""Script for auto-generating api_reference.rst"""
-import glob
-import re
+"""Script for auto-generating api_reference.rst."""
+import importlib
+import inspect
+import typing
 from pathlib import Path
+from typing import TypedDict, Sequence, List, Dict, Literal, Union
+from enum import Enum
+
+from pydantic import BaseModel
 
 ROOT_DIR = Path(__file__).parents[2].absolute()
+HERE = Path(__file__).parent
+
 PKG_DIR = ROOT_DIR / "libs" / "langchain" / "langchain"
 EXP_DIR = ROOT_DIR / "libs" / "experimental" / "langchain_experimental"
-WRITE_FILE = Path(__file__).parent / "api_reference.rst"
-EXP_WRITE_FILE = Path(__file__).parent / "experimental_api_reference.rst"
+WRITE_FILE = HERE / "api_reference.rst"
+EXP_WRITE_FILE = HERE / "experimental_api_reference.rst"
 
 
-def load_members(dir: Path) -> dict:
-    members: dict = {}
-    for py in glob.glob(str(dir) + "/**/*.py", recursive=True):
-        module = py[len(str(dir)) + 1 :].replace(".py", "").replace("/", ".")
-        top_level = module.split(".")[0]
-        if top_level not in members:
-            members[top_level] = {"classes": [], "functions": []}
-        with open(py, "r") as f:
-            for line in f.readlines():
-                cls = re.findall(r"^class ([^_].*)\(", line)
-                members[top_level]["classes"].extend([module + "." + c for c in cls])
-                func = re.findall(r"^def ([^_].*)\(", line)
-                afunc = re.findall(r"^async def ([^_].*)\(", line)
-                func_strings = [module + "." + f for f in func + afunc]
-                members[top_level]["functions"].extend(func_strings)
-    return members
+ClassKind = Literal["TypedDict", "Regular", "Pydantic", "enum"]
 
 
-def construct_doc(pkg: str, members: dict) -> str:
+class ClassInfo(TypedDict):
+    """Information about a class."""
+
+    name: str
+    """The name of the class."""
+    qualified_name: str
+    """The fully qualified name of the class."""
+    kind: ClassKind
+    """The kind of the class."""
+    is_public: bool
+    """Whether the class is public or not."""
+
+
+class FunctionInfo(TypedDict):
+    """Information about a function."""
+
+    name: str
+    """The name of the function."""
+    qualified_name: str
+    """The fully qualified name of the function."""
+    is_public: bool
+    """Whether the function is public or not."""
+
+
+class ModuleMembers(TypedDict):
+    """A dictionary of module members."""
+
+    classes_: Sequence[ClassInfo]
+    functions: Sequence[FunctionInfo]
+
+
+def _load_module_members(module_path: str, namespace: str) -> ModuleMembers:
+    """Load all members of a module.
+
+    Args:
+        module_path: Path to the module.
+        namespace: the namespace of the module.
+
+    Returns:
+        list: A list of loaded module objects.
+    """
+    classes_: List[ClassInfo] = []
+    functions: List[FunctionInfo] = []
+    module = importlib.import_module(module_path)
+    for name, type_ in inspect.getmembers(module):
+        if not hasattr(type_, "__module__"):
+            continue
+        if type_.__module__ != module_path:
+            continue
+
+        if inspect.isclass(type_):
+            if type(type_) == typing._TypedDictMeta:  # type: ignore
+                kind: ClassKind = "TypedDict"
+            elif issubclass(type_, Enum):
+                kind = "enum"
+            elif issubclass(type_, BaseModel):
+                kind = "Pydantic"
+            else:
+                kind = "Regular"
+
+            classes_.append(
+                ClassInfo(
+                    name=name,
+                    qualified_name=f"{namespace}.{name}",
+                    kind=kind,
+                    is_public=not name.startswith("_"),
+                )
+            )
+        elif inspect.isfunction(type_):
+            functions.append(
+                FunctionInfo(
+                    name=name,
+                    qualified_name=f"{namespace}.{name}",
+                    is_public=not name.startswith("_"),
+                )
+            )
+        else:
+            continue
+
+    return ModuleMembers(
+        classes_=classes_,
+        functions=functions,
+    )
+
+
+def _merge_module_members(
+    module_members: Sequence[ModuleMembers],
+) -> ModuleMembers:
+    """Merge module members."""
+    classes_: List[ClassInfo] = []
+    functions: List[FunctionInfo] = []
+    for module in module_members:
+        classes_.extend(module["classes_"])
+        functions.extend(module["functions"])
+
+    return ModuleMembers(
+        classes_=classes_,
+        functions=functions,
+    )
+
+
+def _load_package_modules(
+    package_directory: Union[str, Path]
+) -> Dict[str, ModuleMembers]:
+    """Recursively load modules of a package based on the file system.
+
+    Traversal based on the file system makes it easy to determine which
+    of the modules/packages are part of the package vs. 3rd party or built-in.
+
+    Parameters:
+        package_directory: Path to the package directory.
+
+    Returns:
+        list: A list of loaded module objects.
+    """
+    package_path = (
+        Path(package_directory)
+        if isinstance(package_directory, str)
+        else package_directory
+    )
+    modules_by_namespace = {}
+
+    package_name = package_path.name
+
+    for file_path in package_path.rglob("*.py"):
+        if file_path.name.startswith("_"):
+            continue
+
+        relative_module_name = file_path.relative_to(package_path)
+
+        if relative_module_name.name.startswith("_"):
+            continue
+
+        # Get the full namespace of the module
+        namespace = str(relative_module_name).replace(".py", "").replace("/", ".")
+        # Keep only the top level namespace
+        top_namespace = namespace.split(".")[0]
+
+        try:
+            module_members = _load_module_members(
+                f"{package_name}.{namespace}", namespace
+            )
+            # Merge module members if the namespace already exists
+            if top_namespace in modules_by_namespace:
+                existing_module_members = modules_by_namespace[top_namespace]
+                _module_members = _merge_module_members(
+                    [existing_module_members, module_members]
+                )
+            else:
+                _module_members = module_members
+
+            modules_by_namespace[top_namespace] = _module_members
+
+        except ImportError as e:
+            print(f"Error: Unable to import module '{namespace}' with error: {e}")
+
+    return modules_by_namespace
+
+
+def _construct_doc(pkg: str, members_by_namespace: Dict[str, ModuleMembers]) -> str:
+    """Construct the contents of the reference.rst file for the given package.
+
+    Args:
+        pkg: The package name
+        members_by_namespace: The members of the package, dict organized by top level
+                              module contains a list of classes and functions
+                              inside of the top level namespace.
+
+    Returns:
+        The contents of the reference.rst file.
+    """
     full_doc = f"""\
-=============
+=======================
 ``{pkg}`` API Reference
-=============
+=======================
 
 """
-    for module, _members in sorted(members.items(), key=lambda kv: kv[0]):
-        classes = _members["classes"]
+    namespaces = sorted(members_by_namespace)
+
+    for module in namespaces:
+        _members = members_by_namespace[module]
+        classes = _members["classes_"]
         functions = _members["functions"]
         if not (classes or functions):
             continue
         section = f":mod:`{pkg}.{module}`"
+        underline = "=" * (len(section) + 1)
         full_doc += f"""\
 {section}
-{'=' * (len(section) + 1)}
+{underline}
 
 .. automodule:: {pkg}.{module}
     :no-members:
@@ -52,7 +218,6 @@ def construct_doc(pkg: str, members: dict) -> str:
 """
 
         if classes:
-            cstring = "\n    ".join(sorted(classes))
             full_doc += f"""\
 Classes
 --------------
@@ -60,13 +225,31 @@ Classes
 
 .. autosummary::
     :toctree: {module}
-    :template: class.rst
-
-    {cstring}
-
 """
+
+            for class_ in classes:
+                if not class_["is_public"]:
+                    continue
+
+                if class_["kind"] == "TypedDict":
+                    template = "typeddict.rst"
+                elif class_["kind"] == "enum":
+                    template = "enum.rst"
+                elif class_["kind"] == "Pydantic":
+                    template = "pydantic.rst"
+                else:
+                    template = "class.rst"
+
+                full_doc += f"""\
+    :template: {template}
+    
+    {class_["qualified_name"]}
+    
+"""
+
         if functions:
-            fstring = "\n    ".join(sorted(functions))
+            _functions = [f["qualified_name"] for f in functions if f["is_public"]]
+            fstring = "\n    ".join(sorted(_functions))
             full_doc += f"""\
 Functions
 --------------
@@ -83,12 +266,15 @@ Functions
 
 
 def main() -> None:
-    lc_members = load_members(PKG_DIR)
-    lc_doc = ".. _api_reference:\n\n" + construct_doc("langchain", lc_members)
+    """Generate the reference.rst file for each package."""
+    lc_members = _load_package_modules(PKG_DIR)
+    lc_doc = ".. _api_reference:\n\n" + _construct_doc("langchain", lc_members)
     with open(WRITE_FILE, "w") as f:
         f.write(lc_doc)
-    exp_members = load_members(EXP_DIR)
-    exp_doc = ".. _experimental_api_reference:\n\n" + construct_doc("langchain_experimental", exp_members)
+    exp_members = _load_package_modules(EXP_DIR)
+    exp_doc = ".. _experimental_api_reference:\n\n" + _construct_doc(
+        "langchain_experimental", exp_members
+    )
     with open(EXP_WRITE_FILE, "w") as f:
         f.write(exp_doc)
 

docs/api_reference/requirements.txt

@@ -1,4 +1,5 @@
 -e libs/langchain
+-e libs/experimental
 autodoc_pydantic==1.8.0
 myst_parser
 nbsphinx==0.8.9
@@ -10,4 +11,4 @@ sphinx-panels
 toml
 myst_nb
 sphinx_copybutton
-pydata-sphinx-theme==0.13.1
+pydata-sphinx-theme==0.13.1

docs/api_reference/templates/class.rst

@@ -5,17 +5,6 @@
 
 .. autoclass:: {{ objname }}
 
-   {% block methods %}
-   {% if methods %}
-   .. rubric:: {{ _('Methods') }}
-
-   .. autosummary::
-   {% for item in methods %}
-      ~{{ name }}.{{ item }}
-   {%- endfor %}
-   {% endif %}
-   {% endblock %}
-
    {% block attributes %}
    {% if attributes %}
    .. rubric:: {{ _('Attributes') }}
@@ -27,4 +16,21 @@
    {% endif %}
    {% endblock %}
 
+   {% block methods %}
+   {% if methods %}
+   .. rubric:: {{ _('Methods') }}
+
+   .. autosummary::
+   {% for item in methods %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+
+   {% for item in methods %}
+   .. automethod:: {{ name }}.{{ item }}
+   {%- endfor %}
+
+   {% endif %}
+   {% endblock %}
+
+
 .. example_links:: {{ objname }}

docs/api_reference/templates/enum.rst

@@ -0,0 +1,14 @@
+:mod:`{{module}}`.{{objname}}
+{{ underline }}==============
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+
+    {% block attributes %}
+    {% for item in attributes %}
+    .. autoattribute:: {{ item }}
+    {% endfor %}
+    {% endblock %}
+
+.. example_links:: {{ objname }}

docs/api_reference/templates/pydantic.rst

@@ -0,0 +1,22 @@
+:mod:`{{module}}`.{{objname}}
+{{ underline }}==============
+
+.. currentmodule:: {{ module }}
+
+.. autopydantic_model:: {{ objname }}
+    :model-show-json: False
+    :model-show-config-summary: False
+    :model-show-validator-members: False
+    :model-show-field-summary: False
+    :field-signature-prefix: param
+    :members:
+    :undoc-members:
+    :inherited-members:
+    :member-order: groupwise
+    :show-inheritance: True
+    :special-members: __call__
+
+    {% block attributes %}
+    {% endblock %}
+
+.. example_links:: {{ objname }}

docs/api_reference/templates/typeddict.rst

@@ -0,0 +1,14 @@
+:mod:`{{module}}`.{{objname}}
+{{ underline }}==============
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+
+    {% block attributes %}
+   {% for item in attributes %}
+  .. autoattribute:: {{ item }}
+   {% endfor %}
+   {% endblock %}
+
+.. example_links:: {{ objname }}

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

@@ -19,7 +19,7 @@
   {% block htmltitle %}
   <title>{{ title|striptags|e }}{{ titlesuffix }}</title>
   {% endblock %}
-  <link rel="canonical" href="http://scikit-learn.org/stable/{{pagename}}.html" />
+  <link rel="canonical" href="https://api.python.langchain.com/en/latest/{{pagename}}.html" />
 
   {% if favicon_url %}
   <link rel="shortcut icon" href="{{ favicon_url|e }}"/>

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

@@ -6,17 +6,6 @@
   {%- set top_container_cls = "sk-landing-container" %}
 {%- endif %}
 
-{% if theme_link_to_live_contributing_page|tobool %}
-{# Link to development page for live builds #}
-  {%- set development_link = "https://scikit-learn.org/dev/developers/index.html" %}
-{# Open on a new development page in new window/tab for live builds #}
-  {%- set development_attrs = 'target="_blank" rel="noopener noreferrer"' %}
-{%- else %}
-  {%- set development_link = pathto('developers/index') %}
-  {%- set development_attrs = '' %}
-{%- endif %}
-
-
 <nav id="navbar" class="{{ nav_bar_class }} navbar navbar-expand-md navbar-light bg-light py-0">
   <div class="container-fluid {{ top_container_cls }} px-0">
     {%- if logo_url %}

docs/docs_skeleton/docs/community.md

@@ -0,0 +1,54 @@
+# Community Navigator
+
+Hi! Thanks for being here. We’re lucky to have a community of so many passionate developers building with LangChain–we have so much to teach and learn from each other. Community members contribute code, host meetups, write blog posts, amplify each other’s work, become each other's customers and collaborators, and so much more.
+
+Whether you’re new to LangChain, looking to go deeper, or just want to get more exposure to the world of building with LLMs, this page can point you in the right direction. 
+
+- **🦜 Contribute to LangChain**
+
+- **🌍 Meetups, Events, and Hackathons**
+
+- **📣 Help Us Amplify Your Work**
+
+- **💬 Stay in the loop**
+
+
+# 🦜 Contribute to LangChain
+
+LangChain is the product of over 5,000+ contributions by 1,500+ contributors, and there is ******still****** so much to do together. Here are some ways to get involved:
+
+- **[Open a pull request](https://github.com/langchain-ai/langchain/issues):** we’d appreciate all forms of contributions–new features, infrastructure improvements, better documentation, bug fixes, etc. If you have an improvement or an idea, we’d love to work on it with you.
+- **[Read our contributor guidelines:](https://github.com/langchain-ai/langchain/blob/bbd22b9b761389a5e40fc45b0570e1830aabb707/.github/CONTRIBUTING.md)** We ask contributors to follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow, run a few local checks for formatting, linting, and testing before submitting, and follow certain documentation and testing conventions.
+    - **First time contributor?** [Try one of these PRs with the “good first issue” tag](https://github.com/langchain-ai/langchain/contribute).
+- **Become an expert:** our experts help the community by answering product questions in Discord. If that’s a role you’d like to play, we’d be so grateful! (And we have some special experts-only goodies/perks we can tell you more about). Send us an email to introduce yourself at hello@langchain.dev and we’ll take it from there!
+- **Integrate with LangChain:** if your product integrates with LangChain–or aspires to–we want to help make sure the experience is as smooth as possible for you and end users. Send us an email at hello@langchain.dev and tell us what you’re working on.
+    - **Become an Integration Maintainer:** Partner with our team to ensure your integration stays up-to-date and talk directly with users (and answer their inquiries) in our Discord. Introduce yourself at hello@langchain.dev if you’d like to explore this role.
+
+
+# 🌍 Meetups, Events, and Hackathons
+
+One of our favorite things about working in AI is how much enthusiasm there is for building together. We want to help make that as easy and impactful for you as possible! 
+- **Find a meetup, hackathon, or webinar:** you can find the one for you on on our [global events calendar](https://mirror-feeling-d80.notion.site/0bc81da76a184297b86ca8fc782ee9a3?v=0d80342540df465396546976a50cfb3f).  
+    - **Submit an event to our calendar:** email us at events@langchain.dev with a link to your event page! We can also help you spread the word with our local communities.
+- **Host a meetup:** If you want to bring a group of builders together, we want to help! We can publicize your event on our event calendar/Twitter, share with our local communities in Discord, send swag, or potentially hook you up with a sponsor. Email us at events@langchain.dev to tell us about your event!
+- **Become a meetup sponsor:** we often hear from groups of builders that want to get together, but are blocked or limited on some dimension (space to host, budget for snacks, prizes to distribute, etc.). If you’d like to help, send us an email to events@langchain.dev we can share more about how it works!
+- **Speak at an event:** meetup hosts are always looking for great speakers, presenters, and panelists. If you’d like to do that at an event, send us an email to hello@langchain.dev with more information about yourself, what you want to talk about, and what city you’re based in and we’ll try to match you with an upcoming event!
+- **Tell us about your LLM community:** If you host or participate in a community that would welcome support from LangChain and/or our team, send us an email at hello@langchain.dev and let us know how we can help.
+
+# 📣 Help Us Amplify Your Work
+
+If you’re working on something you’re proud of, and think the LangChain community would benefit from knowing about it, we want to help you show it off.
+
+- **Post about your work and mention us:** we love hanging out on Twitter to see what people in the space are talking about and working on. If you tag [@langchainai](https://twitter.com/LangChainAI), we’ll almost certainly see it and can show you some love.
+- **Publish something on our blog:** if you’re writing about your experience building with LangChain, we’d love to post (or crosspost) it on our blog! E-mail hello@langchain.dev with a draft of your post! Or even an idea for something you want to write about.
+- **Get your product onto our [integrations hub](https://integrations.langchain.com/):** Many developers take advantage of our seamless integrations with other products, and come to our integrations hub to find out who those are. If you want to get your product up there, tell us about it (and how it works with LangChain) at hello@langchain.dev.
+
+# ☀️ Stay in the loop
+
+Here’s where our team hangs out, talks shop, spotlights cool work, and shares what we’re up to. We’d love to see you there too.
+
+- **[Twitter](https://twitter.com/LangChainAI):** we post about what we’re working on and what cool things we’re seeing in the space. If you tag @langchainai in your post, we’ll almost certainly see it, and can snow you some love!
+- **[Discord](https://discord.gg/6adMQxSpJS):** connect with with >30k developers who are building with LangChain
+- **[GitHub](https://github.com/langchain-ai/langchain):** open pull requests, contribute to a discussion, and/or contribute
+- **[Subscribe to our bi-weekly Release Notes](https://6w1pwbss0py.typeform.com/to/KjZB1auB):** a twice/month email roundup of the coolest things going on in our orbit
+- **Slack:** if you’re building an application in production at your company, we’d love to get into a Slack channel together. Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) and we’ll get in touch about setting one up.

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

@@ -8,9 +8,9 @@ import DocCardList from "@theme/DocCardList";
 
 Building applications with language models involves many moving parts. One of the most critical components is ensuring that the outcomes produced by your models are reliable and useful across a broad array of inputs, and that they work well with your application's other software components. Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks. 
 
-The guides in this section review the APIs and functionality LangChain provides to help yous better evaluate your applications. Evaluation and testing are both critical when thinking about deploying LLM applications, since production environments require repeatable and useful outcomes.
+The guides in this section review the APIs and functionality LangChain provides to help you better evaluate your applications. Evaluation and testing are both critical when thinking about deploying LLM applications, since production environments require repeatable and useful outcomes.
 
-LangChain offers various types of evaluators to help you measure performance and integrity on diverse data, and we hope to encourage the the community to create and share other useful evaluators so everyone can improve. These docs will introduce the evaluator types, how to use them, and provide some examples of their use in real-world scenarios.
+LangChain offers various types of evaluators to help you measure performance and integrity on diverse data, and we hope to encourage the community to create and share other useful evaluators so everyone can improve. These docs will introduce the evaluator types, how to use them, and provide some examples of their use in real-world scenarios.
 
 Each evaluator type in LangChain comes with ready-to-use implementations and an extensible API that allows for customization according to your unique requirements. Here are some of the types of evaluators we offer:
 

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

@@ -5,8 +5,8 @@ import DocCardList from "@theme/DocCardList";
 LangSmith helps you trace and evaluate your language model applications and intelligent agents to help you
 move from prototype to production.
 
-Check out the [interactive walkthrough](walkthrough) below to get started.
+Check out the [interactive walkthrough](/docs/guides/langsmith/walkthrough) below to get started.
 
 For more information, please refer to the [LangSmith documentation](https://docs.smith.langchain.com/)
 
-<DocCardList />
+<DocCardList />

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

@@ -12,7 +12,7 @@ Here are the agents available in LangChain.
 
 ### [Zero-shot ReAct](/docs/modules/agents/agent_types/react.html)
 
-This agent uses the [ReAct](https://arxiv.org/pdf/2205.00445.pdf) framework to determine which tool to use
+This agent uses the [ReAct](https://arxiv.org/pdf/2210.03629) framework to determine which tool to use
 based solely on the tool's description. Any number of tools can be provided.
 This agent requires that a description is provided for each tool.
 
@@ -28,7 +28,7 @@ navigating around a browser.
 ### [OpenAI Functions](/docs/modules/agents/agent_types/openai_functions_agent.html)
 
 Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been explicitly fine-tuned to detect when a
-function should to be called and respond with the inputs that should be passed to the function.
+function should be called and respond with the inputs that should be passed to the function.
 The OpenAI Functions Agent is designed to work with these models.
 
 ### [Conversational](/docs/modules/agents/agent_types/chat_conversation_agent.html)

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

@@ -1,6 +1,6 @@
 # OpenAI functions
 
-Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should to be called and respond with the inputs that should be passed to the function.
+Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should be called and respond with the inputs that should be passed to the function.
 In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions.
 The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API.
 

docs/docs_skeleton/docs/modules/index.mdx

@@ -18,5 +18,3 @@ Let chains choose which tools to use given high-level directives
 Persist application state between runs of a chain
 #### [Callbacks](/docs/modules/callbacks/)
 Log and stream intermediate steps of any chain
-#### [Evaluation](/docs/modules/evaluation/)
-Evaluate the performance of a chain.

docs/docs_skeleton/docs/modules/model_io/prompts/index.mdx

@@ -3,10 +3,12 @@ sidebar_position: 0
 ---
 # Prompts
 
-The new way of programming models is through prompts.
-A **prompt** refers to the input to the model.
-This input is often constructed from multiple components.
-LangChain provides several classes and functions to make constructing and working with prompts easy.
+A prompt for a language model is a set of instructions or input provided by a user to
+guide the model's response, helping it understand the context and generate relevant
+and coherent language-based output, such as answering questions, completing sentences,
+or engaging in a conversation.
 
-- [Prompt templates](/docs/modules/model_io/prompts/prompt_templates/): Parametrize model inputs
+LangChain provides several classes and functions to help construct and work with prompts.
+
+- [Prompt templates](/docs/modules/model_io/prompts/prompt_templates/): Parametrized model inputs
 - [Example selectors](/docs/modules/model_io/prompts/example_selectors/): Dynamically select examples to include in prompts

docs/docs_skeleton/docs/modules/model_io/prompts/prompt_templates/index.mdx

@@ -4,18 +4,15 @@ sidebar_position: 0
 
 # Prompt templates
 
-Language models take text as input - that text is commonly referred to as a prompt.
-Typically this is not simply a hardcoded string but rather a combination of a template, some examples, and user input.
-LangChain provides several classes and functions to make constructing and working with prompts easy.
+Prompt templates are pre-defined recipes for generating prompts for language models.
 
-## What is a prompt template?
+A template may include instructions, few shot examples, and specific context and
+questions appropriate for a given task.
 
-A prompt template refers to a reproducible way to generate a prompt. It contains a text string ("the template"), that can take in a set of parameters from the end user and generates a prompt.
+LangChain provides tooling to create and work with prompt templates.
 
