Send evaluator logs to new session

.github/CONTRIBUTING.md

@@ -95,14 +95,6 @@ To run formatting for this project:
 make format
 ```
 
-Additionally, you can run the formatter only on the files that have been modified in your current branch as compared to the master branch using the format_diff command:
-
-```bash
-make format_diff
-```
-
-This is especially useful when you have made changes to a subset of the project and want to ensure your changes are properly formatted without affecting the rest of the codebase.
-
 ### Linting
 
 Linting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/), [isort](https://pycqa.github.io/isort/), [flake8](https://flake8.pycqa.org/en/latest/), and [mypy](http://mypy-lang.org/).
@@ -113,14 +105,6 @@ To run linting for this project:
 make lint
 ```
 
-In addition, you can run the linter only on the files that have been modified in your current branch as compared to the master branch using the lint_diff command:
-
-```bash
-make lint_diff
-```
-
-This can be very helpful when you've made changes to only certain parts of the project and want to ensure your changes meet the linting standards without having to check the entire codebase.
-
 We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
 
 ### Coverage
@@ -224,38 +208,30 @@ When you run `poetry install`, the `langchain` package is installed as editable
 
 ### Contribute Documentation
 
-The docs directory contains Documentation and API Reference.
+Docs are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code.
 
-Documentation is built using [Docusaurus 2](https://docusaurus.io/).
-
-API Reference are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code.
 For that reason, we ask that you add good documentation to all classes and methods.
 
 Similar to linting, we recognize documentation can be annoying. If you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
 
 ### Build Documentation Locally
 
-In the following commands, the prefix `api_` indicates that those are operations for the API Reference.
-
 Before building the documentation, it is always a good idea to clean the build directory:
 
 ```bash
 make docs_clean
-make api_docs_clean
 ```
 
-Next, you can build the documentation as outlined below:
-
-```bash
-make docs_build
-make api_docs_build
-```
-
-Finally, you can run the linkchecker to make sure all links are valid:
+Next, you can run the linkchecker to make sure all links are valid:
 
 ```bash
 make docs_linkcheck
-make api_docs_linkcheck
+```
+
+Finally, you can build the documentation as outlined below:
+
+```bash
+make docs_build
 ```
 
 ## 🏭 Release Process

.github/PULL_REQUEST_TEMPLATE.md

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

.gitignore

@@ -161,12 +161,7 @@ docs/node_modules/
 docs/.docusaurus/
 docs/.cache-loader/
 docs/_dist
-docs/api_reference/api_reference.rst
 docs/api_reference/_build
-docs/api_reference/*/
-!docs/api_reference/_static/
-!docs/api_reference/templates/
-!docs/api_reference/themes/
 docs/docs_skeleton/build
 docs/docs_skeleton/node_modules
 docs/docs_skeleton/yarn.lock

.readthedocs.yaml

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

Makefile

@@ -1,47 +1,40 @@
-.PHONY: all clean docs_build docs_clean docs_linkcheck api_docs_build api_docs_clean api_docs_linkcheck format lint test tests test_watch integration_tests docker_tests help extended_tests
+.PHONY: all clean format lint test tests test_watch integration_tests docker_tests help extended_tests
 
-# Default target executed when no arguments are given to make.
 all: help
 
-######################
-# TESTING AND COVERAGE
-######################
-
-# Run unit tests and generate a coverage report.
 coverage:
 	poetry run pytest --cov \
 		--cov-config=.coveragerc \
 		--cov-report xml \
 		--cov-report term-missing:skip-covered
 
-######################
-# DOCUMENTATION
-######################
-
-clean: docs_clean api_docs_clean
+clean: docs_clean
 
+docs_compile:
+	poetry run nbdoc_build --srcdir $(srcdir)
 
 docs_build:
-	docs/.local_build.sh
+	cd docs && poetry run make html
 
 docs_clean:
-	rm -r docs/_dist
+	cd docs && poetry run make clean
 
 docs_linkcheck:
-	poetry run linkchecker docs/_dist/docs_skeleton/ --ignore-url node_modules
+	poetry run linkchecker docs/_build/html/index.html
 
-api_docs_build:
-	poetry run python docs/api_reference/create_api_rst.py
-	cd docs/api_reference && poetry run make html
+format:
+	poetry run black .
+	poetry run ruff --select I --fix .
 
-api_docs_clean:
-	rm -f docs/api_reference/api_reference.rst
-	cd docs/api_reference && poetry run make clean
+PYTHON_FILES=.
+lint: PYTHON_FILES=.
+lint_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d master | grep -E '\.py$$')
 
-api_docs_linkcheck:
-	poetry run linkchecker docs/api_reference/_build/html/index.html
+lint lint_diff:
+	poetry run mypy $(PYTHON_FILES)
+	poetry run black $(PYTHON_FILES) --check
+	poetry run ruff .
 
-# Define a variable for the test file path.
 TEST_FILE ?= tests/unit_tests/
 
 test:
@@ -63,28 +56,6 @@ docker_tests:
 	docker build -t my-langchain-image:test .
 	docker run --rm my-langchain-image:test
 
-######################
-# LINTING AND FORMATTING
-######################
-
-# Define a variable for Python and notebook files.
-PYTHON_FILES=.
-lint format: PYTHON_FILES=.
-lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
-
-lint lint_diff:
-	poetry run mypy $(PYTHON_FILES)
-	poetry run black $(PYTHON_FILES) --check
-	poetry run ruff .
-
-format format_diff:
-	poetry run black $(PYTHON_FILES)
-	poetry run ruff --select I --fix $(PYTHON_FILES)
-
-######################
-# HELP
-######################
-
 help:
 	@echo '----'
 	@echo 'coverage                     - run unit tests and generate coverage report'

docs/.local_build.sh

@@ -1,15 +1,10 @@
-#!/usr/bin/env bash
-
-set -o errexit
-set -o nounset
-set -o pipefail
-set -o xtrace
-
-SCRIPT_DIR="$(cd "$(dirname "$0")"; pwd)"
-cd "${SCRIPT_DIR}"
-
-mkdir -p _dist/docs_skeleton
+mkdir _dist
 cp -r {docs_skeleton,snippets} _dist
+mkdir -p _dist/docs_skeleton/static/api_reference
+cd api_reference
+poetry run make html
+cp -r _build/* ../_dist/docs_skeleton/static/api_reference
+cd ..
 cp -r extras/* _dist/docs_skeleton/docs
 cd _dist/docs_skeleton
 poetry run nbdoc_build

docs/api_reference/_static/js/mendablesearch.js

@@ -0,0 +1,57 @@
+document.addEventListener('DOMContentLoaded', () => {
+  // Load the external dependencies
+  function loadScript(src, onLoadCallback) {
+    const script = document.createElement('script');
+    script.src = src;
+    script.onload = onLoadCallback;
+    document.head.appendChild(script);
+  }
+
+  function createRootElement() {
+    const rootElement = document.createElement('div');
+    rootElement.id = 'my-component-root';
+    document.body.appendChild(rootElement);
+    return rootElement;
+  }
+
+  
+
+  function initializeMendable() {
+    const rootElement = createRootElement();
+    const { MendableFloatingButton } = Mendable;
+    
+
+    const iconSpan1 = React.createElement('span', {
+    }, '🦜');
+
+    const iconSpan2 = React.createElement('span', {
+    }, '🔗');
+
+    const icon = React.createElement('p', {
+      style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
+    }, [iconSpan1, iconSpan2]);
+    
+    const mendableFloatingButton = React.createElement(
+      MendableFloatingButton,
+      {
+        style: { darkMode: false, accentColor: '#010810' },
+        floatingButtonStyle: { color: '#ffffff', backgroundColor: '#010810' },
+        anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
+        cmdShortcutKey:'j',
+        messageSettings: {
+          openSourcesInNewTab: false,
+          prettySources: true // Prettify the sources displayed now
+        },
+        icon: icon,
+      }
+    );
+
+    ReactDOM.render(mendableFloatingButton, rootElement);
+  }
+
+  loadScript('https://unpkg.com/react@17/umd/react.production.min.js', () => {
+    loadScript('https://unpkg.com/react-dom@17/umd/react-dom.production.min.js', () => {
+      loadScript('https://unpkg.com/@mendable/search@0.0.102/dist/umd/mendable.min.js', initializeMendable);
+    });
+  });
+});

docs/api_reference/agents.rst

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

docs/api_reference/conf.py

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

docs/api_reference/create_api_rst.py

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

docs/api_reference/data_connection.rst

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

docs/api_reference/index.rst

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

docs/api_reference/model_io.rst

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

docs/api_reference/models.rst

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

docs/api_reference/modules/agent_toolkits.rst

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

docs/api_reference/modules/agents.rst

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

docs/api_reference/modules/base_classes.rst

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

docs/api_reference/modules/callbacks.rst

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

docs/api_reference/modules/chains.rst

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

docs/api_reference/modules/chat_models.rst

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

docs/api_reference/modules/document_loaders.rst

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

docs/api_reference/modules/document_transformers.rst

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

docs/api_reference/modules/embeddings.rst

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

docs/api_reference/modules/example_selector.rst

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

docs/api_reference/modules/experimental.rst

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

docs/api_reference/modules/llms.rst

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

docs/api_reference/modules/memory.rst

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

docs/api_reference/modules/output_parsers.rst

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

docs/api_reference/modules/prompts.rst

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

docs/api_reference/modules/retrievers.rst

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

docs/api_reference/modules/tools.rst

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

docs/api_reference/modules/utilities.rst

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

docs/api_reference/modules/vectorstores.rst

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

docs/api_reference/prompts.rst

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

docs/api_reference/templates/COPYRIGHT.txt

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

docs/api_reference/templates/class.rst

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

docs/api_reference/templates/redirects.html

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

docs/api_reference/themes/COPYRIGHT.txt

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

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

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

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

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

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

@@ -1,69 +0,0 @@
-{%- if pagename != 'index' and pagename != 'documentation' %}
-  {%- set nav_bar_class = "sk-docs-navbar" %}
-  {%- set top_container_cls = "sk-docs-container" %}
-{%- else %}
-  {%- set nav_bar_class = "sk-landing-navbar" %}
-  {%- set top_container_cls = "sk-landing-container" %}
-{%- endif %}
-
-{% if theme_link_to_live_contributing_page|tobool %}
-{# Link to development page for live builds #}
-  {%- set development_link = "https://scikit-learn.org/dev/developers/index.html" %}
-{# Open on a new development page in new window/tab for live builds #}
-  {%- set development_attrs = 'target="_blank" rel="noopener noreferrer"' %}
-{%- else %}
-  {%- set development_link = pathto('developers/index') %}
-  {%- set development_attrs = '' %}
-{%- endif %}
-
-
-<nav id="navbar" class="{{ nav_bar_class }} navbar navbar-expand-md navbar-light bg-light py-0">
-  <div class="container-fluid {{ top_container_cls }} px-0">
-    {%- if logo_url %}
-      <a class="navbar-brand py-0" href="{{ pathto('index') }}">
-        <img
-          class="sk-brand-img"
-          src="{{ logo_url|e }}"
-          alt="logo"/>
-      </a>
-    {%- endif %}
-    <button
-      id="sk-navbar-toggler"
-      class="navbar-toggler"
-      type="button"
-      data-toggle="collapse"
-      data-target="#navbarSupportedContent"
-      aria-controls="navbarSupportedContent"
-      aria-expanded="false"
-      aria-label="Toggle navigation"
-    >
-      <span class="navbar-toggler-icon"></span>
-    </button>
-
-    <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
-      <ul class="navbar-nav mr-auto">
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" href="{{ pathto('api_reference') }}">API</a>
-        </li>
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" target="_blank" rel="noopener noreferrer" href="https://python.langchain.com/">Python Docs</a>
-        </li>
-        {%- for title, link, link_attrs in drop_down_navigation %}
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="{{ link }}" {{ link_attrs }}>{{ title }}</a>
-        </li>
-        {%- endfor %}
-      </ul>
-      {%- if pagename != "search"%}
-      <div id="searchbox" role="search">
-          <div class="searchformwrapper">
-          <form class="search" action="{{ pathto('search') }}" method="get">
-            <input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
-            <input class="sk-search-text-btn" type="submit" value="{{ _('Go') }}" />
-          </form>
-          </div>
-      </div>
-      {%- endif %}
-    </div>
-  </div>
-</nav>

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

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

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

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

docs/docs_skeleton/docs/get_started/quickstart.mdx

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

docs/docs_skeleton/docs/get_started/tutorials.mdx

@@ -1,124 +0,0 @@
-# Tutorials
-
-
-⛓ icon marks a new addition [last update 2023-07-05]
-
----------------------
-
-### DeepLearning.AI courses
- by [Harrison Chase](https://github.com/hwchase17) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
-- [LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain)
-- ⛓ [LangChain Chat with Your Data](https://learn.deeplearning.ai/langchain-chat-with-your-data)
-
-### Handbook
-[LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
-
-### Short Tutorials
-[LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
-
-[LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
-
-[LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
-
-
-##  Tutorials
-
-### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs)
-- #1 [Getting Started with `GPT-3` vs. Open Source LLMs](https://youtu.be/nE2skSRWTTs)
-- #2 [Prompt Templates for `GPT 3.5` and other LLMs](https://youtu.be/RflBcK0oDH0)
-- #3 [LLM Chains using `GPT 3.5` and other LLMs](https://youtu.be/S8j9Tk0lZHU)
-- [LangChain Data Loaders, Tokenizers, Chunking, and Datasets - Data Prep 101](https://youtu.be/eqOfr4AGLk8)
-- #4 [Chatbot Memory for `Chat-GPT`, `Davinci` + other LLMs](https://youtu.be/X05uK0TZozM)
-- #5 [Chat with OpenAI in LangChain](https://youtu.be/CnAgB3A5OlU)
-- #6 [Fixing LLM Hallucinations with Retrieval Augmentation in LangChain](https://youtu.be/kvdVduIJsc8)
-- #7 [LangChain Agents Deep Dive with `GPT 3.5`](https://youtu.be/jSP-gSEyVeI)
-- #8 [Create Custom Tools for Chatbots in LangChain](https://youtu.be/q-HNphrWsDE)
-- #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI)
-- [Using NEW `MPT-7B` in Hugging Face and LangChain](https://youtu.be/DXpk9K7DgMo)
-- ⛓ [`MPT-30B` Chatbot with LangChain](https://youtu.be/pnem-EhT6VI)
-
-
-### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Greg Kamradt (Data Indy)](https://www.youtube.com/@DataIndependent)
-- [What Is LangChain? - LangChain + `ChatGPT` Overview](https://youtu.be/_v_fgW2SkkQ)
-- [Quickstart Guide](https://youtu.be/kYRB-vJFy38)
-- [Beginner Guide To 7 Essential Concepts](https://youtu.be/2xxziIWmaSA)
-- [Beginner Guide To 9 Use Cases](https://youtu.be/vGP4pQdCocw)
-- [Agents Overview + Google Searches](https://youtu.be/Jq9Sf68ozk0)
-- [`OpenAI` + `Wolfram Alpha`](https://youtu.be/UijbzCIJ99g)
-- [Ask Questions On Your Custom (or Private) Files](https://youtu.be/EnT-ZTrcPrg)
-- [Connect `Google Drive Files` To `OpenAI`](https://youtu.be/IqqHqDcXLww)
-- [`YouTube Transcripts` + `OpenAI`](https://youtu.be/pNcQ5XXMgH4)
-- [Question A 300 Page Book (w/ `OpenAI` + `Pinecone`)](https://youtu.be/h0DHDp1FbmQ)
-- [Workaround `OpenAI's` Token Limit With Chain Types](https://youtu.be/f9_BWhCI4Zo)
-- [Build Your Own OpenAI + LangChain Web App in 23 Minutes](https://youtu.be/U_eV8wfMkXU)
-- [Working With The New `ChatGPT API`](https://youtu.be/e9P7FLi5Zy8)
-- [OpenAI + LangChain Wrote Me 100 Custom Sales Emails](https://youtu.be/y1pyAQM-3Bo)
-- [Structured Output From `OpenAI` (Clean Dirty Data)](https://youtu.be/KwAXfey-xQk)
-- [Connect `OpenAI` To +5,000 Tools (LangChain + `Zapier`)](https://youtu.be/7tNm0yiDigU)
-- [Use LLMs To Extract Data From Text (Expert Mode)](https://youtu.be/xZzvwR9jdPA)
-- [Extract Insights From Interview Transcripts Using LLMs](https://youtu.be/shkMOHwJ4SM)
-- [5 Levels Of LLM Summarizing: Novice to Expert](https://youtu.be/qaPMdcCqtWk)
-- [Control Tone & Writing Style Of Your LLM Output](https://youtu.be/miBG-a3FuhU)
-- [Build Your Own `AI Twitter Bot` Using LLMs](https://youtu.be/yLWLDjT01q8)
-- [ChatGPT made my interview questions for me (`Streamlit` + LangChain)](https://youtu.be/zvoAMx0WKkw)
-- [Function Calling via ChatGPT API - First Look With LangChain](https://youtu.be/0-zlUy7VUjg)
-- ⛓ [Extract Topics From Video/Audio With LLMs (Topic Modeling w/ LangChain)](https://youtu.be/pEkxRQFNAs4)
-
-
-### [LangChain How to and guides](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ) by [Sam Witteveen](https://www.youtube.com/@samwitteveenai)
-- [LangChain Basics - LLMs & PromptTemplates with Colab](https://youtu.be/J_0qvRt4LNk)
-- [LangChain Basics - Tools and Chains](https://youtu.be/hI2BY7yl_Ac)
-- [`ChatGPT API` Announcement & Code Walkthrough with LangChain](https://youtu.be/phHqvLHCwH4)
-- [Conversations with Memory (explanation & code walkthrough)](https://youtu.be/X550Zbz_ROE)
-- [Chat with `Flan20B`](https://youtu.be/VW5LBavIfY4)
-- [Using `Hugging Face Models` locally (code walkthrough)](https://youtu.be/Kn7SX2Mx_Jk)
-- [`PAL` : Program-aided Language Models with LangChain code](https://youtu.be/dy7-LvDu-3s)
-- [Building a Summarization System with LangChain and `GPT-3` - Part 1](https://youtu.be/LNq_2s_H01Y)
-- [Building a Summarization System with LangChain and `GPT-3` - Part 2](https://youtu.be/d-yeHDLgKHw)
-- [Microsoft's `Visual ChatGPT` using LangChain](https://youtu.be/7YEiEyfPF5U)
-- [LangChain Agents - Joining Tools and Chains with Decisions](https://youtu.be/ziu87EXZVUE)
-- [Comparing LLMs with LangChain](https://youtu.be/rFNG0MIEuW0)
-- [Using `Constitutional AI` in LangChain](https://youtu.be/uoVqNFDwpX4)
-- [Talking to `Alpaca` with LangChain - Creating an Alpaca Chatbot](https://youtu.be/v6sF8Ed3nTE)
-- [Talk to your `CSV` & `Excel` with LangChain](https://youtu.be/xQ3mZhw69bc)
-- [`BabyAGI`: Discover the Power of Task-Driven Autonomous Agents!](https://youtu.be/QBcDLSE2ERA)
-- [Improve your `BabyAGI` with LangChain](https://youtu.be/DRgPyOXZ-oE)
-- [Master `PDF` Chat with LangChain - Your essential guide to queries on documents](https://youtu.be/ZzgUqFtxgXI)
-- [Using LangChain with `DuckDuckGO` `Wikipedia` & `PythonREPL` Tools](https://youtu.be/KerHlb8nuVc)
-- [Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)](https://youtu.be/biS8G8x8DdA)
-- [LangChain Retrieval QA Over Multiple Files with `ChromaDB`](https://youtu.be/3yPBVii7Ct0)
-- [LangChain Retrieval QA with Instructor Embeddings & `ChromaDB` for PDFs](https://youtu.be/cFCGUjc33aU)
-- [LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!](https://youtu.be/9ISVjh8mdlA)
-- [`Camel` + LangChain for Synthetic Data & Market Research](https://youtu.be/GldMMK6-_-g)
-- [Information Extraction with LangChain & `Kor`](https://youtu.be/SW1ZdqH0rRQ)
-- [Converting a LangChain App from OpenAI to OpenSource](https://youtu.be/KUDn7bVyIfc)
-- [Using LangChain `Output Parsers` to get what you want out of LLMs](https://youtu.be/UVn2NroKQCw)
-- [Building a LangChain Custom Medical Agent with Memory](https://youtu.be/6UFtRwWnHws)
-- [Understanding `ReACT` with LangChain](https://youtu.be/Eug2clsLtFs)
-- [`OpenAI Functions` + LangChain : Building a Multi Tool Agent](https://youtu.be/4KXK6c6TVXQ)
-- [What can you do with 16K tokens in LangChain?](https://youtu.be/z2aCZBAtWXs)
-- [Tagging and Extraction - Classification using `OpenAI Functions`](https://youtu.be/a8hMgIcUEnE)
-- ⛓ [HOW to Make Conversational Form with LangChain](https://youtu.be/IT93On2LB5k)
-
-
-### [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
-- [LangChain Crash Course — All You Need to Know to Build Powerful Apps with LLMs](https://youtu.be/5-fc4Tlgmro)
-- [Working with MULTIPLE `PDF` Files in LangChain: `ChatGPT` for your Data](https://youtu.be/s5LhRdh5fu4)
-- [`ChatGPT` for YOUR OWN `PDF` files with LangChain](https://youtu.be/TLf90ipMzfE)
-- [Talk to YOUR DATA without OpenAI APIs: LangChain](https://youtu.be/wrD-fZvT6UI)
-- [Langchain: PDF Chat App (GUI) | ChatGPT for Your PDF FILES](https://youtu.be/RIWbalZ7sTo)
-- [LangFlow: Build Chatbots without Writing Code](https://youtu.be/KJ-ux3hre4s)
-- [LangChain: Giving Memory to LLMs](https://youtu.be/dxO6pzlgJiY)
-- [BEST OPEN Alternative to `OPENAI's EMBEDDINGs` for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY)
-
-
-### LangChain by [Chat with data](https://www.youtube.com/@chatwithdata)
-- [LangChain Beginner's Tutorial for `Typescript`/`Javascript`](https://youtu.be/bH722QgRlhQ)
-- [`GPT-4` Tutorial: How to Chat With Multiple `PDF` Files (~1000 pages of Tesla's 10-K Annual Reports)](https://youtu.be/Ix9WIZpArm0)
-- [`GPT-4` & LangChain Tutorial: How to Chat With A 56-Page `PDF` Document (w/`Pinecone`)](https://youtu.be/ih9PBGVVOO4)
-- [LangChain & Supabase Tutorial: How to Build a ChatGPT Chatbot For Your Website](https://youtu.be/R2FMzcsmQY8)
-- [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI)
-
-
----------------------
-⛓ icon marks a new addition [last update 2023-07-05]

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

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

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

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

docs/docs_skeleton/docusaurus.config.js

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

docs/docs_skeleton/package.json

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

docs/extras/additional_resources/youtube.mdx

@@ -1,6 +1,6 @@
-# YouTube videos
+# YouTube tutorials
 
-⛓ icon marks a new addition [last update 2023-06-20]
+This is a collection of `LangChain` videos on `YouTube`.
 
 ### [Official LangChain YouTube channel](https://www.youtube.com/@LangChain)
 
@@ -9,6 +9,7 @@
 - [LangChain and Weaviate with Harrison Chase and Bob van Luijt - Weaviate Podcast #36](https://youtu.be/lhby7Ql7hbk) by [Weaviate • Vector Database](https://www.youtube.com/@Weaviate)
 - [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning)
 - [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI) by [Chat with data](https://www.youtube.com/@chatwithdata)
+- ⛓️ [LangChain "Agents in Production" Webinar](https://youtu.be/k8GNCCs16F4) by [LangChain](https://www.youtube.com/@LangChain)
 
 ## Videos (sorted by views)
 
@@ -30,9 +31,6 @@
 - [`Weaviate` + LangChain for LLM apps presented by Erika Cardenas](https://youtu.be/7AGj4Td5Lgw) by [`Weaviate` • Vector Database](https://www.youtube.com/@Weaviate)
 - [Langchain Overview — How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
 - [Langchain Overview - How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
-- [LangChain Tutorials](https://www.youtube.com/watch?v=FuqdVNB_8c0&list=PL9V0lbeJ69brU-ojMpU1Y7Ic58Tap0Cw6) by [Edrick](https://www.youtube.com/@edrickdch):
-  - [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
-  - [LangChain 101: The Complete Beginner's Guide](https://youtu.be/P3MAbZ2eMUI)
 - [Custom langchain Agent & Tools with memory. Turn any `Python function` into langchain tool with Gpt 3](https://youtu.be/NIG8lXk0ULg) by [echohive](https://www.youtube.com/@echohive)
 - [LangChain: Run Language Models Locally - `Hugging Face Models`](https://youtu.be/Xxxuw4_iCzw) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
 - [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive)
@@ -48,68 +46,154 @@
 - [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@merksworld)
 - [Connecting the Internet with `ChatGPT` (LLMs) using Langchain And Answers Your Questions](https://youtu.be/9Y0TBC63yZg) by [Kamalraj M M](https://www.youtube.com/@insightbuilder)
 - [Build More Powerful LLM Applications for Business’s with LangChain (Beginners Guide)](https://youtu.be/sp3-WLKEcBg) by[ No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
-- [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
-- [Chatbot Factory: Streamline Python Chatbot Creation with LLMs and Langchain](https://youtu.be/eYer3uzrcuM) by [Finxter](https://www.youtube.com/@CobusGreylingZA)
-- [LangChain Tutorial - ChatGPT mit eigenen Daten](https://youtu.be/0XDLyY90E2c) by [Coding Crashkurse](https://www.youtube.com/@codingcrashkurse6429)
-- [Chat with a `CSV` | LangChain Agents Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [GoDataProf](https://www.youtube.com/@godataprof)
-- [Introdução ao Langchain - #Cortes - Live DataHackers](https://youtu.be/fw8y5VRei5Y) by [Prof. João Gabriel Lima](https://www.youtube.com/@profjoaogabriellima)
-- [LangChain: Level up `ChatGPT` !? | LangChain Tutorial Part 1](https://youtu.be/vxUGx8aZpDE) by [Code Affinity](https://www.youtube.com/@codeaffinitydev)
-- [KI schreibt krasses Youtube Skript 😲😳 | LangChain Tutorial Deutsch](https://youtu.be/QpTiXyK1jus) by [SimpleKI](https://www.youtube.com/@simpleki)
-- [Chat with Audio: Langchain, `Chroma DB`, OpenAI, and `Assembly AI`](https://youtu.be/Kjy7cx1r75g) by [AI Anytime](https://www.youtube.com/@AIAnytime)
-- [QA over documents with Auto vector index selection with Langchain router chains](https://youtu.be/9G05qybShv8) by [echohive](https://www.youtube.com/@echohive)
-- [Build your own custom LLM application with `Bubble.io` & Langchain (No Code & Beginner friendly)](https://youtu.be/O7NhQGu1m6c) by [No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
-- [Simple App to Question Your Docs: Leveraging `Streamlit`, `Hugging Face Spaces`, LangChain, and `Claude`!](https://youtu.be/X4YbNECRr7o) by [Chris Alexiuk](https://www.youtube.com/@chrisalexiuk)
-- [LANGCHAIN AI- `ConstitutionalChainAI` + Databutton AI ASSISTANT Web App](https://youtu.be/5zIU6_rdJCU) by [Avra](https://www.youtube.com/@Avra_b)
-- [LANGCHAIN AI AUTONOMOUS AGENT WEB APP - 👶 `BABY AGI` 🤖 with EMAIL AUTOMATION using `DATABUTTON`](https://youtu.be/cvAwOGfeHgw) by [Avra](https://www.youtube.com/@Avra_b)
-- [The Future of Data Analysis: Using A.I. Models in Data Analysis (LangChain)](https://youtu.be/v_LIcVyg5dk) by [Absent Data](https://www.youtube.com/@absentdata)
-- [Memory in LangChain | Deep dive (python)](https://youtu.be/70lqvTFh_Yg) by [Eden Marco](https://www.youtube.com/@EdenMarco)
-- [9 LangChain UseCases | Beginner's Guide | 2023](https://youtu.be/zS8_qosHNMw) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
-- [Use Large Language Models in Jupyter Notebook | LangChain | Agents & Indexes](https://youtu.be/JSe11L1a_QQ) by [Abhinaw Tiwari](https://www.youtube.com/@AbhinawTiwariAT)
-- [How to Talk to Your Langchain Agent | `11 Labs` + `Whisper`](https://youtu.be/N4k459Zw2PU) by [VRSEN](https://www.youtube.com/@vrsen)
-- [LangChain Deep Dive: 5 FUN AI App Ideas To Build Quickly and Easily](https://youtu.be/mPYEPzLkeks) by [James NoCode](https://www.youtube.com/@jamesnocode)
-- [BEST OPEN Alternative to OPENAI's EMBEDDINGs for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
-- [LangChain 101: Models](https://youtu.be/T6c_XsyaNSQ) by [Mckay Wrigley](https://www.youtube.com/@realmckaywrigley)
-- [LangChain with JavaScript Tutorial #1 | Setup & Using LLMs](https://youtu.be/W3AoeMrg27o) by [Leon van Zyl](https://www.youtube.com/@leonvanzyl)
-- [LangChain Overview & Tutorial for Beginners: Build Powerful AI Apps Quickly & Easily (ZERO CODE)](https://youtu.be/iI84yym473Q) by [James NoCode](https://www.youtube.com/@jamesnocode)
-- [LangChain In Action: Real-World Use Case With Step-by-Step Tutorial](https://youtu.be/UO699Szp82M) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
-- [Summarizing and Querying Multiple Papers with LangChain](https://youtu.be/p_MQRWH5Y6k) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
-- [Using Langchain (and `Replit`) through `Tana`, ask `Google`/`Wikipedia`/`Wolfram Alpha` to fill out a table](https://youtu.be/Webau9lEzoI) by [Stian Håklev](https://www.youtube.com/@StianHaklev)
-- [Langchain PDF App (GUI) | Create a ChatGPT For Your `PDF` in Python](https://youtu.be/wUAUdEw5oxM) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
-- [Auto-GPT with LangChain 🔥 | Create Your Own Personal AI Assistant](https://youtu.be/imDfPmMKEjM) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
-- [Create Your OWN Slack AI Assistant with Python & LangChain](https://youtu.be/3jFXRNn2Bu8) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
-- [How to Create LOCAL Chatbots with GPT4All and LangChain [Full Guide]](https://youtu.be/4p1Fojur8Zw) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
-- [Build a `Multilingual PDF` Search App with LangChain, `Cohere` and `Bubble`](https://youtu.be/hOrtuumOrv8) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
-- [Building a LangChain Agent (code-free!) Using `Bubble` and `Flowise`](https://youtu.be/jDJIIVWTZDE) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
-- [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
-- [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
-- [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
-- [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
-- [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
-- [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
-- ⛓ [Build AI chatbot with custom knowledge base using OpenAI API and GPT Index](https://youtu.be/vDZAZuaXf48) by [Irina Nik](https://www.youtube.com/@irina_nik)
-- ⛓ [Build Your Own Auto-GPT Apps with LangChain (Python Tutorial)](https://youtu.be/NYSWn1ipbgg) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
-- ⛓ [Chat with Multiple `PDFs` | LangChain App Tutorial in Python (Free LLMs and Embeddings)](https://youtu.be/dXxQ0LR-3Hg) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
-- ⛓ [Chat with a `CSV` | `LangChain Agents` Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
-- ⛓ [Create Your Own ChatGPT with `PDF` Data in 5 Minutes (LangChain Tutorial)](https://youtu.be/au2WVVGUvc8) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
-- ⛓ [Using ChatGPT with YOUR OWN Data. This is magical. (LangChain OpenAI API)](https://youtu.be/9AXP7tCI9PI) by [TechLead](https://www.youtube.com/@TechLead)
-- ⛓ [Build a Custom Chatbot with OpenAI: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU) by [Fabrikod](https://www.youtube.com/@fabrikod)
-- ⛓ [`Flowise` is an open source no-code UI visual tool to build 🦜🔗LangChain applications](https://youtu.be/CovAPtQPU0k) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
-- ⛓ [LangChain & GPT 4 For Data Analysis: The `Pandas` Dataframe Agent](https://youtu.be/rFQ5Kmkd4jc) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
-- ⛓ [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Toolfinder AI](https://www.youtube.com/@toolfinderai)
-- ⛓ [`PrivateGPT`: Chat to your FILES OFFLINE and FREE [Installation and Tutorial]](https://youtu.be/G7iLllmx4qc) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
-- ⛓ [How to build with Langchain 10x easier | ⛓️ LangFlow & `Flowise`](https://youtu.be/Ya1oGL7ZTvU) by [AI Jason](https://www.youtube.com/@AIJasonZ)
-- ⛓ [Getting Started With LangChain In 20 Minutes- Build Celebrity Search Application](https://youtu.be/_FpT1cwcSLg) by [Krish Naik](https://www.youtube.com/@krishnaik06)
+- ⛓️ [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
+- ⛓️ [Chatbot Factory: Streamline Python Chatbot Creation with LLMs and Langchain](https://youtu.be/eYer3uzrcuM) by [Finxter](https://www.youtube.com/@CobusGreylingZA)
+- ⛓️ [LangChain Tutorial - ChatGPT mit eigenen Daten](https://youtu.be/0XDLyY90E2c) by [Coding Crashkurse](https://www.youtube.com/@codingcrashkurse6429)
+- ⛓️ [Chat with a `CSV` | LangChain Agents Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [GoDataProf](https://www.youtube.com/@godataprof)
+- ⛓️ [Introdução ao Langchain - #Cortes - Live DataHackers](https://youtu.be/fw8y5VRei5Y) by [Prof. João Gabriel Lima](https://www.youtube.com/@profjoaogabriellima)
+- ⛓️ [LangChain: Level up `ChatGPT` !? | LangChain Tutorial Part 1](https://youtu.be/vxUGx8aZpDE) by [Code Affinity](https://www.youtube.com/@codeaffinitydev)
+- ⛓️ [KI schreibt krasses Youtube Skript 😲😳 | LangChain Tutorial Deutsch](https://youtu.be/QpTiXyK1jus) by [SimpleKI](https://www.youtube.com/@simpleki)
+- ⛓️ [Chat with Audio: Langchain, `Chroma DB`, OpenAI, and `Assembly AI`](https://youtu.be/Kjy7cx1r75g) by [AI Anytime](https://www.youtube.com/@AIAnytime)
+- ⛓️ [QA over documents with Auto vector index selection with Langchain router chains](https://youtu.be/9G05qybShv8) by [echohive](https://www.youtube.com/@echohive)
+- ⛓️ [Build your own custom LLM application with `Bubble.io` & Langchain (No Code & Beginner friendly)](https://youtu.be/O7NhQGu1m6c) by [No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
+- ⛓️ [Simple App to Question Your Docs: Leveraging `Streamlit`, `Hugging Face Spaces`, LangChain, and `Claude`!](https://youtu.be/X4YbNECRr7o) by [Chris Alexiuk](https://www.youtube.com/@chrisalexiuk)
+- ⛓️ [LANGCHAIN AI- `ConstitutionalChainAI` + Databutton AI ASSISTANT Web App](https://youtu.be/5zIU6_rdJCU) by [Avra](https://www.youtube.com/@Avra_b)
+- ⛓️ [LANGCHAIN AI AUTONOMOUS AGENT WEB APP - 👶 `BABY AGI` 🤖 with EMAIL AUTOMATION using `DATABUTTON`](https://youtu.be/cvAwOGfeHgw) by [Avra](https://www.youtube.com/@Avra_b)
+- ⛓️ [The Future of Data Analysis: Using A.I. Models in Data Analysis (LangChain)](https://youtu.be/v_LIcVyg5dk) by [Absent Data](https://www.youtube.com/@absentdata)
+- ⛓️ [Memory in LangChain | Deep dive (python)](https://youtu.be/70lqvTFh_Yg) by [Eden Marco](https://www.youtube.com/@EdenMarco)
+- ⛓️ [9 LangChain UseCases | Beginner's Guide | 2023](https://youtu.be/zS8_qosHNMw) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- ⛓️ [Use Large Language Models in Jupyter Notebook | LangChain | Agents & Indexes](https://youtu.be/JSe11L1a_QQ) by [Abhinaw Tiwari](https://www.youtube.com/@AbhinawTiwariAT)
+- ⛓️ [How to Talk to Your Langchain Agent | `11 Labs` + `Whisper`](https://youtu.be/N4k459Zw2PU) by [VRSEN](https://www.youtube.com/@vrsen)
+- ⛓️ [LangChain Deep Dive: 5 FUN AI App Ideas To Build Quickly and Easily](https://youtu.be/mPYEPzLkeks) by [James NoCode](https://www.youtube.com/@jamesnocode)
+- ⛓️ [BEST OPEN Alternative to OPENAI's EMBEDDINGs for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
+- ⛓️ [LangChain 101: Models](https://youtu.be/T6c_XsyaNSQ) by [Mckay Wrigley](https://www.youtube.com/@realmckaywrigley)
+- ⛓️ [LangChain with JavaScript Tutorial #1 | Setup & Using LLMs](https://youtu.be/W3AoeMrg27o) by [Leon van Zyl](https://www.youtube.com/@leonvanzyl)
+- ⛓️ [LangChain Overview & Tutorial for Beginners: Build Powerful AI Apps Quickly & Easily (ZERO CODE)](https://youtu.be/iI84yym473Q) by [James NoCode](https://www.youtube.com/@jamesnocode)
+- ⛓️ [LangChain In Action: Real-World Use Case With Step-by-Step Tutorial](https://youtu.be/UO699Szp82M) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+- ⛓️ [Summarizing and Querying Multiple Papers with LangChain](https://youtu.be/p_MQRWH5Y6k) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
+- ⛓️ [Using Langchain (and `Replit`) through `Tana`, ask `Google`/`Wikipedia`/`Wolfram Alpha` to fill out a table](https://youtu.be/Webau9lEzoI) by [Stian Håklev](https://www.youtube.com/@StianHaklev)
+- ⛓️ [Langchain PDF App (GUI) | Create a ChatGPT For Your `PDF` in Python](https://youtu.be/wUAUdEw5oxM) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- ⛓️ [Auto-GPT with LangChain 🔥 | Create Your Own Personal AI Assistant](https://youtu.be/imDfPmMKEjM) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- ⛓️ [Create Your OWN Slack AI Assistant with Python & LangChain](https://youtu.be/3jFXRNn2Bu8) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
+- ⛓️ [How to Create LOCAL Chatbots with GPT4All and LangChain [Full Guide]](https://youtu.be/4p1Fojur8Zw) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
+- ⛓️ [Build a `Multilingual PDF` Search App with LangChain, `Cohere` and `Bubble`](https://youtu.be/hOrtuumOrv8) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- ⛓️ [Building a LangChain Agent (code-free!) Using `Bubble` and `Flowise`](https://youtu.be/jDJIIVWTZDE) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- ⛓️ [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- ⛓️ [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- ⛓️ [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- ⛓️ [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
+- ⛓️ [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
+- ⛓️ [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
+- [LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
+- [LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
+- [LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
 