-A prompt template can contain:
-- instructions to the language model,
-- a set of few shot examples to help the language model generate a better response,
-- a question to the language model.
+LangChain strives to create model agnostic templates to make it easy to reuse
+existing templates across different language models.
 
 import GetStarted from "@snippets/modules/model_io/prompts/prompt_templates/get_started.mdx"
 

docs/docs_skeleton/docs/use_cases/apis/api.mdx

@@ -1,9 +0,0 @@
----
-sidebar_position: 0
----
-# API chains
-APIChain enables using LLMs to interact with APIs to retrieve relevant information. Construct the chain by providing a question relevant to the provided API documentation.
-
-import Example from "@snippets/modules/chains/popular/api.mdx"
-
-<Example/>

docs/docs_skeleton/docs/use_cases/summarization/summarize.mdx

@@ -1,8 +0,0 @@
-# Summarization
-
-A summarization chain can be used to summarize multiple documents. One way is to input multiple smaller documents, after they have been divided into chunks, and operate over them with a MapReduceDocumentsChain. You can also choose instead for the chain that does summarization to be a StuffDocumentsChain, or a RefineDocumentsChain.
-
-import Example from "@snippets/modules/chains/popular/summarize.mdx"
-
-<Example/>
-

docs/docs_skeleton/docs/use_cases/web_scraping/index.mdx

@@ -0,0 +1,9 @@
+---
+sidebar_position: 3
+---
+
+# Web Scraping
+
+Web scraping has historically been a challenging endeavor due to the ever-changing nature of website structures, making it tedious for developers to maintain their scraping scripts. Traditional methods often rely on specific HTML tags and patterns which, when altered, can disrupt data extraction processes.
+
+Enter the LLM-based method for parsing HTML: By leveraging the capabilities of LLMs, and especially OpenAI Functions in LangChain's extraction chain, developers can instruct the model to extract only the desired data in a specified format. This method not only streamlines the extraction process but also significantly reduces the time spent on manual debugging and script modifications. Its adaptability means that even if websites undergo significant design changes, the extraction remains consistent and robust. This level of resilience translates to reduced maintenance efforts, cost savings, and ensures a higher quality of extracted data. Compared to its predecessors, LLM-based approach wins out the web scraping domain by transforming a historically cumbersome task into a more automated and efficient process.

docs/docs_skeleton/docusaurus.config.js

@@ -128,6 +128,10 @@ const config = {
           hideable: true,
         },
       },
+      colorMode: {
+        disableSwitch: false,
+        respectPrefersColorScheme: true,
+      },
       prism: {
         theme: {
           ...baseLightCodeBlockTheme,

docs/docs_skeleton/package-lock.json

@@ -12,7 +12,7 @@
         "@docusaurus/preset-classic": "2.4.0",
         "@docusaurus/remark-plugin-npm2yarn": "^2.4.0",
         "@mdx-js/react": "^1.6.22",
-        "@mendable/search": "^0.0.125",
+        "@mendable/search": "^0.0.150",
         "clsx": "^1.2.1",
         "json-loader": "^0.5.7",
         "process": "^0.11.10",
@@ -3212,10 +3212,11 @@
       }
     },
     "node_modules/@mendable/search": {
-      "version": "0.0.125",
-      "resolved": "https://registry.npmjs.org/@mendable/search/-/search-0.0.125.tgz",
-      "integrity": "sha512-Mb1J3zDhOyBZV9cXqJocSOBNYGpe8+LQDqd9n9laPWxosSJcSTUewqtlIbMerrYsScBsxskoSiWgRsc7xF5z0Q==",
+      "version": "0.0.150",
+      "resolved": "https://registry.npmjs.org/@mendable/search/-/search-0.0.150.tgz",
+      "integrity": "sha512-Eb5SeAWlMxzEim/8eJ/Ysn01Pyh39xlPBzRBw/5OyOBhti0HVLXk4wd1Fq2TKgJC2ppQIvhEKO98PUcj9dNDFw==",
       "dependencies": {
+        "html-react-parser": "^4.2.0",
         "posthog-js": "^1.45.1"
       },
       "peerDependencies": {
@@ -8332,6 +8333,33 @@
         "safe-buffer": "~5.1.0"
       }
     },
+    "node_modules/html-dom-parser": {
+      "version": "4.0.0",
+      "resolved": "https://registry.npmjs.org/html-dom-parser/-/html-dom-parser-4.0.0.tgz",
+      "integrity": "sha512-TUa3wIwi80f5NF8CVWzkopBVqVAtlawUzJoLwVLHns0XSJGynss4jiY0mTWpiDOsuyw+afP+ujjMgRh9CoZcXw==",
+      "dependencies": {
+        "domhandler": "5.0.3",
+        "htmlparser2": "9.0.0"
+      }
+    },
+    "node_modules/html-dom-parser/node_modules/htmlparser2": {
+      "version": "9.0.0",
+      "resolved": "https://registry.npmjs.org/htmlparser2/-/htmlparser2-9.0.0.tgz",
+      "integrity": "sha512-uxbSI98wmFT/G4P2zXx4OVx04qWUmyFPrD2/CNepa2Zo3GPNaCaaxElDgwUrwYWkK1nr9fft0Ya8dws8coDLLQ==",
+      "funding": [
+        "https://github.com/fb55/htmlparser2?sponsor=1",
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/fb55"
+        }
+      ],
+      "dependencies": {
+        "domelementtype": "^2.3.0",
+        "domhandler": "^5.0.3",
+        "domutils": "^3.1.0",
+        "entities": "^4.5.0"
+      }
+    },
     "node_modules/html-entities": {
       "version": "2.4.0",
       "resolved": "https://registry.npmjs.org/html-entities/-/html-entities-2.4.0.tgz",
@@ -8375,6 +8403,20 @@
         "node": ">= 12"
       }
     },
+    "node_modules/html-react-parser": {
+      "version": "4.2.0",
+      "resolved": "https://registry.npmjs.org/html-react-parser/-/html-react-parser-4.2.0.tgz",
+      "integrity": "sha512-gzU55AS+FI6qD7XaKe5BLuLFM2Xw0/LodfMWZlxV9uOHe7LCD5Lukx/EgYuBI3c0kLu0XlgFXnSzO0qUUn3Vrg==",
+      "dependencies": {
+        "domhandler": "5.0.3",
+        "html-dom-parser": "4.0.0",
+        "react-property": "2.0.0",
+        "style-to-js": "1.1.3"
+      },
+      "peerDependencies": {
+        "react": "0.14 || 15 || 16 || 17 || 18"
+      }
+    },
     "node_modules/html-tags": {
       "version": "3.3.1",
       "resolved": "https://registry.npmjs.org/html-tags/-/html-tags-3.3.1.tgz",
@@ -11762,6 +11804,11 @@
         "webpack": ">=4.41.1 || 5.x"
       }
     },
+    "node_modules/react-property": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/react-property/-/react-property-2.0.0.tgz",
+      "integrity": "sha512-kzmNjIgU32mO4mmH5+iUyrqlpFQhF8K2k7eZ4fdLSOPFrD1XgEuSBv9LDEgxRXTMBqMd8ppT0x6TIzqE5pdGdw=="
+    },
     "node_modules/react-router": {
       "version": "5.3.4",
       "resolved": "https://registry.npmjs.org/react-router/-/react-router-5.3.4.tgz",
@@ -13127,6 +13174,22 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/style-to-js": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/style-to-js/-/style-to-js-1.1.3.tgz",
+      "integrity": "sha512-zKI5gN/zb7LS/Vm0eUwjmjrXWw8IMtyA8aPBJZdYiQTXj4+wQ3IucOLIOnF7zCHxvW8UhIGh/uZh/t9zEHXNTQ==",
+      "dependencies": {
+        "style-to-object": "0.4.1"
+      }
+    },
+    "node_modules/style-to-js/node_modules/style-to-object": {
+      "version": "0.4.1",
+      "resolved": "https://registry.npmjs.org/style-to-object/-/style-to-object-0.4.1.tgz",
+      "integrity": "sha512-HFpbb5gr2ypci7Qw+IOhnP2zOU7e77b+rzM+wTzXzfi1PrtBCX0E7Pk4wL4iTLnhzZ+JgEGAhX81ebTg/aYjQw==",
+      "dependencies": {
+        "inline-style-parser": "0.1.1"
+      }
+    },
     "node_modules/style-to-object": {
       "version": "0.3.0",
       "resolved": "https://registry.npmjs.org/style-to-object/-/style-to-object-0.3.0.tgz",

docs/docs_skeleton/package.json

@@ -23,7 +23,7 @@
     "@docusaurus/preset-classic": "2.4.0",
     "@docusaurus/remark-plugin-npm2yarn": "^2.4.0",
     "@mdx-js/react": "^1.6.22",
-    "@mendable/search": "^0.0.125",
+    "@mendable/search": "^0.0.150",
     "clsx": "^1.2.1",
     "json-loader": "^0.5.7",
     "process": "^0.11.10",

docs/docs_skeleton/sidebars.js

@@ -75,6 +75,7 @@ module.exports = {
       slug: "additional_resources",
       },
     },
+    'community'
   ],
   integrations: [
     {

docs/docs_skeleton/vercel.json

@@ -556,6 +556,14 @@
       "source": "/docs/integrations/llamacpp",
       "destination": "/docs/integrations/providers/llamacpp"
     },
+    {
+      "source": "/en/latest/integrations/log10.html",
+      "destination": "/docs/integrations/providers/log10"
+    },
+    {
+      "source": "/docs/integrations/log10",
+      "destination": "/docs/integrations/providers/log10"
+    },
     {
       "source": "/en/latest/integrations/mediawikidump.html",
       "destination": "/docs/integrations/providers/mediawikidump"
@@ -3951,6 +3959,10 @@
     {
       "source": "/docs/modules/chains/additional/tagging",
       "destination": "/docs/use_cases/tagging"
+    },
+    {
+      "source": "docs/integrations/providers/agent_with_wandb_tracing",
+      "destination": "docs/integrations/providers/wandb_tracing"
     }
   ]
 }

docs/extras/guides/adapters/openai.ipynb

@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "700a516b",
+   "metadata": {},
+   "source": [
+    "# OpenAI Adapter\n",
+    "\n",
+    "A lot of people get started with OpenAI but want to explore other models. LangChain's integrations with many model providers make this easy to do so. While LangChain has it's own message and model APIs, we've also made it as easy as possible to explore other models by exposing an adapter to adapt LangChain models to the OpenAI api.\n",
+    "\n",
+    "At the moment this only deals with output and does not return other information (token counts, stop reasons, etc)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6017f26a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "from langchain.adapters import openai as lc_openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b522ceda",
+   "metadata": {},
+   "source": [
+    "## ChatCompletion.create"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "1d22eb61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [{\"role\": \"user\", \"content\": \"hi\"}]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d550d3ad",
+   "metadata": {},
+   "source": [
+    "Original OpenAI call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e1d27dfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = openai.ChatCompletion.create(\n",
+    "    messages=messages, \n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "012d81ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result[\"choices\"][0]['message'].to_dict_recursive()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db5b5500",
+   "metadata": {},
+   "source": [
+    "LangChain OpenAI wrapper call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "87c2d515",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "    messages=messages, \n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c67a5ac8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result[\"choices\"][0]['message']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "034ba845",
+   "metadata": {},
+   "source": [
+    "Swapping out model providers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7a2c011c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "    messages=messages, \n",
+    "    model=\"claude-2\", \n",
+    "    temperature=0, \n",
+    "    provider=\"ChatAnthropic\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f7c94827",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': ' Hello!'}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result[\"choices\"][0]['message']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb3f181d",
+   "metadata": {},
+   "source": [
+    "## ChatCompletion.stream"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7b8cd18",
+   "metadata": {},
+   "source": [
+    "Original OpenAI call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "fd8cb1ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ''}\n",
+      "{'content': 'Hello'}\n",
+      "{'content': '!'}\n",
+      "{'content': ' How'}\n",
+      "{'content': ' can'}\n",
+      "{'content': ' I'}\n",
+      "{'content': ' assist'}\n",
+      "{'content': ' you'}\n",
+      "{'content': ' today'}\n",
+      "{'content': '?'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in openai.ChatCompletion.create(\n",
+    "    messages = messages,\n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0,\n",
+    "    stream=True\n",
+    "):\n",
+    "    print(c[\"choices\"][0]['delta'].to_dict_recursive())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b2a076b",
+   "metadata": {},
+   "source": [
+    "LangChain OpenAI wrapper call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "9521218c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ''}\n",
+      "{'content': 'Hello'}\n",
+      "{'content': '!'}\n",
+      "{'content': ' How'}\n",
+      "{'content': ' can'}\n",
+      "{'content': ' I'}\n",
+      "{'content': ' assist'}\n",
+      "{'content': ' you'}\n",
+      "{'content': ' today'}\n",
+      "{'content': '?'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in lc_openai.ChatCompletion.create(\n",
+    "    messages = messages,\n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0,\n",
+    "    stream=True\n",
+    "):\n",
+    "    print(c[\"choices\"][0]['delta'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fc39750",
+   "metadata": {},
+   "source": [
+    "Swapping out model providers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "68f0214e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ' Hello'}\n",
+      "{'content': '!'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in lc_openai.ChatCompletion.create(\n",
+    "    messages = messages,\n",
+    "    model=\"claude-2\", \n",
+    "    temperature=0,\n",
+    "    stream=True,\n",
+    "    provider=\"ChatAnthropic\",\n",
+    "):\n",
+    "    print(c[\"choices\"][0]['delta'])"
+   ]
+  }
+ ],
+ "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": 5
+}

docs/extras/guides/expression_language/cookbook.ipynb

@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "id": "466b65b3",
    "metadata": {},
    "outputs": [],
@@ -171,9 +171,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "decf7710",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -202,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "f799664d",
    "metadata": {},
    "outputs": [],
@@ -347,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "5d3d8ffe",
    "metadata": {},
    "outputs": [],
@@ -368,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "id": "33be32af",
    "metadata": {},
    "outputs": [],
@@ -380,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "id": "df3f3fa2",
    "metadata": {},
    "outputs": [],
@@ -424,9 +422,7 @@
    "cell_type": "code",
    "execution_count": 18,
    "id": "f3040b0c",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -477,9 +473,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "7ee8b2d4",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -515,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 4,
    "id": "3f30c348",
    "metadata": {},
    "outputs": [],
@@ -526,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "id": "64ab1dbf",
    "metadata": {},
    "outputs": [],
@@ -544,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 6,
    "id": "7d628c97",
    "metadata": {},
    "outputs": [],
@@ -559,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 7,
    "id": "f60a5d0f",
    "metadata": {},
    "outputs": [],
@@ -572,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 8,
    "id": "7d007db6",
    "metadata": {},
    "outputs": [],
@@ -589,25 +583,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 16,
    "id": "5c32cc89",
    "metadata": {},
    "outputs": [],
    "source": [
-    "conversational_qa_chain = RunnableMap({\n",
-    "    \"standalone_question\": {\n",
-    "        \"question\": lambda x: x[\"question\"],\n",
-    "        \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
-    "    } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
-    "}) | {\n",
+    "_inputs = RunnableMap(\n",
+    "    {\n",
+    "        \"standalone_question\": {\n",
+    "            \"question\": lambda x: x[\"question\"],\n",
+    "            \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
+    "        } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
+    "    }\n",
+    ")\n",
+    "_context = {\n",
     "    \"context\": itemgetter(\"standalone_question\") | retriever | _combine_documents,\n",
     "    \"question\": lambda x: x[\"standalone_question\"]\n",
-    "} | ANSWER_PROMPT | ChatOpenAI()"
+    "}\n",
+    "conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | ChatOpenAI()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 17,
    "id": "135c8205",
    "metadata": {},
    "outputs": [
@@ -624,7 +622,7 @@
        "AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -638,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 15,
    "id": "424e7e7a",
    "metadata": {},
    "outputs": [
@@ -655,7 +653,7 @@
        "AIMessage(content='Harrison worked at Kensho.', additional_kwargs={}, example=False)"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -667,6 +665,149 @@
     "})"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c5543183",
+   "metadata": {},
+   "source": [
+    "### With Memory and returning source documents\n",
+    "\n",
+    "This shows how to use memory with the above. For memory, we need to manage that outside at the memory. For returning the retrieved documents, we just need to pass them through all the way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e31dd17c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.memory import ConversationBufferMemory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "d4bffe94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "memory = ConversationBufferMemory(return_messages=True, output_key=\"answer\", input_key=\"question\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "733be985",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First we add a step to load memory\n",
+    "# This needs to be a RunnableMap because its the first input\n",
+    "loaded_memory = RunnableMap(\n",
+    "    {\n",
+    "        \"question\": itemgetter(\"question\"),\n",
+    "        \"memory\": memory.load_memory_variables,\n",
+    "    }\n",
+    ")\n",
+    "# Next we add a step to expand memory into the variables\n",
+    "expanded_memory = {\n",
+    "    \"question\": itemgetter(\"question\"),\n",
+    "    \"chat_history\": lambda x: x[\"memory\"][\"history\"]\n",
+    "}\n",
+    "\n",
+    "# Now we calculate the standalone question\n",
+    "standalone_question = {\n",
+    "    \"standalone_question\": {\n",
+    "        \"question\": lambda x: x[\"question\"],\n",
+    "        \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
+    "    } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
+    "}\n",
+    "# Now we retrieve the documents\n",
+    "retrieved_documents = {\n",
+    "    \"docs\": itemgetter(\"standalone_question\") | retriever,\n",
+    "    \"question\": lambda x: x[\"standalone_question\"]\n",
+    "}\n",
+    "# Now we construct the inputs for the final prompt\n",
+    "final_inputs = {\n",
+    "    \"context\": lambda x: _combine_documents(x[\"docs\"]),\n",
+    "    \"question\": itemgetter(\"question\")\n",
+    "}\n",
+    "# And finally, we do the part that returns the answers\n",
+    "answer = {\n",
+    "    \"answer\": final_inputs | ANSWER_PROMPT | ChatOpenAI(),\n",
+    "    \"docs\": itemgetter(\"docs\"),\n",
+    "}\n",
+    "# And now we put it all together!\n",
+    "final_chain = loaded_memory | expanded_memory | standalone_question | retrieved_documents | answer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "806e390c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False),\n",
+       " 'docs': [Document(page_content='harrison worked at kensho', metadata={})]}"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = {\"question\": \"where did harrison work?\"}\n",
+    "result = final_chain.invoke(inputs)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "977399fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note that the memory does not save automatically\n",
+    "# This will be improved in the future\n",
+    "# For now you need to save it yourself\n",
+    "memory.save_context(inputs, {\"answer\": result[\"answer\"].content})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "f94f7de4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'history': [HumanMessage(content='where did harrison work?', additional_kwargs={}, example=False),\n",
+       "  AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)]}"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memory.load_memory_variables({})"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0f2bf8d3",
@@ -1391,10 +1532,299 @@
     "response"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4927a727-b4c8-453c-8c83-bd87b4fcac14",
+   "metadata": {},
+   "source": [
+    "## Moderation\n",
+    "\n",
+    "This shows how to add in moderation (or other safeguards) around your LLM application."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "4f5f6449-940a-4f5c-97c0-39b71c3e2a68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import OpenAIModerationChain\n",
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "fcb8312b-7e7a-424f-a3ec-76738c9a9d21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moderate = OpenAIModerationChain()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b24b9148-f6b0-4091-8ea8-d3fb281bd950",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = OpenAI()\n",
+    "prompt = ChatPromptTemplate.from_messages([\n",
+    "    (\"system\", \"repeat after me: {input}\")\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "1c8ed87c-9ca6-4559-bf60-d40e94a0af08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = prompt | model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "5256b9bd-381a-42b0-bfa8-7e6d18f853cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nYou are stupid.'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"input\": \"you are stupid\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "fe6e3b33-dc9a-49d5-b194-ba750c58a628",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moderated_chain = chain | moderate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "d8ba0cbd-c739-4d23-be9f-6ae092bd5ffb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'input': '\\n\\nYou are stupid.',\n",
+       " 'output': \"Text was found that violates OpenAI's content policy.\"}"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "moderated_chain.invoke({\"input\": \"you are stupid\"})"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "179d3c03",
+   "id": "a0a85ba4-f782-47b8-b16f-8b7a61d6dab7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Conversational Retrieval With Memory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92c87dd8-bb6f-4f32-a30d-8f5459ce6265",
+   "metadata": {},
+   "source": [
+    "## Fallbacks\n",
+    "\n",
+    "With LCEL you can easily introduce fallbacks for any Runnable component, like an LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1b1cb744-31fc-4261-ab25-65fe1fcad559",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='To get to the other side.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
+    "bad_llm = ChatOpenAI(model_name=\"gpt-fake\")\n",
+    "good_llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
+    "llm = bad_llm.with_fallbacks([good_llm])\n",
+    "\n",
+    "llm.invoke(\"Why did the the chicken cross the road?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8cf3982-03f6-49b3-8ff5-7cd12444f19c",
+   "metadata": {},
+   "source": [
+    "Looking at the trace, we can see that the first model failed but the second succeeded, so we still got an output: https://smith.langchain.com/public/dfaf0bf6-d86d-43e9-b084-dd16a56df15c/r\n",
+    "\n",
+    "We can add an arbitrary sequence of fallbacks, which will be executed in order:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "31819be0-7f40-4e67-b5ab-61340027b948",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='To get to the other side.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm = bad_llm.with_fallbacks([bad_llm, bad_llm, good_llm])\n",
+    "\n",
+    "llm.invoke(\"Why did the the chicken cross the road?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acad6e88-8046-450e-b005-db7e50f33b80",
+   "metadata": {},
+   "source": [
+    "Trace: https://smith.langchain.com/public/c09efd01-3184-4369-a225-c9da8efcaf47/r\n",
+    "\n",
+    "We can continue to use our Runnable with fallbacks the same way we use any Runnable, mean we can include it in sequences:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bab114a1-bb93-4b7e-a639-e7e00f21aebc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='To show off its incredible jumping skills! Kangaroos are truly amazing creatures.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You're a nice assistant who always includes a compliment in your response\"),\n",
+    "        (\"human\", \"Why did the {animal} cross the road\"),\n",
+    "    ]\n",
+    ")\n",
+    "chain = prompt | llm\n",
+    "chain.invoke({\"animal\": \"kangaroo\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58340afa-8187-4ffe-9bd2-7912fb733a15",
+   "metadata": {},
+   "source": [
+    "Trace: https://smith.langchain.com/public/ba03895f-f8bd-4c70-81b7-8b930353eabd/r\n",
+    "\n",
+    "Note, since every sequence of Runnables is itself a Runnable, we can create fallbacks for whole Sequences. We can also continue using the full interface, including asynchronous calls, batched calls, and streams:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "45aa3170-b2e6-430d-887b-bd879048060a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\"\\n\\nAnswer: The rabbit crossed the road to get to the other side. That's quite clever of him!\",\n",
+       " '\\n\\nAnswer: The turtle crossed the road to get to the other side. You must be pretty clever to come up with that riddle!']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "chat_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You're a nice assistant who always includes a compliment in your response\"),\n",
+    "        (\"human\", \"Why did the {animal} cross the road\"),\n",
+    "    ]\n",
+    ")\n",
+    "chat_model = ChatOpenAI(model_name=\"gpt-fake\")\n",
+    "\n",
+    "prompt_template = \"\"\"Instructions: You should always include a compliment in your response.\n",
+    "\n",
+    "Question: Why did the {animal} cross the road?\"\"\"\n",
+    "prompt = PromptTemplate.from_template(prompt_template)\n",
+    "llm = OpenAI()\n",
+    "\n",
+    "bad_chain = chat_prompt | chat_model\n",
+    "good_chain = prompt | llm\n",
+    "chain = bad_chain.with_fallbacks([good_chain])\n",
+    "await chain.abatch([{\"animal\": \"rabbit\"}, {\"animal\": \"turtle\"}])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af6731c6-0c73-4b1d-a433-6e8f6ecce2bb",
+   "metadata": {},
+   "source": [
+    "Traces: \n",
+    "1. https://smith.langchain.com/public/ccd73236-9ae5-48a6-94b5-41210be18a46/r\n",
+    "2. https://smith.langchain.com/public/f43f608e-075c-45c7-bf73-b64e4d3f3082/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d2fe1fe-506b-4ee5-8056-8b9df801765f",
    "metadata": {},
    "outputs": [],
    "source": []

docs/extras/guides/expression_language/interface.ipynb

@@ -19,9 +19,22 @@
     "- `ainvoke`: call the chain on an input async\n",
     "- `abatch`: call the chain on a list of inputs async\n",
     "\n",
-    "The type of the input varies by component. For a prompt it is a dictionary, for a retriever it is a single string, for a model either a single string, a list of chat messages, or a PromptValue.\n",
+    "The type of the input varies by component:\n",
     "\n",
-    "The output type also varies by component. For an LLM it is a string, for a ChatModel it's a ChatMessage, for a prompt it's a PromptValue, for a retriever it's a list of documents.\n",
+    "| Component | Input Type |\n",
+    "| --- | --- |\n",
+    "|Prompt|Dictionary|\n",
+    "|Retriever|Single string|\n",
+    "|Model| Single string, list of chat messages or a PromptValue|\n",
+    "\n",
+    "The output type also varies by component:\n",
+    "\n",
+    "| Component | Output Type |\n",
+    "| --- | --- |\n",
+    "| LLM | String |\n",
+    "| ChatModel | ChatMessage |\n",
+    "| Prompt | PromptValue |\n",
+    "| Retriever | List of documents |\n",
     "\n",
     "Let's take a look at these methods! To do so, we'll create a super simple PromptTemplate + ChatModel chain."
    ]
@@ -95,7 +108,7 @@
    ],
    "source": [
     "for s in chain.stream({\"topic\": \"bears\"}):\n",
-    "    print(s.content, end=\"\")"
+    "    print(s.content, end=\"\", flush=True)"
    ]
   },
   {
@@ -183,7 +196,7 @@
    ],
    "source": [
     "async for s in chain.astream({\"topic\": \"bears\"}):\n",
-    "    print(s.content, end=\"\")"
+    "    print(s.content, end=\"\", flush=True)"
    ]
   },
   {