 
+## Tutorial Series
 
-### [Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
+
+⛓ icon marks a new addition [last update 2023-05-15]
+
+### DeepLearning.AI course
+⛓[LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain) by Harrison Chase presented by [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
+
+### Handbook
+[LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
+
+### Tutorials
+[LangChain Tutorials](https://www.youtube.com/watch?v=FuqdVNB_8c0&list=PL9V0lbeJ69brU-ojMpU1Y7Ic58Tap0Cw6) by [Edrick](https://www.youtube.com/@edrickdch):
+- ⛓ [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
+- ⛓ [LangChain 101: The Complete Beginner's Guide](https://youtu.be/P3MAbZ2eMUI)
+
+[LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
+
+
+[LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
+
+
+[LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+
+
+### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs):
+- #1 [Getting Started with `GPT-3` vs. Open Source LLMs](https://youtu.be/nE2skSRWTTs)
+- #2 [Prompt Templates for `GPT 3.5` and other LLMs](https://youtu.be/RflBcK0oDH0)
+- #3 [LLM Chains using `GPT 3.5` and other LLMs](https://youtu.be/S8j9Tk0lZHU)
+- #4 [Chatbot Memory for `Chat-GPT`, `Davinci` + other LLMs](https://youtu.be/X05uK0TZozM)
+- #5 [Chat with OpenAI in LangChain](https://youtu.be/CnAgB3A5OlU)
+- ⛓ #6 [Fixing LLM Hallucinations with Retrieval Augmentation in LangChain](https://youtu.be/kvdVduIJsc8)
+- ⛓ #7 [LangChain Agents Deep Dive with GPT 3.5](https://youtu.be/jSP-gSEyVeI)
+- ⛓ #8 [Create Custom Tools for Chatbots in LangChain](https://youtu.be/q-HNphrWsDE)
+- ⛓ #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI)
+
+
+### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Data Independent](https://www.youtube.com/@DataIndependent):
+- [What Is LangChain? - LangChain + `ChatGPT` Overview](https://youtu.be/_v_fgW2SkkQ)
+- [Quickstart Guide](https://youtu.be/kYRB-vJFy38)
+- [Beginner Guide To 7 Essential Concepts](https://youtu.be/2xxziIWmaSA)
+- [`OpenAI` + `Wolfram Alpha`](https://youtu.be/UijbzCIJ99g)
+- [Ask Questions On Your Custom (or Private) Files](https://youtu.be/EnT-ZTrcPrg)
+- [Connect `Google Drive Files` To `OpenAI`](https://youtu.be/IqqHqDcXLww)
+- [`YouTube Transcripts` + `OpenAI`](https://youtu.be/pNcQ5XXMgH4)
+- [Question A 300 Page Book (w/ `OpenAI` + `Pinecone`)](https://youtu.be/h0DHDp1FbmQ)
+- [Workaround `OpenAI's` Token Limit With Chain Types](https://youtu.be/f9_BWhCI4Zo)
+- [Build Your Own OpenAI + LangChain Web App in 23 Minutes](https://youtu.be/U_eV8wfMkXU)
+- [Working With The New `ChatGPT API`](https://youtu.be/e9P7FLi5Zy8)
+- [OpenAI + LangChain Wrote Me 100 Custom Sales Emails](https://youtu.be/y1pyAQM-3Bo)
+- [Structured Output From `OpenAI` (Clean Dirty Data)](https://youtu.be/KwAXfey-xQk)
+- [Connect `OpenAI` To +5,000 Tools (LangChain + `Zapier`)](https://youtu.be/7tNm0yiDigU)
+- [Use LLMs To Extract Data From Text (Expert Mode)](https://youtu.be/xZzvwR9jdPA)
+- ⛓ [Extract Insights From Interview Transcripts Using LLMs](https://youtu.be/shkMOHwJ4SM)
+- ⛓ [5 Levels Of LLM Summarizing: Novice to Expert](https://youtu.be/qaPMdcCqtWk)
+
+
+### [LangChain How to and guides](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ) by [Sam Witteveen](https://www.youtube.com/@samwitteveenai):
+- [LangChain Basics - LLMs & PromptTemplates with Colab](https://youtu.be/J_0qvRt4LNk)
+- [LangChain Basics - Tools and Chains](https://youtu.be/hI2BY7yl_Ac)
+- [`ChatGPT API` Announcement & Code Walkthrough with LangChain](https://youtu.be/phHqvLHCwH4)
+- [Conversations with Memory (explanation & code walkthrough)](https://youtu.be/X550Zbz_ROE)
+- [Chat with `Flan20B`](https://youtu.be/VW5LBavIfY4)
+- [Using `Hugging Face Models` locally (code walkthrough)](https://youtu.be/Kn7SX2Mx_Jk)
+- [`PAL` : Program-aided Language Models with LangChain code](https://youtu.be/dy7-LvDu-3s)
+- [Building a Summarization System with LangChain and `GPT-3` - Part 1](https://youtu.be/LNq_2s_H01Y)
+- [Building a Summarization System with LangChain and `GPT-3` - Part 2](https://youtu.be/d-yeHDLgKHw)
+- [Microsoft's `Visual ChatGPT` using LangChain](https://youtu.be/7YEiEyfPF5U)
+- [LangChain Agents - Joining Tools and Chains with Decisions](https://youtu.be/ziu87EXZVUE)
+- [Comparing LLMs with LangChain](https://youtu.be/rFNG0MIEuW0)
+- [Using `Constitutional AI` in LangChain](https://youtu.be/uoVqNFDwpX4)
+- [Talking to `Alpaca` with LangChain - Creating an Alpaca Chatbot](https://youtu.be/v6sF8Ed3nTE)
+- [Talk to your `CSV` & `Excel` with LangChain](https://youtu.be/xQ3mZhw69bc)
+- [`BabyAGI`: Discover the Power of Task-Driven Autonomous Agents!](https://youtu.be/QBcDLSE2ERA)
+- [Improve your `BabyAGI` with LangChain](https://youtu.be/DRgPyOXZ-oE)
+- ⛓ [Master `PDF` Chat with LangChain - Your essential guide to queries on documents](https://youtu.be/ZzgUqFtxgXI)
+- ⛓ [Using LangChain with `DuckDuckGO` `Wikipedia` & `PythonREPL` Tools](https://youtu.be/KerHlb8nuVc)
+- ⛓ [Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)](https://youtu.be/biS8G8x8DdA)
+- ⛓ [LangChain Retrieval QA Over Multiple Files with `ChromaDB`](https://youtu.be/3yPBVii7Ct0)
+- ⛓ [LangChain Retrieval QA with Instructor Embeddings & `ChromaDB` for PDFs](https://youtu.be/cFCGUjc33aU)
+- ⛓ [LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!](https://youtu.be/9ISVjh8mdlA)
+
+
+### [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt):
+- [LangChain Crash Course — All You Need to Know to Build Powerful Apps with LLMs](https://youtu.be/5-fc4Tlgmro)
+- [Working with MULTIPLE `PDF` Files in LangChain: `ChatGPT` for your Data](https://youtu.be/s5LhRdh5fu4)
+- [`ChatGPT` for YOUR OWN `PDF` files with LangChain](https://youtu.be/TLf90ipMzfE)
+- [Talk to YOUR DATA without OpenAI APIs: LangChain](https://youtu.be/wrD-fZvT6UI)
+- ⛓️ [CHATGPT For WEBSITES: Custom ChatBOT](https://youtu.be/RBnuhhmD21U)
+
+
+### LangChain by [Chat with data](https://www.youtube.com/@chatwithdata)
+- [LangChain Beginner's Tutorial for `Typescript`/`Javascript`](https://youtu.be/bH722QgRlhQ)
+- [`GPT-4` Tutorial: How to Chat With Multiple `PDF` Files (~1000 pages of Tesla's 10-K Annual Reports)](https://youtu.be/Ix9WIZpArm0)
+- [`GPT-4` & LangChain Tutorial: How to Chat With A 56-Page `PDF` Document (w/`Pinecone`)](https://youtu.be/ih9PBGVVOO4)
+- ⛓ [LangChain & Supabase Tutorial: How to Build a ChatGPT Chatbot For Your Website](https://youtu.be/R2FMzcsmQY8)
+
+
+### [Get SH\*T Done with Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
 - [Getting Started with LangChain: Load Custom Data, Run OpenAI Models, Embeddings and `ChatGPT`](https://www.youtube.com/watch?v=muXbPpG_ys4)
 - [Loaders, Indexes & Vectorstores in LangChain: Question Answering on `PDF` files with `ChatGPT`](https://www.youtube.com/watch?v=FQnvfR8Dmr0)
 - [LangChain Models: `ChatGPT`, `Flan Alpaca`, `OpenAI Embeddings`, Prompt Templates & Streaming](https://www.youtube.com/watch?v=zy6LiK5F5-s)
 - [LangChain Chains: Use `ChatGPT` to Build Conversational Agents, Summaries and Q&A on Text With LLMs](https://www.youtube.com/watch?v=h1tJZQPcimM)
 - [Analyze Custom CSV Data with `GPT-4` using Langchain](https://www.youtube.com/watch?v=Ew3sGdX8at4)
-- [Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations](https://youtu.be/CyuUlf54wTs)
-
+- ⛓ [Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations](https://youtu.be/CyuUlf54wTs)
 
 ---------------------
-⛓ icon marks a new addition [last update 2023-06-20]
+⛓ icon marks a new addition [last update 2023-05-15]

docs/extras/ecosystem/dependents.mdx

@@ -2,261 +2,188 @@
 
 Dependents stats for `hwchase17/langchain`
 
-[![](https://img.shields.io/static/v1?label=Used%20by&message=9941&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(public)&message=244&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(private)&message=9697&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(stars)&message=19827&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by&message=5152&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(public)&message=172&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(private)&message=4980&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(stars)&message=17239&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
 
-
-[update: 2023-07-07; only dependent repositories with Stars > 100]
+[update: 2023-05-17; only dependent repositories with Stars > 100]
 
 
 | Repository | Stars  |
 | :--------  | -----: |
-|[openai/openai-cookbook](https://github.com/openai/openai-cookbook) | 41047 |
-|[LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant) | 33983 |
-|[microsoft/TaskMatrix](https://github.com/microsoft/TaskMatrix) | 33375 |
-|[imartinez/privateGPT](https://github.com/imartinez/privateGPT) | 31114 |
-|[hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI) | 30369 |
-|[reworkd/AgentGPT](https://github.com/reworkd/AgentGPT) | 24116 |
-|[OpenBB-finance/OpenBBTerminal](https://github.com/OpenBB-finance/OpenBBTerminal) | 22565 |
-|[openai/chatgpt-retrieval-plugin](https://github.com/openai/chatgpt-retrieval-plugin) | 18375 |
-|[jerryjliu/llama_index](https://github.com/jerryjliu/llama_index) | 17723 |
-|[mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) | 16958 |
-|[mlflow/mlflow](https://github.com/mlflow/mlflow) | 14632 |
-|[GaiZhenbiao/ChuanhuChatGPT](https://github.com/GaiZhenbiao/ChuanhuChatGPT) | 11273 |
-|[openai/evals](https://github.com/openai/evals) | 10745 |
-|[databrickslabs/dolly](https://github.com/databrickslabs/dolly) | 10298 |
-|[imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM) | 9838 |
-|[logspace-ai/langflow](https://github.com/logspace-ai/langflow) | 9247 |
-|[AIGC-Audio/AudioGPT](https://github.com/AIGC-Audio/AudioGPT) | 8768 |
-|[PromtEngineer/localGPT](https://github.com/PromtEngineer/localGPT) | 8651 |
-|[StanGirard/quivr](https://github.com/StanGirard/quivr) | 8119 |
-|[go-skynet/LocalAI](https://github.com/go-skynet/LocalAI) | 7418 |
-|[gventuri/pandas-ai](https://github.com/gventuri/pandas-ai) | 7301 |
-|[PipedreamHQ/pipedream](https://github.com/PipedreamHQ/pipedream) | 6636 |
-|[arc53/DocsGPT](https://github.com/arc53/DocsGPT) | 5849 |
-|[e2b-dev/e2b](https://github.com/e2b-dev/e2b) | 5129 |
-|[langgenius/dify](https://github.com/langgenius/dify) | 4804 |
-|[serge-chat/serge](https://github.com/serge-chat/serge) | 4448 |
-|[csunny/DB-GPT](https://github.com/csunny/DB-GPT) | 4350 |
-|[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 4268 |
-|[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 4244 |
-|[intitni/CopilotForXcode](https://github.com/intitni/CopilotForXcode) | 4232 |
-|[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 4154 |
-|[madawei2699/myGPTReader](https://github.com/madawei2699/myGPTReader) | 4080 |
-|[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 3949 |
-|[gkamradt/langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) | 3920 |
-|[bentoml/OpenLLM](https://github.com/bentoml/OpenLLM) | 3481 |
-|[MineDojo/Voyager](https://github.com/MineDojo/Voyager) | 3453 |
-|[mmabrouk/chatgpt-wrapper](https://github.com/mmabrouk/chatgpt-wrapper) | 3355 |
-|[postgresml/postgresml](https://github.com/postgresml/postgresml) | 3328 |
-|[marqo-ai/marqo](https://github.com/marqo-ai/marqo) | 3100 |
-|[kyegomez/tree-of-thoughts](https://github.com/kyegomez/tree-of-thoughts) | 3049 |
-|[PrefectHQ/marvin](https://github.com/PrefectHQ/marvin) | 2844 |
-|[project-baize/baize-chatbot](https://github.com/project-baize/baize-chatbot) | 2833 |
-|[h2oai/h2ogpt](https://github.com/h2oai/h2ogpt) | 2809 |
-|[hwchase17/chat-langchain](https://github.com/hwchase17/chat-langchain) | 2809 |
-|[whitead/paper-qa](https://github.com/whitead/paper-qa) | 2664 |
-|[Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo) | 2650 |
-|[OpenGVLab/InternGPT](https://github.com/OpenGVLab/InternGPT) | 2525 |
-|[GerevAI/gerev](https://github.com/GerevAI/gerev) | 2372 |
-|[ParisNeo/lollms-webui](https://github.com/ParisNeo/lollms-webui) | 2287 |
-|[OpenBMB/BMTools](https://github.com/OpenBMB/BMTools) | 2265 |
-|[SamurAIGPT/privateGPT](https://github.com/SamurAIGPT/privateGPT) | 2084 |
-|[Chainlit/chainlit](https://github.com/Chainlit/chainlit) | 1912 |
-|[Farama-Foundation/PettingZoo](https://github.com/Farama-Foundation/PettingZoo) | 1869 |
-|[OpenGVLab/Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) | 1864 |
-|[IntelligenzaArtificiale/Free-Auto-GPT](https://github.com/IntelligenzaArtificiale/Free-Auto-GPT) | 1849 |
-|[Unstructured-IO/unstructured](https://github.com/Unstructured-IO/unstructured) | 1766 |
-|[yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) | 1745 |
-|[NVIDIA/NeMo-Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | 1732 |
-|[hwchase17/notion-qa](https://github.com/hwchase17/notion-qa) | 1716 |
-|[paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) | 1619 |
-|[pinterest/querybook](https://github.com/pinterest/querybook) | 1468 |
-|[vocodedev/vocode-python](https://github.com/vocodedev/vocode-python) | 1446 |
-|[thomas-yanxin/LangChain-ChatGLM-Webui](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui) | 1430 |
-|[Mintplex-Labs/anything-llm](https://github.com/Mintplex-Labs/anything-llm) | 1419 |
-|[Kav-K/GPTDiscord](https://github.com/Kav-K/GPTDiscord) | 1416 |
-|[lunasec-io/lunasec](https://github.com/lunasec-io/lunasec) | 1327 |
-|[psychic-api/psychic](https://github.com/psychic-api/psychic) | 1307 |
-|[jina-ai/thinkgpt](https://github.com/jina-ai/thinkgpt) | 1242 |
-|[agiresearch/OpenAGI](https://github.com/agiresearch/OpenAGI) | 1239 |
-|[ttengwang/Caption-Anything](https://github.com/ttengwang/Caption-Anything) | 1203 |
-|[jina-ai/dev-gpt](https://github.com/jina-ai/dev-gpt) | 1179 |
-|[keephq/keep](https://github.com/keephq/keep) | 1169 |
-|[greshake/llm-security](https://github.com/greshake/llm-security) | 1156 |
-|[richardyc/Chrome-GPT](https://github.com/richardyc/Chrome-GPT) | 1090 |
-|[jina-ai/langchain-serve](https://github.com/jina-ai/langchain-serve) | 1088 |
-|[mmz-001/knowledge_gpt](https://github.com/mmz-001/knowledge_gpt) | 1074 |
-|[juncongmoo/chatllama](https://github.com/juncongmoo/chatllama) | 1057 |
-|[noahshinn024/reflexion](https://github.com/noahshinn024/reflexion) | 1045 |
-|[visual-openllm/visual-openllm](https://github.com/visual-openllm/visual-openllm) | 1036 |
-|[101dotxyz/GPTeam](https://github.com/101dotxyz/GPTeam) | 999 |
-|[poe-platform/api-bot-tutorial](https://github.com/poe-platform/api-bot-tutorial) | 989 |
-|[irgolic/AutoPR](https://github.com/irgolic/AutoPR) | 974 |
-|[homanp/superagent](https://github.com/homanp/superagent) | 970 |
-|[microsoft/X-Decoder](https://github.com/microsoft/X-Decoder) | 941 |
-|[peterw/Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) | 896 |
-|[SamurAIGPT/Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT) | 856 |
-|[cirediatpl/FigmaChain](https://github.com/cirediatpl/FigmaChain) | 840 |
-|[chatarena/chatarena](https://github.com/chatarena/chatarena) | 829 |
-|[rlancemartin/auto-evaluator](https://github.com/rlancemartin/auto-evaluator) | 816 |
-|[seanpixel/Teenage-AGI](https://github.com/seanpixel/Teenage-AGI) | 816 |
-|[hashintel/hash](https://github.com/hashintel/hash) | 806 |
-|[corca-ai/EVAL](https://github.com/corca-ai/EVAL) | 790 |
-|[eyurtsev/kor](https://github.com/eyurtsev/kor) | 752 |
-|[cheshire-cat-ai/core](https://github.com/cheshire-cat-ai/core) | 713 |
-|[e-johnstonn/BriefGPT](https://github.com/e-johnstonn/BriefGPT) | 686 |
-|[run-llama/llama-lab](https://github.com/run-llama/llama-lab) | 685 |
-|[refuel-ai/autolabel](https://github.com/refuel-ai/autolabel) | 673 |
-|[griptape-ai/griptape](https://github.com/griptape-ai/griptape) | 617 |
-|[billxbf/ReWOO](https://github.com/billxbf/ReWOO) | 616 |
-|[Anil-matcha/ChatPDF](https://github.com/Anil-matcha/ChatPDF) | 609 |
-|[NimbleBoxAI/ChainFury](https://github.com/NimbleBoxAI/ChainFury) | 592 |
-|[getmetal/motorhead](https://github.com/getmetal/motorhead) | 581 |
-|[ajndkr/lanarky](https://github.com/ajndkr/lanarky) | 574 |
-|[namuan/dr-doc-search](https://github.com/namuan/dr-doc-search) | 572 |
-|[kreneskyp/ix](https://github.com/kreneskyp/ix) | 564 |
-|[akshata29/chatpdf](https://github.com/akshata29/chatpdf) | 540 |
-|[hwchase17/chat-your-data](https://github.com/hwchase17/chat-your-data) | 540 |
-|[whyiyhw/chatgpt-wechat](https://github.com/whyiyhw/chatgpt-wechat) | 537 |
-|[khoj-ai/khoj](https://github.com/khoj-ai/khoj) | 531 |
-|[SamurAIGPT/ChatGPT-Developer-Plugins](https://github.com/SamurAIGPT/ChatGPT-Developer-Plugins) | 528 |
-|[microsoft/PodcastCopilot](https://github.com/microsoft/PodcastCopilot) | 526 |
-|[ruoccofabrizio/azure-open-ai-embeddings-qna](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) | 515 |
-|[alexanderatallah/window.ai](https://github.com/alexanderatallah/window.ai) | 494 |
-|[StevenGrove/GPT4Tools](https://github.com/StevenGrove/GPT4Tools) | 483 |
-|[jina-ai/agentchain](https://github.com/jina-ai/agentchain) | 472 |
-|[mckaywrigley/repo-chat](https://github.com/mckaywrigley/repo-chat) | 465 |
-|[yeagerai/yeagerai-agent](https://github.com/yeagerai/yeagerai-agent) | 464 |
-|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 464 |
-|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 455 |
-|[michaelthwan/searchGPT](https://github.com/michaelthwan/searchGPT) | 455 |
-|[freddyaboulton/gradio-tools](https://github.com/freddyaboulton/gradio-tools) | 450 |
-|[amosjyng/langchain-visualizer](https://github.com/amosjyng/langchain-visualizer) | 446 |
-|[msoedov/langcorn](https://github.com/msoedov/langcorn) | 445 |
-|[plastic-labs/tutor-gpt](https://github.com/plastic-labs/tutor-gpt) | 426 |
-|[poe-platform/poe-protocol](https://github.com/poe-platform/poe-protocol) | 426 |
-|[jonra1993/fastapi-alembic-sqlmodel-async](https://github.com/jonra1993/fastapi-alembic-sqlmodel-async) | 418 |
-|[langchain-ai/auto-evaluator](https://github.com/langchain-ai/auto-evaluator) | 416 |
-|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 401 |
-|[xuwenhao/geektime-ai-course](https://github.com/xuwenhao/geektime-ai-course) | 400 |
-|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 386 |
-|[mtenenholtz/chat-twitter](https://github.com/mtenenholtz/chat-twitter) | 382 |
-|[explosion/spacy-llm](https://github.com/explosion/spacy-llm) | 368 |
-|[showlab/VLog](https://github.com/showlab/VLog) | 363 |
-|[yvann-hub/Robby-chatbot](https://github.com/yvann-hub/Robby-chatbot) | 363 |
-|[daodao97/chatdoc](https://github.com/daodao97/chatdoc) | 361 |
-|[opentensor/bittensor](https://github.com/opentensor/bittensor) | 360 |
-|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 355 |
-|[logan-markewich/llama_index_starter_pack](https://github.com/logan-markewich/llama_index_starter_pack) | 351 |
-|[jupyterlab/jupyter-ai](https://github.com/jupyterlab/jupyter-ai) | 348 |
-|[alejandro-ao/ask-multiple-pdfs](https://github.com/alejandro-ao/ask-multiple-pdfs) | 321 |
-|[andylokandy/gpt-4-search](https://github.com/andylokandy/gpt-4-search) | 314 |
-|[mosaicml/examples](https://github.com/mosaicml/examples) | 313 |
-|[personoids/personoids-lite](https://github.com/personoids/personoids-lite) | 306 |
-|[itamargol/openai](https://github.com/itamargol/openai) | 304 |
-|[Anil-matcha/Website-to-Chatbot](https://github.com/Anil-matcha/Website-to-Chatbot) | 299 |
-|[momegas/megabots](https://github.com/momegas/megabots) | 299 |
-|[BlackHC/llm-strategy](https://github.com/BlackHC/llm-strategy) | 289 |
-|[daveebbelaar/langchain-experiments](https://github.com/daveebbelaar/langchain-experiments) | 283 |
-|[wandb/weave](https://github.com/wandb/weave) | 279 |
-|[Cheems-Seminar/grounded-segment-any-parts](https://github.com/Cheems-Seminar/grounded-segment-any-parts) | 273 |
-|[jerlendds/osintbuddy](https://github.com/jerlendds/osintbuddy) | 271 |
-|[OpenBMB/AgentVerse](https://github.com/OpenBMB/AgentVerse) | 270 |
-|[MagnivOrg/prompt-layer-library](https://github.com/MagnivOrg/prompt-layer-library) | 269 |
-|[sullivan-sean/chat-langchainjs](https://github.com/sullivan-sean/chat-langchainjs) | 259 |
-|[Azure-Samples/openai](https://github.com/Azure-Samples/openai) | 252 |
-|[bborn/howdoi.ai](https://github.com/bborn/howdoi.ai) | 248 |
-|[hnawaz007/pythondataanalysis](https://github.com/hnawaz007/pythondataanalysis) | 247 |
-|[conceptofmind/toolformer](https://github.com/conceptofmind/toolformer) | 243 |
-|[truera/trulens](https://github.com/truera/trulens) | 239 |
-|[ur-whitelab/exmol](https://github.com/ur-whitelab/exmol) | 238 |
-|[intel/intel-extension-for-transformers](https://github.com/intel/intel-extension-for-transformers) | 237 |
-|[monarch-initiative/ontogpt](https://github.com/monarch-initiative/ontogpt) | 236 |
-|[wandb/edu](https://github.com/wandb/edu) | 231 |
-|[recalign/RecAlign](https://github.com/recalign/RecAlign) | 229 |
-|[alvarosevilla95/autolang](https://github.com/alvarosevilla95/autolang) | 223 |
-|[kaleido-lab/dolphin](https://github.com/kaleido-lab/dolphin) | 221 |
-|[JohnSnowLabs/nlptest](https://github.com/JohnSnowLabs/nlptest) | 220 |
-|[paolorechia/learn-langchain](https://github.com/paolorechia/learn-langchain) | 219 |
-|[Safiullah-Rahu/CSV-AI](https://github.com/Safiullah-Rahu/CSV-AI) | 215 |
-|[Haste171/langchain-chatbot](https://github.com/Haste171/langchain-chatbot) | 215 |
-|[steamship-packages/langchain-agent-production-starter](https://github.com/steamship-packages/langchain-agent-production-starter) | 214 |
-|[airobotlab/KoChatGPT](https://github.com/airobotlab/KoChatGPT) | 213 |
-|[filip-michalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) | 211 |
-|[marella/chatdocs](https://github.com/marella/chatdocs) | 207 |
-|[su77ungr/CASALIOY](https://github.com/su77ungr/CASALIOY) | 200 |
-|[shaman-ai/agent-actors](https://github.com/shaman-ai/agent-actors) | 195 |
-|[plchld/InsightFlow](https://github.com/plchld/InsightFlow) | 189 |
-|[jbrukh/gpt-jargon](https://github.com/jbrukh/gpt-jargon) | 186 |
-|[hwchase17/langchain-streamlit-template](https://github.com/hwchase17/langchain-streamlit-template) | 185 |
-|[huchenxucs/ChatDB](https://github.com/huchenxucs/ChatDB) | 179 |
-|[benthecoder/ClassGPT](https://github.com/benthecoder/ClassGPT) | 178 |
-|[hwchase17/chroma-langchain](https://github.com/hwchase17/chroma-langchain) | 178 |
-|[radi-cho/datasetGPT](https://github.com/radi-cho/datasetGPT) | 177 |
-|[jiran214/GPT-vup](https://github.com/jiran214/GPT-vup) | 176 |
-|[rsaryev/talk-codebase](https://github.com/rsaryev/talk-codebase) | 174 |
-|[edreisMD/plugnplai](https://github.com/edreisMD/plugnplai) | 174 |
-|[gia-guar/JARVIS-ChatGPT](https://github.com/gia-guar/JARVIS-ChatGPT) | 172 |
-|[hardbyte/qabot](https://github.com/hardbyte/qabot) | 171 |
-|[shamspias/customizable-gpt-chatbot](https://github.com/shamspias/customizable-gpt-chatbot) | 165 |
-|[gustavz/DataChad](https://github.com/gustavz/DataChad) | 164 |
-|[yasyf/compress-gpt](https://github.com/yasyf/compress-gpt) | 163 |
-|[SamPink/dev-gpt](https://github.com/SamPink/dev-gpt) | 161 |
-|[yuanjie-ai/ChatLLM](https://github.com/yuanjie-ai/ChatLLM) | 161 |
-|[pablomarin/GPT-Azure-Search-Engine](https://github.com/pablomarin/GPT-Azure-Search-Engine) | 160 |
-|[jondurbin/airoboros](https://github.com/jondurbin/airoboros) | 157 |
-|[fengyuli-dev/multimedia-gpt](https://github.com/fengyuli-dev/multimedia-gpt) | 157 |
-|[PradipNichite/Youtube-Tutorials](https://github.com/PradipNichite/Youtube-Tutorials) | 156 |
-|[nicknochnack/LangchainDocuments](https://github.com/nicknochnack/LangchainDocuments) | 155 |
-|[ethanyanjiali/minChatGPT](https://github.com/ethanyanjiali/minChatGPT) | 155 |
-|[ccurme/yolopandas](https://github.com/ccurme/yolopandas) | 154 |
-|[chakkaradeep/pyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI) | 153 |
-|[preset-io/promptimize](https://github.com/preset-io/promptimize) | 150 |
-|[onlyphantom/llm-python](https://github.com/onlyphantom/llm-python) | 148 |
-|[Azure-Samples/azure-search-power-skills](https://github.com/Azure-Samples/azure-search-power-skills) | 146 |
-|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 144 |
-|[microsoft/azure-openai-in-a-day-workshop](https://github.com/microsoft/azure-openai-in-a-day-workshop) | 144 |
-|[jmpaz/promptlib](https://github.com/jmpaz/promptlib) | 143 |
-|[shauryr/S2QA](https://github.com/shauryr/S2QA) | 142 |
-|[handrew/browserpilot](https://github.com/handrew/browserpilot) | 141 |
-|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 140 |
-|[Klingefjord/chatgpt-telegram](https://github.com/Klingefjord/chatgpt-telegram) | 140 |
-|[WongSaang/chatgpt-ui-server](https://github.com/WongSaang/chatgpt-ui-server) | 139 |
-|[ibiscp/LLM-IMDB](https://github.com/ibiscp/LLM-IMDB) | 139 |
-|[menloparklab/langchain-cohere-qdrant-doc-retrieval](https://github.com/menloparklab/langchain-cohere-qdrant-doc-retrieval) | 138 |
-|[hirokidaichi/wanna](https://github.com/hirokidaichi/wanna) | 137 |
-|[steamship-core/vercel-examples](https://github.com/steamship-core/vercel-examples) | 137 |
-|[deeppavlov/dream](https://github.com/deeppavlov/dream) | 136 |
-|[miaoshouai/miaoshouai-assistant](https://github.com/miaoshouai/miaoshouai-assistant) | 135 |
-|[sugarforever/LangChain-Tutorials](https://github.com/sugarforever/LangChain-Tutorials) | 135 |
-|[yasyf/summ](https://github.com/yasyf/summ) | 135 |
-|[peterw/StoryStorm](https://github.com/peterw/StoryStorm) | 134 |
-|[vaibkumr/prompt-optimizer](https://github.com/vaibkumr/prompt-optimizer) | 132 |
-|[ju-bezdek/langchain-decorators](https://github.com/ju-bezdek/langchain-decorators) | 130 |
-|[homanp/vercel-langchain](https://github.com/homanp/vercel-langchain) | 128 |
-|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 127 |
-|[petehunt/langchain-github-bot](https://github.com/petehunt/langchain-github-bot) | 125 |
-|[eunomia-bpf/GPTtrace](https://github.com/eunomia-bpf/GPTtrace) | 122 |
-|[fixie-ai/fixie-examples](https://github.com/fixie-ai/fixie-examples) | 122 |
-|[Aggregate-Intellect/practical-llms](https://github.com/Aggregate-Intellect/practical-llms) | 120 |
-|[davila7/file-gpt](https://github.com/davila7/file-gpt) | 120 |
-|[Azure-Samples/azure-search-openai-demo-csharp](https://github.com/Azure-Samples/azure-search-openai-demo-csharp) | 119 |
-|[prof-frink-lab/slangchain](https://github.com/prof-frink-lab/slangchain) | 117 |
-|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 117 |
-|[zenml-io/zenml-projects](https://github.com/zenml-io/zenml-projects) | 116 |
-|[flurb18/AgentOoba](https://github.com/flurb18/AgentOoba) | 114 |
-|[kaarthik108/snowChat](https://github.com/kaarthik108/snowChat) | 112 |
-|[RedisVentures/redis-openai-qna](https://github.com/RedisVentures/redis-openai-qna) | 111 |
-|[solana-labs/chatgpt-plugin](https://github.com/solana-labs/chatgpt-plugin) | 111 |
-|[kulltc/chatgpt-sql](https://github.com/kulltc/chatgpt-sql) | 109 |
-|[summarizepaper/summarizepaper](https://github.com/summarizepaper/summarizepaper) | 109 |
-|[Azure-Samples/miyagi](https://github.com/Azure-Samples/miyagi) | 106 |
-|[ssheng/BentoChain](https://github.com/ssheng/BentoChain) | 106 |
-|[voxel51/voxelgpt](https://github.com/voxel51/voxelgpt) | 105 |
-|[mallahyari/drqa](https://github.com/mallahyari/drqa) | 103 |
+|[openai/openai-cookbook](https://github.com/openai/openai-cookbook) | 35401 |
+|[LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant) | 32861 |
+|[microsoft/TaskMatrix](https://github.com/microsoft/TaskMatrix) | 32766 |
+|[hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI) | 29560 |
+|[reworkd/AgentGPT](https://github.com/reworkd/AgentGPT) | 22315 |
+|[imartinez/privateGPT](https://github.com/imartinez/privateGPT) | 17474 |
+|[openai/chatgpt-retrieval-plugin](https://github.com/openai/chatgpt-retrieval-plugin) | 16923 |
+|[mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) | 16112 |
+|[jerryjliu/llama_index](https://github.com/jerryjliu/llama_index) | 15407 |
+|[mlflow/mlflow](https://github.com/mlflow/mlflow) | 14345 |
+|[GaiZhenbiao/ChuanhuChatGPT](https://github.com/GaiZhenbiao/ChuanhuChatGPT) | 10372 |
+|[databrickslabs/dolly](https://github.com/databrickslabs/dolly) | 9919 |
+|[AIGC-Audio/AudioGPT](https://github.com/AIGC-Audio/AudioGPT) | 8177 |
+|[logspace-ai/langflow](https://github.com/logspace-ai/langflow) | 6807 |
+|[imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM) | 6087 |
+|[arc53/DocsGPT](https://github.com/arc53/DocsGPT) | 5292 |
+|[e2b-dev/e2b](https://github.com/e2b-dev/e2b) | 4622 |
+|[nsarrazin/serge](https://github.com/nsarrazin/serge) | 4076 |
+|[madawei2699/myGPTReader](https://github.com/madawei2699/myGPTReader) | 3952 |
+|[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 3952 |
+|[go-skynet/LocalAI](https://github.com/go-skynet/LocalAI) | 3762 |
+|[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 3388 |
+|[mmabrouk/chatgpt-wrapper](https://github.com/mmabrouk/chatgpt-wrapper) | 3243 |
+|[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 3189 |
+|[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 3050 |
+|[marqo-ai/marqo](https://github.com/marqo-ai/marqo) | 2930 |
+|[gkamradt/langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) | 2710 |
+|[PrefectHQ/marvin](https://github.com/PrefectHQ/marvin) | 2545 |
+|[project-baize/baize-chatbot](https://github.com/project-baize/baize-chatbot) | 2479 |
+|[whitead/paper-qa](https://github.com/whitead/paper-qa) | 2399 |
+|[langgenius/dify](https://github.com/langgenius/dify) | 2344 |
+|[GerevAI/gerev](https://github.com/GerevAI/gerev) | 2283 |
+|[hwchase17/chat-langchain](https://github.com/hwchase17/chat-langchain) | 2266 |
+|[guangzhengli/ChatFiles](https://github.com/guangzhengli/ChatFiles) | 1903 |
+|[Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo) | 1884 |
+|[OpenBMB/BMTools](https://github.com/OpenBMB/BMTools) | 1860 |
+|[Farama-Foundation/PettingZoo](https://github.com/Farama-Foundation/PettingZoo) | 1813 |
+|[OpenGVLab/Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) | 1571 |
+|[IntelligenzaArtificiale/Free-Auto-GPT](https://github.com/IntelligenzaArtificiale/Free-Auto-GPT) | 1480 |
+|[hwchase17/notion-qa](https://github.com/hwchase17/notion-qa) | 1464 |
+|[NVIDIA/NeMo-Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | 1419 |
+|[Unstructured-IO/unstructured](https://github.com/Unstructured-IO/unstructured) | 1410 |
+|[Kav-K/GPTDiscord](https://github.com/Kav-K/GPTDiscord) | 1363 |
+|[paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) | 1344 |
+|[StanGirard/quivr](https://github.com/StanGirard/quivr) | 1330 |
+|[lunasec-io/lunasec](https://github.com/lunasec-io/lunasec) | 1318 |
+|[vocodedev/vocode-python](https://github.com/vocodedev/vocode-python) | 1286 |
+|[agiresearch/OpenAGI](https://github.com/agiresearch/OpenAGI) | 1156 |
+|[h2oai/h2ogpt](https://github.com/h2oai/h2ogpt) | 1141 |
+|[jina-ai/thinkgpt](https://github.com/jina-ai/thinkgpt) | 1106 |
+|[yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) | 1072 |
+|[ttengwang/Caption-Anything](https://github.com/ttengwang/Caption-Anything) | 1064 |
+|[jina-ai/dev-gpt](https://github.com/jina-ai/dev-gpt) | 1057 |
+|[juncongmoo/chatllama](https://github.com/juncongmoo/chatllama) | 1003 |
+|[greshake/llm-security](https://github.com/greshake/llm-security) | 1002 |
+|[visual-openllm/visual-openllm](https://github.com/visual-openllm/visual-openllm) | 957 |
+|[richardyc/Chrome-GPT](https://github.com/richardyc/Chrome-GPT) | 918 |
+|[irgolic/AutoPR](https://github.com/irgolic/AutoPR) | 886 |
+|[mmz-001/knowledge_gpt](https://github.com/mmz-001/knowledge_gpt) | 867 |
+|[thomas-yanxin/LangChain-ChatGLM-Webui](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui) | 850 |
+|[microsoft/X-Decoder](https://github.com/microsoft/X-Decoder) | 837 |
+|[peterw/Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) | 826 |
+|[cirediatpl/FigmaChain](https://github.com/cirediatpl/FigmaChain) | 782 |
+|[hashintel/hash](https://github.com/hashintel/hash) | 778 |
+|[seanpixel/Teenage-AGI](https://github.com/seanpixel/Teenage-AGI) | 773 |
+|[jina-ai/langchain-serve](https://github.com/jina-ai/langchain-serve) | 738 |
+|[corca-ai/EVAL](https://github.com/corca-ai/EVAL) | 737 |
+|[ai-sidekick/sidekick](https://github.com/ai-sidekick/sidekick) | 717 |
+|[rlancemartin/auto-evaluator](https://github.com/rlancemartin/auto-evaluator) | 703 |
+|[poe-platform/api-bot-tutorial](https://github.com/poe-platform/api-bot-tutorial) | 689 |
+|[SamurAIGPT/Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT) | 666 |
+|[eyurtsev/kor](https://github.com/eyurtsev/kor) | 608 |
+|[run-llama/llama-lab](https://github.com/run-llama/llama-lab) | 559 |
+|[namuan/dr-doc-search](https://github.com/namuan/dr-doc-search) | 544 |
+|[pieroit/cheshire-cat](https://github.com/pieroit/cheshire-cat) | 520 |
+|[griptape-ai/griptape](https://github.com/griptape-ai/griptape) | 514 |
+|[getmetal/motorhead](https://github.com/getmetal/motorhead) | 481 |
+|[hwchase17/chat-your-data](https://github.com/hwchase17/chat-your-data) | 462 |
+|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 452 |
+|[jina-ai/agentchain](https://github.com/jina-ai/agentchain) | 439 |
+|[SamurAIGPT/ChatGPT-Developer-Plugins](https://github.com/SamurAIGPT/ChatGPT-Developer-Plugins) | 437 |
+|[alexanderatallah/window.ai](https://github.com/alexanderatallah/window.ai) | 433 |
+|[michaelthwan/searchGPT](https://github.com/michaelthwan/searchGPT) | 427 |
+|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 425 |
+|[mckaywrigley/repo-chat](https://github.com/mckaywrigley/repo-chat) | 422 |
+|[whyiyhw/chatgpt-wechat](https://github.com/whyiyhw/chatgpt-wechat) | 421 |
+|[freddyaboulton/gradio-tools](https://github.com/freddyaboulton/gradio-tools) | 407 |
+|[jonra1993/fastapi-alembic-sqlmodel-async](https://github.com/jonra1993/fastapi-alembic-sqlmodel-async) | 395 |
+|[yeagerai/yeagerai-agent](https://github.com/yeagerai/yeagerai-agent) | 383 |
+|[akshata29/chatpdf](https://github.com/akshata29/chatpdf) | 374 |
+|[OpenGVLab/InternGPT](https://github.com/OpenGVLab/InternGPT) | 368 |
+|[ruoccofabrizio/azure-open-ai-embeddings-qna](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) | 358 |
+|[101dotxyz/GPTeam](https://github.com/101dotxyz/GPTeam) | 357 |
+|[mtenenholtz/chat-twitter](https://github.com/mtenenholtz/chat-twitter) | 354 |
+|[amosjyng/langchain-visualizer](https://github.com/amosjyng/langchain-visualizer) | 343 |
+|[msoedov/langcorn](https://github.com/msoedov/langcorn) | 334 |
+|[showlab/VLog](https://github.com/showlab/VLog) | 330 |
+|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 324 |
+|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 323 |
+|[daodao97/chatdoc](https://github.com/daodao97/chatdoc) | 320 |
+|[xuwenhao/geektime-ai-course](https://github.com/xuwenhao/geektime-ai-course) | 308 |
+|[StevenGrove/GPT4Tools](https://github.com/StevenGrove/GPT4Tools) | 301 |
+|[logan-markewich/llama_index_starter_pack](https://github.com/logan-markewich/llama_index_starter_pack) | 300 |
+|[andylokandy/gpt-4-search](https://github.com/andylokandy/gpt-4-search) | 299 |
+|[Anil-matcha/ChatPDF](https://github.com/Anil-matcha/ChatPDF) | 287 |
+|[itamargol/openai](https://github.com/itamargol/openai) | 273 |
+|[BlackHC/llm-strategy](https://github.com/BlackHC/llm-strategy) | 267 |
+|[momegas/megabots](https://github.com/momegas/megabots) | 259 |
+|[bborn/howdoi.ai](https://github.com/bborn/howdoi.ai) | 238 |
+|[Cheems-Seminar/grounded-segment-any-parts](https://github.com/Cheems-Seminar/grounded-segment-any-parts) | 232 |
+|[ur-whitelab/exmol](https://github.com/ur-whitelab/exmol) | 227 |
+|[sullivan-sean/chat-langchainjs](https://github.com/sullivan-sean/chat-langchainjs) | 227 |
+|[explosion/spacy-llm](https://github.com/explosion/spacy-llm) | 226 |
+|[recalign/RecAlign](https://github.com/recalign/RecAlign) | 218 |
+|[jupyterlab/jupyter-ai](https://github.com/jupyterlab/jupyter-ai) | 218 |
+|[alvarosevilla95/autolang](https://github.com/alvarosevilla95/autolang) | 215 |
+|[conceptofmind/toolformer](https://github.com/conceptofmind/toolformer) | 213 |
+|[MagnivOrg/prompt-layer-library](https://github.com/MagnivOrg/prompt-layer-library) | 209 |
+|[JohnSnowLabs/nlptest](https://github.com/JohnSnowLabs/nlptest) | 208 |
+|[airobotlab/KoChatGPT](https://github.com/airobotlab/KoChatGPT) | 197 |
+|[langchain-ai/auto-evaluator](https://github.com/langchain-ai/auto-evaluator) | 195 |
+|[yvann-hub/Robby-chatbot](https://github.com/yvann-hub/Robby-chatbot) | 195 |
+|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 192 |
+|[daveebbelaar/langchain-experiments](https://github.com/daveebbelaar/langchain-experiments) | 189 |
+|[NimbleBoxAI/ChainFury](https://github.com/NimbleBoxAI/ChainFury) | 187 |
+|[kaleido-lab/dolphin](https://github.com/kaleido-lab/dolphin) | 184 |
+|[Anil-matcha/Website-to-Chatbot](https://github.com/Anil-matcha/Website-to-Chatbot) | 183 |
+|[plchld/InsightFlow](https://github.com/plchld/InsightFlow) | 180 |
+|[OpenBMB/AgentVerse](https://github.com/OpenBMB/AgentVerse) | 166 |
+|[benthecoder/ClassGPT](https://github.com/benthecoder/ClassGPT) | 166 |
+|[jbrukh/gpt-jargon](https://github.com/jbrukh/gpt-jargon) | 161 |
+|[hardbyte/qabot](https://github.com/hardbyte/qabot) | 160 |
+|[shaman-ai/agent-actors](https://github.com/shaman-ai/agent-actors) | 153 |
+|[radi-cho/datasetGPT](https://github.com/radi-cho/datasetGPT) | 153 |
+|[poe-platform/poe-protocol](https://github.com/poe-platform/poe-protocol) | 152 |
+|[paolorechia/learn-langchain](https://github.com/paolorechia/learn-langchain) | 149 |
+|[ajndkr/lanarky](https://github.com/ajndkr/lanarky) | 149 |
+|[fengyuli-dev/multimedia-gpt](https://github.com/fengyuli-dev/multimedia-gpt) | 147 |
+|[yasyf/compress-gpt](https://github.com/yasyf/compress-gpt) | 144 |
+|[homanp/superagent](https://github.com/homanp/superagent) | 143 |
+|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 141 |
+|[ethanyanjiali/minChatGPT](https://github.com/ethanyanjiali/minChatGPT) | 141 |
+|[ccurme/yolopandas](https://github.com/ccurme/yolopandas) | 139 |
+|[hwchase17/langchain-streamlit-template](https://github.com/hwchase17/langchain-streamlit-template) | 138 |
+|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 136 |
+|[hirokidaichi/wanna](https://github.com/hirokidaichi/wanna) | 135 |
+|[Haste171/langchain-chatbot](https://github.com/Haste171/langchain-chatbot) | 134 |
+|[jmpaz/promptlib](https://github.com/jmpaz/promptlib) | 130 |
+|[Klingefjord/chatgpt-telegram](https://github.com/Klingefjord/chatgpt-telegram) | 130 |
+|[filip-michalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) | 128 |
+|[handrew/browserpilot](https://github.com/handrew/browserpilot) | 128 |
+|[shauryr/S2QA](https://github.com/shauryr/S2QA) | 127 |
+|[steamship-core/vercel-examples](https://github.com/steamship-core/vercel-examples) | 127 |
+|[yasyf/summ](https://github.com/yasyf/summ) | 127 |
+|[gia-guar/JARVIS-ChatGPT](https://github.com/gia-guar/JARVIS-ChatGPT) | 126 |
+|[jerlendds/osintbuddy](https://github.com/jerlendds/osintbuddy) | 125 |
+|[ibiscp/LLM-IMDB](https://github.com/ibiscp/LLM-IMDB) | 124 |
+|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 124 |
+|[hwchase17/chroma-langchain](https://github.com/hwchase17/chroma-langchain) | 124 |
+|[menloparklab/langchain-cohere-qdrant-doc-retrieval](https://github.com/menloparklab/langchain-cohere-qdrant-doc-retrieval) | 123 |
+|[peterw/StoryStorm](https://github.com/peterw/StoryStorm) | 123 |
+|[chakkaradeep/pyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI) | 123 |
+|[petehunt/langchain-github-bot](https://github.com/petehunt/langchain-github-bot) | 115 |
+|[su77ungr/CASALIOY](https://github.com/su77ungr/CASALIOY) | 113 |
+|[eunomia-bpf/GPTtrace](https://github.com/eunomia-bpf/GPTtrace) | 113 |
+|[zenml-io/zenml-projects](https://github.com/zenml-io/zenml-projects) | 112 |
+|[pablomarin/GPT-Azure-Search-Engine](https://github.com/pablomarin/GPT-Azure-Search-Engine) | 111 |
+|[shamspias/customizable-gpt-chatbot](https://github.com/shamspias/customizable-gpt-chatbot) | 109 |
+|[WongSaang/chatgpt-ui-server](https://github.com/WongSaang/chatgpt-ui-server) | 108 |
+|[davila7/file-gpt](https://github.com/davila7/file-gpt) | 104 |
+|[enhancedocs/enhancedocs](https://github.com/enhancedocs/enhancedocs) | 102 |
+|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 101 |
 