docs/extras/integrations/callbacks/argilla.ipynb

@@ -147,7 +147,7 @@
     "    api_key=os.environ[\"ARGILLA_API_KEY\"],\n",
     ")\n",
     "\n",
-    "dataset.push_to_argilla(\"langchain-dataset\")"
+    "dataset.push_to_argilla(\"langchain-dataset\");"
    ]
   },
   {

docs/extras/integrations/callbacks/labelstudio.ipynb

@@ -0,0 +1,382 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Label Studio\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"https://labelstudio-pub.s3.amazonaws.com/lc/open-source-data-labeling-platform.png\" width=\"400\"/>\n",
+    "</div>\n",
+    "\n",
+    "Label Studio is an open-source data labeling platform that provides LangChain with flexibility when it comes to labeling data for fine-tuning large language models (LLMs). It also enables the preparation of custom training data and the collection and evaluation of responses through human feedback.\n",
+    "\n",
+    "In this guide, you will learn how to connect a LangChain pipeline to Label Studio to:\n",
+    "\n",
+    "- Aggregate all input prompts, conversations, and responses in a single LabelStudio project. This consolidates all the data in one place for easier labeling and analysis.\n",
+    "- Refine prompts and responses to create a dataset for supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) scenarios. The labeled data can be used to further train the LLM to improve its performance.\n",
+    "- Evaluate model responses through human feedback. LabelStudio provides an interface for humans to review and provide feedback on model responses, allowing evaluation and iteration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Installation and setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "First install latest versions of Label Studio and Label Studio API client:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -U label-studio label-studio-sdk openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Next, run `label-studio` on the command line to start the local LabelStudio instance at `http://localhost:8080`. See the [Label Studio installation guide](https://labelstud.io/guide/install) for more options."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "You'll need a token to make API calls.\n",
+    "\n",
+    "Open your LabelStudio instance in your browser, go to `Account & Settings > Access Token` and copy the key.\n",
+    "\n",
+    "Set environment variables with your LabelStudio URL, API key and OpenAI API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ['LABEL_STUDIO_URL'] = '<YOUR-LABEL-STUDIO-URL>'  # e.g. http://localhost:8080\n",
+    "os.environ['LABEL_STUDIO_API_KEY'] = '<YOUR-LABEL-STUDIO-API-KEY>'\n",
+    "os.environ['OPENAI_API_KEY'] = '<YOUR-OPENAI-API-KEY>'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Collecting LLMs prompts and responses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data used for labeling is stored in projects within Label Studio. Every project is identified by an XML configuration that details the specifications for input and output data. \n",
+    "\n",
+    "Create a project that takes human input in text format and outputs an editable LLM response in a text area:\n",
+    "\n",
+    "```xml\n",
+    "<View>\n",
+    "<Style>\n",
+    "    .prompt-box {\n",
+    "        background-color: white;\n",
+    "        border-radius: 10px;\n",
+    "        box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);\n",
+    "        padding: 20px;\n",
+    "    }\n",
+    "</Style>\n",
+    "<View className=\"root\">\n",
+    "    <View className=\"prompt-box\">\n",
+    "        <Text name=\"prompt\" value=\"$prompt\"/>\n",
+    "    </View>\n",
+    "    <TextArea name=\"response\" toName=\"prompt\"\n",
+    "              maxSubmissions=\"1\" editable=\"true\"\n",
+    "              required=\"true\"/>\n",
+    "</View>\n",
+    "<Header value=\"Rate the response:\"/>\n",
+    "<Rating name=\"rating\" toName=\"prompt\"/>\n",
+    "</View>\n",
+    "```\n",
+    "\n",
+    "1. To create a project in Label Studio, click on the \"Create\" button. \n",
+    "2. Enter a name for your project in the \"Project Name\" field, such as `My Project`.\n",
+    "3. Navigate to `Labeling Setup > Custom Template` and paste the XML configuration provided above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "You can collect input LLM prompts and output responses in a LabelStudio project, connecting it via `LabelStudioCallbackHandler`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.callbacks import LabelStudioCallbackHandler\n",
+    "\n",
+    "llm = OpenAI(\n",
+    "    temperature=0,\n",
+    "    callbacks=[\n",
+    "        LabelStudioCallbackHandler(\n",
+    "            project_name=\"My Project\"\n",
+    "    )]\n",
+    ")\n",
+    "print(llm(\"Tell me a joke\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "In the Label Studio, open `My Project`. You will see the prompts, responses, and metadata like the model name. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Collecting Chat model Dialogues"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also track and display full chat dialogues in LabelStudio, with the ability to rate and modify the last response:\n",
+    "\n",
+    "1. Open Label Studio and click on the \"Create\" button.\n",
+    "2. Enter a name for your project in the \"Project Name\" field, such as `New Project with Chat`.\n",
+    "3. Navigate to Labeling Setup > Custom Template and paste the following XML configuration:\n",
+    "\n",
+    "```xml\n",
+    "<View>\n",
+    "<View className=\"root\">\n",
+    "     <Paragraphs name=\"dialogue\"\n",
+    "               value=\"$prompt\"\n",
+    "               layout=\"dialogue\"\n",
+    "               textKey=\"content\"\n",
+    "               nameKey=\"role\"\n",
+    "               granularity=\"sentence\"/>\n",
+    "  <Header value=\"Final response:\"/>\n",
+    "    <TextArea name=\"response\" toName=\"dialogue\"\n",
+    "              maxSubmissions=\"1\" editable=\"true\"\n",
+    "              required=\"true\"/>\n",
+    "</View>\n",
+    "<Header value=\"Rate the response:\"/>\n",
+    "<Rating name=\"rating\" toName=\"dialogue\"/>\n",
+    "</View>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain.callbacks import LabelStudioCallbackHandler\n",
+    "\n",
+    "chat_llm = ChatOpenAI(callbacks=[\n",
+    "    LabelStudioCallbackHandler(\n",
+    "        mode=\"chat\",\n",
+    "        project_name=\"New Project with Chat\",\n",
+    "    )\n",
+    "])\n",
+    "llm_results = chat_llm([\n",
+    "    SystemMessage(content=\"Always use a lot of emojis\"),\n",
+    "    HumanMessage(content=\"Tell me a joke\")\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In Label Studio, open \"New Project with Chat\". Click on a created task to view dialog history and edit/annotate responses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Custom Labeling Configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "You can modify the default labeling configuration in LabelStudio to add more target labels like response sentiment, relevance, and many [other types annotator's feedback](https://labelstud.io/tags/).\n",
+    "\n",
+    "New labeling configuration can be added from UI: go to `Settings > Labeling Interface` and set up a custom configuration with additional tags like `Choices` for sentiment or `Rating` for relevance. Keep in mind that [`TextArea` tag](https://labelstud.io/tags/textarea) should be presented in any configuration to display the LLM responses.\n",
+    "\n",
+    "Alternatively, you can specify the labeling configuration on the initial call before project creation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ls = LabelStudioCallbackHandler(project_config='''\n",
+    "<View>\n",
+    "<Text name=\"prompt\" value=\"$prompt\"/>\n",
+    "<TextArea name=\"response\" toName=\"prompt\"/>\n",
+    "<TextArea name=\"user_feedback\" toName=\"prompt\"/>\n",
+    "<Rating name=\"rating\" toName=\"prompt\"/>\n",
+    "<Choices name=\"sentiment\" toName=\"prompt\">\n",
+    "    <Choice value=\"Positive\"/>\n",
+    "    <Choice value=\"Negative\"/>\n",
+    "</Choices>\n",
+    "</View>\n",
+    "''')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that if the project doesn't exist, it will be created with the specified labeling configuration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Other parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "The `LabelStudioCallbackHandler` accepts several optional parameters:\n",
+    "\n",
+    "- **api_key** - Label Studio API key. Overrides environmental variable `LABEL_STUDIO_API_KEY`.\n",
+    "- **url** - Label Studio URL. Overrides `LABEL_STUDIO_URL`, default `http://localhost:8080`.\n",
+    "- **project_id** - Existing Label Studio project ID. Overrides `LABEL_STUDIO_PROJECT_ID`. Stores data in this project.\n",
+    "- **project_name** - Project name if project ID not specified. Creates a new project. Default is `\"LangChain-%Y-%m-%d\"` formatted with the current date.\n",
+    "- **project_config** - [custom labeling configuration](#custom-labeling-configuration)\n",
+    "- **mode**: use this shortcut to create target configuration from scratch:\n",
+    "   - `\"prompt\"` - Single prompt, single response. Default.\n",
+    "   - `\"chat\"` - Multi-turn chat mode.\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "labelops",
+   "language": "python",
+   "name": "labelops"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/extras/integrations/callbacks/streamlit.md

@@ -71,3 +71,6 @@ or any other local ENV management tool.
 
 Currently `StreamlitCallbackHandler` is geared towards use with a LangChain Agent Executor. Support for additional agent types,
 use directly with Chains, etc will be added in the future.
+
+You may also be interested in using
+[StreamlitChatMessageHistory](/docs/integrations/memory/streamlit_chat_message_history) for LangChain.

docs/extras/integrations/chat/anyscale.ipynb

@@ -0,0 +1,225 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "642fd21c-600a-47a1-be96-6e1438b421a9",
+   "metadata": {},
+   "source": [
+    "# Anyscale\n",
+    "\n",
+    "This notebook demonstrates the use of `langchain.chat_models.ChatAnyscale` for [Anyscale Endpoints](https://endpoints.anyscale.com/).\n",
+    "\n",
+    "* Set `ANYSCALE_API_KEY` environment variable\n",
+    "* or use the `anyscale_api_key` keyword argument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# !pip install openai"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "d00d850917865298"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "72340871-ae2f-415f-b399-0777d32dc379",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"ANYSCALE_API_KEY\"] = getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d7fc704-3ea0-4c35-96e7-89fcae6c73fa",
+   "metadata": {},
+   "source": [
+    "# Let's try out each model offered on Anyscale Endpoints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0dc9428d-4217-47d2-97de-f784b1764186",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['meta-llama/Llama-2-70b-chat-hf', 'meta-llama/Llama-2-7b-chat-hf', 'meta-llama/Llama-2-13b-chat-hf'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.chat_models import ChatAnyscale\n",
+    "\n",
+    "chats = {\n",
+    "    model: ChatAnyscale(model_name=model, temperature=1.0)\n",
+    "    for model in ChatAnyscale.get_available_models()\n",
+    "}\n",
+    "\n",
+    "print(chats.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c4f124a-eaf7-4d78-a2c0-b0aa23fb25c4",
+   "metadata": {},
+   "source": [
+    "# We can use async methods and other stuff supported by ChatOpenAI\n",
+    "\n",
+    "This way, the three requests will only take as long as the longest individual request."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1f94f5d2-569e-4a2c-965e-de53c2845fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "\n",
+    "from langchain.schema import SystemMessage, HumanMessage\n",
+    "\n",
+    "messages = [\n",
+    "    SystemMessage(\n",
+    "        content=\"You are a helpful AI that shares everything you know.\"\n",
+    "    ),\n",
+    "    HumanMessage(\n",
+    "        content=\"Tell me technical facts about yourself. Are you a transformer model? How many billions of parameters do you have?\"\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "async def get_msgs():\n",
+    "    tasks = [\n",
+    "        chat.apredict_messages(messages)\n",
+    "        for chat in chats.values()\n",
+    "    ]\n",
+    "    responses = await asyncio.gather(*tasks)\n",
+    "    return dict(zip(chats.keys(), responses))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b2ced871-869a-4ca6-a2ec-6bfececdf7da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nest_asyncio\n",
+    "\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bc605fa5-9501-470d-a6c9-cd868d2145ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tmeta-llama/Llama-2-70b-chat-hf\n",
+      "\n",
+      "Greetings! I'm just an AI, I don't have a personal identity like humans do, but I'm here to help you with any questions you have.\n",
+      "\n",
+      "I'm a large language model, which means I'm trained on a large corpus of text data to generate language outputs that are coherent and natural-sounding. My architecture is based on a transformer model, which is a type of neural network that's particularly well-suited for natural language processing tasks.\n",
+      "\n",
+      "As for my parameters, I have a few billion parameters, but I don't have access to the exact number as it's not relevant to my functioning. My training data includes a vast amount of text from various sources, including books, articles, and websites, which I use to learn patterns and relationships in language.\n",
+      "\n",
+      "I'm designed to be a helpful tool for a variety of tasks, such as answering questions, providing information, and generating text. I'm constantly learning and improving my abilities through machine learning algorithms and feedback from users like you.\n",
+      "\n",
+      "I hope this helps! Is there anything else you'd like to know about me or my capabilities?\n",
+      "\n",
+      "---\n",
+      "\n",
+      "\tmeta-llama/Llama-2-7b-chat-hf\n",
+      "\n",
+      "Ah, a fellow tech enthusiast! *adjusts glasses* I'm glad to share some technical details about myself. 🤓\n",
+      "Indeed, I'm a transformer model, specifically a BERT-like language model trained on a large corpus of text data. My architecture is based on the transformer framework, which is a type of neural network designed for natural language processing tasks. 🏠\n",
+      "As for the number of parameters, I have approximately 340 million. *winks* That's a pretty hefty number, if I do say so myself! These parameters allow me to learn and represent complex patterns in language, such as syntax, semantics, and more. 🤔\n",
+      "But don't ask me to do math in my head – I'm a language model, not a calculating machine! 😅 My strengths lie in understanding and generating human-like text, so feel free to chat with me anytime you'd like. 💬\n",
+      "Now, do you have any more technical questions for me? Or would you like to engage in a nice chat? 😊\n",
+      "\n",
+      "---\n",
+      "\n",
+      "\tmeta-llama/Llama-2-13b-chat-hf\n",
+      "\n",
+      "Hello! As a friendly and helpful AI, I'd be happy to share some technical facts about myself.\n",
+      "\n",
+      "I am a transformer-based language model, specifically a variant of the BERT (Bidirectional Encoder Representations from Transformers) architecture. BERT was developed by Google in 2018 and has since become one of the most popular and widely-used AI language models.\n",
+      "\n",
+      "Here are some technical details about my capabilities:\n",
+      "\n",
+      "1. Parameters: I have approximately 340 million parameters, which are the numbers that I use to learn and represent language. This is a relatively large number of parameters compared to some other languages models, but it allows me to learn and understand complex language patterns and relationships.\n",
+      "2. Training: I was trained on a large corpus of text data, including books, articles, and other sources of written content. This training allows me to learn about the structure and conventions of language, as well as the relationships between words and phrases.\n",
+      "3. Architectures: My architecture is based on the transformer model, which is a type of neural network that is particularly well-suited for natural language processing tasks. The transformer model uses self-attention mechanisms to allow the model to \"attend\" to different parts of the input text, allowing it to capture long-range dependencies and contextual relationships.\n",
+      "4. Precision: I am capable of generating text with high precision and accuracy, meaning that I can produce text that is close to human-level quality in terms of grammar, syntax, and coherence.\n",
+      "5. Generative capabilities: In addition to being able to generate text based on prompts and questions, I am also capable of generating text based on a given topic or theme. This allows me to create longer, more coherent pieces of text that are organized around a specific idea or concept.\n",
+      "\n",
+      "Overall, I am a powerful and versatile language model that is capable of a wide range of natural language processing tasks. I am constantly learning and improving, and I am here to help answer any questions you may have!\n",
+      "\n",
+      "---\n",
+      "\n",
+      "CPU times: user 371 ms, sys: 15.5 ms, total: 387 ms\n",
+      "Wall time: 12 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "response_dict = asyncio.run(get_msgs())\n",
+    "\n",
+    "for model_name, response in response_dict.items():\n",
+    "    print(f'\\t{model_name}')\n",
+    "    print()\n",
+    "    print(response.content)\n",
+    "    print('\\n---\\n')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/chat/azure_chat_openai.ipynb

@@ -74,6 +74,124 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f27fa24d",
+   "metadata": {},
+   "source": [
+    "## Model Version\n",
+    "Azure OpenAI responses contain `model` property, which is name of the model used to generate the response. However unlike native OpenAI responses, it does not contain the version of the model, which is set on the deplyoment in Azure. This makes it tricky to know which version of the model was used to generate the response, which as result can lead to e.g. wrong total cost calculation with `OpenAICallbackHandler`.\n",
+    "\n",
+    "To solve this problem, you can pass `model_version` parameter to `AzureChatOpenAI` class, which will be added to the model name in the llm output. This way you can easily distinguish between different versions of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0531798a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks import get_openai_callback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3fd97dfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BASE_URL = \"https://{endpoint}.openai.azure.com\"\n",
+    "API_KEY = \"...\"\n",
+    "DEPLOYMENT_NAME = \"gpt-35-turbo\" # in Azure, this deployment has version 0613 - input and output tokens are counted separately"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "aceddb72",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Cost (USD): $0.000054\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = AzureChatOpenAI(\n",
+    "    openai_api_base=BASE_URL,\n",
+    "    openai_api_version=\"2023-05-15\",\n",
+    "    deployment_name=DEPLOYMENT_NAME,\n",
+    "    openai_api_key=API_KEY,\n",
+    "    openai_api_type=\"azure\",\n",
+    ")\n",
+    "with get_openai_callback() as cb:\n",
+    "    model(\n",
+    "        [\n",
+    "            HumanMessage(\n",
+    "                content=\"Translate this sentence from English to French. I love programming.\"\n",
+    "            )\n",
+    "        ]\n",
+    "    )\n",
+    "    print(f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\") # without specifying the model version, flat-rate 0.002 USD per 1k input and output tokens is used\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e61eefd",
+   "metadata": {},
+   "source": [
+    "We can provide the model version to `AzureChatOpenAI` constructor. It will get appended to the model name returned by Azure OpenAI and cost will be counted correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8d5e54e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Cost (USD): $0.000044\n"
+     ]
+    }
+   ],
+   "source": [
+    "model0613 = AzureChatOpenAI(\n",
+    "    openai_api_base=BASE_URL,\n",
+    "    openai_api_version=\"2023-05-15\",\n",
+    "    deployment_name=DEPLOYMENT_NAME,\n",
+    "    openai_api_key=API_KEY,\n",
+    "    openai_api_type=\"azure\",\n",
+    "    model_version=\"0613\"\n",
+    ")\n",
+    "with get_openai_callback() as cb:\n",
+    "    model0613(\n",
+    "        [\n",
+    "            HumanMessage(\n",
+    "                content=\"Translate this sentence from English to French. I love programming.\"\n",
+    "            )\n",
+    "        ]\n",
+    "    )\n",
+    "    print(f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99682534",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -92,7 +210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,

docs/extras/integrations/document_loaders/airbyte_cdk.ipynb

@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f3a5ebf",
+   "metadata": {},
+   "source": [
+    "# Airbyte CDK"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ac77b1-449b-44f7-b8f3-3494d55c286e",
+   "metadata": {},
+   "source": [
+    ">[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.\n",
+    "\n",
+    "A lot of source connectors are implemented using the [Airbyte CDK](https://docs.airbyte.com/connector-development/cdk-python/). This loader allows to run any of these connectors and return the data as documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b06fbde",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e9dc79",
+   "metadata": {},
+   "source": [
+    "First, you need to install the `airbyte-cdk` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d35e4e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install airbyte-cdk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "085aa658",
+   "metadata": {},
+   "source": [
+    "Then, either install an existing connector from the [Airbyte Github repository](https://github.com/airbytehq/airbyte/tree/master/airbyte-integrations/connectors) or create your own connector using the [Airbyte CDK](https://docs.airbyte.io/connector-development/connector-development).\n",
+    "\n",
+    "For example, to install the Github connector, run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6d04ef4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install \"source_github@git+https://github.com/airbytehq/airbyte.git@master#subdirectory=airbyte-integrations/connectors/source-github\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36069b74",
+   "metadata": {},
+   "source": [
+    "Some sources are also published as regular packages on PyPI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae855210",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02208f52",
+   "metadata": {},
+   "source": [
+    "Now you can create an `AirbyteCDKLoader` based on the imported source. It takes a `config` object that's passed to the connector. You also have to pick the stream you want to retrieve records from by name (`stream_name`). Check the connectors documentation page and spec definition for more information on the config object and available streams. For the Github connectors these are:\n",
+    "* [https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-github/source_github/spec.json](https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-github/source_github/spec.json).\n",
+    "* [https://docs.airbyte.com/integrations/sources/github/](https://docs.airbyte.com/integrations/sources/github/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a99e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.document_loaders.airbyte import AirbyteCDKLoader\n",
+    "from source_github.source import SourceGithub # plug in your own source here\n",
+    "\n",
+    "config = {\n",
+    "    # your github configuration\n",
+    "    \"credentials\": {\n",
+    "        \"api_url\": \"api.github.com\",\n",
+    "        \"personal_access_token\": \"<token>\"\n",
+    "    },\n",
+    "    \"repository\": \"<repo>\",\n",
+    "    \"start_date\": \"<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>\"\n",
+    "}\n",
+    "\n",
+    "issues_loader = AirbyteCDKLoader(source_class=SourceGithub, config=config, stream_name=\"issues\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cea23fc",
+   "metadata": {},
+   "source": [
+    "Now you can load documents the usual way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dae75cdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = issues_loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a93dc2a",
+   "metadata": {},
+   "source": [
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1782db09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_iterator = issues_loader.lazy_load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a124086",
+   "metadata": {},
+   "source": [
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5671395d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document\n",
+    "\n",
+    "def handle_record(record, id):\n",
+    "    return Document(page_content=record.data[\"title\"] + \"\\n\" + (record.data[\"body\"] or \"\"), metadata=record.data)\n",
+    "\n",
+    "issues_loader = AirbyteCDKLoader(source_class=SourceGithub, config=config, stream_name=\"issues\", record_handler=handle_record)\n",
+    "\n",
+    "docs = issues_loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "223eb8bc",
+   "metadata": {},
+   "source": [
+    "## Incremental loads\n",
+    "\n",
+    "Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.\n",
+    "\n",
+    "To take advantage of this, store the `last_state` property of the loader and pass it in when creating the loader again. This will ensure that only new records are loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7061e735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last_state = issues_loader.last_state # store safely\n",
+    "\n",
+    "incremental_issue_loader = AirbyteCDKLoader(source_class=SourceGithub, config=config, stream_name=\"issues\", state=last_state)\n",
+    "\n",
+    "new_docs = incremental_issue_loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/airbyte_gong.ipynb

@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f3a5ebf",
+   "metadata": {},
+   "source": [
+    "# Airbyte Gong"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ac77b1-449b-44f7-b8f3-3494d55c286e",
+   "metadata": {},
+   "source": [
+    ">[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.\n",
+    "\n",
+    "This loader exposes the Gong connector as a document loader, allowing you to load various Gong objects as documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6847a40c",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b06fbde",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e9dc79",
+   "metadata": {},
+   "source": [
+    "First, you need to install the `airbyte-source-gong` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d35e4e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install airbyte-source-gong"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae855210",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02208f52",
+   "metadata": {},
+   "source": [
+    "Check out the [Airbyte documentation page](https://docs.airbyte.com/integrations/sources/gong/) for details about how to configure the reader.\n",
+    "The JSON schema the config object should adhere to can be found on Github: [https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-gong/source_gong/spec.yaml](https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-gong/source_gong/spec.yaml).\n",
+    "\n",
+    "The general shape looks like this:\n",
+    "```python\n",
+    "{\n",
+    "  \"access_key\": \"<access key name>\",\n",
+    "  \"access_key_secret\": \"<access key secret>\",\n",
+    "  \"start_date\": \"<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>\",\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a99e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.document_loaders.airbyte import AirbyteGongLoader\n",
+    "\n",
+    "config = {\n",
+    "    # your gong configuration\n",
+    "}\n",
+    "\n",
+    "loader = AirbyteGongLoader(config=config, stream_name=\"calls\") # check the documentation linked above for a list of all streams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cea23fc",
+   "metadata": {},
+   "source": [
+    "Now you can load documents the usual way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dae75cdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a93dc2a",
+   "metadata": {},
+   "source": [
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1782db09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_iterator = loader.lazy_load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a124086",
+   "metadata": {},
+   "source": [
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To process documents, create a class inheriting from the base loader and implement the `_handle_records` method yourself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5671395d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document\n",
+    "\n",
+    "def handle_record(record, id):\n",
+    "    return Document(page_content=record.data[\"title\"], metadata=record.data)\n",
+    "\n",
+    "loader = AirbyteGongLoader(config=config, record_handler=handle_record, stream_name=\"calls\")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "223eb8bc",
+   "metadata": {},
+   "source": [
+    "## Incremental loads\n",
+    "\n",
+    "Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.\n",
+    "\n",
+    "To take advantage of this, store the `last_state` property of the loader and pass it in when creating the loader again. This will ensure that only new records are loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7061e735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last_state = loader.last_state # store safely\n",
+    "\n",
+    "incremental_loader = AirbyteGongLoader(config=config, stream_name=\"calls\", state=last_state)\n",
+    "\n",
+    "new_docs = incremental_loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/airbyte_hubspot.ipynb