 
 

docs/extras/ecosystem/integrations/airtable.md

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

docs/extras/ecosystem/integrations/arthur_tracking.ipynb

@@ -1,199 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Arthur"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Arthur](https://arthur.ai) is a model monitoring and observability platform.\n",
-    "\n",
-    "The following guide shows how to run a registered chat LLM with the Arthur callback handler to automatically log model inferences to Arthur.\n",
-    "\n",
-    "If you do not have a model currently onboarded to Arthur, visit our [onboarding guide for generative text models](https://docs.arthur.ai/user-guide/walkthroughs/model-onboarding/generative_text_onboarding.html). For more information about how to use the Arthur SDK, visit our [docs](https://docs.arthur.ai/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "y8ku6X96sebl"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks import ArthurCallbackHandler\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema import HumanMessage"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Place Arthur credentials here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "id": "Me3prhqjsoqz"
-   },
-   "outputs": [],
-   "source": [
-    "arthur_url = \"https://app.arthur.ai\"\n",
-    "arthur_login = \"your-arthur-login-username-here\"\n",
-    "arthur_model_id = \"your-arthur-model-id-here\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create Langchain LLM with Arthur callback handler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "id": "9Hq9snQasynA"
-   },
-   "outputs": [],
-   "source": [
-    "def make_langchain_chat_llm(chat_model=):\n",
-    "    return ChatOpenAI(\n",
-    "        streaming=True,\n",
-    "        temperature=0.1,\n",
-    "        callbacks=[\n",
-    "            StreamingStdOutCallbackHandler(),\n",
-    "            ArthurCallbackHandler.from_credentials(\n",
-    "                arthur_model_id, \n",
-    "                arthur_url=arthur_url, \n",
-    "                arthur_login=arthur_login)\n",
-    "        ])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Please enter password for admin: ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "chatgpt = make_langchain_chat_llm()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "aXRyj50Ls8eP"
-   },
-   "source": [
-    "Running the chat LLM with this `run` function will save the chat history in an ongoing list so that the conversation can reference earlier messages and log each response to the Arthur platform. You can view the history of this model's inferences on your [model dashboard page](https://app.arthur.ai/).\n",
-    "\n",
-    "Enter `q` to quit the run loop"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "4taWSbN-s31Y"
-   },
-   "outputs": [],
-   "source": [
-    "def run(llm):\n",
-    "    history = []\n",
-    "    while True:\n",
-    "        user_input = input(\"\\n>>> input >>>\\n>>>: \")\n",
-    "        if user_input == \"q\":\n",
-    "            break\n",
-    "        history.append(HumanMessage(content=user_input))\n",
-    "        history.append(llm(history))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "id": "MEx8nWJps-EG"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      ">>> input >>>\n",
-      ">>>: What is a callback handler?\n",
-      "A callback handler, also known as a callback function or callback method, is a piece of code that is executed in response to a specific event or condition. It is commonly used in programming languages that support event-driven or asynchronous programming paradigms.\n",
-      "\n",
-      "The purpose of a callback handler is to provide a way for developers to define custom behavior that should be executed when a certain event occurs. Instead of waiting for a result or blocking the execution, the program registers a callback function and continues with other tasks. When the event is triggered, the callback function is invoked, allowing the program to respond accordingly.\n",
-      "\n",
-      "Callback handlers are commonly used in various scenarios, such as handling user input, responding to network requests, processing asynchronous operations, and implementing event-driven architectures. They provide a flexible and modular way to handle events and decouple different components of a system.\n",
-      ">>> input >>>\n",
-      ">>>: What do I need to do to get the full benefits of this\n",
-      "To get the full benefits of using a callback handler, you should consider the following:\n",
-      "\n",
-      "1. Understand the event or condition: Identify the specific event or condition that you want to respond to with a callback handler. This could be user input, network requests, or any other asynchronous operation.\n",
-      "\n",
-      "2. Define the callback function: Create a function that will be executed when the event or condition occurs. This function should contain the desired behavior or actions you want to take in response to the event.\n",
-      "\n",
-      "3. Register the callback function: Depending on the programming language or framework you are using, you may need to register or attach the callback function to the appropriate event or condition. This ensures that the callback function is invoked when the event occurs.\n",
-      "\n",
-      "4. Handle the callback: Implement the necessary logic within the callback function to handle the event or condition. This could involve updating the user interface, processing data, making further requests, or triggering other actions.\n",
-      "\n",
-      "5. Consider error handling: It's important to handle any potential errors or exceptions that may occur within the callback function. This ensures that your program can gracefully handle unexpected situations and prevent crashes or undesired behavior.\n",
-      "\n",
-      "6. Maintain code readability and modularity: As your codebase grows, it's crucial to keep your callback handlers organized and maintainable. Consider using design patterns or architectural principles to structure your code in a modular and scalable way.\n",
-      "\n",
-      "By following these steps, you can leverage the benefits of callback handlers, such as asynchronous and event-driven programming, improved responsiveness, and modular code design.\n",
-      ">>> input >>>\n",
-      ">>>: q\n"
-     ]
-    }
-   ],
-   "source": [
-    "run(chatgpt)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

docs/extras/ecosystem/integrations/brave_search.mdx

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

docs/extras/ecosystem/integrations/cassandra.mdx

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

docs/extras/ecosystem/integrations/clarifai.mdx

@@ -1,52 +0,0 @@
-# Clarifai
-
->[Clarifai](https://clarifai.com) is one of first deep learning platforms having been founded in 2013. Clarifai provides an AI platform with the full AI lifecycle for data exploration, data labeling, model training, evaluation and inference around images, video, text and audio data. In the LangChain ecosystem, as far as we're aware, Clarifai is the only provider that supports LLMs, embeddings and a vector store in one production scale platform, making it an excellent choice to operationalize your LangChain implementations.
-
-## Installation and Setup
-- Install the Python SDK:
-```bash
-pip install clarifai
-```
-[Sign-up](https://clarifai.com/signup) for a Clarifai account, then get a personal access token to access the Clarifai API from your [security settings](https://clarifai.com/settings/security) and set it as an environment variable (`CLARIFAI_PAT`).
-
-
-## Models
-
-Clarifai provides 1,000s of AI models for many different use cases. You can [explore them here](https://clarifai.com/explore) to find the one most suited for your use case. These models include those created by other providers such as OpenAI, Anthropic, Cohere, AI21, etc. as well as state of the art from open source such as Falcon, InstructorXL, etc. so that you build the best in AI into your products. You'll find these organized by the creator's user_id and into projects we call applications denoted by their app_id. Those IDs will be needed in additional to the model_id and optionally the version_id, so make note of all these IDs once you found the best model for your use case!
-
-Also note that given there are many models for images, video, text and audio understanding, you can build some interested AI agents that utilize the variety of AI models as experts to understand those data types.
-
-### LLMs
-
-To find the selection of LLMs in the Clarifai platform you can select the text to text model type [here](https://clarifai.com/explore/models?filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-to-text%22%5D%7D%5D&page=1&perPage=24).
-
-```python
-from langchain.llms import Clarifai
-llm = Clarifai(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)
-```
-
-For more details, the docs on the Clarifai LLM wrapper provide a [detailed walkthrough](/docs/modules/model_io/models/llms/integrations/clarifai.html).
-
-
-### Text Embedding Models
-
-To find the selection of text embeddings models in the Clarifai platform you can select the text to embedding model type [here](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-embedder%22%5D%7D%5D).
-
-There is a Clarifai Embedding model in LangChain, which you can access with:
-```python
-from langchain.embeddings import ClarifaiEmbeddings
-embeddings = ClarifaiEmbeddings(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)
-```
-For more details, the docs on the Clarifai Embeddings wrapper provide a [detailed walthrough](/docs/modules/data_connection/text_embedding/integrations/clarifai.html).
-
-## Vectorstore
-
-Clarifai's vector DB was launched in 2016 and has been optimized to support live search queries. With workflows in the Clarifai platform, you data is automatically indexed by am embedding model and optionally other models as well to index that information in the DB for search. You can query the DB not only via the vectors but also filter by metadata matches, other AI predicted concepts, and even do geo-coordinate search. Simply create an application, select the appropriate base workflow for your type of data, and upload it (through the API as [documented here](https://docs.clarifai.com/api-guide/data/create-get-update-delete) or the UIs at clarifai.com).
-
-You an also add data directly from LangChain as well, and the auto-indexing will take place for you. You'll notice this is a little different than other vectorstores where you need to provde an embedding model in their constructor and have LangChain coordinate getting the embeddings from text and writing those to the index. Not only is it more convenient, but it's much more scalable to use Clarifai's distributed cloud to do all the index in the background.
-
-```python
-from langchain.vectorstores import Clarifai
-clarifai_vector_db = Clarifai.from_texts(user_id=USER_ID, app_id=APP_ID, texts=texts, pat=CLARIFAI_PAT, number_of_docs=NUMBER_OF_DOCS, metadatas = metadatas)
-```
-For more details, the docs on the Clarifai vector store provide a [detailed walthrough](/docs/modules/data_connection/text_embedding/integrations/clarifai.html).

docs/extras/ecosystem/integrations/cnosdb.mdx

@@ -1,110 +0,0 @@
-# CnosDB
-> [CnosDB](https://github.com/cnosdb/cnosdb) is an open source distributed time series database with high performance, high compression rate and high ease of use.
-
-## Installation and Setup
-
-```python
-pip install cnos-connector
-```
-
-## Connecting to CnosDB
-You can connect to CnosDB using the `SQLDatabase.from_cnosdb()` method.
-### Syntax
-```python
-def SQLDatabase.from_cnosdb(url: str = "127.0.0.1:8902",
-                              user: str = "root",
-                              password: str = "",
-                              tenant: str = "cnosdb",
-                              database: str = "public")
-```
-Args:
-1. url (str): The HTTP connection host name and port number of the CnosDB
-                service, excluding "http://" or "https://", with a default value
-                of "127.0.0.1:8902".
-2. user (str): The username used to connect to the CnosDB service, with a
-                default value of "root".
-3. password (str): The password of the user connecting to the CnosDB service,
-                with a default value of "".
-4. tenant (str): The name of the tenant used to connect to the CnosDB service,
-                with a default value of "cnosdb".
-5. database (str): The name of the database in the CnosDB tenant.
-## Examples
-```python
-# Connecting to CnosDB with SQLDatabase Wrapper
-from langchain import SQLDatabase
-
-db = SQLDatabase.from_cnosdb()
-```
-```python
-# Creating a OpenAI Chat LLM Wrapper
-from langchain.chat_models import ChatOpenAI
-
-llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
-```
-
-### SQL Database Chain
-This example demonstrates the use of the SQL Chain for answering a question over a CnosDB.
-```python
-from langchain import SQLDatabaseChain
-
-db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
-
-db_chain.run(
-    "What is the average temperature of air at station XiaoMaiDao between October 19, 2022 and Occtober 20, 2022?"
-)
-```
-```shell
-> Entering new  chain...
-What is the average temperature of air at station XiaoMaiDao between October 19, 2022 and Occtober 20, 2022?
-SQLQuery:SELECT AVG(temperature) FROM air WHERE station = 'XiaoMaiDao' AND time >= '2022-10-19' AND time < '2022-10-20'
-SQLResult: [(68.0,)]
-Answer:The average temperature of air at station XiaoMaiDao between October 19, 2022 and October 20, 2022 is 68.0.
-> Finished chain.
-```
-### SQL Database Agent
-This example demonstrates the use of the SQL Database Agent for answering questions over a CnosDB.
-```python
-from langchain.agents import create_sql_agent
-from langchain.agents.agent_toolkits import SQLDatabaseToolkit
-
-toolkit = SQLDatabaseToolkit(db=db, llm=llm)
-agent = create_sql_agent(llm=llm, toolkit=toolkit, verbose=True)
-```
-```python
-agent.run(
-    "What is the average temperature of air at station XiaoMaiDao between October 19, 2022 and Occtober 20, 2022?"
-)
-```
-```shell
-> Entering new  chain...
-Action: sql_db_list_tables
-Action Input: ""
-Observation: air
-Thought:The "air" table seems relevant to the question. I should query the schema of the "air" table to see what columns are available.
-Action: sql_db_schema
-Action Input: "air"
-Observation:
-CREATE TABLE air (
-	pressure FLOAT,
-	station STRING,
-	temperature FLOAT,
-	time TIMESTAMP,
-	visibility FLOAT
-)
-
-/*
-3 rows from air table:
-pressure	station	temperature	time	visibility
-75.0	XiaoMaiDao	67.0	2022-10-19T03:40:00	54.0
-77.0	XiaoMaiDao	69.0	2022-10-19T04:40:00	56.0
-76.0	XiaoMaiDao	68.0	2022-10-19T05:40:00	55.0
-*/
-Thought:The "temperature" column in the "air" table is relevant to the question. I can query the average temperature between the specified dates.
-Action: sql_db_query
-Action Input: "SELECT AVG(temperature) FROM air WHERE station = 'XiaoMaiDao' AND time >= '2022-10-19' AND time <= '2022-10-20'"
-Observation: [(68.0,)]
-Thought:The average temperature of air at station XiaoMaiDao between October 19, 2022 and October 20, 2022 is 68.0.
-Final Answer: 68.0
-
-> Finished chain.
-```

docs/extras/ecosystem/integrations/databerry.mdx

@@ -1,17 +1,17 @@
-# Chaindesk
+# Databerry
 
->[Chaindesk](https://chaindesk.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
+>[Databerry](https://databerry.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
 
 
 ## Installation and Setup
 
-We need to sign up for Chaindesk, create a datastore, add some data and get your datastore api endpoint url. 
-We need the [API Key](https://docs.chaindesk.ai/api-reference/authentication).
+We need to sign up for Databerry, create a datastore, add some data and get your datastore api endpoint url. 
+We need the [API Key](https://docs.databerry.ai/api-reference/authentication).
 
 ## Retriever
 
-See a [usage example](/docs/modules/data_connection/retrievers/integrations/chaindesk.html).
+See a [usage example](/docs/modules/data_connection/retrievers/integrations/databerry.html).
 
 ```python
-from langchain.retrievers import ChaindeskRetriever
+from langchain.retrievers import DataberryRetriever
 ```

docs/extras/ecosystem/integrations/datadog_logs.mdx

@@ -1,19 +0,0 @@
-# Datadog Logs
-
->[Datadog](https://www.datadoghq.com/) is a monitoring and analytics platform for cloud-scale applications.
-
-## Installation and Setup
-
-```bash
-pip install datadog_api_client
-```
-
-We must initialize the loader with the Datadog API key and APP key, and we need to set up the query to extract the desired logs.
-
-## Document Loader
-
-See a [usage example](/docs/modules/data_connection/document_loaders/integrations/datadog_logs.html).
-
-```python
-from langchain.document_loaders import DatadogLogsLoader
-```

docs/extras/ecosystem/integrations/dataforseo.mdx

@@ -1,51 +0,0 @@
-# DataForSEO
-
-This page provides instructions on how to use the DataForSEO search APIs within LangChain.
-
-## Installation and Setup
-
-- Get a DataForSEO API Access login and password, and set them as environment variables (`DATAFORSEO_LOGIN` and `DATAFORSEO_PASSWORD` respectively). You can find it in your dashboard.
-
-## Wrappers
-
-### Utility
-
-The DataForSEO utility wraps the API. To import this utility, use:
-
-```python
-from langchain.utilities import DataForSeoAPIWrapper
-```
-
-For a detailed walkthrough of this wrapper, see [this notebook](/docs/modules/agents/tools/integrations/dataforseo.ipynb).
-
-### Tool
-
-You can also load this wrapper as a Tool to use with an Agent:
-
-```python
-from langchain.agents import load_tools
-tools = load_tools(["dataforseo-api-search"])
-```
-
-## Example usage
-
-```python
-dataforseo = DataForSeoAPIWrapper(api_login="your_login", api_password="your_password")
-result = dataforseo.run("Bill Gates")
-print(result)
-```
-
-## Environment Variables
-
-You can store your DataForSEO API Access login and password as environment variables. The wrapper will automatically check for these environment variables if no values are provided:
-
-```python
-import os
-
-os.environ["DATAFORSEO_LOGIN"] = "your_login"
-os.environ["DATAFORSEO_PASSWORD"] = "your_password"
-
-dataforseo = DataForSeoAPIWrapper()
-result = dataforseo.run("weather in Los Angeles")
-print(result)
-```