@@ -0,0 +1,208 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f3a5ebf",
+   "metadata": {},
+   "source": [
+    "# Airbyte Hubspot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ac77b1-449b-44f7-b8f3-3494d55c286e",
+   "metadata": {},
+   "source": [
+    ">[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.\n",
+    "\n",
+    "This loader exposes the Hubspot connector as a document loader, allowing you to load various Hubspot objects as documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6847a40c",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b06fbde",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e9dc79",
+   "metadata": {},
+   "source": [
+    "First, you need to install the `airbyte-source-hubspot` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d35e4e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install airbyte-source-hubspot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae855210",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02208f52",
+   "metadata": {},
+   "source": [
+    "Check out the [Airbyte documentation page](https://docs.airbyte.com/integrations/sources/hubspot/) for details about how to configure the reader.\n",
+    "The JSON schema the config object should adhere to can be found on Github: [https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-hubspot/source_hubspot/spec.yaml](https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-hubspot/source_hubspot/spec.yaml).\n",
+    "\n",
+    "The general shape looks like this:\n",
+    "```python\n",
+    "{\n",
+    "  \"start_date\": \"<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>\",\n",
+    "  \"credentials\": {\n",
+    "    \"credentials_title\": \"Private App Credentials\",\n",
+    "    \"access_token\": \"<access token of your private app>\"\n",
+    "  }\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a99e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.document_loaders.airbyte import AirbyteHubspotLoader\n",
+    "\n",
+    "config = {\n",
+    "    # your hubspot configuration\n",
+    "}\n",
+    "\n",
+    "loader = AirbyteHubspotLoader(config=config, stream_name=\"products\") # check the documentation linked above for a list of all streams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cea23fc",
+   "metadata": {},
+   "source": [
+    "Now you can load documents the usual way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dae75cdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a93dc2a",
+   "metadata": {},
+   "source": [
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1782db09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_iterator = loader.lazy_load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a124086",
+   "metadata": {},
+   "source": [
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To process documents, create a class inheriting from the base loader and implement the `_handle_records` method yourself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5671395d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document\n",
+    "\n",
+    "def handle_record(record, id):\n",
+    "    return Document(page_content=record.data[\"title\"], metadata=record.data)\n",
+    "\n",
+    "loader = AirbyteHubspotLoader(config=config, record_handler=handle_record, stream_name=\"products\")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "223eb8bc",
+   "metadata": {},
+   "source": [
+    "## Incremental loads\n",
+    "\n",
+    "Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.\n",
+    "\n",
+    "To take advantage of this, store the `last_state` property of the loader and pass it in when creating the loader again. This will ensure that only new records are loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7061e735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last_state = loader.last_state # store safely\n",
+    "\n",
+    "incremental_loader = AirbyteHubspotLoader(config=config, stream_name=\"products\", state=last_state)\n",
+    "\n",
+    "new_docs = incremental_loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/airbyte_salesforce.ipynb

@@ -0,0 +1,213 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f3a5ebf",
+   "metadata": {},
+   "source": [
+    "# Airbyte Salesforce"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ac77b1-449b-44f7-b8f3-3494d55c286e",
+   "metadata": {},
+   "source": [
+    ">[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.\n",
+    "\n",
+    "This loader exposes the Salesforce connector as a document loader, allowing you to load various Salesforce objects as documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6847a40c",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b06fbde",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e9dc79",
+   "metadata": {},
+   "source": [
+    "First, you need to install the `airbyte-source-salesforce` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d35e4e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install airbyte-source-salesforce"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae855210",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02208f52",
+   "metadata": {},
+   "source": [
+    "Check out the [Airbyte documentation page](https://docs.airbyte.com/integrations/sources/salesforce/) for details about how to configure the reader.\n",
+    "The JSON schema the config object should adhere to can be found on Github: [https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-salesforce/source_salesforce/spec.yaml](https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-salesforce/source_salesforce/spec.yaml).\n",
+    "\n",
+    "The general shape looks like this:\n",
+    "```python\n",
+    "{\n",
+    "  \"client_id\": \"<oauth client id>\",\n",
+    "  \"client_secret\": \"<oauth client secret>\",\n",
+    "  \"refresh_token\": \"<oauth refresh token>\",\n",
+    "  \"start_date\": \"<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>\",\n",
+    "  \"is_sandbox\": False, # set to True if you're using a sandbox environment\n",
+    "  \"streams_criteria\": [ # Array of filters for salesforce objects that should be loadable\n",
+    "    {\"criteria\": \"exacts\", \"value\": \"Account\"}, # Exact name of salesforce object\n",
+    "    {\"criteria\": \"starts with\", \"value\": \"Asset\"}, # Prefix of the name\n",
+    "    # Other allowed criteria: ends with, contains, starts not with, ends not with, not contains, not exacts\n",
+    "  ],\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a99e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.document_loaders.airbyte import AirbyteSalesforceLoader\n",
+    "\n",
+    "config = {\n",
+    "    # your salesforce configuration\n",
+    "}\n",
+    "\n",
+    "loader = AirbyteSalesforceLoader(config=config, stream_name=\"asset\") # check the documentation linked above for a list of all streams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cea23fc",
+   "metadata": {},
+   "source": [
+    "Now you can load documents the usual way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dae75cdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a93dc2a",
+   "metadata": {},
+   "source": [
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1782db09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_iterator = loader.lazy_load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a124086",
+   "metadata": {},
+   "source": [
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5671395d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document\n",
+    "\n",
+    "def handle_record(record, id):\n",
+    "    return Document(page_content=record.data[\"title\"], metadata=record.data)\n",
+    "\n",
+    "loader = AirbyteSalesforceLoader(config=config, record_handler=handle_record, stream_name=\"asset\")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "223eb8bc",
+   "metadata": {},
+   "source": [
+    "## Incremental loads\n",
+    "\n",
+    "Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.\n",
+    "\n",
+    "To take advantage of this, store the `last_state` property of the loader and pass it in when creating the loader again. This will ensure that only new records are loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7061e735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last_state = loader.last_state # store safely\n",
+    "\n",
+    "incremental_loader = AirbyteSalesforceLoader(config=config, stream_name=\"asset\", state=last_state)\n",
+    "\n",
+    "new_docs = incremental_loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/airbyte_shopify.ipynb

@@ -0,0 +1,209 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f3a5ebf",
+   "metadata": {},
+   "source": [
+    "# Airbyte Shopify"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ac77b1-449b-44f7-b8f3-3494d55c286e",
+   "metadata": {},
+   "source": [
+    ">[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.\n",
+    "\n",
+    "This loader exposes the Shopify connector as a document loader, allowing you to load various Shopify objects as documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6847a40c",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b06fbde",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e9dc79",
+   "metadata": {},
+   "source": [
+    "First, you need to install the `airbyte-source-shopify` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d35e4e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install airbyte-source-shopify"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae855210",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02208f52",
+   "metadata": {},
+   "source": [
+    "Check out the [Airbyte documentation page](https://docs.airbyte.com/integrations/sources/shopify/) for details about how to configure the reader.\n",
+    "The JSON schema the config object should adhere to can be found on Github: [https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-shopify/source_shopify/spec.json](https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-shopify/source_shopify/spec.json).\n",
+    "\n",
+    "The general shape looks like this:\n",
+    "```python\n",
+    "{\n",
+    "    \"start_date\": \"<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>\",\n",
+    "    \"shop\": \"<name of the shop you want to retrieve documents from>\",\n",
+    "    \"credentials\": {\n",
+    "        \"auth_method\": \"api_password\",\n",
+    "        \"api_password\": \"<your api password>\"\n",
+    "    }\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a99e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.document_loaders.airbyte import AirbyteShopifyLoader\n",
+    "\n",
+    "config = {\n",
+    "    # your shopify configuration\n",
+    "}\n",
+    "\n",
+    "loader = AirbyteShopifyLoader(config=config, stream_name=\"orders\") # check the documentation linked above for a list of all streams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cea23fc",
+   "metadata": {},
+   "source": [
+    "Now you can load documents the usual way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dae75cdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a93dc2a",
+   "metadata": {},
+   "source": [
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1782db09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_iterator = loader.lazy_load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a124086",
+   "metadata": {},
+   "source": [
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5671395d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document\n",
+    "\n",
+    "def handle_record(record, id):\n",
+    "    return Document(page_content=record.data[\"title\"], metadata=record.data)\n",
+    "\n",
+    "loader = AirbyteShopifyLoader(config=config, record_handler=handle_record, stream_name=\"orders\")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "223eb8bc",
+   "metadata": {},
+   "source": [
+    "## Incremental loads\n",
+    "\n",
+    "Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.\n",
+    "\n",
+    "To take advantage of this, store the `last_state` property of the loader and pass it in when creating the loader again. This will ensure that only new records are loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7061e735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last_state = loader.last_state # store safely\n",
+    "\n",
+    "incremental_loader = AirbyteShopifyLoader(config=config, stream_name=\"orders\", state=last_state)\n",
+    "\n",
+    "new_docs = incremental_loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/airbyte_stripe.ipynb

@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f3a5ebf",
+   "metadata": {},
+   "source": [
+    "# Airbyte Stripe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ac77b1-449b-44f7-b8f3-3494d55c286e",
+   "metadata": {},
+   "source": [
+    ">[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.\n",
+    "\n",
+    "This loader exposes the Stripe connector as a document loader, allowing you to load various Stripe objects as documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6847a40c",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b06fbde",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e9dc79",
+   "metadata": {},
+   "source": [
+    "First, you need to install the `airbyte-source-stripe` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d35e4e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install airbyte-source-stripe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae855210",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02208f52",
+   "metadata": {},
+   "source": [
+    "Check out the [Airbyte documentation page](https://docs.airbyte.com/integrations/sources/stripe/) for details about how to configure the reader.\n",
+    "The JSON schema the config object should adhere to can be found on Github: [https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-stripe/source_stripe/spec.yaml](https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-stripe/source_stripe/spec.yaml).\n",
+    "\n",
+    "The general shape looks like this:\n",
+    "```python\n",
+    "{\n",
+    "  \"client_secret\": \"<secret key>\",\n",
+    "  \"account_id\": \"<account id>\",\n",
+    "  \"start_date\": \"<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>\",\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a99e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.document_loaders.airbyte import AirbyteStripeLoader\n",
+    "\n",
+    "config = {\n",
+    "    # your stripe configuration\n",
+    "}\n",
+    "\n",
+    "loader = AirbyteStripeLoader(config=config, stream_name=\"invoices\") # check the documentation linked above for a list of all streams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cea23fc",
+   "metadata": {},
+   "source": [
+    "Now you can load documents the usual way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dae75cdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a93dc2a",
+   "metadata": {},
+   "source": [
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1782db09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_iterator = loader.lazy_load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a124086",
+   "metadata": {},
+   "source": [
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5671395d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document\n",
+    "\n",
+    "def handle_record(record, id):\n",
+    "    return Document(page_content=record.data[\"title\"], metadata=record.data)\n",
+    "\n",
+    "loader = AirbyteStripeLoader(config=config, record_handler=handle_record, stream_name=\"invoices\")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "223eb8bc",
+   "metadata": {},
+   "source": [
+    "## Incremental loads\n",
+    "\n",
+    "Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.\n",
+    "\n",
+    "To take advantage of this, store the `last_state` property of the loader and pass it in when creating the loader again. This will ensure that only new records are loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7061e735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last_state = loader.last_state # store safely\n",
+    "\n",
+    "incremental_loader = AirbyteStripeLoader(config=config, record_handler=handle_record, stream_name=\"invoices\", state=last_state)\n",
+    "\n",
+    "new_docs = incremental_loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/airbyte_typeform.ipynb

@@ -0,0 +1,209 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f3a5ebf",
+   "metadata": {},
+   "source": [
+    "# Airbyte Typeform"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ac77b1-449b-44f7-b8f3-3494d55c286e",
+   "metadata": {},
+   "source": [
+    ">[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.\n",
+    "\n",
+    "This loader exposes the Typeform connector as a document loader, allowing you to load various Typeform objects as documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6847a40c",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b06fbde",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e9dc79",
+   "metadata": {},
+   "source": [
+    "First, you need to install the `airbyte-source-typeform` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d35e4e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install airbyte-source-typeform"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae855210",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02208f52",
+   "metadata": {},
+   "source": [
+    "Check out the [Airbyte documentation page](https://docs.airbyte.com/integrations/sources/typeform/) for details about how to configure the reader.\n",
+    "The JSON schema the config object should adhere to can be found on Github: [https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-typeform/source_typeform/spec.json](https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-typeform/source_typeform/spec.json).\n",
+    "\n",
+    "The general shape looks like this:\n",
+    "```python\n",
+    "{\n",
+    "  \"credentials\": {\n",
+    "    \"auth_type\": \"Private Token\",\n",
+    "    \"access_token\": \"<your auth token>\"\n",
+    "  },\n",
+    "  \"start_date\": \"<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>\",\n",
+    "  \"form_ids\": [\"<id of form to load records for>\"] # if omitted, records from all forms will be loaded\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a99e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.document_loaders.airbyte import AirbyteTypeformLoader\n",
+    "\n",
+    "config = {\n",
+    "    # your typeform configuration\n",
+    "}\n",
+    "\n",
+    "loader = AirbyteTypeformLoader(config=config, stream_name=\"forms\") # check the documentation linked above for a list of all streams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cea23fc",
+   "metadata": {},
+   "source": [
+    "Now you can load documents the usual way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dae75cdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a93dc2a",
+   "metadata": {},
+   "source": [
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1782db09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_iterator = loader.lazy_load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a124086",
+   "metadata": {},
+   "source": [
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5671395d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document\n",
+    "\n",
+    "def handle_record(record, id):\n",
+    "    return Document(page_content=record.data[\"title\"], metadata=record.data)\n",
+    "\n",
+    "loader = AirbyteTypeformLoader(config=config, record_handler=handle_record, stream_name=\"forms\")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "223eb8bc",
+   "metadata": {},
+   "source": [
+    "## Incremental loads\n",
+    "\n",
+    "Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.\n",
+    "\n",
+    "To take advantage of this, store the `last_state` property of the loader and pass it in when creating the loader again. This will ensure that only new records are loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7061e735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last_state = loader.last_state # store safely\n",
+    "\n",
+    "incremental_loader = AirbyteTypeformLoader(config=config, record_handler=handle_record, stream_name=\"forms\", state=last_state)\n",
+    "\n",
+    "new_docs = incremental_loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/airbyte_zendesk_support.ipynb

@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f3a5ebf",
+   "metadata": {},
+   "source": [
+    "# Airbyte Zendesk Support"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ac77b1-449b-44f7-b8f3-3494d55c286e",
+   "metadata": {},
+   "source": [
+    ">[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.\n",
+    "\n",
+    "This loader exposes the Zendesk Support connector as a document loader, allowing you to load various objects as documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6847a40c",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b06fbde",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e9dc79",
+   "metadata": {},
+   "source": [
+    "First, you need to install the `airbyte-source-zendesk-support` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d35e4e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install airbyte-source-zendesk-support"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae855210",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02208f52",
+   "metadata": {},
+   "source": [
+    "Check out the [Airbyte documentation page](https://docs.airbyte.com/integrations/sources/zendesk-support/) for details about how to configure the reader.\n",
+    "The JSON schema the config object should adhere to can be found on Github: [https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-zendesk-support/source_zendesk_support/spec.json](https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-zendesk-support/source_zendesk_support/spec.json).\n",
+    "\n",
+    "The general shape looks like this:\n",
+    "```python\n",
+    "{\n",
+    "  \"subdomain\": \"<your zendesk subdomain>\",\n",
+    "  \"start_date\": \"<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>\",\n",
+    "  \"credentials\": {\n",
+    "    \"credentials\": \"api_token\",\n",
+    "    \"email\": \"<your email>\",\n",
+    "    \"api_token\": \"<your api token>\"\n",
+    "  }\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a99e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.document_loaders.airbyte import AirbyteZendeskSupportLoader\n",
+    "\n",
+    "config = {\n",
+    "    # your zendesk-support configuration\n",
+    "}\n",
+    "\n",
+    "loader = AirbyteZendeskSupportLoader(config=config, stream_name=\"tickets\") # check the documentation linked above for a list of all streams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cea23fc",
+   "metadata": {},
+   "source": [
+    "Now you can load documents the usual way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dae75cdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a93dc2a",
+   "metadata": {},
+   "source": [
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1782db09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_iterator = loader.lazy_load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a124086",
+   "metadata": {},
+   "source": [
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5671395d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.docstore.document import Document\n",
+    "\n",
+    "def handle_record(record, id):\n",
+    "    return Document(page_content=record.data[\"title\"], metadata=record.data)\n",
+    "\n",
+    "loader = AirbyteZendeskSupportLoader(config=config, record_handler=handle_record, stream_name=\"tickets\")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "223eb8bc",
+   "metadata": {},
+   "source": [
+    "## Incremental loads\n",
+    "\n",
+    "Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.\n",
+    "\n",
+    "To take advantage of this, store the `last_state` property of the loader and pass it in when creating the loader again. This will ensure that only new records are loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7061e735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "last_state = loader.last_state # store safely\n",
+    "\n",
+    "incremental_loader = AirbyteZendeskSupportLoader(config=config, stream_name=\"tickets\", state=last_state)\n",
+    "\n",
+    "new_docs = incremental_loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/arcgis.ipynb

@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "62359e08-cf80-4210-a30c-f450000e65b9",
+   "metadata": {},
+   "source": [
+    "# ArcGISLoader\n",
+    "\n",
+    "This notebook demonstrates the use of the `langchain.document_loaders.ArcGISLoader` class.\n",
+    "\n",
+    "You will need to install the ArcGIS API for Python `arcgis` and, optionally, `bs4.BeautifulSoup`.\n",
+    "\n",
+    "You can use an `arcgis.gis.GIS` object for authenticated data loading, or leave it blank to access public data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b782cab5-0584-4e2a-9073-009fb8dc93a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import ArcGISLoader\n",
+    "\n",
+    "\n",
+    "url = \"https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7\"\n",
+    "\n",
+    "loader = ArcGISLoader(url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aa3053cf-4127-43ea-bf56-e378b348091f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4.04 ms, sys: 1.63 ms, total: 5.67 ms\n",
+      "Wall time: 644 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a2444519-9117-4feb-8bb9-8931ce286fa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['url', 'layer_description', 'item_description', 'layer_properties'])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0].metadata.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6b6e9107-6a80-4ef7-8149-3013faa2de76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KeysView({\n",
+       "  \"currentVersion\": 10.81,\n",
+       "  \"id\": 7,\n",
+       "  \"name\": \"Beach Ramps\",\n",
+       "  \"type\": \"Feature Layer\",\n",
+       "  \"description\": \"\",\n",
+       "  \"geometryType\": \"esriGeometryPoint\",\n",
+       "  \"sourceSpatialReference\": {\n",
+       "    \"wkid\": 2881,\n",
+       "    \"latestWkid\": 2881\n",
+       "  },\n",
+       "  \"copyrightText\": \"\",\n",
+       "  \"parentLayer\": null,\n",
+       "  \"subLayers\": [],\n",
+       "  \"minScale\": 750000,\n",
+       "  \"maxScale\": 0,\n",
+       "  \"drawingInfo\": {\n",
+       "    \"renderer\": {\n",
+       "      \"type\": \"simple\",\n",
+       "      \"symbol\": {\n",
+       "        \"type\": \"esriPMS\",\n",
+       "        \"url\": \"9bb2e5ca499bb68aa3ee0d4e1ecc3849\",\n",
+       "        \"imageData\": \"iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAJJJREFUOI3NkDEKg0AQRZ9kkSnSGBshR7DJqdJYeg7BMpcS0uQWQsqoCLExkcUJzGqT38zw2fcY1rEzbp7vjXz0EXC7gBxs1ABcG/8CYkCcDqwyLqsV+RlV0I/w7PzuJBArr1VB20H58Ls6h+xoFITkTwWpQJX7XSIBAnFwVj7MLAjJV/AC6G3QoAmK+74Lom04THTBEp/HCSc6AAAAAElFTkSuQmCC\",\n",
+       "        \"contentType\": \"image/png\",\n",
+       "        \"width\": 12,\n",
+       "        \"height\": 12,\n",
+       "        \"angle\": 0,\n",
+       "        \"xoffset\": 0,\n",
+       "        \"yoffset\": 0\n",
+       "      },\n",
+       "      \"label\": \"\",\n",
+       "      \"description\": \"\"\n",
+       "    },\n",
+       "    \"transparency\": 0,\n",
+       "    \"labelingInfo\": null\n",
+       "  },\n",
+       "  \"defaultVisibility\": true,\n",
+       "  \"extent\": {\n",
+       "    \"xmin\": -81.09480168806815,\n",
+       "    \"ymin\": 28.858349245353473,\n",
+       "    \"xmax\": -80.77512908572814,\n",
+       "    \"ymax\": 29.41078388840041,\n",
+       "    \"spatialReference\": {\n",
+       "      \"wkid\": 4326,\n",
+       "      \"latestWkid\": 4326\n",
+       "    }\n",
+       "  },\n",
+       "  \"hasAttachments\": false,\n",
+       "  \"htmlPopupType\": \"esriServerHTMLPopupTypeNone\",\n",
+       "  \"displayField\": \"AccessName\",\n",
+       "  \"typeIdField\": null,\n",
+       "  \"subtypeFieldName\": null,\n",
+       "  \"subtypeField\": null,\n",
+       "  \"defaultSubtypeCode\": null,\n",
+       "  \"fields\": [\n",
+       "    {\n",
+       "      \"name\": \"OBJECTID\",\n",
+       "      \"type\": \"esriFieldTypeOID\",\n",
+       "      \"alias\": \"OBJECTID\",\n",
+       "      \"domain\": null\n",
+       "    },\n",
+       "    {\n",
+       "      \"name\": \"Shape\",\n",
+       "      \"type\": \"esriFieldTypeGeometry\",\n",
+       "      \"alias\": \"Shape\",\n",
+       "      \"domain\": null\n",
+       "    },\n",
+       "    {\n",
+       "      \"name\": \"AccessName\",\n",
+       "      \"type\": \"esriFieldTypeString\",\n",
+       "      \"alias\": \"AccessName\",\n",
+       "      \"length\": 40,\n",
+       "      \"domain\": null\n",
+       "    },\n",
+       "    {\n",
+       "      \"name\": \"AccessID\",\n",
+       "      \"type\": \"esriFieldTypeString\",\n",
+       "      \"alias\": \"AccessID\",\n",
+       "      \"length\": 50,\n",
+       "      \"domain\": null\n",
+       "    },\n",
+       "    {\n",
+       "      \"name\": \"AccessType\",\n",
+       "      \"type\": \"esriFieldTypeString\",\n",
+       "      \"alias\": \"AccessType\",\n",
+       "      \"length\": 25,\n",
+       "      \"domain\": null\n",
+       "    },\n",
+       "    {\n",
+       "      \"name\": \"GeneralLoc\",\n",
+       "      \"type\": \"esriFieldTypeString\",\n",
+       "      \"alias\": \"GeneralLoc\",\n",
+       "      \"length\": 100,\n",
+       "      \"domain\": null\n",
+       "    },\n",
+       "    {\n",
+       "      \"name\": \"MilePost\",\n",
+       "      \"type\": \"esriFieldTypeDouble\",\n",
+       "      \"alias\": \"MilePost\",\n",
+       "      \"domain\": null\n",
+       "    },\n",
+       "    {\n",
+       "      \"name\": \"City\",\n",
+       "      \"type\": \"esriFieldTypeString\",\n",
+       "      \"alias\": \"City\",\n",
+       "      \"length\": 50,\n",
+       "      \"domain\": null\n",
+       "    },\n",
+       "    {\n",
+       "      \"name\": \"AccessStatus\",\n",
+       "      \"type\": \"esriFieldTypeString\",\n",
+       "      \"alias\": \"AccessStatus\",\n",
+       "      \"length\": 50,\n",
+       "      \"domain\": null\n",
+       "    },\n",
+       "    {\n",
+       "      \"name\": \"Entry_Date_Time\",\n",
+       "      \"type\": \"esriFieldTypeDate\",\n",
+       "      \"alias\": \"Entry_Date_Time\",\n",
+       "      \"length\": 8,\n",
+       "      \"domain\": null\n",
+       "    },\n",
+       "    {\n",
+       "      \"name\": \"DrivingZone\",\n",
+       "      \"type\": \"esriFieldTypeString\",\n",
+       "      \"alias\": \"DrivingZone\",\n",
+       "      \"length\": 50,\n",
+       "      \"domain\": null\n",
+       "    }\n",
+       "  ],\n",
+       "  \"geometryField\": {\n",
+       "    \"name\": \"Shape\",\n",
+       "    \"type\": \"esriFieldTypeGeometry\",\n",
+       "    \"alias\": \"Shape\"\n",
+       "  },\n",
+       "  \"indexes\": null,\n",
+       "  \"subtypes\": [],\n",
+       "  \"relationships\": [],\n",
+       "  \"canModifyLayer\": true,\n",
+       "  \"canScaleSymbols\": false,\n",
+       "  \"hasLabels\": false,\n",
+       "  \"capabilities\": \"Map,Query,Data\",\n",
+       "  \"maxRecordCount\": 1000,\n",
+       "  \"supportsStatistics\": true,\n",
+       "  \"supportsAdvancedQueries\": true,\n",
+       "  \"supportedQueryFormats\": \"JSON, geoJSON\",\n",
+       "  \"isDataVersioned\": false,\n",
+       "  \"ownershipBasedAccessControlForFeatures\": {\n",
+       "    \"allowOthersToQuery\": true\n",
+       "  },\n",
+       "  \"useStandardizedQueries\": true,\n",
+       "  \"advancedQueryCapabilities\": {\n",
+       "    \"useStandardizedQueries\": true,\n",
+       "    \"supportsStatistics\": true,\n",
+       "    \"supportsHavingClause\": true,\n",
+       "    \"supportsCountDistinct\": true,\n",
+       "    \"supportsOrderBy\": true,\n",
+       "    \"supportsDistinct\": true,\n",
+       "    \"supportsPagination\": true,\n",
+       "    \"supportsTrueCurve\": true,\n",
+       "    \"supportsReturningQueryExtent\": true,\n",
+       "    \"supportsQueryWithDistance\": true,\n",
+       "    \"supportsSqlExpression\": true\n",
+       "  },\n",
+       "  \"supportsDatumTransformation\": true,\n",
+       "  \"dateFieldsTimeReference\": null,\n",
+       "  \"supportsCoordinatesQuantization\": true\n",
+       "})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0].metadata['layer_properties'].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1d132b7d-5a13-4d66-98e8-785ffdf87af0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"OBJECTID\": 2, \"AccessName\": \"27TH AV\", \"AccessID\": \"NS-141\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"3600 BLK S ATLANTIC AV\", \"MilePost\": 4.83, \"City\": \"NEW SMYRNA BEACH\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691397348000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 7, \"AccessName\": \"BEACHWAY AV\", \"AccessID\": \"NS-106\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"1400 N ATLANTIC AV\", \"MilePost\": 1.57, \"City\": \"NEW SMYRNA BEACH\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691397348000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 10, \"AccessName\": \"SEABREEZE BLVD\", \"AccessID\": \"DB-051\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"500 BLK N ATLANTIC AV\", \"MilePost\": 14.24, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691394892000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 13, \"AccessName\": \"GRANADA BLVD\", \"AccessID\": \"OB-030\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"20 BLK OCEAN SHORE BLVD\", \"MilePost\": 10.02, \"City\": \"ORMOND BEACH\", \"AccessStatus\": \"4X4 ONLY\", \"Entry_Date_Time\": 1691394952000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 16, \"AccessName\": \"INTERNATIONAL SPEEDWAY BLVD\", \"AccessID\": \"DB-059\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"300 BLK S ATLANTIC AV\", \"MilePost\": 15.27, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691395174000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 26, \"AccessName\": \"UNIVERSITY BLVD\", \"AccessID\": \"DB-048\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"900 BLK N ATLANTIC AV\", \"MilePost\": 13.74, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691394892000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 36, \"AccessName\": \"BEACH ST\", \"AccessID\": \"PI-097\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"4890 BLK S ATLANTIC AV\", \"MilePost\": 25.85, \"City\": \"PONCE INLET\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691397348000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 40, \"AccessName\": \"BOTEFUHR AV\", \"AccessID\": \"DBS-067\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"1900 BLK S ATLANTIC AV\", \"MilePost\": 16.68, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"4X4 ONLY\", \"Entry_Date_Time\": 1691395124000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 41, \"AccessName\": \"SILVER BEACH AV\", \"AccessID\": \"DB-064\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"1000 BLK S ATLANTIC AV\", \"MilePost\": 15.98, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691395174000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 50, \"AccessName\": \"3RD AV\", \"AccessID\": \"NS-118\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"1200 BLK HILL ST\", \"MilePost\": 3.25, \"City\": \"NEW SMYRNA BEACH\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691397348000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 58, \"AccessName\": \"DUNLAWTON BLVD\", \"AccessID\": \"DBS-078\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"3400 BLK S ATLANTIC AV\", \"MilePost\": 20.61, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691397348000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 63, \"AccessName\": \"MILSAP RD\", \"AccessID\": \"OB-037\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"700 BLK S ATLANTIC AV\", \"MilePost\": 11.52, \"City\": \"ORMOND BEACH\", \"AccessStatus\": \"4X4 ONLY\", \"Entry_Date_Time\": 1691394952000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 68, \"AccessName\": \"EMILIA AV\", \"AccessID\": \"DBS-082\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"3790 BLK S ATLANTIC AV\", \"MilePost\": 21.38, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691397348000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 92, \"AccessName\": \"FLAGLER AV\", \"AccessID\": \"NS-110\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"500 BLK FLAGLER AV\", \"MilePost\": 2.57, \"City\": \"NEW SMYRNA BEACH\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691397348000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 94, \"AccessName\": \"CRAWFORD RD\", \"AccessID\": \"NS-108\", \"AccessType\": \"OPEN VEHICLE RAMP - PASS\", \"GeneralLoc\": \"800 BLK N ATLANTIC AV\", \"MilePost\": 2.19, \"City\": \"NEW SMYRNA BEACH\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691397348000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 122, \"AccessName\": \"HARTFORD AV\", \"AccessID\": \"DB-043\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"1890 BLK N ATLANTIC AV\", \"MilePost\": 12.76, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"CLOSED - SEASONAL\", \"Entry_Date_Time\": 1691394832000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 125, \"AccessName\": \"WILLIAMS AV\", \"AccessID\": \"DB-042\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"2200 BLK N ATLANTIC AV\", \"MilePost\": 12.5, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"4X4 ONLY\", \"Entry_Date_Time\": 1691394952000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 134, \"AccessName\": \"CARDINAL DR\", \"AccessID\": \"OB-036\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"600 BLK S ATLANTIC AV\", \"MilePost\": 11.27, \"City\": \"ORMOND BEACH\", \"AccessStatus\": \"4X4 ONLY\", \"Entry_Date_Time\": 1691394952000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 229, \"AccessName\": \"EL PORTAL ST\", \"AccessID\": \"DBS-076\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"3200 BLK S ATLANTIC AV\", \"MilePost\": 20.04, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691397348000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 230, \"AccessName\": \"HARVARD DR\", \"AccessID\": \"OB-038\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"900 BLK S ATLANTIC AV\", \"MilePost\": 11.72, \"City\": \"ORMOND BEACH\", \"AccessStatus\": \"4X4 ONLY\", \"Entry_Date_Time\": 1691394952000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 232, \"AccessName\": \"VAN AV\", \"AccessID\": \"DBS-075\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"3100 BLK S ATLANTIC AV\", \"MilePost\": 19.6, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"OPEN\", \"Entry_Date_Time\": 1691397348000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 233, \"AccessName\": \"ROCKEFELLER DR\", \"AccessID\": \"OB-034\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"400 BLK S ATLANTIC AV\", \"MilePost\": 10.9, \"City\": \"ORMOND BEACH\", \"AccessStatus\": \"CLOSED - SEASONAL\", \"Entry_Date_Time\": 1691394832000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 235, \"AccessName\": \"MINERVA RD\", \"AccessID\": \"DBS-069\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"2300 BLK S ATLANTIC AV\", \"MilePost\": 17.52, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"4X4 ONLY\", \"Entry_Date_Time\": 1691395124000, \"DrivingZone\": \"YES\"}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for doc in docs:\n",
+    "    print(doc.page_content)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/async_chromium.ipynb