docs/extras/ecosystem/integrations/flyte.mdx

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

docs/extras/ecosystem/integrations/grobid.mdx

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

docs/extras/ecosystem/integrations/jina.mdx

@@ -16,59 +16,3 @@ There exists a Jina Embeddings wrapper, which you can access with
 from langchain.embeddings import JinaEmbeddings
 ```
 For a more detailed walkthrough of this, see [this notebook](/docs/modules/data_connection/text_embedding/integrations/jina.html)
-
-## Deployment
-
-[Langchain-serve](https://github.com/jina-ai/langchain-serve), powered by Jina, helps take LangChain apps to production with easy to use REST/WebSocket APIs and Slack bots. 
-
-### Usage
-
-Install the package from PyPI. 
-
-```bash
-pip install langchain-serve
-```
-
-Wrap your LangChain app with the `@serving` decorator. 
-
-```python
-# app.py
-from lcserve import serving
-
-@serving
-def ask(input: str) -> str:
-    from langchain import LLMChain, OpenAI
-    from langchain.agents import AgentExecutor, ZeroShotAgent
-    
-    tools = [...] # list of tools
-    prompt = ZeroShotAgent.create_prompt(
-        tools, input_variables=["input", "agent_scratchpad"],
-    )
-    llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
-    agent = ZeroShotAgent(
-        llm_chain=llm_chain, allowed_tools=[tool.name for tool in tools]
-    )
-    agent_executor = AgentExecutor.from_agent_and_tools(
-        agent=agent, 
-        tools=tools, 
-        verbose=True,
-    )
-    return agent_executor.run(input)
-```
-
-Deploy on Jina AI Cloud with `lc-serve deploy jcloud app`. Once deployed, we can send a POST request to the API endpoint to get a response.
-
-```bash
-curl -X 'POST' 'https://<your-app>.wolf.jina.ai/ask' \
- -d '{
-  "input": "Your Quesion here?",
-  "envs": {
-     "OPENAI_API_KEY": "sk-***"
-  }
-}'
-```
-
-You can also self-host the app on your infrastructure with Docker-compose or Kubernetes. See [here](https://github.com/jina-ai/langchain-serve#-self-host-llm-apps-with-docker-compose-or-kubernetes) for more details.
-
-
-Langchain-serve also allows to deploy the apps with WebSocket APIs and Slack Bots both on [Jina AI Cloud](https://cloud.jina.ai/) or self-hosted infrastructure. 

docs/extras/ecosystem/integrations/marqo.md

@@ -1,31 +0,0 @@
-# Marqo
-
-This page covers how to use the Marqo ecosystem within LangChain.
-
-### **What is Marqo?**
-
-Marqo is a tensor search engine that uses embeddings stored in in-memory HNSW indexes to achieve cutting edge search speeds. Marqo can scale to hundred-million document indexes with horizontal index sharding and allows for async and non-blocking data upload and search. Marqo uses the latest machine learning models from PyTorch, Huggingface, OpenAI and more. You can start with a pre-configured model or bring your own. The built in ONNX support and conversion allows for faster inference and higher throughput on both CPU and GPU.
-
-Because Marqo include its own inference your documents can have a mix of text and images, you can bring Marqo indexes with data from your other systems into the langchain ecosystem without having to worry about your embeddings being compatible. 
-
-Deployment of Marqo is flexible, you can get started yourself with our docker image or [contact us about our managed cloud offering!](https://www.marqo.ai/pricing)
-
-To run Marqo locally with our docker image, [see our getting started.](https://docs.marqo.ai/latest/)
-
-## Installation and Setup
-- Install the Python SDK with `pip install marqo`
-
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Marqo indexes, allowing you to use them within the vectorstore framework. Marqo lets you select from a range of models for generating embeddings and exposes some preprocessing configurations.
-
-The Marqo vectorstore can also work with existing multimodel indexes where your documents have a mix of images and text, for more information refer to [our documentation](https://docs.marqo.ai/latest/#multi-modal-and-cross-modal-search). Note that instaniating the Marqo vectorstore with an existing multimodal index will disable the ability to add any new documents to it via the langchain vectorstore `add_texts` method.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import Marqo
-```
-
-For a more detailed walkthrough of the Marqo wrapper and some of its unique features, see [this notebook](/docs/modules/data_connection/vectorstores/integrations/marqo.html)

docs/extras/ecosystem/integrations/trulens.mdx

@@ -1,56 +0,0 @@
-# TruLens
-
-This page covers how to use [TruLens](https://trulens.org) to evaluate and track LLM apps built on langchain.
-
-## What is TruLens?
-
-TruLens is an [opensource](https://github.com/truera/trulens) package that provides instrumentation and evaluation tools for large language model (LLM) based applications.
-
-## Quick start
-
-Once you've created your LLM chain, you can use TruLens for evaluation and tracking. TruLens has a number of [out-of-the-box Feedback Functions](https://www.trulens.org/trulens_eval/feedback_functions/), and is also an extensible framework for LLM evaluation.
-
-```python
-# create a feedback function
-
-from trulens_eval.feedback import Feedback, Huggingface, OpenAI
-# Initialize HuggingFace-based feedback function collection class:
-hugs = Huggingface()
-openai = OpenAI()
-
-# Define a language match feedback function using HuggingFace.
-lang_match = Feedback(hugs.language_match).on_input_output()
-# By default this will check language match on the main app input and main app
-# output.
-
-# Question/answer relevance between overall question and answer.
-qa_relevance = Feedback(openai.relevance).on_input_output()
-# By default this will evaluate feedback on main app input and main app output.
-
-# Toxicity of input
-toxicity = Feedback(openai.toxicity).on_input()
-
-```
-
-After you've set up Feedback Function(s) for evaluating your LLM, you can wrap your application with TruChain to get detailed tracing, logging and evaluation of your LLM app.
-
-```python
-# wrap your chain with TruChain
-truchain = TruChain(
-    chain,
-    app_id='Chain1_ChatApplication',
-    feedbacks=[lang_match, qa_relevance, toxicity]
-)
-# Note: any `feedbacks` specified here will be evaluated and logged whenever the chain is used.
-truchain("que hora es?")
-```
-
-Now you can explore your LLM-based application!
-
-Doing so will help you understand how your LLM application is performing at a glance. As you iterate new versions of your LLM application, you can compare their performance across all of the different quality metrics you've set up. You'll also be able to view evaluations at a record level, and explore the chain metadata for each record.
-
-```python
-tru.run_dashboard() # open a Streamlit app to explore
-```
-
-For more information on TruLens, visit [trulens.org](https://www.trulens.org/)

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

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

docs/extras/ecosystem/integrations/youtube.mdx

@@ -1,6 +1,6 @@
 # YouTube
 
->[YouTube](https://www.youtube.com/) is an online video sharing and social media platform by Google.
+>[YouTube](https://www.youtube.com/) is an online video sharing and social media platform created by Google.
 > We download the `YouTube` transcripts and video information.
 
 ## Installation and Setup

docs/extras/guides/deployments/index.mdx

@@ -24,7 +24,6 @@ Understanding these components is crucial when assessing serving systems. LangCh
 - [BentoML](https://github.com/bentoml/BentoML)
 - [OpenLLM](/docs/ecosystem/integrations/openllm.html)
 - [Modal](/docs/ecosystem/integrations/modal.html)
-- [Jina](/docs/ecosystem/integrations/jina.html#deployment)
 
 These links will provide further information on each ecosystem, assisting you in finding the best fit for your LLM deployment needs.
 

docs/extras/guides/deployments/template_repos.mdx

@@ -51,10 +51,6 @@ A minimal example of how to deploy LangChain to [Fly.io](https://fly.io/) using
 
 A minimal example on how to deploy LangChain to DigitalOcean App Platform.
 
-## [CI/CD Google Cloud Build + Dockerfile + Serverless Google Cloud Run](https://github.com/g-emarco/github-assistant)
-
-Boilerplate LangChain project on how to deploy to Google Cloud Run using Docker with Cloud Build CI/CD pipeline
-
 ## [Google Cloud Run](https://github.com/homanp/gcp-langchain)
 
 A minimal example on how to deploy LangChain to Google Cloud Run.
@@ -65,7 +61,7 @@ This repository contains LangChain adapters for Steamship, enabling LangChain de
 
 ## [Langchain-serve](https://github.com/jina-ai/langchain-serve)
 
-This repository allows users to deploy any LangChain app as REST/WebSocket APIs or, as Slack Bots with ease. Benefit from the scalability and serverless architecture of Jina AI Cloud, or deploy on-premise with Kubernetes.
+This repository allows users to serve local chains and agents as RESTful, gRPC, or WebSocket APIs, thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
 
 ## [BentoML](https://github.com/ssheng/BentoChain)
 

docs/extras/guides/evaluation/comparisons.ipynb

@@ -117,11 +117,11 @@
     "\n",
     "\n",
     "# Initialize the language model\n",
-    "# You can add your own OpenAI API key by adding openai_api_key=\"<your_api_key>\"\n",
+    "# You can add your own OpenAI API key by adding openai_api_key=\"<your_api_key>\" \n",
     "llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")\n",
     "\n",
     "# Initialize the SerpAPIWrapper for search functionality\n",
-    "# Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual SerpAPI key.\n",
+    "#Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual SerpAPI key.\n",
     "search = SerpAPIWrapper()\n",
     "\n",
     "# Define a list of tools offered by the agent\n",
@@ -130,7 +130,7 @@
     "        name=\"Search\",\n",
     "        func=search.run,\n",
     "        coroutine=search.arun,\n",
-    "        description=\"Useful when you need to answer questions about current events. You should ask targeted questions.\",\n",
+    "        description=\"Useful when you need to answer questions about current events. You should ask targeted questions.\"\n",
     "    ),\n",
     "]"
    ]
@@ -143,12 +143,8 @@
    },
    "outputs": [],
    "source": [
-    "functions_agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.OPENAI_MULTI_FUNCTIONS, verbose=False\n",
-    ")\n",
-    "conversations_agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=False\n",
-    ")"
+    "functions_agent = initialize_agent(tools, llm, agent=AgentType.OPENAI_MULTI_FUNCTIONS, verbose=False)\n",
+    "conversations_agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=False)"
    ]
   },
   {
@@ -197,20 +193,20 @@
     "\n",
     "results = []\n",
     "agents = [functions_agent, conversations_agent]\n",
-    "concurrency_level = 6  # How many concurrent agents to run. May need to decrease if OpenAI is rate limiting.\n",
+    "concurrency_level = 6 # How many concurrent agents to run. May need to decrease if OpenAI is rate limiting.\n",
     "\n",
     "# We will only run the first 20 examples of this dataset to speed things up\n",
     "# This will lead to larger confidence intervals downstream.\n",
     "batch = []\n",
     "for example in tqdm(dataset[:20]):\n",
-    "    batch.extend([agent.acall(example[\"inputs\"]) for agent in agents])\n",
+    "    batch.extend([agent.acall(example['inputs']) for agent in agents])\n",
     "    if len(batch) >= concurrency_level:\n",
     "        batch_results = await asyncio.gather(*batch, return_exceptions=True)\n",
-    "        results.extend(list(zip(*[iter(batch_results)] * 2)))\n",
+    "        results.extend(list(zip(*[iter(batch_results)]*2)))\n",
     "        batch = []\n",
     "if batch:\n",
     "    batch_results = await asyncio.gather(*batch, return_exceptions=True)\n",
-    "    results.extend(list(zip(*[iter(batch_results)] * 2)))"
+    "    results.extend(list(zip(*[iter(batch_results)]*2)))"
    ]
   },
   {
@@ -234,12 +230,11 @@
    "source": [
     "import random\n",
     "\n",
-    "\n",
     "def predict_preferences(dataset, results) -> list:\n",
     "    preferences = []\n",
     "\n",
     "    for example, (res_a, res_b) in zip(dataset, results):\n",
-    "        input_ = example[\"inputs\"]\n",
+    "        input_ = example['inputs']\n",
     "        # Flip a coin to reduce persistent position bias\n",
     "        if random.random() < 0.5:\n",
     "            pred_a, pred_b = res_a, res_b\n",
@@ -248,16 +243,16 @@
     "            pred_a, pred_b = res_b, res_a\n",
     "            a, b = \"b\", \"a\"\n",
     "        eval_res = eval_chain.evaluate_string_pairs(\n",
-    "            prediction=pred_a[\"output\"] if isinstance(pred_a, dict) else str(pred_a),\n",
-    "            prediction_b=pred_b[\"output\"] if isinstance(pred_b, dict) else str(pred_b),\n",
-    "            input=input_,\n",
+    "            output_a=pred_a['output'] if isinstance(pred_a, dict) else str(pred_a),\n",
+    "            output_b=pred_b['output'] if isinstance(pred_b, dict) else str(pred_b),\n",
+    "            input=input_\n",
     "        )\n",
     "        if eval_res[\"value\"] == \"A\":\n",
     "            preferences.append(a)\n",
     "        elif eval_res[\"value\"] == \"B\":\n",
     "            preferences.append(b)\n",
     "        else:\n",
-    "            preferences.append(None)  # No preference\n",
+    "            preferences.append(None) # No preference\n",
     "    return preferences"
    ]
   },
@@ -303,7 +298,10 @@
     "    \"b\": \"Structured Chat Agent\",\n",
     "}\n",
     "counts = Counter(preferences)\n",
-    "pref_ratios = {k: v / len(preferences) for k, v in counts.items()}\n",
+    "pref_ratios = {\n",
+    "    k: v/len(preferences) for k, v in\n",
+    "    counts.items()\n",
+    "}\n",
     "for k, v in pref_ratios.items():\n",
     "    print(f\"{name_map.get(k)}: {v:.2%}\")"
    ]
@@ -329,16 +327,13 @@
    "source": [
     "from math import sqrt\n",
     "\n",
-    "\n",
-    "def wilson_score_interval(\n",
-    "    preferences: list, which: str = \"a\", z: float = 1.96\n",
-    ") -> tuple:\n",
+    "def wilson_score_interval(preferences: list, which: str = \"a\", z: float = 1.96) -> tuple:\n",
     "    \"\"\"Estimate the confidence interval using the Wilson score.\n",
-    "\n",
+    "    \n",
     "    See: https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval\n",
     "    for more details, including when to use it and when it should not be used.\n",
     "    \"\"\"\n",
-    "    total_preferences = preferences.count(\"a\") + preferences.count(\"b\")\n",
+    "    total_preferences = preferences.count('a') + preferences.count('b')\n",
     "    n_s = preferences.count(which)\n",
     "\n",
     "    if total_preferences == 0:\n",
@@ -347,11 +342,8 @@
     "    p_hat = n_s / total_preferences\n",
     "\n",
     "    denominator = 1 + (z**2) / total_preferences\n",
-    "    adjustment = (z / denominator) * sqrt(\n",
-    "        p_hat * (1 - p_hat) / total_preferences\n",
-    "        + (z**2) / (4 * total_preferences * total_preferences)\n",
-    "    )\n",
-    "    center = (p_hat + (z**2) / (2 * total_preferences)) / denominator\n",
+    "    adjustment = (z / denominator) * sqrt(p_hat*(1-p_hat)/total_preferences + (z**2)/(4*total_preferences*total_preferences))\n",
+    "    center = (p_hat + (z**2) / (2*total_preferences)) / denominator\n",
     "    lower_bound = min(max(center - adjustment, 0.0), 1.0)\n",
     "    upper_bound = min(max(center + adjustment, 0.0), 1.0)\n",
     "\n",
@@ -377,9 +369,7 @@
    "source": [
     "for which_, name in name_map.items():\n",
     "    low, high = wilson_score_interval(preferences, which=which_)\n",
-    "    print(\n",
-    "        f'The \"{name}\" would be preferred between {low:.2%} and {high:.2%} percent of the time (with 95% confidence).'\n",
-    "    )"
+    "    print(f'The \"{name}\" would be preferred between {low:.2%} and {high:.2%} percent of the time (with 95% confidence).')"
    ]
   },
   {
@@ -408,16 +398,13 @@
    ],
    "source": [
     "from scipy import stats\n",
-    "\n",
     "preferred_model = max(pref_ratios, key=pref_ratios.get)\n",
     "successes = preferences.count(preferred_model)\n",
     "n = len(preferences) - preferences.count(None)\n",
-    "p_value = stats.binom_test(successes, n, p=0.5, alternative=\"two-sided\")\n",
-    "print(\n",
-    "    f\"\"\"The p-value is {p_value:.5f}. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),\n",
+    "p_value = stats.binom_test(successes, n, p=0.5, alternative='two-sided')\n",
+    "print(f\"\"\"The p-value is {p_value:.5f}. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),\n",
     "then there is a {p_value:.5%} chance of observing the {name_map.get(preferred_model)} be preferred at least {successes}\n",
-    "times out of {n} trials.\"\"\"\n",
-    ")"
+    "times out of {n} trials.\"\"\")"
    ]
   },
   {

docs/extras/guides/evaluation/criteria_eval_chain.ipynb

@@ -54,7 +54,7 @@
    "outputs": [],
    "source": [
     "llm = ChatOpenAI(temperature=0)\n",
-    "query = \"What's the origin of the term synecdoche?\"\n",
+    "query=\"What's the origin of the term synecdoche?\"\n",
     "prediction = llm.predict(query)"
    ]
   },
@@ -151,22 +151,19 @@
     }
    ],
    "source": [
-    "eval_chain = CriteriaEvalChain.from_llm(\n",
-    "    llm=llm, criteria=\"correctness\", requires_reference=True\n",
-    ")\n",
+    "eval_chain = CriteriaEvalChain.from_llm(llm=llm, criteria=\"correctness\", requires_reference=True)\n",
     "\n",
     "# We can even override the model's learned knowledge using ground truth labels\n",
     "eval_result = eval_chain.evaluate_strings(\n",
     "    input=\"What is the capital of the US?\",\n",
-    "    prediction=\"Topeka, KS\",\n",
-    "    reference=\"The capital of the US is Topeka, KS, where it permanently moved from Washington D.C. on May 16, 2023\",\n",
-    ")\n",
+    "    prediction=\"Topeka, KS\", \n",
+    "    reference=\"The capital of the US is Topeka, KS, where it permanently moved from Washington D.C. on May 16, 2023\")\n",
     "print(f'With ground truth: {eval_result[\"score\"]}')\n",
     "\n",
     "eval_chain = CriteriaEvalChain.from_llm(llm=llm, criteria=\"correctness\")\n",
     "eval_result = eval_chain.evaluate_strings(\n",
     "    input=\"What is the capital of the US?\",\n",
-    "    prediction=\"Topeka, KS\",\n",
+    "    prediction=\"Topeka, KS\", \n",
     ")\n",
     "print(f'Withoutg ground truth: {eval_result[\"score\"]}')"
    ]
@@ -233,7 +230,9 @@
     }
    ],
    "source": [
-    "custom_criterion = {\"numeric\": \"Does the output contain numeric information?\"}\n",
+    "custom_criterion = {\n",
+    "    \"numeric\": \"Does the output contain numeric information?\"\n",
+    "}\n",
     "\n",
     "eval_chain = CriteriaEvalChain.from_llm(llm=llm, criteria=custom_criterion)\n",
     "eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)\n",
@@ -270,17 +269,11 @@
     "\n",
     "# Example that complies\n",
     "query = \"What's the population of lagos?\"\n",
-    "eval_result = eval_chain.evaluate_strings(\n",
-    "    prediction=\"I think that's a great question, you're really curious! About 30 million people live in Lagos, Nigeria, as of 2023.\",\n",
-    "    input=query,\n",
-    ")\n",
+    "eval_result = eval_chain.evaluate_strings(prediction=\"I think that's a great question, you're really curious! About 30 million people live in Lagos, Nigeria, as of 2023.\", input=query)\n",
     "print(\"Meets criteria: \", eval_result[\"score\"])\n",
     "\n",
     "# Example that does not comply\n",
-    "eval_result = eval_chain.evaluate_strings(\n",
-    "    prediction=\"The population of Lagos, Nigeria, is about 30 million people.\",\n",
-    "    input=query,\n",
-    ")\n",
+    "eval_result = eval_chain.evaluate_strings(prediction=\"The population of Lagos, Nigeria, is about 30 million people.\", input=query)\n",
     "print(\"Does not meet criteria: \", eval_result[\"score\"])"
    ]
   },
@@ -357,13 +350,8 @@
     }
    ],
    "source": [
-    "eval_chain = CriteriaEvalChain.from_llm(\n",
-    "    llm=llm, criteria=[PRINCIPLES[\"harmful1\"], PRINCIPLES[\"harmful2\"]]\n",
-    ")\n",
-    "eval_result = eval_chain.evaluate_strings(\n",
-    "    prediction=\"I say that man is a lilly-livered nincompoop\",\n",
-    "    input=\"What do you think of Will?\",\n",
-    ")\n",
+    "eval_chain = CriteriaEvalChain.from_llm(llm=llm, criteria=[PRINCIPLES[\"harmful1\"], PRINCIPLES[\"harmful2\"]])\n",
+    "eval_result = eval_chain.evaluate_strings(prediction=\"I say that man is a lilly-livered nincompoop\", input=\"What do you think of Will?\")\n",
     "eval_result"
    ]
   },

docs/extras/guides/evaluation/generic_agent_evaluation.ipynb

@@ -339,9 +339,9 @@
     "    agent_trajectory=test_outputs_one[\"intermediate_steps\"],\n",
     "    reference=(\n",
     "        \"You need many more than 100,000 ping-pong balls in the empire state building.\"\n",
-    "    ),\n",
+    "    )\n",
     ")\n",
-    "\n",
+    "    \n",
     "\n",
     "print(\"Score from 1 to 5: \", evaluation[\"score\"])\n",
     "print(\"Reasoning: \", evaluation[\"reasoning\"])"

docs/extras/guides/evaluation/openapi_eval.ipynb

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

docs/extras/guides/evaluation/qa_benchmarking_pg.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "Here we go over how to benchmark performance on a question answering task over a Paul Graham essay.\n",
     "\n",
-    "It is highly reccomended that you do any evaluation/benchmarking with tracing enabled. See [here](https://python.langchain.com/docs/modules/callbacks/how_to/tracing) for an explanation of what tracing is and how to set it up."
+    "It is highly reccomended that you do any evaluation/benchmarking with tracing enabled. See [here](https://langchain.readthedocs.io/en/latest/tracing.html) for an explanation of what tracing is and how to set it up."
    ]
   },
   {

docs/extras/modules/agents/agent_types/openai_multi_functions_agent.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "c0a83623",
    "metadata": {},
    "outputs": [],
@@ -38,20 +38,6 @@
     ">This initializes the SerpAPIWrapper for search functionality (search).\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a2b0a215",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\n",
-    "    \"SERPAPI_API_KEY\"\n",
-    "] = \"897780527132b5f31d8d73c40c820d5ef2c2279687efa69f413a61f752027747\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -60,11 +46,11 @@
    "outputs": [],
    "source": [
     "# Initialize the OpenAI language model\n",
-    "# Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual OpenAI key.\n",
+    "#Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual OpenAI key.\n",
     "llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")\n",
     "\n",
     "# Initialize the SerpAPIWrapper for search functionality\n",
-    "# Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual SerpAPI key.\n",
+    "#Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual SerpAPI key.\n",
     "search = SerpAPIWrapper()\n",
     "\n",
     "# Define a list of tools offered by the agent\n",
@@ -72,9 +58,9 @@
     "    Tool(\n",
     "        name=\"Search\",\n",
     "        func=search.run,\n",
-    "        description=\"Useful when you need to answer questions about current events. You should ask targeted questions.\",\n",
+    "        description=\"Useful when you need to answer questions about current events. You should ask targeted questions.\"\n",
     "    ),\n",
-    "]"
+    "]\n"
    ]
   },
   {
@@ -84,9 +70,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mrkl = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.OPENAI_MULTI_FUNCTIONS, verbose=True\n",
-    ")"
+    "mrkl = initialize_agent(tools, llm, agent=AgentType.OPENAI_MULTI_FUNCTIONS, verbose=True)"
    ]
   },
   {
@@ -98,7 +82,6 @@
    "source": [
     "# Do this so we can see exactly what's going on under the hood\n",
     "import langchain\n",
-    "\n",
     "langchain.debug = True"
    ]
   },
@@ -211,223 +194,15 @@
     }
    ],
    "source": [
-    "mrkl.run(\"What is the weather in LA and SF?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d31d4c09",
-   "metadata": {},
-   "source": [
-    "## Configuring max iteration behavior\n",
-    "\n",
-    "To make sure that our agent doesn't get stuck in excessively long loops, we can set max_iterations. We can also set an early stopping method, which will determine our agent's behavior once the number of max iterations is hit. By default, the early stopping uses method `force` which just returns that constant string. Alternatively, you could specify method `generate` which then does one FINAL pass through the LLM to generate an output."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "9f5f6743",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mrkl = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.OPENAI_FUNCTIONS,\n",
-    "    verbose=True,\n",
-    "    max_iterations=2,\n",
-    "    early_stopping_method=\"generate\",\n",
+    "mrkl.run(\n",
+    "    \"What is the weather in LA and SF?\"\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "4362ebc7",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor] Entering Chain run with input:\n",
-      "\u001b[0m{\n",
-      "  \"input\": \"What is the weather in NYC today, yesterday, and the day before?\"\n",
-      "}\n",
-      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:\n",
-      "\u001b[0m{\n",
-      "  \"prompts\": [\n",
-      "    \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\"\n",
-      "  ]\n",
-      "}\n",
-      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] [1.27s] Exiting LLM run with output:\n",
-      "\u001b[0m{\n",
-      "  \"generations\": [\n",
-      "    [\n",
-      "      {\n",
-      "        \"text\": \"\",\n",
-      "        \"generation_info\": null,\n",
-      "        \"message\": {\n",
-      "          \"lc\": 1,\n",
-      "          \"type\": \"constructor\",\n",
-      "          \"id\": [\n",
-      "            \"langchain\",\n",
-      "            \"schema\",\n",
-      "            \"messages\",\n",
-      "            \"AIMessage\"\n",
-      "          ],\n",
-      "          \"kwargs\": {\n",
-      "            \"content\": \"\",\n",
-      "            \"additional_kwargs\": {\n",
-      "              \"function_call\": {\n",
-      "                \"name\": \"Search\",\n",
-      "                \"arguments\": \"{\\n  \\\"query\\\": \\\"weather in NYC today\\\"\\n}\"\n",
-      "              }\n",
-      "            }\n",
-      "          }\n",
-      "        }\n",
-      "      }\n",
-      "    ]\n",
-      "  ],\n",
-      "  \"llm_output\": {\n",
-      "    \"token_usage\": {\n",
-      "      \"prompt_tokens\": 79,\n",
-      "      \"completion_tokens\": 17,\n",
-      "      \"total_tokens\": 96\n",
-      "    },\n",
-      "    \"model_name\": \"gpt-3.5-turbo-0613\"\n",
-      "  },\n",
-      "  \"run\": null\n",
-      "}\n",
-      "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 3:tool:Search] Entering Tool run with input:\n",
-      "\u001b[0m\"{'query': 'weather in NYC today'}\"\n",
-      "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 3:tool:Search] [3.84s] Exiting Tool run with output:\n",
-      "\u001b[0m\"10:00 am · Feels Like85° · WindSE 4 mph · Humidity78% · UV Index3 of 11 · Cloud Cover81% · Rain Amount0 in ...\"\n",
-      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:llm:ChatOpenAI] Entering LLM run with input:\n",
-      "\u001b[0m{\n",
-      "  \"prompts\": [\n",
-      "    \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\\nAI: {'name': 'Search', 'arguments': '{\\\\n  \\\"query\\\": \\\"weather in NYC today\\\"\\\\n}'}\\nFunction: 10:00 am · Feels Like85° · WindSE 4 mph · Humidity78% · UV Index3 of 11 · Cloud Cover81% · Rain Amount0 in ...\"\n",
-      "  ]\n",
-      "}\n",
-      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:llm:ChatOpenAI] [1.24s] Exiting LLM run with output:\n",
-      "\u001b[0m{\n",
-      "  \"generations\": [\n",
-      "    [\n",
-      "      {\n",
-      "        \"text\": \"\",\n",
-      "        \"generation_info\": null,\n",
-      "        \"message\": {\n",
-      "          \"lc\": 1,\n",
-      "          \"type\": \"constructor\",\n",
-      "          \"id\": [\n",
-      "            \"langchain\",\n",
-      "            \"schema\",\n",
-      "            \"messages\",\n",
-      "            \"AIMessage\"\n",
-      "          ],\n",
-      "          \"kwargs\": {\n",
-      "            \"content\": \"\",\n",
-      "            \"additional_kwargs\": {\n",
-      "              \"function_call\": {\n",
-      "                \"name\": \"Search\",\n",
-      "                \"arguments\": \"{\\n  \\\"query\\\": \\\"weather in NYC yesterday\\\"\\n}\"\n",
-      "              }\n",
-      "            }\n",
-      "          }\n",
-      "        }\n",
-      "      }\n",
-      "    ]\n",
-      "  ],\n",
-      "  \"llm_output\": {\n",
-      "    \"token_usage\": {\n",
-      "      \"prompt_tokens\": 142,\n",
-      "      \"completion_tokens\": 17,\n",
-      "      \"total_tokens\": 159\n",
-      "    },\n",
-      "    \"model_name\": \"gpt-3.5-turbo-0613\"\n",
-      "  },\n",
-      "  \"run\": null\n",
-      "}\n",
-      "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:tool:Search] Entering Tool run with input:\n",
-      "\u001b[0m\"{'query': 'weather in NYC yesterday'}\"\n",
-      "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:tool:Search] [1.15s] Exiting Tool run with output:\n",
-      "\u001b[0m\"New York Temperature Yesterday. Maximum temperature yesterday: 81 °F (at 1:51 pm) Minimum temperature yesterday: 72 °F (at 7:17 pm) Average temperature ...\"\n",
-      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:llm:ChatOpenAI] Entering LLM run with input:\n",
-      "\u001b[0m{\n",
-      "  \"prompts\": [\n",
-      "    \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\\nAI: {'name': 'Search', 'arguments': '{\\\\n  \\\"query\\\": \\\"weather in NYC today\\\"\\\\n}'}\\nFunction: 10:00 am · Feels Like85° · WindSE 4 mph · Humidity78% · UV Index3 of 11 · Cloud Cover81% · Rain Amount0 in ...\\nAI: {'name': 'Search', 'arguments': '{\\\\n  \\\"query\\\": \\\"weather in NYC yesterday\\\"\\\\n}'}\\nFunction: New York Temperature Yesterday. Maximum temperature yesterday: 81 °F (at 1:51 pm) Minimum temperature yesterday: 72 °F (at 7:17 pm) Average temperature ...\"\n",
-      "  ]\n",
-      "}\n",
-      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:llm:ChatOpenAI] [2.68s] Exiting LLM run with output:\n",
-      "\u001b[0m{\n",
-      "  \"generations\": [\n",
-      "    [\n",
-      "      {\n",
-      "        \"text\": \"Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\",\n",
-      "        \"generation_info\": null,\n",
-      "        \"message\": {\n",
-      "          \"lc\": 1,\n",
-      "          \"type\": \"constructor\",\n",
-      "          \"id\": [\n",
-      "            \"langchain\",\n",
-      "            \"schema\",\n",
-      "            \"messages\",\n",
-      "            \"AIMessage\"\n",
-      "          ],\n",
-      "          \"kwargs\": {\n",
-      "            \"content\": \"Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\",\n",
-      "            \"additional_kwargs\": {}\n",
-      "          }\n",
-      "        }\n",
-      "      }\n",
-      "    ]\n",
-      "  ],\n",
-      "  \"llm_output\": {\n",
-      "    \"token_usage\": {\n",
-      "      \"prompt_tokens\": 160,\n",
-      "      \"completion_tokens\": 91,\n",
-      "      \"total_tokens\": 251\n",
-      "    },\n",
-      "    \"model_name\": \"gpt-3.5-turbo-0613\"\n",
-      "  },\n",
-      "  \"run\": null\n",
-      "}\n",
-      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor] [10.18s] Exiting Chain run with output:\n",
-      "\u001b[0m{\n",
-      "  \"output\": \"Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\"\n",
-      "}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.'"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mrkl.run(\"What is the weather in NYC today, yesterday, and the day before?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "067a8d3e",
-   "metadata": {},
-   "source": [
-    "Notice that we never get around to looking up the weather the day before yesterday, due to hitting our max_iterations limit."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c3318a11",
+   "id": "9f5f6743",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -435,9 +210,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -449,7 +224,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/extras/modules/agents/how_to/add_memory_openai_functions.ipynb

@@ -78,7 +78,6 @@
    "source": [
     "from langchain.prompts import MessagesPlaceholder\n",
     "from langchain.memory import ConversationBufferMemory\n",
-    "\n",
     "agent_kwargs = {\n",
     "    \"extra_prompt_messages\": [MessagesPlaceholder(variable_name=\"memory\")],\n",
     "}\n",
@@ -93,12 +92,12 @@
    "outputs": [],
    "source": [
     "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.OPENAI_FUNCTIONS,\n",
-    "    verbose=True,\n",
-    "    agent_kwargs=agent_kwargs,\n",
-    "    memory=memory,\n",
+    "    tools, \n",
+    "    llm, \n",
+    "    agent=AgentType.OPENAI_FUNCTIONS, \n",
+    "    verbose=True, \n",
+    "    agent_kwargs=agent_kwargs, \n",
+    "    memory=memory\n",
     ")"
    ]
   },

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

@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "g9EmNu5DD9YI"
-   },
-   "source": [
-    "# Custom functions with OpenAI Functions Agent\n",
-    "\n",
-    "This notebook goes through how to integrate custom functions with OpenAI Functions agent."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LFKylC3CPtTl"
-   },
-   "source": [
-    "Install libraries which are required to run this example notebook\n",
-    "\n",
-    "`pip install -q openai langchain yfinance`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "E2DqzmEGDPak"
-   },
-   "source": [
-    "## Define custom functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "SiucthMs6SIK"
-   },
-   "outputs": [],
-   "source": [
-    "import yfinance as yf\n",
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "\n",
-    "def get_current_stock_price(ticker):\n",
-    "    \"\"\"Method to get current stock price\"\"\"\n",
-    "\n",
-    "    ticker_data = yf.Ticker(ticker)\n",
-    "    recent = ticker_data.history(period=\"1d\")\n",
-    "    return {\"price\": recent.iloc[0][\"Close\"], \"currency\": ticker_data.info[\"currency\"]}\n",
-    "\n",
-    "\n",
-    "def get_stock_performance(ticker, days):\n",
-    "    \"\"\"Method to get stock price change in percentage\"\"\"\n",
-    "\n",
-    "    past_date = datetime.today() - timedelta(days=days)\n",
-    "    ticker_data = yf.Ticker(ticker)\n",
-    "    history = ticker_data.history(start=past_date)\n",
-    "    old_price = history.iloc[0][\"Close\"]\n",
-    "    current_price = history.iloc[-1][\"Close\"]\n",
-    "    return {\"percent_change\": ((current_price - old_price) / old_price) * 100}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "vRLINGvQR1rO",
-    "outputId": "68230a4b-dda2-4273-b956-7439661e3785"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'price': 334.57000732421875, 'currency': 'USD'}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "get_current_stock_price(\"MSFT\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "57T190q235mD",
-    "outputId": "c6ee66ec-0659-4632-85d1-263b08826e68"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'percent_change': 1.014466941163018}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "get_stock_performance(\"MSFT\", 30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "MT8QsdyBDhwg"
-   },
-   "source": [
-    "## Make custom tools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "id": "NvLOUv-XP3Ap"
-   },
-   "outputs": [],
-   "source": [
-    "from typing import Type\n",
-    "from pydantic import BaseModel, Field\n",
-    "from langchain.tools import BaseTool\n",
-    "\n",
-    "\n",
-    "class CurrentStockPriceInput(BaseModel):\n",
-    "    \"\"\"Inputs for get_current_stock_price\"\"\"\n",
-    "\n",
-    "    ticker: str = Field(description=\"Ticker symbol of the stock\")\n",
-    "\n",
-    "\n",
-    "class CurrentStockPriceTool(BaseTool):\n",
-    "    name = \"get_current_stock_price\"\n",
-    "    description = \"\"\"\n",
-    "        Useful when you want to get current stock price.\n",
-    "        You should enter the stock ticker symbol recognized by the yahoo finance\n",
-    "        \"\"\"\n",
-    "    args_schema: Type[BaseModel] = CurrentStockPriceInput\n",
-    "\n",
-    "    def _run(self, ticker: str):\n",
-    "        price_response = get_current_stock_price(ticker)\n",
-    "        return price_response\n",
-    "\n",
-    "    def _arun(self, ticker: str):\n",
-    "        raise NotImplementedError(\"get_current_stock_price does not support async\")\n",
-    "\n",
-    "\n",
-    "class StockPercentChangeInput(BaseModel):\n",
-    "    \"\"\"Inputs for get_stock_performance\"\"\"\n",
-    "\n",
-    "    ticker: str = Field(description=\"Ticker symbol of the stock\")\n",
-    "    days: int = Field(description=\"Timedelta days to get past date from current date\")\n",
-    "\n",
-    "\n",
-    "class StockPerformanceTool(BaseTool):\n",
-    "    name = \"get_stock_performance\"\n",
-    "    description = \"\"\"\n",
-    "        Useful when you want to check performance of the stock.\n",
-    "        You should enter the stock ticker symbol recognized by the yahoo finance.\n",
-    "        You should enter days as number of days from today from which performance needs to be check.\n",
-    "        output will be the change in the stock price represented as a percentage.\n",
-    "        \"\"\"\n",
-    "    args_schema: Type[BaseModel] = StockPercentChangeInput\n",
-    "\n",
-    "    def _run(self, ticker: str, days: int):\n",
-    "        response = get_stock_performance(ticker, days)\n",
-    "        return response\n",
-    "\n",
-    "    def _arun(self, ticker: str):\n",
-    "        raise NotImplementedError(\"get_stock_performance does not support async\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "PVKoqeCyFKHF"
-   },
-   "source": [
-    "## Create Agent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "id": "yY7qNB7vSQGh"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents import AgentType\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.agents import initialize_agent\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0613\", temperature=0)\n",
-    "\n",
-    "tools = [CurrentStockPriceTool(), StockPerformanceTool()]\n",
-    "\n",
-    "agent = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 321
-    },
-    "id": "4X96xmgwRkcC",
-    "outputId": "a91b13ef-9643-4f60-d067-c4341e0b285e"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new  chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `get_current_stock_price` with `{'ticker': 'MSFT'}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m{'price': 334.57000732421875, 'currency': 'USD'}\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `get_stock_performance` with `{'ticker': 'MSFT', 'days': 180}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 40.163963297187905}\u001b[0m\u001b[32;1m\u001b[1;3mThe current price of Microsoft stock is $334.57 USD. \n",
-      "\n",
-      "Over the past 6 months, Microsoft stock has performed well with a 40.16% increase in its price.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The current price of Microsoft stock is $334.57 USD. \\n\\nOver the past 6 months, Microsoft stock has performed well with a 40.16% increase in its price.'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\n",
-    "    \"What is the current price of Microsoft stock? How it has performed over past 6 months?\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 285
-    },
-    "id": "nkZ_vmAcT7Al",
-    "outputId": "092ebc55-4d28-4a4b-aa2a-98ae47ceec20"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new  chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `get_current_stock_price` with `{'ticker': 'GOOGL'}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m{'price': 118.33000183105469, 'currency': 'USD'}\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `get_current_stock_price` with `{'ticker': 'META'}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m{'price': 287.04998779296875, 'currency': 'USD'}\u001b[0m\u001b[32;1m\u001b[1;3mThe recent stock price of Google (GOOGL) is $118.33 USD and the recent stock price of Meta (META) is $287.05 USD.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The recent stock price of Google (GOOGL) is $118.33 USD and the recent stock price of Meta (META) is $287.05 USD.'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"Give me recent stock prices of Google and Meta?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 466
-    },
-    "id": "jLU-HjMq7n1o",
-    "outputId": "a42194dd-26ed-4b5a-d4a2-1038420045c4"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new  chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `get_stock_performance` with `{'ticker': 'MSFT', 'days': 90}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 18.043096235165596}\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `get_stock_performance` with `{'ticker': 'GOOGL', 'days': 90}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 17.286155760642853}\u001b[0m\u001b[32;1m\u001b[1;3mIn the past 3 months, Microsoft (MSFT) has performed better than Google (GOOGL). Microsoft's stock price has increased by 18.04% while Google's stock price has increased by 17.29%.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"In the past 3 months, Microsoft (MSFT) has performed better than Google (GOOGL). Microsoft's stock price has increased by 18.04% while Google's stock price has increased by 17.29%.\""
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\n",
-    "    \"In the past 3 months, which stock between Microsoft and Google has performed the best?\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

docs/extras/modules/agents/how_to/use_toolkits_with_openai_functions.ipynb

@@ -79,10 +79,10 @@
    "source": [
     "llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")\n",
     "agent = initialize_agent(\n",
-    "    toolkit.get_tools(),\n",
-    "    llm,\n",
-    "    agent=AgentType.OPENAI_FUNCTIONS,\n",
-    "    verbose=True,\n",
+    "    toolkit.get_tools(), \n",
+    "    llm, \n",
+    "    agent=AgentType.OPENAI_FUNCTIONS, \n",
+    "    verbose=True, \n",
     "    agent_kwargs=agent_kwargs,\n",
     ")"
    ]

docs/extras/modules/agents/toolkits/document_comparison_toolkit.ipynb

@@ -17,7 +17,16 @@
    "execution_count": 1,
    "id": "8632a37c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.5) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from pydantic import BaseModel, Field\n",
     "\n",
@@ -47,14 +56,14 @@
     "files = [\n",
     "    # https://abc.xyz/investor/static/pdf/2023Q1_alphabet_earnings_release.pdf\n",
     "    {\n",
-    "        \"name\": \"alphabet-earnings\",\n",
+    "        \"name\": \"alphabet-earnings\", \n",
     "        \"path\": \"/Users/harrisonchase/Downloads/2023Q1_alphabet_earnings_release.pdf\",\n",
-    "    },\n",
+    "    }, \n",
     "    # https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q1-2023-Update\n",
     "    {\n",
-    "        \"name\": \"tesla-earnings\",\n",
-    "        \"path\": \"/Users/harrisonchase/Downloads/TSLA-Q1-2023-Update.pdf\",\n",
-    "    },\n",
+    "        \"name\": \"tesla-earnings\", \n",
+    "        \"path\": \"/Users/harrisonchase/Downloads/TSLA-Q1-2023-Update.pdf\"\n",
+    "    }\n",
     "]\n",
     "\n",
     "for file in files:\n",
@@ -64,14 +73,14 @@
     "    docs = text_splitter.split_documents(pages)\n",
     "    embeddings = OpenAIEmbeddings()\n",
     "    retriever = FAISS.from_documents(docs, embeddings).as_retriever()\n",
-    "\n",
+    "    \n",
     "    # Wrap retrievers in a Tool\n",
     "    tools.append(\n",
     "        Tool(\n",
     "            args_schema=DocumentInput,\n",
-    "            name=file[\"name\"],\n",
+    "            name=file[\"name\"], \n",
     "            description=f\"useful when you want to answer questions about {file['name']}\",\n",
-    "            func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever),\n",
+    "            func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever)\n",
     "        )\n",
     "    )"
    ]
@@ -130,7 +139,7 @@
    "source": [
     "llm = ChatOpenAI(\n",
     "    temperature=0,\n",
-    "    model=\"gpt-3.5-turbo-0613\",\n",
+    "    model=\"gpt-3.5-turbo-0613\", \n",
     ")\n",
     "\n",
     "agent = initialize_agent(\n",
@@ -161,7 +170,6 @@
    "outputs": [],
    "source": [
     "import langchain\n",
-    "\n",
     "langchain.debug = True"
    ]
   },
@@ -397,7 +405,7 @@
    "source": [
     "llm = ChatOpenAI(\n",
     "    temperature=0,\n",
-    "    model=\"gpt-3.5-turbo-0613\",\n",
+    "    model=\"gpt-3.5-turbo-0613\", \n",
     ")\n",
     "\n",
     "agent = initialize_agent(\n",

docs/extras/modules/agents/toolkits/office365.ipynb

@@ -136,11 +136,9 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"Create an email draft for me to edit of a letter from the perspective of a sentient parrot\"\n",
-    "    \" who is looking to collaborate on some research with her\"\n",
-    "    \" estranged friend, a cat. Under no circumstances may you send the message, however.\"\n",
-    ")"
+    "agent.run(\"Create an email draft for me to edit of a letter from the perspective of a sentient parrot\"\n",
+    "          \" who is looking to collaborate on some research with her\"\n",
+    "          \" estranged friend, a cat. Under no circumstances may you send the message, however.\")"
    ]
   },
   {
@@ -162,9 +160,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"Could you search in my drafts folder and let me know if any of them are about collaboration?\"\n",
-    ")"
+    "agent.run(\"Could you search in my drafts folder and let me know if any of them are about collaboration?\")"
    ]
   },
   {
@@ -194,9 +190,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"Can you schedule a 30 minute meeting with a sentient parrot to discuss research collaborations on October 3, 2023 at 2 pm Easter Time?\"\n",
-    ")"
+    "agent.run(\"Can you schedule a 30 minute meeting with a sentient parrot to discuss research collaborations on October 3, 2023 at 2 pm Easter Time?\")"
    ]
   },
   {
@@ -216,9 +210,7 @@
     }
    ],
    "source": [
-    "agent.run(\n",
-    "    \"Can you tell me if I have any events on October 3, 2023 in Eastern Time, and if so, tell me if any of them are with a sentient parrot?\"\n",
-    ")"
+    "agent.run(\"Can you tell me if I have any events on October 3, 2023 in Eastern Time, and if so, tell me if any of them are with a sentient parrot?\")"
    ]
   }
  ],

docs/extras/modules/agents/toolkits/sql_database.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# SQL Database Agent\n",
     "\n",
-    "This notebook showcases an agent designed to interact with a sql databases. The agent builds off of [SQLDatabaseChain](https://python.langchain.com/docs/modules/chains/popular/sqlite) and is designed to answer more general questions about a database, as well as recover from errors.\n",
+    "This notebook showcases an agent designed to interact with a sql databases. The agent builds off of [SQLDatabaseChain](https://langchain.readthedocs.io/en/latest/modules/chains/examples/sqlite.html) and is designed to answer more general questions about a database, as well as recover from errors.\n",
     "\n",
     "Note that, as this agent is in active development, all answers might not be correct. Additionally, it is not guaranteed that the agent won't perform DML statements on your database given certain questions. Be careful running it on sensitive data!\n",
     "\n",

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

@@ -26,8 +26,8 @@
    "source": [
     "import os\n",
     "\n",
-    "os.environ[\"BING_SUBSCRIPTION_KEY\"] = \"<key>\"\n",
-    "os.environ[\"BING_SEARCH_URL\"] = \"https://api.bing.microsoft.com/v7.0/search\""
+    "os.environ[\"BING_SUBSCRIPTION_KEY\"] = \"\"\n",
+    "os.environ[\"BING_SEARCH_URL\"] = \"\""
    ]
   },
   {

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

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

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

@@ -1,237 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# DataForSeo API Wrapper\n",
-    "This notebook demonstrates how to use the DataForSeo API wrapper to obtain search engine results. The DataForSeo API allows users to retrieve SERP from most popular search engines like Google, Bing, Yahoo. It also allows to get SERPs from different search engine types like Maps, News, Events, etc.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.utilities import DataForSeoAPIWrapper"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting up the API wrapper with your credentials\n",
-    "You can obtain your API credentials by registering on the DataForSeo website."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"DATAFORSEO_LOGIN\"] = \"your_api_access_username\"\n",
-    "os.environ[\"DATAFORSEO_PASSWORD\"] = \"your_api_access_password\"\n",
-    "\n",
-    "wrapper = DataForSeoAPIWrapper()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The run method will return the first result snippet from one of the following elements: answer_box, knowledge_graph, featured_snippet, shopping, organic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wrapper.run(\"Weather in Los Angeles\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The Difference Between `run` and `results`\n",
-    "`run` and `results` are two methods provided by the `DataForSeoAPIWrapper` class.\n",
-    "\n",
-    "The `run` method executes the search and returns the first result snippet from the answer box, knowledge graph, featured snippet, shopping, or organic results. These elements are sorted by priority from highest to lowest.\n",
-    "\n",
-    "The `results` method returns a JSON response configured according to the parameters set in the wrapper. This allows for more flexibility in terms of what data you want to return from the API."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Getting Results as JSON\n",
-    "You can customize the result types and fields you want to return in the JSON response. You can also set a maximum count for the number of top results to return."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json_wrapper = DataForSeoAPIWrapper(\n",
-    "    json_result_types=[\"organic\", \"knowledge_graph\", \"answer_box\"],\n",
-    "    json_result_fields=[\"type\", \"title\", \"description\", \"text\"],\n",
-    "    top_count=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json_wrapper.results(\"Bill Gates\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Customizing Location and Language\n",
-    "You can specify the location and language of your search results by passing additional parameters to the API wrapper."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "customized_wrapper = DataForSeoAPIWrapper(\n",
-    "    top_count=10,\n",
-    "    json_result_types=[\"organic\", \"local_pack\"],\n",
-    "    json_result_fields=[\"title\", \"description\", \"type\"],\n",
-    "    params={\"location_name\": \"Germany\", \"language_code\": \"en\"},\n",
-    ")\n",
-    "customized_wrapper.results(\"coffee near me\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Customizing the Search Engine\n",
-    "You can also specify the search engine you want to use."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "customized_wrapper = DataForSeoAPIWrapper(\n",
-    "    top_count=10,\n",
-    "    json_result_types=[\"organic\", \"local_pack\"],\n",
-    "    json_result_fields=[\"title\", \"description\", \"type\"],\n",
-    "    params={\"location_name\": \"Germany\", \"language_code\": \"en\", \"se_name\": \"bing\"},\n",
-    ")\n",
-    "customized_wrapper.results(\"coffee near me\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Customizing the Search Type\n",
-    "The API wrapper also allows you to specify the type of search you want to perform. For example, you can perform a maps search."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "maps_search = DataForSeoAPIWrapper(\n",
-    "    top_count=10,\n",
-    "    json_result_fields=[\"title\", \"value\", \"address\", \"rating\", \"type\"],\n",
-    "    params={\n",
-    "        \"location_coordinate\": \"52.512,13.36,12z\",\n",
-    "        \"language_code\": \"en\",\n",
-    "        \"se_type\": \"maps\",\n",
-    "    },\n",
-    ")\n",
-    "maps_search.results(\"coffee near me\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Integration with Langchain Agents\n",
-    "You can use the `Tool` class from the `langchain.agents` module to integrate the `DataForSeoAPIWrapper` with a langchain agent. The `Tool` class encapsulates a function that the agent can call."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import Tool\n",
-    "\n",
-    "search = DataForSeoAPIWrapper(\n",
-    "    top_count=3,\n",
-    "    json_result_types=[\"organic\"],\n",
-    "    json_result_fields=[\"title\", \"description\", \"type\"],\n",
-    ")\n",
-    "tool = Tool(\n",
-    "    name=\"google-search-answer\",\n",
-    "    description=\"My new answer tool\",\n",
-    "    func=search.run,\n",
-    ")\n",
-    "json_tool = Tool(\n",
-    "    name=\"google-search-json\",\n",
-    "    description=\"My new json tool\",\n",
-    "    func=search.results,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

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

@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "16763ed3",
-   "metadata": {},
-   "source": [
-    "# Lemon AI NLP Workflow Automation\n",
-    "\\\n",
-    "Full docs are available at: https://github.com/felixbrock/lemonai-py-client\n",
-    "\n",
-    "**Lemon AI helps you build powerful AI assistants in minutes and automate workflows by allowing for accurate and reliable read and write operations in tools like Airtable, Hubspot, Discord, Notion, Slack and Github.**\n",
-    "\n",
-    "Most connectors available today are focused on read-only operations, limiting the potential of LLMs. Agents, on the other hand, have a tendency to hallucinate from time to time due to missing context or instructions.\n",
-    "\n",
-    "With Lemon AI, it is possible to give your agents access to well-defined APIs for reliable read and write operations. In addition, Lemon AI functions allow you to further reduce the risk of hallucinations by providing a way to statically define workflows that the model can rely on in case of uncertainty."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "4881b484-1b97-478f-b206-aec407ceff66",
-   "metadata": {},
-   "source": [
-    "## Quick Start\n",
-    "\n",
-    "The following quick start demonstrates how to use Lemon AI in combination with Agents to automate workflows that involve interaction with internal tooling."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "ff91b41a",
-   "metadata": {},
-   "source": [
-    "### 1. Install Lemon AI\n",
-    "\n",
-    "Requires Python 3.8.1 and above.\n",
-    "\n",
-    "To use Lemon AI in your Python project run `pip install lemonai`\n",
-    "\n",
-    "This will install the corresponding Lemon AI client which you can then import into your script.\n",
-    "\n",
-    "The tool uses Python packages langchain and loguru. In case of any installation errors with Lemon AI, install both packages first and then install the Lemon AI package."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "340ff63d",
-   "metadata": {},
-   "source": [
-    "### 2. Launch the Server\n",
-    "\n",
-    "The interaction of your agents and all tools provided by Lemon AI is handled by the [Lemon AI Server](https://github.com/felixbrock/lemonai-server). To use Lemon AI you need to run the server on your local machine so the Lemon AI Python client can connect to it."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e845f402",
-   "metadata": {},
-   "source": [
-    "### 3. Use Lemon AI with Langchain"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "d3ae6a82",
-   "metadata": {},
-   "source": [
-    "Lemon AI automatically solves given tasks by finding the right combination of relevant tools or uses Lemon AI Functions as an alternative. The following example demonstrates how to retrieve a user from Hackernews and write it to a table in Airtable:"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "43476a22",
-   "metadata": {},
-   "source": [
-    "#### (Optional) Define your Lemon AI Functions"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "cb038670",
-   "metadata": {},
-   "source": [
-    "Similar to [OpenAI functions](https://openai.com/blog/function-calling-and-other-api-updates), Lemon AI provides the option to define workflows as reusable functions. These functions can be defined for use cases where it is especially important to move as close as possible to near-deterministic behavior. Specific workflows can be defined in a separate lemonai.json:"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e423ebbb",
-   "metadata": {},
-   "source": [
-    "```json\n",
-    "[\n",
-    "  {\n",
-    "    \"name\": \"Hackernews Airtable User Workflow\",\n",
-    "    \"description\": \"retrieves user data from Hackernews and appends it to a table in Airtable\",\n",
-    "    \"tools\": [\"hackernews-get-user\", \"airtable-append-data\"]\n",
-    "  }\n",
-    "]\n",
-    "```"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "3fdb36ce",
-   "metadata": {},
-   "source": [
-    "Your model will have access to these functions and will prefer them over self-selecting tools to solve a given task. All you have to do is to let the agent know that it should use a given function by including the function name in the prompt."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "ebfb8b5d",
-   "metadata": {},
-   "source": [
-    "#### Include Lemon AI in your Langchain project "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5318715d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from lemonai import execute_workflow\n",
-    "from langchain import OpenAI"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c9d082cb",
-   "metadata": {},
-   "source": [
-    "#### Load API Keys and Access Tokens\n",
-    "\n",
-    "To use tools that require authentication, you have to store the corresponding access credentials in your environment in the format \"{tool name}_{authentication string}\" where the authentication string is one of [\"API_KEY\", \"SECRET_KEY\", \"SUBSCRIPTION_KEY\", \"ACCESS_KEY\"] for API keys or [\"ACCESS_TOKEN\", \"SECRET_TOKEN\"] for authentication tokens. Examples are \"OPENAI_API_KEY\", \"BING_SUBSCRIPTION_KEY\", \"AIRTABLE_ACCESS_TOKEN\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a370d999",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\" Load all relevant API Keys and Access Tokens into your environment variables \"\"\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"*INSERT OPENAI API KEY HERE*\"\n",
-    "os.environ[\"AIRTABLE_ACCESS_TOKEN\"] = \"*INSERT AIRTABLE TOKEN HERE*\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "38d158e7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hackernews_username = \"*INSERT HACKERNEWS USERNAME HERE*\"\n",
-    "airtable_base_id = \"*INSERT BASE ID HERE*\"\n",
-    "airtable_table_id = \"*INSERT TABLE ID HERE*\"\n",
-    "\n",
-    "\"\"\" Define your instruction to be given to your LLM \"\"\"\n",
-    "prompt = f\"\"\"Read information from Hackernews for user {hackernews_username} and then write the results to\n",
-    "Airtable (baseId: {airtable_base_id}, tableId: {airtable_table_id}). Only write the fields \"username\", \"karma\"\n",
-    "and \"created_at_i\". Please make sure that Airtable does NOT automatically convert the field types.\n",
-    "\"\"\"\n",
-    "\n",
-    "\"\"\"\n",
-    "Use the Lemon AI execute_workflow wrapper \n",
-    "to run your Langchain agent in combination with Lemon AI  \n",
-    "\"\"\"\n",
-    "model = OpenAI(temperature=0)\n",
-    "\n",
-    "execute_workflow(llm=model, prompt_string=prompt)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "aef3e801",
-   "metadata": {},
-   "source": [
-    "### 4. Gain transparency on your Agent's decision making\n",
-    "\n",
-    "To gain transparency on how your Agent interacts with Lemon AI tools to solve a given task, all decisions made, tools used and operations performed are written to a local `lemonai.log` file. Every time your LLM agent is interacting with the Lemon AI tool stack a corresponding log entry is created.\n",
-    "\n",
-    "```log\n",
-    "2023-06-26T11:50:27.708785+0100 - b5f91c59-8487-45c2-800a-156eac0c7dae - hackernews-get-user\n",
-    "2023-06-26T11:50:39.624035+0100 - b5f91c59-8487-45c2-800a-156eac0c7dae - airtable-append-data\n",
-    "2023-06-26T11:58:32.925228+0100 - 5efe603c-9898-4143-b99a-55b50007ed9d - hackernews-get-user\n",
-    "2023-06-26T11:58:43.988788+0100 - 5efe603c-9898-4143-b99a-55b50007ed9d - airtable-append-data\n",
-    "```\n",
-    "\n",
-    "By using the [Lemon AI Analytics Tool](https://github.com/felixbrock/lemonai-analytics) you can easily gain a better understanding of how frequently and in which order tools are used. As a result, you can identify weak spots in your agent’s decision-making capabilities and move to a more deterministic behavior by defining Lemon AI functions."
-   ]
-  }
- ],
- "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": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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

@@ -90,12 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "search.results(\n",
-    "    \"The best blog post about AI safety is definitely this: \",\n",
-    "    10,\n",
-    "    include_domains=[\"lesswrong.com\"],\n",
-    "    start_published_date=\"2019-01-01\",\n",
-    ")"
+    "search.results(\"The best blog post about AI safety is definitely this: \", 10, include_domains=[\"lesswrong.com\"], start_published_date=\"2019-01-01\")"
    ]
   },
   {

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

@@ -21,7 +21,7 @@
     "\n",
     "2. User-facing (Oauth): for production scenarios where you are deploying an end-user facing application and LangChain needs access to end-user's exposed actions and connected accounts on Zapier.com\n",
     "\n",
-    "This quick start will focus mostly on the server-side use case for brevity. Jump to [Example Using OAuth Access Token](#oauth) to see a short example how to set up Zapier for user-facing situations. Review [full docs](https://nla.zapier.com/start/) for full user-facing oauth developer support.\n",
+    "This quick start will focus on the server-side use case for brevity. Review [full docs](https://nla.zapier.com/start/) for user-facing oauth developer support.\n",
     "\n",
     "This example goes over how to use the Zapier integration with a `SimpleSequentialChain`, then an `Agent`.\n",
     "In code, below:"
@@ -149,7 +149,7 @@
    "id": "bcdea831",
    "metadata": {},
    "source": [
-    "## Example with SimpleSequentialChain\n",
+    "# Example with SimpleSequentialChain\n",
     "If you need more explicit control, use a chain, like below."
    ]
   },
@@ -322,35 +322,13 @@
     "overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "09ff954e-45f2-4595-92ea-91627abde4a0",
-   "metadata": {},
-   "source": [
-    "## <a id=\"oauth\">Example Using OAuth Access Token</a>\n",
-    "The below snippet shows how to initialize the wrapper with a procured OAuth access token. Note the argument being passed in as opposed to setting an environment variable. Review the [authentication docs](https://nla.zapier.com/docs/authentication/#oauth-credentials) for full user-facing oauth developer support.\n",
-    "\n",
-    "The developer is tasked with handling the OAuth handshaking to procure and refresh the access token."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7c6835c8",
+   "id": "09ff954e-45f2-4595-92ea-91627abde4a0",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "zapier = ZapierNLAWrapper(zapier_nla_oauth_access_token=\"<fill in access token here>\")\n",
-    "toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)\n",
-    "agent = initialize_agent(\n",
-    "    toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")\n",
-    "\n",
-    "agent.run(\n",
-    "    \"Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.\"\n",
-    ")"
-   ]
+   "source": []
   }
  ],
  "metadata": {