@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ad553e51",
+   "metadata": {},
+   "source": [
+    "# Async Chromium\n",
+    "\n",
+    "Chromium is one of the browsers supported by Playwright, a library used to control browser automation. \n",
+    "\n",
+    "By running `p.chromium.launch(headless=True)`, we are launching a headless instance of Chromium. \n",
+    "\n",
+    "Headless mode means that the browser is running without a graphical user interface.\n",
+    "\n",
+    "`AsyncChromiumLoader` load the page, and then we use `Html2TextTransformer` to trasnform to text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c3a4c19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -q playwright beautifulsoup4\n",
+    "! playwright install"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dd2cdea7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'<!DOCTYPE html><html lang=\"en\"><head><script src=\"https://s0.2mdn.net/instream/video/client.js\" asyn'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.document_loaders import AsyncChromiumLoader\n",
+    "urls = [\"https://www.wsj.com\"]\n",
+    "loader = AsyncChromiumLoader(urls)\n",
+    "docs = loader.load()\n",
+    "docs[0].page_content[0:100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "013caa7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Skip to Main ContentSkip to SearchSkip to... Select * Top News * What's News *\\nFeatured Stories * Retirement * Life & Arts * Hip-Hop * Sports * Video *\\nEconomy * Real Estate * Sports * CMO * CIO * CFO * Risk & Compliance *\\nLogistics Report * Sustainable Business * Heard on the Street * Barron’s *\\nMarketWatch * Mansion Global * Penta * Opinion * Journal Reports * Sponsored\\nOffers Explore Our Brands * WSJ * * * * * Barron's * * * * * MarketWatch * * *\\n* * IBD # The Wall Street Journal SubscribeSig\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.document_transformers import Html2TextTransformer\n",
+    "html2text = Html2TextTransformer()\n",
+    "docs_transformed = html2text.transform_documents(docs)\n",
+    "docs_transformed[0].page_content[0:500]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/example_data/sample_rss_feeds.opml

@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<opml version="1.0">
+    <head>
+        <title>Sample RSS feed subscriptions</title>
+    </head>
+    <body>
+        <outline text="Tech" title="Tech">
+            <outline type="rss" text="Engadget" title="Engadget" xmlUrl="http://www.engadget.com/rss-full.xml" htmlUrl="http://www.engadget.com"/>
+            <outline type="rss" text="Ars Technica - All content" title="Ars Technica - All content" xmlUrl="http://feeds.arstechnica.com/arstechnica/index/" htmlUrl="https://arstechnica.com"/>
+        </outline>
+    </body>
+</opml>

docs/extras/integrations/document_loaders/google_cloud_storage_file.ipynb

@@ -73,13 +73,27 @@
     "loader.load()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "41c8a46f",
+   "metadata": {},
+   "source": [
+    "If you want to use an alternative loader, you can provide a custom function, for example:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "eba3002d",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from langchain.document_loaders import PyPDFLoader\n",
+    "def load_pdf(file_path):\n",
+    "    return PyPDFLoader(file_path)\n",
+    "\n",
+    "loader = GCSFileLoader(project_name=\"aist\", bucket=\"testing-hwc\", blob=\"fake.pdf\", loader_func=load_pdf)"
+   ]
   }
  ],
  "metadata": {

docs/extras/integrations/document_loaders/grobid.ipynb

@@ -9,66 +9,16 @@
     "\n",
     "GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents.\n",
     "\n",
-    "It is particularly good for sturctured PDFs, like academic papers.\n",
+    "It is designed and expected to be used to parse academic papers, where it works particularly well. Note: if the articles supplied to Grobid are large documents (e.g. dissertations) exceeding a certain number of elements, they might not be processed. \n",
     "\n",
-    "This loader uses GROBIB to parse PDFs into `Documents` that retain metadata associated with the section of text.\n",
+    "This loader uses Grobid to parse PDFs into `Documents` that retain metadata associated with the section of text.\n",
     "\n",
     "---\n",
+    "The best approach is to install Grobid via docker, see https://grobid.readthedocs.io/en/latest/Grobid-docker/. \n",
     "\n",
-    "For users on `Mac` - \n",
+    "(Note: additional instructions can be found [here](https://python.langchain.com/docs/extras/integrations/providers/grobid.mdx).)\n",
     "\n",
-    "(Note: additional instructions can be found [here](https://python.langchain.com/docs/ecosystem/integrations/grobid.mdx).)\n",
-    "\n",
-    "Install Java (Apple Silicon):\n",
-    "```\n",
-    "$ arch -arm64 brew install openjdk@11\n",
-    "$ brew --prefix openjdk@11\n",
-    "/opt/homebrew/opt/openjdk@ 11\n",
-    "```\n",
-    "\n",
-    "In `~/.zshrc`:\n",
-    "```\n",
-    "export JAVA_HOME=/opt/homebrew/opt/openjdk@11\n",
-    "export PATH=$JAVA_HOME/bin:$PATH\n",
-    "```\n",
-    "\n",
-    "Then, in Terminal:\n",
-    "```\n",
-    "$ source ~/.zshrc\n",
-    "```\n",
-    "\n",
-    "Confirm install:\n",
-    "```\n",
-    "$ which java\n",
-    "/opt/homebrew/opt/openjdk@11/bin/java\n",
-    "$ java -version \n",
-    "openjdk version \"11.0.19\" 2023-04-18\n",
-    "OpenJDK Runtime Environment Homebrew (build 11.0.19+0)\n",
-    "OpenJDK 64-Bit Server VM Homebrew (build 11.0.19+0, mixed mode)\n",
-    "```\n",
-    "\n",
-    "Then, get [Grobid](https://grobid.readthedocs.io/en/latest/Install-Grobid/#getting-grobid):\n",
-    "```\n",
-    "$ curl -LO https://github.com/kermitt2/grobid/archive/0.7.3.zip\n",
-    "$ unzip 0.7.3.zip\n",
-    "```\n",
-    "                   \n",
-    "Build\n",
-    "```\n",
-    "$ ./gradlew clean install\n",
-    "```\n",
-    "\n",
-    "Then, run the server:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2d8992fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')"
+    "Once grobid is up-and-running you can interact as described below. \n"
    ]
   },
   {

docs/extras/integrations/document_loaders/huawei_obs_directory.ipynb

@@ -0,0 +1,178 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c83b6a4c",
+   "metadata": {},
+   "source": [
+    "# Huawei OBS Directory\n",
+    "The following code demonstrates how to load objects from the Huawei OBS (Object Storage Service) as documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2191935",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the required package\n",
+    "# pip install esdk-obs-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "55fca3b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import OBSDirectoryLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c3ed419f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "endpoint = \"your-endpoint\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3428fd4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure your access credentials\\n\n",
+    "config = {\n",
+    "    \"ak\": \"your-access-key\",\n",
+    "    \"sk\": \"your-secret-key\"\n",
+    "}\n",
+    "loader = OBSDirectoryLoader(\"your-bucket-name\", endpoint=endpoint, config=config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9beede9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e20a839",
+   "metadata": {},
+   "source": [
+    "## Specify a Prefix for Loading\n",
+    "If you want to load objects with a specific prefix from the bucket, you can use the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "125f311d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = OBSDirectoryLoader(\"your-bucket-name\", endpoint=endpoint, config=config, prefix=\"test_prefix\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3488037",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84c82c0a",
+   "metadata": {},
+   "source": [
+    "## Get Authentication Information from ECS\n",
+    "If your langchain is deployed on Huawei Cloud ECS and [Agency is set up](https://support.huaweicloud.com/intl/en-us/usermanual-ecs/ecs_03_0166.html#section7), the loader can directly get the security token from ECS without needing access key and secret key. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1db99969",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = {\"get_token_from_ecs\": True}\n",
+    "loader = OBSDirectoryLoader(\"your-bucket-name\", endpoint=endpoint, config=config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57dd9f35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30205d25",
+   "metadata": {},
+   "source": [
+    "## Use a Public Bucket\n",
+    "If your bucket's bucket policy allows anonymous access (anonymous users have `listBucket` and `GetObject` permissions), you can directly load the objects without configuring the `config` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4dfa2ef0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = OBSDirectoryLoader(\"your-bucket-name\", endpoint=endpoint)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67d4c1d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/huawei_obs_file.ipynb

@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4394a872",
+   "metadata": {},
+   "source": [
+    "# Huawei OBS File\n",
+    "The following code demonstrates how to load an object from the Huawei OBS (Object Storage Service) as document."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c43d811b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the required package\n",
+    "# pip install esdk-obs-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5e16bae6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.obs_file import OBSFileLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "75cc7e7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "endpoint = \"your-endpoint\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f9816984",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from obs import ObsClient\n",
+    "obs_client = ObsClient(access_key_id=\"your-access-key\", secret_access_key=\"your-secret-key\", server=endpoint)\n",
+    "loader = OBSFileLoader(\"your-bucket-name\", \"your-object-key\", client=obs_client)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6143b39b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "633e05ca",
+   "metadata": {},
+   "source": [
+    "## Each Loader with Separate Authentication Information\n",
+    "If you don't need to reuse OBS connections between different loaders, you can directly configure the `config`. The loader will use the config information to initialize its own OBS client."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a5dd6a5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure your access credentials\\n\n",
+    "config = {\n",
+    "    \"ak\": \"your-access-key\",\n",
+    "    \"sk\": \"your-secret-key\"\n",
+    "}\n",
+    "loader = OBSFileLoader(\"your-bucket-name\", \"your-object-key\",endpoint=endpoint, config=config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a741f1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e2e611c",
+   "metadata": {},
+   "source": [
+    "## Get Authentication Information from ECS\n",
+    "If your langchain is deployed on Huawei Cloud ECS and [Agency is set up](https://support.huaweicloud.com/intl/en-us/usermanual-ecs/ecs_03_0166.html#section7), the loader can directly get the security token from ECS without needing access key and secret key. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "338fafef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = {\"get_token_from_ecs\": True}\n",
+    "loader = OBSFileLoader(\"your-bucket-name\", \"your-object-key\", endpoint=endpoint, config=config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73976c55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b77aa18c",
+   "metadata": {},
+   "source": [
+    "## Access a Publicly Accessible Object\n",
+    "If the object you want to access allows anonymous user access (anonymous users have `GetObject` permission), you can directly load the object without configuring the `config` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "df83d121",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = OBSFileLoader(\"your-bucket-name\", \"your-object-key\", endpoint=endpoint)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82a844ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader.load()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/news.ipynb

@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2dfc4698",
+   "metadata": {},
+   "source": [
+    "# News URL\n",
+    "\n",
+    "This covers how to load HTML news articles from a list of URLs into a document format that we can use downstream."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "16c3699e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-02T21:18:18.886031400Z",
+     "start_time": "2023-08-02T21:18:17.682345Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import NewsURLLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "836fbac1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-02T21:18:18.895539800Z",
+     "start_time": "2023-08-02T21:18:18.895539800Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "urls = [\n",
+    "    \"https://www.bbc.com/news/world-us-canada-66388172\",\n",
+    "    \"https://www.bbc.com/news/entertainment-arts-66384971\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33089aba-ff74-4d00-8f40-9449c29587cc",
+   "metadata": {},
+   "source": [
+    "Pass in urls to load them into Documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "00f46fda",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-02T21:18:19.227074500Z",
+     "start_time": "2023-08-02T21:18:18.895539800Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First article:  page_content='In testimony to the congressional committee examining the 6 January riot, Mrs Powell said she did not review all of the many claims of election fraud she made, telling them that \"no reasonable person\" would view her claims as fact. Neither she nor her representatives have commented.' metadata={'title': 'Donald Trump indictment: What do we know about the six co-conspirators?', 'link': 'https://www.bbc.com/news/world-us-canada-66388172', 'authors': [], 'language': 'en', 'description': 'Six people accused of helping Mr Trump undermine the election have been described by prosecutors.', 'publish_date': None}\n",
+      "\n",
+      "Second article:  page_content='Ms Williams added: \"If there\\'s anything that I can do in my power to ensure that dancers or singers or whoever decides to work with her don\\'t have to go through that same experience, I\\'m going to do that.\"' metadata={'title': \"Lizzo dancers Arianna Davis and Crystal Williams: 'No one speaks out, they are scared'\", 'link': 'https://www.bbc.com/news/entertainment-arts-66384971', 'authors': [], 'language': 'en', 'description': 'The US pop star is being sued for sexual harassment and fat-shaming but has yet to comment.', 'publish_date': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = NewsURLLoader(urls=urls)\n",
+    "data = loader.load()\n",
+    "print(\"First article: \", data[0])\n",
+    "print(\"\\nSecond article: \", data[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Use nlp=True to run nlp analysis and generate keywords + summary"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "98ac26c488315bff"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b68a26b3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-02T21:18:19.585758200Z",
+     "start_time": "2023-08-02T21:18:19.227074500Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First article:  page_content='In testimony to the congressional committee examining the 6 January riot, Mrs Powell said she did not review all of the many claims of election fraud she made, telling them that \"no reasonable person\" would view her claims as fact. Neither she nor her representatives have commented.' metadata={'title': 'Donald Trump indictment: What do we know about the six co-conspirators?', 'link': 'https://www.bbc.com/news/world-us-canada-66388172', 'authors': [], 'language': 'en', 'description': 'Six people accused of helping Mr Trump undermine the election have been described by prosecutors.', 'publish_date': None, 'keywords': ['powell', 'know', 'donald', 'trump', 'review', 'indictment', 'telling', 'view', 'reasonable', 'person', 'testimony', 'coconspirators', 'riot', 'representatives', 'claims'], 'summary': 'In testimony to the congressional committee examining the 6 January riot, Mrs Powell said she did not review all of the many claims of election fraud she made, telling them that \"no reasonable person\" would view her claims as fact.\\nNeither she nor her representatives have commented.'}\n",
+      "\n",
+      "Second article:  page_content='Ms Williams added: \"If there\\'s anything that I can do in my power to ensure that dancers or singers or whoever decides to work with her don\\'t have to go through that same experience, I\\'m going to do that.\"' metadata={'title': \"Lizzo dancers Arianna Davis and Crystal Williams: 'No one speaks out, they are scared'\", 'link': 'https://www.bbc.com/news/entertainment-arts-66384971', 'authors': [], 'language': 'en', 'description': 'The US pop star is being sued for sexual harassment and fat-shaming but has yet to comment.', 'publish_date': None, 'keywords': ['davis', 'lizzo', 'singers', 'experience', 'crystal', 'ensure', 'arianna', 'theres', 'williams', 'power', 'going', 'dancers', 'im', 'speaks', 'work', 'ms', 'scared'], 'summary': 'Ms Williams added: \"If there\\'s anything that I can do in my power to ensure that dancers or singers or whoever decides to work with her don\\'t have to go through that same experience, I\\'m going to do that.\"'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = NewsURLLoader(urls=urls, nlp=True)\n",
+    "data = loader.load()\n",
+    "print(\"First article: \", data[0])\n",
+    "print(\"\\nSecond article: \", data[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "['powell',\n 'know',\n 'donald',\n 'trump',\n 'review',\n 'indictment',\n 'telling',\n 'view',\n 'reasonable',\n 'person',\n 'testimony',\n 'coconspirators',\n 'riot',\n 'representatives',\n 'claims']"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0].metadata['keywords']"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-02T21:18:19.585758200Z",
+     "start_time": "2023-08-02T21:18:19.585758200Z"
+    }
+   },
+   "id": "ae37e004e0284b1d"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'In testimony to the congressional committee examining the 6 January riot, Mrs Powell said she did not review all of the many claims of election fraud she made, telling them that \"no reasonable person\" would view her claims as fact.\\nNeither she nor her representatives have commented.'"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0].metadata['summary']"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-02T21:18:19.598966800Z",
+     "start_time": "2023-08-02T21:18:19.594950200Z"
+    }
+   },
+   "id": "7676155fb175e53e"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/nuclia.ipynb

@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nuclia Understanding API document loader\n",
+    "\n",
+    "[Nuclia](https://nuclia.com) automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.\n",
+    "\n",
+    "The Nuclia Understanding API supports the processing of unstructured data, including text, web pages, documents, and audio/video contents. It extracts all texts wherever they are (using speech-to-text or OCR when needed), it also extracts metadata, embedded files (like images in a PDF), and web links. If machine learning is enabled, it identifies entities, provides a summary of the content and generates embeddings for all the sentences.\n",
+    "\n",
+    "To use the Nuclia Understanding API, you need to have a Nuclia account. You can create one for free at [https://nuclia.cloud](https://nuclia.cloud), and then [create a NUA key](https://docs.nuclia.dev/docs/docs/using/understanding/intro)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install --upgrade protobuf\n",
+    "#!pip install nucliadb-protos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"NUCLIA_ZONE\"] = \"<YOUR_ZONE>\"  # e.g. europe-1\n",
+    "os.environ[\"NUCLIA_NUA_KEY\"] = \"<YOUR_API_KEY>\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use the Nuclia document loader, you need to instantiate a `NucliaUnderstandingAPI` tool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools.nuclia import NucliaUnderstandingAPI\n",
+    "\n",
+    "nua = NucliaUnderstandingAPI(enable_ml=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.nuclia import NucliaLoader\n",
+    "\n",
+    "loader = NucliaLoader(\"./interview.mp4\", nua)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can now call the `load` the document in a loop until you get the document."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "pending = True\n",
+    "while pending:\n",
+    "    time.sleep(15)\n",
+    "    docs = loader.load()\n",
+    "    if len(docs) > 0:\n",
+    "        print(docs[0].page_content)\n",
+    "        print(docs[0].metadata)\n",
+    "        pending = False\n",
+    "    else:\n",
+    "        print(\"waiting...\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Retrieved information\n",
+    "\n",
+    "Nuclia returns the following information:\n",
+    "\n",
+    "- file metadata\n",
+    "- extracted text\n",
+    "- nested text (like text in an embedded image)\n",
+    "- paragraphs and sentences splitting (defined by the position of their first and last characters, plus start time and end time for a video or audio file)\n",
+    "- links\n",
+    "- a thumbnail\n",
+    "- embedded files\n",
+    "\n",
+    "Note:\n",
+    "\n",
+    "  Generated files (thumbnail, extracted embedded files, etc.) are provided as a token. You can download them with the [`/processing/download` endpoint](https://docs.nuclia.dev/docs/api#operation/Download_binary_file_processing_download_get).\n",
+    "\n",
+    "  Also at any level, if an attribute exceeds a certain size, it will be put in a downloadable file and will be replaced in the document by a file pointer. This will consist of `{\"file\": {\"uri\": \"JWT_TOKEN\"}}`. The rule is that if the size of the message is greater than 1000000 characters, the biggest parts will be moved to downloadable files. First, the compression process will target vectors. If that is not enough, it will target large field metadata, and finally it will target extracted text.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/extras/integrations/document_loaders/pubmed.ipynb

@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3df0dcf8",
+   "metadata": {},
+   "source": [
+    "# PubMed\n",
+    "\n",
+    ">[PubMed®](https://pubmed.ncbi.nlm.nih.gov/) by `The National Center for Biotechnology Information, National Library of Medicine` comprises more than 35 million citations for biomedical literature from `MEDLINE`, life science journals, and online books. Citations may include links to full text content from `PubMed Central` and publisher web sites."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "aecaff63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import PubMedLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f2f7e8d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = PubMedLoader(\"chatgpt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ed115aa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b68d3264-b893-45e4-8ab0-077b25a586dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9f4626d2-068d-4aed-9ffe-ad754ad4b4cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'uid': '37548997',\n",
+       " 'Title': 'Performance of ChatGPT on the Situational Judgement Test-A Professional Dilemmas-Based Examination for Doctors in the United Kingdom.',\n",
+       " 'Published': '2023-08-07',\n",
+       " 'Copyright Information': '©Robin J Borchert, Charlotte R Hickman, Jack Pepys, Timothy J Sadler. Originally published in JMIR Medical Education (https://mededu.jmir.org), 07.08.2023.'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[1].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8000f687-b500-4cce-841b-70d6151304da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"BACKGROUND: ChatGPT is a large language model that has performed well on professional examinations in the fields of medicine, law, and business. However, it is unclear how ChatGPT would perform on an examination assessing professionalism and situational judgement for doctors.\\nOBJECTIVE: We evaluated the performance of ChatGPT on the Situational Judgement Test (SJT): a national examination taken by all final-year medical students in the United Kingdom. This examination is designed to assess attributes such as communication, teamwork, patient safety, prioritization skills, professionalism, and ethics.\\nMETHODS: All questions from the UK Foundation Programme Office's (UKFPO's) 2023 SJT practice examination were inputted into ChatGPT. For each question, ChatGPT's answers and rationales were recorded and assessed on the basis of the official UK Foundation Programme Office scoring template. Questions were categorized into domains of Good Medical Practice on the basis of the domains referenced in the rationales provided in the scoring sheet. Questions without clear domain links were screened by reviewers and assigned one or multiple domains. ChatGPT's overall performance, as well as its performance across the domains of Good Medical Practice, was evaluated.\\nRESULTS: Overall, ChatGPT performed well, scoring 76% on the SJT but scoring full marks on only a few questions (9%), which may reflect possible flaws in ChatGPT's situational judgement or inconsistencies in the reasoning across questions (or both) in the examination itself. ChatGPT demonstrated consistent performance across the 4 outlined domains in Good Medical Practice for doctors.\\nCONCLUSIONS: Further research is needed to understand the potential applications of large language models, such as ChatGPT, in medical education for standardizing questions and providing consistent rationales for examinations assessing professionalism and ethics.\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[1].page_content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1070e571-697d-4c33-9a4f-0b2dd6909629",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/recursive_url_loader.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "We may want to process load all URLs under a root directory.\n",
     "\n",
-    "For example, let's look at the [LangChain JS documentation](https://js.langchain.com/docs/).\n",
+    "For example, let's look at the [Python 3.9 Document](https://docs.python.org/3.9/).\n",
     "\n",
     "This has many interesting child pages that we may want to read in bulk.\n",
     "\n",
@@ -19,13 +19,28 @@
     " \n",
     "We do this using the `RecursiveUrlLoader`.\n",
     "\n",
-    "This also gives us the flexibility to exclude some children (e.g., the `api` directory with > 800 child pages)."
+    "This also gives us the flexibility to exclude some children, customize the extractor, and more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1be8094f",
+   "metadata": {},
+   "source": [
+    "# Parameters\n",
+    "- url: str, the target url to crawl.\n",
+    "- exclude_dirs: Optional[str], webpage directories to exclude.\n",
+    "- use_async: Optional[bool], wether to use async requests, using async requests is usually faster in large tasks. However, async will disable the lazy loading feature(the function still works, but it is not lazy). By default, it is set to False.\n",
+    "- extractor: Optional[Callable[[str], str]], a function to extract the text of the document from the webpage, by default it returns the page as it is. It is recommended to use tools like goose3 and beautifulsoup to extract the text. By default, it just returns the page as it is.\n",
+    "- max_depth: Optional[int] = None, the maximum depth to crawl. By default, it is set to 2. If you need to crawl the whole website, set it to a number that is large enough would simply do the job.\n",
+    "- timeout: Optional[int] = None, the timeout for each request, in the unit of seconds. By default, it is set to 10.\n",
+    "- prevent_outside: Optional[bool] = None, whether to prevent crawling outside the root url. By default, it is set to True."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "2e3532b2",
+   "execution_count": null,
+   "id": "23c18539",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,13 +57,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "d69e5620",
+   "execution_count": null,
+   "id": "55394afe",
    "metadata": {},
    "outputs": [],
    "source": [
-    "url = \"https://js.langchain.com/docs/modules/memory/examples/\"\n",
-    "loader = RecursiveUrlLoader(url=url)\n",
+    "from bs4 import BeautifulSoup as Soup\n",
+    "\n",
+    "url = \"https://docs.python.org/3.9/\"\n",
+    "loader = RecursiveUrlLoader(url=url, max_depth=2, extractor=lambda x: Soup(x, \"html.parser\").text)\n",
     "docs = loader.load()"
    ]
   },
@@ -61,7 +78,7 @@
     {
      "data": {
       "text/plain": [
-       "12"
+       "'\\n\\n\\n\\n\\nPython Frequently Asked Questions — Python 3.'"
       ]
      },
      "execution_count": 3,
@@ -70,19 +87,21 @@
     }
    ],
    "source": [
-    "len(docs)"
+    "docs[0].page_content[:50]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "89355b7c",
+   "id": "13bd7e16",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'\\n\\n\\n\\n\\nBuffer Window Memory | 🦜️🔗 Langchain\\n\\n\\n\\n\\n\\nSki'"
+       "{'source': 'https://docs.python.org/3.9/library/index.html',\n",
+       " 'title': 'The Python Standard Library — Python 3.9.17 documentation',\n",
+       " 'language': None}"
       ]
      },
      "execution_count": 4,
@@ -91,137 +110,48 @@
     }
    ],
    "source": [
-    "docs[0].page_content[:50]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "13bd7e16",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'source': 'https://js.langchain.com/docs/modules/memory/examples/buffer_window_memory',\n",
-       " 'title': 'Buffer Window Memory | 🦜️🔗 Langchain',\n",
-       " 'description': 'BufferWindowMemory keeps track of the back-and-forths in conversation, and then uses a window of size k to surface the last k back-and-forths to use as memory.',\n",
-       " 'language': 'en'}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[0].metadata"
+    "docs[-1].metadata"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "40fc13ef",
+   "id": "5866e5a6",
    "metadata": {},
    "source": [
-    "Now, let's try a more extensive example, the `docs` root dir.\n",
-    "\n",
-    "We will skip everything under `api`.\n",
-    "\n",
-    "For this, we can `lazy_load`  each page as we crawl the tree, using `WebBaseLoader` to load each as we go."
+    "However, since it's hard to perform a perfect filter, you may still see some irrelevant results in the results. You can perform a filter on the returned documents by yourself, if it's needed. Most of the time, the returned results are good enough."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ec8ecef",
+   "metadata": {},
+   "source": [
+    "Testing on LangChain docs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5c938b9f",
+   "execution_count": 2,
+   "id": "349b5598",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://js.langchain.com/docs/\"\n",
-    "exclude_dirs = [\"https://js.langchain.com/docs/api/\"]\n",
-    "loader = RecursiveUrlLoader(url=url, exclude_dirs=exclude_dirs)\n",
-    "# Lazy load each\n",
-    "docs = [print(doc) or doc for doc in loader.lazy_load()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "30ff61d3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load all pages\n",
-    "docs = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "457e30f3",
-   "metadata": {
-    "scrolled": true
-   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "188"
+       "8"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "url = \"https://js.langchain.com/docs/modules/memory/integrations/\"\n",
+    "loader = RecursiveUrlLoader(url=url)\n",
+    "docs = loader.load()\n",
     "len(docs)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "bca80b4a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\n\\n\\n\\n\\nAgent Simulations | 🦜️🔗 Langchain\\n\\n\\n\\n\\n\\nSkip t'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[0].page_content[:50]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "df97cf22",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'source': 'https://js.langchain.com/docs/use_cases/agent_simulations/',\n",
-       " 'title': 'Agent Simulations | 🦜️🔗 Langchain',\n",
-       " 'description': 'Agent simulations involve taking multiple agents and having them interact with each other.',\n",
-       " 'language': 'en'}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[0].metadata"
-   ]
   }
  ],
  "metadata": {

docs/extras/integrations/document_loaders/rss.ipynb

@@ -0,0 +1,311 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2dfc4698",
+   "metadata": {},
+   "source": [
+    "# RSS Feeds\n",
+    "\n",
+    "This covers how to load HTML news articles from a list of RSS feed URLs into a document format that we can use downstream."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7c2cd52-c1f7-4a06-8539-b0117da91fba",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "!pip install feedparser newspaper3k listparser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "16c3699e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import RSSFeedLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "836fbac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "urls = [\"https://news.ycombinator.com/rss\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33089aba-ff74-4d00-8f40-9449c29587cc",
+   "metadata": {},
+   "source": [
+    "Pass in urls to load them into Documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00f46fda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = RSSFeedLoader(urls=urls)\n",
+    "data = loader.load()\n",
+    "print(len(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "b447468cc42266d0",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(next Rich)\n",
+      "\n",
+      "04 August 2023\n",
+      "\n",
+      "Rich Hickey\n",
+      "\n",
+      "It is with a mixture of heartache and optimism that I announce today my (long planned) retirement from commercial software development, and my employment at Nubank. It’s been thrilling to see Clojure and Datomic successfully applied at scale.\n",
+      "\n",
+      "I look forward to continuing to lead ongoing work maintaining and enhancing Clojure with Alex, Stu, Fogus and many others, as an independent developer once again. We have many useful things planned for 1.12 and beyond. The community remains friendly, mature and productive, and is taking Clojure into many interesting new domains.\n",
+      "\n",
+      "I want to highlight and thank Nubank for their ongoing sponsorship of Alex, Fogus and the core team, as well as the Clojure community at large.\n",
+      "\n",
+      "Stu will continue to lead the development of Datomic at Nubank, where the Datomic team grows and thrives. I’m particularly excited to see where the new free availability of Datomic will lead.\n",
+      "\n",
+      "My time with Cognitect remains the highlight of my career. I have learned from absolutely everyone on our team, and am forever grateful to all for our interactions. There are too many people to thank here, but I must extend my sincerest appreciation and love to Stu and Justin for (repeatedly) taking a risk on me and my ideas, and for being the best of partners and friends, at all times fully embodying the notion of integrity. And of course to Alex Miller - who possesses in abundance many skills I lack, and without whose indomitable spirit, positivity and friendship Clojure would not have become what it did.\n",
+      "\n",
+      "I have made many friends through Clojure and Cognitect, and I hope to nurture those friendships moving forward.\n",
+      "\n",
+      "Retirement returns me to the freedom and independence I had when originally developing Clojure. The journey continues!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c36d3b0d329faf2a",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "You can pass arguments to the NewsURLLoader which it uses to load articles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "5fdada62470d3019",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Error fetching or processing https://twitter.com/andrewmccalip/status/1687405505604734978, exception: You must `parse()` an article first!\n",
+      "Error processing entry https://twitter.com/andrewmccalip/status/1687405505604734978, exception: list index out of range\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = RSSFeedLoader(urls=urls, nlp=True)\n",
+    "data = loader.load()\n",
+    "print(len(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "11d71963f7735c1d",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['nubank',\n",
+       " 'alex',\n",
+       " 'stu',\n",
+       " 'taking',\n",
+       " 'team',\n",
+       " 'remains',\n",
+       " 'rich',\n",
+       " 'clojure',\n",
+       " 'thank',\n",
+       " 'planned',\n",
+       " 'datomic']"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0].metadata['keywords']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9fb64ba0e8780966",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'It’s been thrilling to see Clojure and Datomic successfully applied at scale.\\nI look forward to continuing to lead ongoing work maintaining and enhancing Clojure with Alex, Stu, Fogus and many others, as an independent developer once again.\\nThe community remains friendly, mature and productive, and is taking Clojure into many interesting new domains.\\nI want to highlight and thank Nubank for their ongoing sponsorship of Alex, Fogus and the core team, as well as the Clojure community at large.\\nStu will continue to lead the development of Datomic at Nubank, where the Datomic team grows and thrives.'"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0].metadata['summary']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98ac26c488315bff",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "You can also use an OPML file such as a Feedly export.  Pass in either a URL or the OPML contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "8b6f07ae526a897c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Error fetching http://www.engadget.com/rss-full.xml, exception: Error fetching http://www.engadget.com/rss-full.xml, exception: document declared as us-ascii, but parsed as utf-8\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"example_data/sample_rss_feeds.opml\", \"r\") as f:\n",
+    "    loader = RSSFeedLoader(opml=f.read())\n",
+    "data = loader.load()\n",
+    "print(len(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b68a26b3",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The electric vehicle startup Fisker made a splash in Huntington Beach last night, showing off a range of new EVs it plans to build alongside the Fisker Ocean, which is slowly beginning deliveries in Europe and the US. With shades of Lotus circa 2010, it seems there\\'s something for most tastes, with a powerful four-door GT, a versatile pickup truck, and an affordable electric city car.\\n\\n\"We want the world to know that we have big plans and intend to move into several different segments, redefining each with our unique blend of design, innovation, and sustainability,\" said CEO Henrik Fisker.\\n\\nStarting with the cheapest, the Fisker PEAR—a cutesy acronym for \"Personal Electric Automotive Revolution\"—is said to use 35 percent fewer parts than other small EVs. Although it\\'s a smaller car, the PEAR seats six thanks to front and rear bench seats. Oh, and it has a frunk, which the company is calling the \"froot,\" something that will satisfy some British English speakers like Ars\\' friend and motoring journalist Jonny Smith.\\n\\nBut most exciting is the price—starting at $29,900 and scheduled for 2025. Fisker plans to contract with Foxconn to build the PEAR in Lordstown, Ohio, meaning it would be eligible for federal tax incentives.\\n\\nAdvertisement\\n\\nThe Fisker Alaska is the company\\'s pickup truck, built on a modified version of the platform used by the Ocean. It has an extendable cargo bed, which can be as little as 4.5 feet (1,371 mm) or as much as 9.2 feet (2,804 mm) long. Fisker claims it will be both the lightest EV pickup on sale and the most sustainable pickup truck in the world. Range will be an estimated 230–240 miles (370–386 km).\\n\\nThis, too, is slated for 2025, and also at a relatively affordable price, starting at $45,400. Fisker hopes to build this car in North America as well, although it isn\\'t saying where that might take place.\\n\\nFinally, there\\'s the Ronin, a four-door GT that bears more than a passing resemblance to the Fisker Karma, Henrik Fisker\\'s 2012 creation. There\\'s no price for this one, but Fisker says its all-wheel drive powertrain will boast 1,000 hp (745 kW) and will hit 60 mph from a standing start in two seconds—just about as fast as modern tires will allow. Expect a massive battery in this one, as Fisker says it\\'s targeting a 600-mile (956 km) range.\\n\\n\"Innovation and sustainability, along with design, are our three brand values. By 2027, we intend to produce the world’s first climate-neutral vehicle, and as our customers reinvent their relationships with mobility, we want to be a leader in software-defined transportation,\" Fisker said.'"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5a0cbe8-18a6-4af2-b447-7abb8b734451",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "poetry-venv",
+   "language": "python",
+   "name": "poetry-venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/tensorflow_datasets.ipynb

@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bda1f3f5",
+   "metadata": {},
+   "source": [
+    "# TensorFlow Datasets\n",
+    "\n",
+    ">[TensorFlow Datasets](https://www.tensorflow.org/datasets) is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as [tf.data.Datasets](https://www.tensorflow.org/api_docs/python/tf/data/Dataset), enabling easy-to-use and high-performance input pipelines. To get started see the [guide](https://www.tensorflow.org/datasets/overview) and the [list of datasets](https://www.tensorflow.org/datasets/catalog/overview#all_datasets).\n",
+    "\n",
+    "This notebook shows how to load `TensorFlow Datasets` into a Document format that we can use downstream."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b7a1eef-7bf7-4e7d-8bfc-c4e27c9488cb",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2abd5578-aa3d-46b9-99af-8b262f0b3df8",
+   "metadata": {},
+   "source": [
+    "You need to install `tensorflow` and `tensorflow-datasets` python packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e589036-351e-4c63-b734-c9a05fadb880",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tensorflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b674aaea-ed3a-4541-8414-260a8f67f623",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install tensorflow-datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95f05e1c-195e-4e2b-ae8e-8d6637f15be6",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e66e211e-9419-4dbb-b3cd-afc3cf984305",
+   "metadata": {},
+   "source": [
+    "As an example, we use the [`mlqa/en` dataset](https://www.tensorflow.org/datasets/catalog/mlqa#mlqaen).\n",
+    "\n",
+    ">`MLQA` (`Multilingual Question Answering Dataset`) is a benchmark dataset for evaluating multilingual question answering performance. The dataset consists of 7 languages: Arabic, German, Spanish, English, Hindi, Vietnamese, Chinese.\n",
+    ">\n",
+    ">- Homepage: https://github.com/facebookresearch/MLQA\n",
+    ">- Source code: `tfds.datasets.mlqa.Builder`\n",
+    ">- Download size: 72.21 MiB\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8968d645-c81c-4e3b-82bc-a3cbb5ddd93a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Feature structure of `mlqa/en` dataset:\n",
+    "\n",
+    "FeaturesDict({\n",
+    "    'answers': Sequence({\n",
+    "        'answer_start': int32,\n",
+    "        'text': Text(shape=(), dtype=string),\n",
+    "    }),\n",
+    "    'context': Text(shape=(), dtype=string),\n",
+    "    'id': string,\n",
+    "    'question': Text(shape=(), dtype=string),\n",
+    "    'title': Text(shape=(), dtype=string),\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "30fcaba5-cc9b-4a0e-a8f4-c047018451c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import tensorflow_datasets as tfds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "e307dd67-029e-4ee3-a65f-e085c09b0b8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<_TakeDataset element_spec={'answers': {'answer_start': TensorSpec(shape=(None,), dtype=tf.int32, name=None), 'text': TensorSpec(shape=(None,), dtype=tf.string, name=None)}, 'context': TensorSpec(shape=(), dtype=tf.string, name=None), 'id': TensorSpec(shape=(), dtype=tf.string, name=None), 'question': TensorSpec(shape=(), dtype=tf.string, name=None), 'title': TensorSpec(shape=(), dtype=tf.string, name=None)}>"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# try directly access this dataset:\n",
+    "ds = tfds.load('mlqa/en', split='test')\n",
+    "ds = ds.take(1)  # Only take a single example\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c9c4b08-d94f-4b53-add0-93769811644e",
+   "metadata": {},
+   "source": [
+    "Now we have to create a custom function to convert dataset sample into a Document.\n",
+    "\n",
+    "This is a requirement. There is no standard format for the TF datasets that's why we need to make a custom transformation function.\n",
+    "\n",
+    "Let's use `context` field as the `Document.page_content` and place other fields in the `Document.metadata`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "78844113-f8d8-48a8-8105-685280b6cfa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='After completing the journey around South America, on 23 February 2006, Queen Mary 2 met her namesake, the original RMS Queen Mary, which is permanently docked at Long Beach, California. Escorted by a flotilla of smaller ships, the two Queens exchanged a \"whistle salute\" which was heard throughout the city of Long Beach. Queen Mary 2 met the other serving Cunard liners Queen Victoria and Queen Elizabeth 2 on 13 January 2008 near the Statue of Liberty in New York City harbour, with a celebratory fireworks display; Queen Elizabeth 2 and Queen Victoria made a tandem crossing of the Atlantic for the meeting. This marked the first time three Cunard Queens have been present in the same location. Cunard stated this would be the last time these three ships would ever meet, due to Queen Elizabeth 2\\'s impending retirement from service in late 2008. However this would prove not to be the case, as the three Queens met in Southampton on 22 April 2008. Queen Mary 2 rendezvoused with Queen Elizabeth 2  in Dubai on Saturday 21 March 2009, after the latter ship\\'s retirement, while both ships were berthed at Port Rashid. With the withdrawal of Queen Elizabeth 2 from Cunard\\'s fleet and its docking in Dubai, Queen Mary 2 became the only ocean liner left in active passenger service.' metadata={'id': '5116f7cccdbf614d60bcd23498274ffd7b1e4ec7', 'title': 'RMS Queen Mary 2', 'question': 'What year did Queen Mary 2 complete her journey around South America?', 'answer': '2006'}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-08-03 14:27:08.482983: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def decode_to_str(item: tf.Tensor) -> str:\n",
+    "    return item.numpy().decode('utf-8')\n",
+    "\n",
+    "def mlqaen_example_to_document(example: dict) -> Document:\n",
+    "    return Document(\n",
+    "        page_content=decode_to_str(example[\"context\"]),\n",
+    "        metadata={\n",
+    "            \"id\": decode_to_str(example[\"id\"]),\n",
+    "            \"title\": decode_to_str(example[\"title\"]),\n",
+    "            \"question\": decode_to_str(example[\"question\"]),\n",
+    "            \"answer\": decode_to_str(example[\"answers\"][\"text\"][0]),\n",
+    "        },\n",
+    "    )\n",
+    "    \n",
+    "    \n",
+    "for example in ds:  \n",
+    "    doc = mlqaen_example_to_document(example)\n",
+    "    print(doc)\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "2d43c834-5145-4793-9558-8e301ccaf3b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema import Document\n",
+    "from langchain.document_loaders import TensorflowDatasetLoader\n",
+    "\n",
+    "loader = TensorflowDatasetLoader(\n",
+    "        dataset_name=\"mlqa/en\",\n",
+    "        split_name=\"test\",\n",
+    "        load_max_docs=3,\n",
+    "        sample_to_document_function=mlqaen_example_to_document,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e29b954c-1407-4797-ae21-6ba8937156be",
+   "metadata": {},
+   "source": [
+    "`TensorflowDatasetLoader` has these parameters:\n",
+    "- `dataset_name`: the name of the dataset to load\n",
+    "- `split_name`: the name of the split to load. Defaults to \"train\".\n",
+    "- `load_max_docs`: a limit to the number of loaded documents. Defaults to 100.\n",
+    "- `sample_to_document_function`: a function that converts a dataset sample to a Document\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "700e4ef2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-08-03 14:27:22.998964: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "len(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "9138940a-e9fe-4145-83e8-77589b5272c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'After completing the journey around South America, on 23 February 2006, Queen Mary 2 met her namesake, the original RMS Queen Mary, which is permanently docked at Long Beach, California. Escorted by a flotilla of smaller ships, the two Queens exchanged a \"whistle salute\" which was heard throughout the city of Long Beach. Queen Mary 2 met the other serving Cunard liners Queen Victoria and Queen Elizabeth 2 on 13 January 2008 near the Statue of Liberty in New York City harbour, with a celebratory fireworks display; Queen Elizabeth 2 and Queen Victoria made a tandem crossing of the Atlantic for the meeting. This marked the first time three Cunard Queens have been present in the same location. Cunard stated this would be the last time these three ships would ever meet, due to Queen Elizabeth 2\\'s impending retirement from service in late 2008. However this would prove not to be the case, as the three Queens met in Southampton on 22 April 2008. Queen Mary 2 rendezvoused with Queen Elizabeth 2  in Dubai on Saturday 21 March 2009, after the latter ship\\'s retirement, while both ships were berthed at Port Rashid. With the withdrawal of Queen Elizabeth 2 from Cunard\\'s fleet and its docking in Dubai, Queen Mary 2 became the only ocean liner left in active passenger service.'"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "2f7f7832-fe4d-4a58-892d-bb987cdbed0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'id': '5116f7cccdbf614d60bcd23498274ffd7b1e4ec7',\n",
+       " 'title': 'RMS Queen Mary 2',\n",
+       " 'question': 'What year did Queen Mary 2 complete her journey around South America?',\n",
+       " 'answer': '2006'}"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "125d073c-4f4f-4ae6-a0c7-9e9db3cc8d69",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_loaders/unstructured_file.ipynb

@@ -18,8 +18,7 @@
    "outputs": [],
    "source": [
     "# # Install package\n",
-    "!pip install \"unstructured[local-inference]\"\n",
-    "!pip install layoutparser[layoutmodels,tesseract]"
+    "!pip install \"unstructured[all-docs]\"\n"
    ]
   },
   {

docs/extras/integrations/document_loaders/youtube_audio.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e48afb8d",
    "metadata": {},
@@ -11,7 +12,8 @@
     "\n",
     "Below we show how to easily go from a YouTube url to text to chat!\n",
     "\n",
-    "We wil use the `OpenAIWhisperParser`, which will use the OpenAI Whisper API to transcribe audio to text.\n",
+    "We wil use the `OpenAIWhisperParser`, which will use the OpenAI Whisper API to transcribe audio to text, \n",
+    "and the  `OpenAIWhisperParserLocal` for local support and running on private clouds or on premise.\n",
     "\n",
     "Note: You will need to have an `OPENAI_API_KEY` supplied."
    ]
@@ -24,7 +26,7 @@
    "outputs": [],
    "source": [
     "from langchain.document_loaders.generic import GenericLoader\n",
-    "from langchain.document_loaders.parsers import OpenAIWhisperParser\n",
+    "from langchain.document_loaders.parsers import OpenAIWhisperParser, OpenAIWhisperParserLocal\n",
     "from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader"
    ]
   },
@@ -46,7 +48,8 @@
    "outputs": [],
    "source": [
     "! pip install yt_dlp\n",
-    "! pip install pydub"
+    "! pip install pydub\n",
+    "! pip install librosa"
    ]
   },
   {
@@ -63,6 +66,18 @@
     "Let's take the first lecture of Andrej Karpathy's YouTube course as an example! "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8682f256",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set a flag to switch between local and remote parsing\n",
+    "# change this to True if you want to use local parsing\n",
+    "local = False"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -102,7 +117,10 @@
     "save_dir = \"~/Downloads/YouTube\"\n",
     "\n",
     "# Transcribe the videos to text\n",
-    "loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParser())\n",
+    "if local:\n",
+    "    loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParserLocal())\n",
+    "else:\n",
+    "    loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParser())\n",
     "docs = loader.load()"
    ]
   },
@@ -275,7 +293,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -289,7 +307,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.11"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "97cc609b13305c559618ec78a438abc56230b9381f827f22d070313b9a1f3777"
+   }
   }
  },
  "nbformat": 4,

docs/extras/integrations/document_transformers/beautiful_soup.ipynb

@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2ed9a4c2",
+   "metadata": {},
+   "source": [
+    "# Beautiful Soup\n",
+    "\n",
+    "Beautiful Soup offers fine-grained control over HTML content, enabling specific tag extraction, removal, and content cleaning. \n",
+    "\n",
+    "It's suited for cases where you want to extract specific information and clean up the HTML content according to your needs.\n",
+    "\n",
+    "For example, we can scrape text content within `<p>, <li>, <div>, and <a>` tags from the HTML content:\n",
+    "\n",
+    "* `<p>`: The paragraph tag. It defines a paragraph in HTML and is used to group together related sentences and/or phrases.\n",
+    " \n",
+    "* `<li>`: The list item tag. It is used within ordered (`<ol>`) and unordered (`<ul>`) lists to define individual items within the list.\n",
+    " \n",
+    "* `<div>`: The division tag. It is a block-level element used to group other inline or block-level elements.\n",
+    " \n",
+    "* `<a>`: The anchor tag. It is used to define hyperlinks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dd710e5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import AsyncChromiumLoader\n",
+    "from langchain.document_transformers import BeautifulSoupTransformer\n",
+    "\n",
+    "# Load HTML\n",
+    "loader = AsyncChromiumLoader([\"https://www.wsj.com\"])\n",
+    "html = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "052b64dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Transform\n",
+    "bs_transformer = BeautifulSoupTransformer()\n",
+    "docs_transformed = bs_transformer.transform_documents(html,tags_to_extract=[\"p\", \"li\", \"div\", \"a\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b53a5307",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Conservative legal activists are challenging Amazon, Comcast and others using many of the same tools that helped kill affirmative-action programs in colleges.1,2099 min read U.S. stock indexes fell and government-bond prices climbed, after Moody’s lowered credit ratings for 10 smaller U.S. banks and said it was reviewing ratings for six larger ones. The Dow industrials dropped more than 150 points.3 min read Penn Entertainment’s Barstool Sportsbook app will be rebranded as ESPN Bet this fall as '"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_transformed[0].page_content[0:500]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/document_transformers/nuclia_transformer.ipynb

@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nuclia Understanding API document transformer\n",
+    "\n",
+    "[Nuclia](https://nuclia.com) automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.\n",
+    "\n",
+    "The Nuclia Understanding API document transformer splits text into paragraphs and sentences, identifies entities, provides a summary of the text and generates embeddings for all the sentences.\n",
+    "\n",
+    "To use the Nuclia Understanding API, you need to have a Nuclia account. You can create one for free at [https://nuclia.cloud](https://nuclia.cloud), and then [create a NUA key](https://docs.nuclia.dev/docs/docs/using/understanding/intro).\n",
+    "\n",
+    "from langchain.document_transformers.nuclia_text_transform import NucliaTextTransformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install --upgrade protobuf\n",
+    "#!pip install nucliadb-protos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"NUCLIA_ZONE\"] = \"<YOUR_ZONE>\"  # e.g. europe-1\n",
+    "os.environ[\"NUCLIA_NUA_KEY\"] = \"<YOUR_API_KEY>\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use the Nuclia document transformer, you need to instantiate a `NucliaUnderstandingAPI` tool with `enable_ml` set to `True`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools.nuclia import NucliaUnderstandingAPI\n",
+    "\n",
+    "nua = NucliaUnderstandingAPI(enable_ml=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Nuclia document transformer must be called in async mode, so you need to use the `atransform_documents` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "\n",
+    "from langchain.document_transformers.nuclia_text_transform import NucliaTextTransformer\n",
+    "from langchain.schema.document import Document\n",
+    "\n",
+    "\n",
+    "async def process():\n",
+    "    documents = [\n",
+    "        Document(page_content=\"<TEXT 1>\", metadata={}),\n",
+    "        Document(page_content=\"<TEXT 2>\", metadata={}),\n",
+    "        Document(page_content=\"<TEXT 3>\", metadata={}),\n",
+    "    ]\n",
+    "    nuclia_transformer = NucliaTextTransformer(nua)\n",
+    "    transformed_documents = await nuclia_transformer.atransform_documents(documents)\n",
+    "    print(transformed_documents)\n",
+    "\n",
+    "\n",
+    "asyncio.run(process())"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/extras/integrations/llms/Fireworks.ipynb

@@ -0,0 +1,242 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cc6caafa",
+   "metadata": {},
+   "source": [
+    "# Fireworks\n",
+    "\n",
+    ">[Fireworks](https://app.fireworks.ai/) accelerates product development on generative AI by creating an innovative AI experiment and production platform. \n",
+    "\n",
+    "This example goes over how to use LangChain to interact with `Fireworks` models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "60b6dbb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms.fireworks import Fireworks, FireworksChat\n",
+    "from langchain import PromptTemplate, LLMChain\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccff689e",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "\n",
+    "Contact Fireworks AI for the an API Key to access our models\n",
+    "\n",
+    "Set up your model using a model id. If the model is not set, the default model is fireworks-llama-v2-7b-chat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ca87a2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize a Fireworks LLM\n",
+    "os.environ['FIREWORKS_API_KEY'] = \"<YOUR_API_KEY>\"  # Change this to your own API key\n",
+    "llm = Fireworks(model_id=\"accounts/fireworks/models/llama-v2-13b-chat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acc24d0c",
+   "metadata": {},
+   "source": [
+    "# Calling the Model\n",
+    "\n",
+    "You can use the LLMs to call the model for specified prompt(s). \n",
+    "\n",
+    "Currently supported models: \n",
+    "\n",
+    "*   Falcon\n",
+    "    *   `accounts/fireworks/models/falcon-7b`\n",
+    "    *   `accounts/fireworks/models/falcon-40b-w8a16`\n",
+    "*   Llama 2\n",
+    "    *   `accounts/fireworks/models/llama-v2-7b`\n",
+    "    *   `accounts/fireworks/models/llama-v2-7b-w8a16`\n",
+    "    *   `accounts/fireworks/models/llama-v2-7b-chat`\n",
+    "    *   `accounts/fireworks/models/llama-v2-7b-chat-w8a16`\n",
+    "    *   `accounts/fireworks/models/llama-v2-13b`\n",
+    "    *   `accounts/fireworks/models/llama-v2-13b-w8a16`\n",
+    "    *   `accounts/fireworks/models/llama-v2-13b-chat`\n",
+    "    *   `accounts/fireworks/models/llama-v2-13b-chat-w8a16`\n",
+    "    *   `accounts/fireworks/models/llama-v2-70b-chat-4gpu`\n",
+    "*   StarCoder\n",
+    "    *   `accounts/fireworks/models/starcoder-1b-w8a16-1gpu`\n",
+    "    *   `accounts/fireworks/models/starcoder-3b-w8a16-1gpu`\n",
+    "    *   `accounts/fireworks/models/starcoder-7b-w8a16-1gpu`\n",
+    "    *   `accounts/fireworks/models/starcoder-16b-w8a16`\n",
+    "\n",
+    "See the full, most up-to-date list on [app.fireworks.ai](https://app.fireworks.ai)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bf0a425c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Is it Tom Brady, Aaron Rodgers, or someone else? It's a tough question to answer, and there are strong arguments for each of these quarterbacks. Here are some of the reasons why each of these quarterbacks could be considered the best:\n",
+      "\n",
+      "Tom Brady:\n",
+      "\n",
+      "* He has the most Super Bowl wins (6) of any quarterback in NFL history.\n",
+      "* He has been named Super Bowl MVP four times, more than any other player.\n",
+      "* He has led the New England Patriots to 18 playoff victories, the most in NFL history.\n",
+      "* He has thrown for over 70,000 yards in his career, the most of any quarterback in NFL history.\n",
+      "* He has thrown for 50 or more touchdowns in a season four times, the most of any quarterback in NFL history.\n",
+      "\n",
+      "Aaron Rodgers:\n",
+      "\n",
+      "* He has led the Green Bay Packers to a Super Bowl victory in 2010.\n",
+      "* He has been named Super Bowl MVP once.\n",
+      "* He has thrown for over 40,000 yards in his career, the most of any quarterback in NFL history.\n",
+      "* He has thrown for 40 or more touchdowns in a season three times, the most of any quarterback in NFL history.\n",
+      "* He has a career passer rating of 103.1, the highest of any quarterback in NFL history.\n",
+      "\n",
+      "So, who's the best quarterback in the NFL? It's a tough call, but here's my opinion:\n",
+      "\n",
+      "I think Aaron Rodgers is the best quarterback in the NFL right now. He has led the Packers to a Super Bowl victory and has had some incredible seasons, including the 2011 season when he threw for 45 touchdowns and just 6 interceptions. He has a strong arm, great accuracy, and is incredibly mobile for a quarterback of his size. He also has a great sense of timing and knows when to take risks and when to play it safe.\n",
+      "\n",
+      "Tom Brady is a close second, though. He has an incredible track record of success, including six Super Bowl victories, and has been one of the most consistent quarterbacks in the league for the past two decades. He has a strong arm and is incredibly accurate\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Single prompt\n",
+    "output = llm(\"Who's the best quarterback in the NFL?\")\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "afc7de6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[Generation(text='\\nThe best cricket player in 2016 is a matter of opinion, but some of the top contenders for the title include:\\n\\n1. Virat Kohli (India): Kohli had a phenomenal year in 2016, scoring over 1,000 runs in Test cricket, including four centuries, and averaging over 70. He also scored heavily in ODI cricket, with an average of over 80.\\n2. Steve Smith (Australia): Smith had a remarkable year in 2016, leading Australia to a Test series victory in India and scoring over 1,000 runs in the format, including five centuries. He also averaged over 60 in ODI cricket.\\n3. KL Rahul (India): Rahul had a breakout year in 2016, scoring over 1,000 runs in Test cricket, including four centuries, and averaging over 60. He also scored heavily in ODI cricket, with an average of over 70.\\n4. Joe Root (England): Root had a solid year in 2016, scoring over 1,000 runs in Test cricket, including four centuries, and averaging over 50. He also scored heavily in ODI cricket, with an average of over 80.\\n5. Quinton de Kock (South Africa): De Kock had a remarkable year in 2016, scoring over 1,000 runs in ODI cricket, including six centuries, and averaging over 80. He also scored heavily in Test cricket, with an average of over 50.\\n\\nThese are just a few of the top contenders for the title of best cricket player in 2016, but there were many other talented players who also had impressive years. Ultimately, the answer to this question is subjective and depends on individual opinions and criteria for evaluation.', generation_info=None)], [Generation(text=\"\\nThis is a tough one, as there are so many great players in the league right now. But if I had to choose one, I'd say LeBron James is the best basketball player in the league. He's a once-in-a-generation talent who can dominate the game in so many ways. He's got incredible speed, strength, and court vision, and he's always finding new ways to improve his game. Plus, he's been doing it at an elite level for over a decade now, which is just amazing.\\n\\nBut don't just take my word for it - there are plenty of other great players in the league who could make a strong case for being the best. Guys like Kevin Durant, Steph Curry, James Harden, and Giannis Antetokounmpo are all having incredible seasons, and they've all got their own unique skills and strengths that make them special. So ultimately, it's up to you to decide who you think is the best basketball player in the league.\", generation_info=None)]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calling multiple prompts\n",
+    "output = llm.generate([\n",
+    "    \"Who's the best cricket player in 2016?\",\n",
+    "    \"Who's the best basketball player in the league?\",\n",
+    "])\n",
+    "print(output.generations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b801c20d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Kansas City in December is quite cold, with temperatures typically r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Setting additional parameters: temperature, max_tokens, top_p\n",
+    "llm = Fireworks(model_id=\"accounts/fireworks/models/llama-v2-13b-chat\", temperature=0.7, max_tokens=15, top_p=1.0)\n",
+    "print(llm(\"What's the weather like in Kansas City in December?\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "137662a6",
+   "metadata": {},
+   "source": [
+    "# Create and Run Chain\n",
+    "\n",
+    "Create a prompt template to be used with the LLM Chain. Once this prompt template is created, initialize the chain with the LLM and prompt template, and run the chain with the specified prompts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fd2c6bc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Naming a company can be a fun and creative process! Here are a few name ideas for a company that makes football helmets:\n",
+      "\n",
+      "1. Helix Headgear: This name plays off the idea of the helix shape of a football helmet and could be a memorable and catchy name for a company.\n",
+      "2. Gridiron Gear: \"Gridiron\" is a term used to describe a football field, and \"gear\" refers to the products the company sells. This name is straightforward and easy to understand.\n",
+      "3. Cushion Crusaders: This name emphasizes the protective qualities of football helmets and could appeal to customers looking for safety-conscious products.\n",
+      "4. Helmet Heroes: This name has a fun, heroic tone and could appeal to customers looking for high-quality products.\n",
+      "5. Tackle Tech: \"Tackle\" is a term used in football to describe a player's attempt to stop an opponent, and \"tech\" refers to the technology used in the helmets. This name could appeal to customers interested in innovative products.\n",
+      "6. Padded Protection: This name emphasizes the protective qualities of football helmets and could appeal to customers looking for products that prioritize safety.\n",
+      "7. Gridiron Gear Co.: This name is simple and straightforward, and it clearly conveys the company's focus on football-related products.\n",
+      "8. Helmet Haven: This name has a soothing, protective tone and could appeal to customers looking for a reliable brand.\n",
+      "\n",
+      "Remember to choose a name that reflects your company's values and mission, and that resonates with your target market. Good luck with your company!\n"
+     ]
+    }
+   ],
+   "source": [
+    "human_message_prompt = HumanMessagePromptTemplate.from_template(\"What is a good name for a company that makes {product}?\")\n",
+    "chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])\n",
+    "chat = FireworksChat()\n",
+    "chain = LLMChain(llm=chat, prompt=chat_prompt_template)\n",
+    "output = chain.run(\"football helmets\")\n",
+    "\n",
+    "print(output)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/llms/bedrock.ipynb

@@ -31,12 +31,11 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.llms.bedrock import Bedrock\n",
+    "from langchain.llms import Bedrock\n",
     "\n",
     "llm = Bedrock(\n",
     "    credentials_profile_name=\"bedrock-admin\",\n",
-    "    model_id=\"amazon.titan-tg1-large\",\n",
-    "    endpoint_url=\"custom_endpoint_url\",\n",
+    "    model_id=\"amazon.titan-tg1-large\"\n",
     ")"
    ]
   },

docs/extras/integrations/llms/llamacpp.ipynb

@@ -4,12 +4,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Llama-cpp\n",
+    "# Llama.cpp\n",
     "\n",
-    "[llama-cpp](https://github.com/abetlen/llama-cpp-python) is a Python binding for [llama.cpp](https://github.com/ggerganov/llama.cpp). \n",
+    "[llama-cpp-python](https://github.com/abetlen/llama-cpp-python) is a Python binding for [llama.cpp](https://github.com/ggerganov/llama.cpp). \n",
     "It supports [several LLMs](https://github.com/ggerganov/llama.cpp).\n",
     "\n",
-    "This notebook goes over how to run `llama-cpp` within LangChain."
+    "This notebook goes over how to run `llama-cpp-python` within LangChain."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "source": [
     "## Installation\n",
     "\n",
-    "There is a bunch of options how to install the llama-cpp package: \n",
+    "There are different options on how to install the llama-cpp package: \n",
     "- only CPU usage\n",
     "- CPU + GPU (using one of many BLAS backends)\n",
     "- Metal GPU (MacOS with Apple Silicon Chip) \n",
@@ -61,7 +61,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**IMPORTANT**: If you have already installed a cpu only version of the package, you need to reinstall it from scratch: consider the following command: "
+    "**IMPORTANT**: If you have already installed the CPU only version of the package, you need to reinstall it from scratch. Consider the following command: "
    ]
   },
   {
@@ -79,7 +79,7 @@
    "source": [
     "### Installation with Metal\n",
     "\n",
-    "`lama.cpp` supports Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks. Use the `FORCE_CMAKE=1` environment variable to force the use of cmake and install the pip package for the Metal support ([source](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md)).\n",
+    "`llama.cpp` supports Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks. Use the `FORCE_CMAKE=1` environment variable to force the use of cmake and install the pip package for the Metal support ([source](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md)).\n",
     "\n",
     "Example installation with Metal Support:"
    ]
@@ -143,7 +143,7 @@
     "\n",
     "#### Compiling and installing\n",
     "\n",
-    "In the same command prompt (anaconda prompt) you set the variables, you can cd into `llama-cpp-python` directory and run the following commands.\n",
+    "In the same command prompt (anaconda prompt) you set the variables, you can `cd` into `llama-cpp-python` directory and run the following commands.\n",
     "\n",
     "```\n",
     "python setup.py clean\n",
@@ -164,7 +164,9 @@
    "source": [
     "Make sure you are following all instructions to [install all necessary model files](https://github.com/ggerganov/llama.cpp).\n",
     "\n",
-    "You don't need an `API_TOKEN`!"
+    "You don't need an `API_TOKEN` as you will run the LLM locally.\n",
+    "\n",
+    "It is worth understanding which models are suitable to be used on the desired machine."
    ]
   },
   {
@@ -227,7 +229,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`Llama-v2`"
+    "Example using a LLaMA 2 7B model"
    ]
   },
   {
@@ -304,7 +306,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`Llama-v1`"
+    "Example using a LLaMA v1 model"
    ]
   },
   {
@@ -381,7 +383,7 @@
    "source": [
     "### GPU\n",
     "\n",
-    "If the installation with BLAS backend was correct, you will see an `BLAS = 1` indicator in model properties.\n",
+    "If the installation with BLAS backend was correct, you will see a `BLAS = 1` indicator in model properties.\n",
     "\n",
     "Two of the most important parameters for use with GPU are:\n",
     "\n",
@@ -473,22 +475,15 @@
     "llm_chain.run(question)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Metal\n",
     "\n",
-    "If the installation with Metal was correct, you will see an `NEON = 1` indicator in model properties.\n",
+    "If the installation with Metal was correct, you will see a `NEON = 1` indicator in model properties.\n",
     "\n",
-    "Two of the most important parameters for use with GPU are:\n",
+    "Two of the most important GPU parameters are:\n",
     "\n",
     "- `n_gpu_layers` - determines how many layers of the model are offloaded to your Metal GPU, in the most case, set it to `1` is enough for Metal\n",
     "- `n_batch` - how many tokens are processed in parallel, default is 8, set to bigger number.\n",
@@ -522,7 +517,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The rest are almost same as GPU, the console log will show the following log to indicate the Metal was enable properly.\n",
+    "The console log will show the following log to indicate Metal was enable properly.\n",
     "\n",
     "```\n",
     "ggml_metal_init: allocating\n",
@@ -530,7 +525,9 @@
     "...\n",
     "```\n",
     "\n",
-    "You also could check the `Activity Monitor` by watching the % GPU of the process, the % CPU will drop dramatically after turn on `n_gpu_layers=1`. Also for the first time call LLM, the performance might be slow due to the model compilation in Metal GPU."
+    "You also could check `Activity Monitor` by watching the GPU usage of the process, the CPU usage will drop dramatically after turn on `n_gpu_layers=1`. \n",
+    "\n",
+    "For the first call to the LLM, the performance may be slow due to the model compilation in Metal GPU."
    ]
   }
  ],

docs/extras/integrations/llms/ollama.ipynb

@@ -0,0 +1,340 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ollama\n",
+    "\n",
+    "[Ollama](https://ollama.ai/) allows you to run open-source large language models, such as Llama 2, locally.\n",
+    "\n",
+    "Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. \n",
+    "\n",
+    "It optimizes setup and configuration details, including GPU usage.\n",
+    "\n",
+    "For a complete list of supported models and model variants, see the [Ollama model library](https://github.com/jmorganca/ollama#model-library).\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
+    "\n",
+    "* [Download](https://ollama.ai/download)\n",
+    "* Fetch a model, e.g., `Llama-7b`: `ollama pull llama2`\n",
+    "* Run `ollama run llama2`\n",
+    "\n",
+    "\n",
+    "## Usage\n",
+    "\n",
+    "You can see a full list of supported parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain.llms.ollama.Ollama.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import Ollama\n",
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler                                  \n",
+    "llm = Ollama(base_url=\"http://localhost:11434\", \n",
+    "             model=\"llama2\", \n",
+    "             callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With `StreamingStdOutCallbackHandler`, you will see tokens streamed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Great! The history of Artificial Intelligence (AI) is a fascinating and complex topic that spans several decades. Here's a brief overview:\n",
+      "\n",
+      "1. Early Years (1950s-1960s): The term \"Artificial Intelligence\" was coined in 1956 by computer scientist John McCarthy. However, the concept of AI dates back to ancient Greece, where mythical creatures like Talos and Hephaestus were created to perform tasks without any human intervention. In the 1950s and 1960s, researchers began exploring ways to replicate human intelligence using computers, leading to the development of simple AI programs like ELIZA (1966) and PARRY (1972).\n",
+      "2. Rule-Based Systems (1970s-1980s): As computing power increased, researchers developed rule-based systems, such as Mycin (1976), which could diagnose medical conditions based on a set of rules. This period also saw the rise of expert systems, like EDICT (1985), which mimicked human experts in specific domains.\n",
+      "3. Machine Learning (1990s-2000s): With the advent of big data and machine learning algorithms, AI evolved to include neural networks, decision trees, and other techniques for training models on large datasets. This led to the development of applications like speech recognition (e.g., Siri, Alexa), image recognition (e.g., Google Image Search), and natural language processing (e.g., chatbots).\n",
+      "4. Deep Learning (2010s-present): The rise of deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has enabled AI to perform complex tasks like image and speech recognition, natural language processing, and even autonomous driving. Companies like Google, Facebook, and Baidu have invested heavily in deep learning research, leading to breakthroughs in areas like facial recognition, object detection, and machine translation.\n",
+      "5. Current Trends (present-future): AI is currently being applied to various industries, including healthcare, finance, education, and entertainment. With the growth of cloud computing, edge AI, and autonomous systems, we can expect to see more sophisticated AI applications in the near future. However, there are also concerns about the ethical implications of AI, such as data privacy, algorithmic bias, and job displacement.\n",
+      "\n",
+      "Remember, AI has a long history, and its development is an ongoing process. As technology advances, we can expect to see even more innovative applications of AI in various fields."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\nGreat! The history of Artificial Intelligence (AI) is a fascinating and complex topic that spans several decades. Here\\'s a brief overview:\\n\\n1. Early Years (1950s-1960s): The term \"Artificial Intelligence\" was coined in 1956 by computer scientist John McCarthy. However, the concept of AI dates back to ancient Greece, where mythical creatures like Talos and Hephaestus were created to perform tasks without any human intervention. In the 1950s and 1960s, researchers began exploring ways to replicate human intelligence using computers, leading to the development of simple AI programs like ELIZA (1966) and PARRY (1972).\\n2. Rule-Based Systems (1970s-1980s): As computing power increased, researchers developed rule-based systems, such as Mycin (1976), which could diagnose medical conditions based on a set of rules. This period also saw the rise of expert systems, like EDICT (1985), which mimicked human experts in specific domains.\\n3. Machine Learning (1990s-2000s): With the advent of big data and machine learning algorithms, AI evolved to include neural networks, decision trees, and other techniques for training models on large datasets. This led to the development of applications like speech recognition (e.g., Siri, Alexa), image recognition (e.g., Google Image Search), and natural language processing (e.g., chatbots).\\n4. Deep Learning (2010s-present): The rise of deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has enabled AI to perform complex tasks like image and speech recognition, natural language processing, and even autonomous driving. Companies like Google, Facebook, and Baidu have invested heavily in deep learning research, leading to breakthroughs in areas like facial recognition, object detection, and machine translation.\\n5. Current Trends (present-future): AI is currently being applied to various industries, including healthcare, finance, education, and entertainment. With the growth of cloud computing, edge AI, and autonomous systems, we can expect to see more sophisticated AI applications in the near future. However, there are also concerns about the ethical implications of AI, such as data privacy, algorithmic bias, and job displacement.\\n\\nRemember, AI has a long history, and its development is an ongoing process. As technology advances, we can expect to see even more innovative applications of AI in various fields.'"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm(\"Tell me about the history of AI\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RAG\n",
+    "\n",
+    "We can use Olama with RAG, [just as shown here](https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa).\n",
+    "\n",
+    "Let's use the 13b model:\n",
+    "\n",
+    "```\n",
+    "ollama pull llama2:13b\n",
+    "ollama run llama2:13b \n",
+    "```\n",
+    "\n",
+    "Let's also use local embeddings from `GPT4AllEmbeddings` and `Chroma`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install gpt4all chromadb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import WebBaseLoader\n",
+    "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
+    "data = loader.load()\n",
+    "\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
+    "all_splits = text_splitter.split_documents(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found model file at  /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.embeddings import GPT4AllEmbeddings\n",
+    "\n",
+    "vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"What are the approaches to Task Decomposition?\"\n",
+    "docs = vectorstore.similarity_search(question)\n",
+    "len(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import PromptTemplate\n",
+    "\n",
+    "# Prompt\n",
+    "template = \"\"\"Use the following pieces of context to answer the question at the end. \n",
+    "If you don't know the answer, just say that you don't know, don't try to make up an answer. \n",
+    "Use three sentences maximum and keep the answer as concise as possible. \n",
+    "{context}\n",
+    "Question: {question}\n",
+    "Helpful Answer:\"\"\"\n",
+    "QA_CHAIN_PROMPT = PromptTemplate(\n",
+    "    input_variables=[\"context\", \"question\"],\n",
+    "    template=template,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# LLM\n",
+    "from langchain.llms import Ollama\n",
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "llm = Ollama(base_url=\"http://localhost:11434\",\n",
+    "             model=\"llama2\",\n",
+    "             verbose=True,\n",
+    "             callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# QA chain\n",
+    "from langchain.chains import RetrievalQA\n",
+    "qa_chain = RetrievalQA.from_chain_type(\n",
+    "    llm,\n",
+    "    retriever=vectorstore.as_retriever(),\n",
+    "    chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Task decomposition can be approached in different ways for AI agents, including:\n",
+      "\n",
+      "1. Using simple prompts like \"Steps for XYZ.\" or \"What are the subgoals for achieving XYZ?\" to guide the LLM.\n",
+      "2. Providing task-specific instructions, such as \"Write a story outline\" for writing a novel.\n",
+      "3. Utilizing human inputs to help the AI agent understand the task and break it down into smaller steps."
+     ]
+    }
+   ],
+   "source": [
+    "question = \"What are the various approaches to Task Decomposition for AI Agents?\"\n",
+    "result = qa_chain({\"query\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also get logging for tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Task decomposition can be approached in three ways: (1) using simple prompting like \"Steps for XYZ.\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions, or (3) with human inputs.{'model': 'llama2', 'created_at': '2023-08-08T04:01:09.005367Z', 'done': True, 'context': [1, 29871, 1, 13, 9314, 14816, 29903, 6778, 13, 13, 3492, 526, 263, 8444, 29892, 3390, 1319, 322, 15993, 20255, 29889, 29849, 1234, 408, 1371, 3730, 408, 1950, 29892, 1550, 1641, 9109, 29889, 3575, 6089, 881, 451, 3160, 738, 10311, 1319, 29892, 443, 621, 936, 29892, 11021, 391, 29892, 7916, 391, 29892, 304, 27375, 29892, 18215, 29892, 470, 27302, 2793, 29889, 3529, 9801, 393, 596, 20890, 526, 5374, 635, 443, 5365, 1463, 322, 6374, 297, 5469, 29889, 13, 13, 3644, 263, 1139, 947, 451, 1207, 738, 4060, 29892, 470, 338, 451, 2114, 1474, 16165, 261, 296, 29892, 5649, 2020, 2012, 310, 22862, 1554, 451, 1959, 29889, 960, 366, 1016, 29915, 29873, 1073, 278, 1234, 304, 263, 1139, 29892, 3113, 1016, 29915, 29873, 6232, 2089, 2472, 29889, 13, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 29961, 25580, 29962, 4803, 278, 1494, 12785, 310, 3030, 304, 1234, 278, 1139, 472, 278, 1095, 29889, 29871, 13, 3644, 366, 1016, 29915, 29873, 1073, 278, 1234, 29892, 925, 1827, 393, 366, 1016, 29915, 29873, 1073, 29892, 1016, 29915, 29873, 1018, 304, 1207, 701, 385, 1234, 29889, 29871, 13, 11403, 2211, 25260, 7472, 322, 3013, 278, 1234, 408, 3022, 895, 408, 1950, 29889, 29871, 13, 5398, 26227, 508, 367, 2309, 313, 29896, 29897, 491, 365, 26369, 411, 2560, 9508, 292, 763, 376, 7789, 567, 363, 1060, 29979, 29999, 7790, 29876, 29896, 19602, 376, 5618, 526, 278, 1014, 1484, 1338, 363, 3657, 15387, 1060, 29979, 29999, 29973, 613, 313, 29906, 29897, 491, 773, 3414, 29899, 14940, 11994, 29936, 321, 29889, 29887, 29889, 376, 6113, 263, 5828, 27887, 1213, 363, 5007, 263, 9554, 29892, 470, 313, 29941, 29897, 411, 5199, 10970, 29889, 13, 13, 5398, 26227, 508, 367, 2309, 313, 29896, 29897, 491, 365, 26369, 411, 2560, 9508, 292, 763, 376, 7789, 567, 363, 1060, 29979, 29999, 7790, 29876, 29896, 19602, 376, 5618, 526, 278, 1014, 1484, 1338, 363, 3657, 15387, 1060, 29979, 29999, 29973, 613, 313, 29906, 29897, 491, 773, 3414, 29899, 14940, 11994, 29936, 321, 29889, 29887, 29889, 376, 6113, 263, 5828, 27887, 1213, 363, 5007, 263, 9554, 29892, 470, 313, 29941, 29897, 411, 5199, 10970, 29889, 13, 13, 5398, 26227, 508, 367, 2309, 313, 29896, 29897, 491, 365, 26369, 411, 2560, 9508, 292, 763, 376, 7789, 567, 363, 1060, 29979, 29999, 7790, 29876, 29896, 19602, 376, 5618, 526, 278, 1014, 1484, 1338, 363, 3657, 15387, 1060, 29979, 29999, 29973, 613, 313, 29906, 29897, 491, 773, 3414, 29899, 14940, 11994, 29936, 321, 29889, 29887, 29889, 376, 6113, 263, 5828, 27887, 1213, 363, 5007, 263, 9554, 29892, 470, 313, 29941, 29897, 411, 5199, 10970, 29889, 13, 13, 1451, 16047, 267, 297, 1472, 29899, 8489, 18987, 322, 3414, 26227, 29901, 1858, 9450, 975, 263, 3309, 29891, 4955, 322, 17583, 3902, 8253, 278, 1650, 2913, 3933, 18066, 292, 29889, 365, 26369, 29879, 21117, 304, 10365, 13900, 746, 20050, 411, 15668, 4436, 29892, 3907, 963, 3109, 16424, 9401, 304, 25618, 1058, 5110, 515, 14260, 322, 1059, 29889, 13, 16492, 29901, 1724, 526, 278, 13501, 304, 9330, 897, 510, 3283, 29973, 13, 29648, 1319, 673, 29901, 518, 29914, 25580, 29962, 13, 5398, 26227, 508, 367, 26733, 297, 2211, 5837, 29901, 313, 29896, 29897, 773, 2560, 9508, 292, 763, 376, 7789, 567, 363, 1060, 29979, 29999, 7790, 29876, 29896, 19602, 376, 5618, 526, 278, 1014, 1484, 1338, 363, 3657, 15387, 1060, 29979, 29999, 29973, 613, 313, 29906, 29897, 491, 773, 3414, 29899, 14940, 11994, 29892, 470, 313, 29941, 29897, 411, 5199, 10970, 29889, 2], 'total_duration': 1364428708, 'load_duration': 1246375, 'sample_count': 62, 'sample_duration': 44859000, 'prompt_eval_count': 1, 'eval_count': 62, 'eval_duration': 1313002000}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.schema import LLMResult\n",
+    "from langchain.callbacks.base import BaseCallbackHandler\n",
+    "\n",
+    "class GenerationStatisticsCallback(BaseCallbackHandler):\n",
+    "    def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
+    "        print(response.generations[0][0].generation_info)\n",
+    "        \n",
+    "callback_manager = CallbackManager([StreamingStdOutCallbackHandler(), GenerationStatisticsCallback()])\n",
+    "\n",
+    "llm = Ollama(base_url=\"http://localhost:11434\",\n",
+    "             model=\"llama2\",\n",
+    "             verbose=True,\n",
+    "             callback_manager=callback_manager)\n",
+    "\n",
+    "qa_chain = RetrievalQA.from_chain_type(\n",
+    "    llm,\n",
+    "    retriever=vectorstore.as_retriever(),\n",
+    "    chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT},\n",
+    ")\n",
+    "\n",
+    "question = \"What are the approaches to Task Decomposition?\"\n",
+    "result = qa_chain({\"query\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`eval_count` / (`eval_duration`/10e9)  gets `tok / s`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "47.22003469910937"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "62 / (1313002000/1000/1000/1000)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/extras/integrations/llms/symblai_nebula.ipynb

@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "9597802c",
+   "metadata": {},
+   "source": [
+    "# Nebula\n",
+    "\n",
+    "[Nebula](https://symbl.ai/nebula/) is a fully-managed Conversation platform, on which you can build, deploy, and manage scalable AI applications.\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with the [Nebula platform](https://docs.symbl.ai/docs/nebula-llm-overview). \n",
+    "\n",
+    "It will send the requests to Nebula Service endpoint, which concatenates `SYMBLAI_NEBULA_SERVICE_URL` and `SYMBLAI_NEBULA_SERVICE_PATH`, with a token defined in `SYMBLAI_NEBULA_SERVICE_TOKEN`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f15ebe0d",
+   "metadata": {},
+   "source": [
+    "### Integrate with a LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5472a7cd-af26-48ca-ae9b-5f6ae73c74d2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"NEBULA_SERVICE_URL\"] = NEBULA_SERVICE_URL\n",
+    "os.environ[\"NEBULA_SERVICE_PATH\"] = NEBULA_SERVICE_PATH\n",
+    "os.environ[\"NEBULA_SERVICE_API_KEY\"] = NEBULA_SERVICE_API_KEY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fb585dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenLLM\n",
+    "\n",
+    "llm = OpenLLM(\n",
+    "    conversation=\"<Drop your text conversation that you want to ask Nebula to analyze here>\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "035dea0f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain import PromptTemplate, LLMChain\n",
+    "\n",
+    "template = \"Identify the {count} main objectives or goals mentioned in this context concisely in less points. Emphasize on key intents.\"\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"count\"])\n",
+    "\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "\n",
+    "generated = llm_chain.run(count=\"five\")\n",
+    "print(generated)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/llms/titan_takeoff.ipynb

@@ -0,0 +1,169 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titan Takeoff\n",
+    "\n",
+    "TitanML helps businesses build and deploy better, smaller, cheaper, and faster NLP models through our training, compression, and inference optimization platform. \n",
+    "\n",
+    "Our inference server, [Titan Takeoff](https://docs.titanml.co/docs/titan-takeoff/getting-started) enables deployment of LLMs locally on your hardware in a single command. Most generative model architectures are supported, such as Falcon, Llama 2, GPT2, T5 and many more."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "\n",
+    "To get started with Iris Takeoff, all you need is to have docker and python installed on your local system. If you wish to use the server with gpu suport, then you will need to install docker with cuda support.\n",
+    "\n",
+    "For Mac and Windows users, make sure you have the docker daemon running! You can check this by running docker ps in your terminal. To start the daemon, open the docker desktop app.\n",
+    "\n",
+    "Run the following command to install the Iris CLI that will enable you to run the takeoff server:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pip install titan-iris"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Choose a Model\n",
+    "Iris Takeoff supports many of the most powerful generative text models, such as Falcon, MPT, and Llama. See the [supported models](https://docs.titanml.co/docs/titan-takeoff/supported-models) for more information. For information about using your own models, see the [custom models](https://docs.titanml.co/docs/titan-takeoff/Advanced/custom-models).\n",
+    "\n",
+    "Going forward in this demo we will be using the falcon 7B instruct model. This is a good open source model that is trained to follow instructions, and is small enough to easily inference even on CPUs.\n",
+    "\n",
+    "## Taking off\n",
+    "Models are referred to by their model id on HuggingFace. Takeoff uses port 8000 by default, but can be configured to use another port. There is also support to use a Nvidia GPU by specifing cuda for the device flag.\n",
+    "\n",
+    "To start the takeoff server, run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu\n",
+    "iris takeoff --model tiiuae/falcon-7b-instruct --device cuda # Nvidia GPU required\n",
+    "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu --port 5000 # run on port 5000 (default: 8000)\n",
+    "```"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will then be directed to a login page, where you will need to create an account to proceed.\n",
+    "After logging in, run the command onscreen to check whether the server is ready. When it is ready, you can start using the Takeoff integration\n",
+    "\n",
+    "## Inferencing your model\n",
+    "To access your LLM, use the TitanTakeoff LLM wrapper:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import TitanTakeoff\n",
+    "\n",
+    "llm = TitanTakeoff(\n",
+    "    port=8000,\n",
+    "    generate_max_length=128,\n",
+    "    temperature=1.0\n",
+    ")\n",
+    "\n",
+    "prompt = \"What is the largest planet in the solar system?\"\n",
+    "\n",
+    "llm(prompt)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No parameters are needed by default, but a port can be specified and [generation parameters](https://docs.titanml.co/docs/titan-takeoff/Advanced/generation-parameters) can be supplied.\n",
+    "\n",
+    "### Streaming\n",
+    "Streaming is also supported via the streaming flag:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "\n",
+    "llm = TitanTakeoff(port=8000, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), streaming=True)\n",
+    "\n",
+    "prompt = \"What is the capital of France?\"\n",
+    "\n",
+    "llm(prompt)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Integration with LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import PromptTemplate, LLMChain\n",
+    "\n",
+    "llm = TitanTakeoff()\n",
+    "\n",
+    "template = \"What is the capital of {country}\"\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"country\"])\n",
+    "\n",
+    "llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
+    "\n",
+    "generated = llm_chain.run(country=\"Belgium\")\n",
+    "print(generated)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/extras/integrations/llms/vllm.ipynb

@@ -0,0 +1,196 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "499c3142-2033-437d-a60a-731988ac6074",
+   "metadata": {},
+   "source": [
+    "# vLLM\n",
+    "\n",
+    "[vLLM](https://vllm.readthedocs.io/en/latest/index.html) is a fast and easy-to-use library for LLM inference and serving, offering:\n",
+    "* State-of-the-art serving throughput \n",
+    "* Efficient management of attention key and value memory with PagedAttention\n",
+    "* Continuous batching of incoming requests\n",
+    "* Optimized CUDA kernels\n",
+    "\n",
+    "This notebooks goes over how to use a LLM with langchain and vLLM.\n",
+    "\n",
+    "To use, you should have the `vllm` python package installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8a3f2666-5c75-4797-967a-7915a247bf33",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install vllm -q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "84e350f7-21f6-455b-b1f0-8b0116a2fd49",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO 08-06 11:37:33 llm_engine.py:70] Initializing an LLM engine with config: model='mosaicml/mpt-7b', tokenizer='mosaicml/mpt-7b', tokenizer_mode=auto, trust_remote_code=True, dtype=torch.bfloat16, use_dummy_weights=False, download_dir=None, use_np_weights=False, tensor_parallel_size=1, seed=0)\n",
+      "INFO 08-06 11:37:41 llm_engine.py:196] # GPU blocks: 861, # CPU blocks: 512\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processed prompts: 100%|██████████| 1/1 [00:00<00:00,  2.00it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "What is the capital of France ? The capital of France is Paris.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.llms import VLLM\n",
+    "\n",
+    "llm = VLLM(model=\"mosaicml/mpt-7b\",\n",
+    "           trust_remote_code=True,  # mandatory for hf models\n",
+    "           max_new_tokens=128,\n",
+    "           top_k=10,\n",
+    "           top_p=0.95,\n",
+    "           temperature=0.8,\n",
+    ")\n",
+    "\n",
+    "print(llm(\"What is the capital of France ?\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94a3b41d-8329-4f8f-94f9-453d7f132214",
+   "metadata": {},
+   "source": [
+    "## Integrate the model in an LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5605b7a1-fa63-49c1-934d-8b4ef8d71dd5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processed prompts: 100%|██████████| 1/1 [00:01<00:00,  1.34s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "1. The first Pokemon game was released in 1996.\n",
+      "2. The president was Bill Clinton.\n",
+      "3. Clinton was president from 1993 to 2001.\n",
+      "4. The answer is Clinton.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain import PromptTemplate, LLMChain\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "\n",
+    "question = \"Who was the US president in the year the first Pokemon game was released?\"\n",
+    "\n",
+    "print(llm_chain.run(question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56826aba-d08b-4838-8bfa-ca96e463b25d",
+   "metadata": {},
+   "source": [
+    "## Distributed Inference\n",
+    "\n",
+    "vLLM supports distributed tensor-parallel inference and serving. \n",
+    "\n",
+    "To run multi-GPU inference with the LLM class, set the `tensor_parallel_size` argument to the number of GPUs you want to use. For example, to run inference on 4 GPUs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8c25c35-47b5-459d-9985-3cf546e9ac16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import VLLM\n",
+    "\n",
+    "llm = VLLM(model=\"mosaicml/mpt-30b\",\n",
+    "           tensor_parallel_size=4,\n",
+    "           trust_remote_code=True,  # mandatory for hf models\n",
+    ")\n",
+    "\n",
+    "llm(\"What is the future of AI?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_pytorch_p310",
+   "language": "python",
+   "name": "conda_pytorch_p310"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/memory/rockset_chat_message_history.ipynb

@@ -0,0 +1,67 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Rockset Chat Message History\n",
+    "\n",
+    "This notebook goes over how to use [Rockset](https://rockset.com/docs) to store chat message history. \n",
+    "\n",
+    "To begin, with get your API key from the [Rockset console](https://console.rockset.com/apikeys). Find your API region for the Rockset [API reference](https://rockset.com/docs/rest-api#introduction)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.memory.chat_message_histories import RocksetChatMessageHistory\n",
+    "from rockset import RocksetClient, Regions\n",
+    "\n",
+    "history = RocksetChatMessageHistory(\n",
+    "    session_id=\"MySession\",\n",
+    "    client=RocksetClient(\n",
+    "        api_key=\"YOUR API KEY\", \n",
+    "        host=Regions.usw2a1 # us-west-2 Oregon\n",
+    "    ),\n",
+    "    collection=\"langchain_demo\",\n",
+    "    sync=True\n",
+    ")\n",
+    "history.add_user_message(\"hi!\")\n",
+    "history.add_ai_message(\"whats up?\")\n",
+    "print(history.messages)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output should be something like:\n",
+    "\n",
+    "```python\n",
+    "[\n",
+    "    HumanMessage(content='hi!', additional_kwargs={'id': '2e62f1c2-e9f7-465e-b551-49bae07fe9f0'}, example=False), \n",
+    "    AIMessage(content='whats up?', additional_kwargs={'id': 'b9be8eda-4c18-4cf8-81c3-e91e876927d0'}, example=False)\n",
+    "]\n",
+    "\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/extras/integrations/memory/streamlit_chat_message_history.ipynb

@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "91c6a7ef",
+   "metadata": {},
+   "source": [
+    "# Streamlit Chat Message History\n",
+    "\n",
+    "This notebook goes over how to store and use chat message history in a Streamlit app. StreamlitChatMessageHistory will store messages in\n",
+    "[Streamlit session state](https://docs.streamlit.io/library/api-reference/session-state)\n",
+    "at the specified `key=`. The default key is `\"langchain_messages\"`.\n",
+    "\n",
+    "- Note, StreamlitChatMessageHistory only works when run in a Streamlit app.\n",
+    "- You may also be interested in [StreamlitCallbackHandler](/docs/integrations/callbacks/streamlit) for LangChain.\n",
+    "- For more on Streamlit check out their\n",
+    "[getting started documentation](https://docs.streamlit.io/library/get-started).\n",
+    "\n",
+    "You can see the [full app example running here](https://langchain-st-memory.streamlit.app/), and more examples in\n",
+    "[github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d15e3302",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.memory import StreamlitChatMessageHistory\n",
+    "\n",
+    "history = StreamlitChatMessageHistory(key=\"chat_messages\")\n",
+    "\n",
+    "history.add_user_message(\"hi!\")\n",
+    "history.add_ai_message(\"whats up?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64fc465e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "history.messages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b60dc735",
+   "metadata": {},
+   "source": [
+    "You can integrate StreamlitChatMessageHistory into ConversationBufferMemory and chains or agents as usual. The history will be persisted across re-runs of the Streamlit app within a given user session. A given StreamlitChatMessageHistory will NOT be persisted or shared across user sessions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42ab5bf3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.memory.chat_message_histories import StreamlitChatMessageHistory\n",
+    "\n",
+    "# Optionally, specify your own session_state key for storing messages\n",
+    "msgs = StreamlitChatMessageHistory(key=\"special_app_key\")\n",
+    "\n",
+    "memory = ConversationBufferMemory(memory_key=\"history\", chat_memory=msgs)\n",
+    "if len(msgs.messages) == 0:\n",
+    "    msgs.add_ai_message(\"How can I help you?\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a29252de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "template = \"\"\"You are an AI chatbot having a conversation with a human.\n",
+    "\n",
+    "{history}\n",
+    "Human: {human_input}\n",
+    "AI: \"\"\"\n",
+    "prompt = PromptTemplate(input_variables=[\"history\", \"human_input\"], template=template)\n",
+    "\n",
+    "# Add the memory to an LLMChain as usual\n",
+    "llm_chain = LLMChain(llm=OpenAI(), prompt=prompt, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cd99b4b",
+   "metadata": {},
+   "source": [
+    "Conversational Streamlit apps will often re-draw each previous chat message on every re-run. This is easy to do by iterating through `StreamlitChatMessageHistory.messages`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bdb637b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import streamlit as st\n",
+    "\n",
+    "for msg in msgs.messages:\n",
+    "    st.chat_message(msg.type).write(msg.content)\n",
+    "\n",
+    "if prompt := st.chat_input():\n",
+    "    st.chat_message(\"human\").write(prompt)\n",
+    "\n",
+    "    # As usual, new messages are added to StreamlitChatMessageHistory when the Chain is called.\n",
+    "    response = llm_chain.run(prompt)\n",
+    "    st.chat_message(\"ai\").write(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7adaf3d6",
+   "metadata": {},
+   "source": [
+    "**[View the final app](https://langchain-st-memory.streamlit.app/).**"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "poetry-venv",
+   "language": "python",
+   "name": "poetry-venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/providers/bageldb.mdx

@@ -0,0 +1,21 @@
+# BagelDB
+
+> [BagelDB](https://www.bageldb.ai/) (`Open Vector Database for AI`), is like GitHub for AI data.
+It is a collaborative platform where users can create,
+share, and manage vector datasets. It can support private projects for independent developers,
+internal collaborations for enterprises, and public contributions for data DAOs.
+
+## Installation and Setup
+
+```bash
+pip install betabageldb
+```
+
+
+## VectorStore
+
+See a [usage example](/docs/integrations/vectorstores/bageldb).
+
+```python
+from langchain.vectorstores import Bagel
+```