maybe

.devcontainer/docker-compose.yaml

@@ -2,7 +2,7 @@ version: '3'
 services:
   langchain:
     build:
-      dockerfile: libs/langchain/dev.Dockerfile
+      dockerfile: dev.Dockerfile
       context: ..
     volumes:
    # Update this to wherever you want VS Code to mount the folder of your project

.github/CONTRIBUTING.md

@@ -69,14 +69,6 @@ This project uses [Poetry](https://python-poetry.org/) as a dependency manager.
 3. Tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
 4. Continue with the following steps.
 
-There are two separate projects in this repository:
-- `langchain`: core langchain code, abstractions, and use cases
-- `langchain.experimental`: more experimental code
-
-Each of these has their OWN development environment. 
-In order to run any of the commands below, please move into their respective directories.
-For example, to contribute to `langchain` run `cd libs/langchain` before getting started with the below.
-
 To install requirements:
 
 ```bash
@@ -103,14 +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/).
@@ -121,42 +105,8 @@ 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.
 
-### Spellcheck
-
-Spellchecking for this project is done via [codespell](https://github.com/codespell-project/codespell).
-Note that `codespell` finds common typos, so could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.
-
-To check spelling for this project:
-
-```bash
-make spell_check
-```
-
-To fix spelling in place:
-
-```bash
-make spell_fix
-```
-
-If codespell is incorrectly flagging a word, you can skip spellcheck for that word by adding it to the codespell config in the `pyproject.toml` file.
-
-```python
-[tool.codespell]
-...
-# Add here:
-ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure'
-```
-
 ### Coverage
 
 Code coverage (i.e. the amount of code that is covered by unit tests) helps identify areas of the code that are potentially more or less brittle.
@@ -256,43 +206,32 @@ When you run `poetry install`, the `langchain` package is installed as editable
 
 ## Documentation
 
-While the code is split between `langchain` and `langchain.experimental`, the documentation is one holistic thing.
-This covers how to get started contributing to documentation.
-
 ### 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

@@ -7,18 +7,16 @@ Replace this comment with:
   - Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
   - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
 
-Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
-
 If you're adding a new integration, please include:
   1. a test for the integration, preferably unit tests that do not rely on network access,
   2. an example notebook showing its use.
 
 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
 

.github/actions/poetry_setup/action.yml

@@ -52,13 +52,11 @@ runs:
 
     - name: Check Poetry File
       shell: bash
-      working-directory: ${{ inputs.working-directory }}
       run: |
         poetry check
 
     - name: Check lock file
       shell: bash
-      working-directory: ${{ inputs.working-directory }}
       run: |
         poetry lock --check
 

.github/workflows/codespell.yml

@@ -1,22 +0,0 @@
----
-name: Codespell
-
-on:
-  push:
-    branches: [master]
-  pull_request:
-    branches: [master]
-
-permissions:
-  contents: read
-
-jobs:
-  codespell:
-    name: Check for spelling errors
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v3
-      - name: Codespell
-        uses: codespell-project/actions-codespell@v2

.github/workflows/langchain_ci.yml

@@ -1,27 +0,0 @@
----
-name: libs/langchain CI
-
-on:
-  push:
-    branches: [ master ]
-  pull_request:
-    paths:
-      - '.github/workflows/_lint.yml'
-      - '.github/workflows/_test.yml'
-      - '.github/workflows/langchain_ci.yml'
-      - 'libs/langchain/**'
-  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
-
-jobs:
-  lint:
-    uses:
-      ./.github/workflows/_lint.yml
-    with:
-      working-directory: libs/langchain
-    secrets: inherit
-  test:
-    uses:
-      ./.github/workflows/_test.yml
-    with:
-      working-directory: libs/langchain
-    secrets: inherit

.github/workflows/langchain_experimental_ci.yml

@@ -1,29 +0,0 @@
----
-name: libs/langchain-experimental CI
-
-on:
-  push:
-    branches: [ master ]
-  pull_request:
-    paths:
-      - '.github/workflows/_lint.yml'
-      - '.github/workflows/_test.yml'
-      - '.github/workflows/langchain_experimental_ci.yml'
-      - 'libs/langchain/**'
-      - 'libs/experimental/**'
-  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
-
-jobs:
-  lint:
-    uses:
-      ./.github/workflows/_lint.yml
-    with:
-      working-directory: libs/experimental
-    secrets: inherit
-  test:
-    uses:
-      ./.github/workflows/_test.yml
-    with:
-      working-directory: libs/experimental
-      test_type: '["core"]'
-    secrets: inherit

.github/workflows/langchain_experimental_release.yml

@@ -1,20 +0,0 @@
----
-name: libs/langchain-experimental Release
-
-on:
-  pull_request:
-    types:
-      - closed
-    branches:
-      - master
-    paths:
-      - 'libs/experimental/pyproject.toml'
-  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
-
-jobs:
-  release:
-    uses:
-      ./.github/workflows/_release.yml
-    with:
-      working-directory: libs/experimental
-    secrets: inherit

.github/workflows/langchain_release.yml

@@ -1,20 +0,0 @@
----
-name: libs/langchain Release
-
-on:
-  pull_request:
-    types:
-      - closed
-    branches:
-      - master
-    paths:
-      - 'libs/langchain/pyproject.toml'
-  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
-
-jobs:
-  release:
-    uses:
-      ./.github/workflows/_release.yml
-    with:
-      working-directory: libs/langchain
-    secrets: inherit

.github/workflows/lint.yml

@@ -1,21 +1,15 @@
 name: lint
 
 on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
+  push:
+    branches: [master]
+  pull_request:
 
 env:
   POETRY_VERSION: "1.4.2"
 
 jobs:
   build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -37,10 +31,6 @@ jobs:
       - name: Install dependencies
         run: |
           poetry install
-      - name: Install langchain editable
-        if: ${{ inputs.working-directory != 'langchain' }}
-        run: |
-          pip install -e ../langchain
       - name: Analysing the code with our lint
         run: |
           make lint

.github/workflows/release.yml

@@ -1,12 +1,13 @@
 name: release
 
 on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
+  pull_request:
+    types:
+      - closed
+    branches:
+      - master
+    paths:
+      - 'pyproject.toml'
 
 env:
   POETRY_VERSION: "1.4.2"
@@ -17,9 +18,6 @@ jobs:
         ${{ github.event.pull_request.merged == true }}
         && ${{ contains(github.event.pull_request.labels.*.name, 'release') }}
     runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
     steps:
       - uses: actions/checkout@v3
       - name: Install poetry

.github/workflows/test.yml

@@ -1,25 +1,16 @@
 name: test
 
 on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-      test_type:
-        type: string
-        description: "Test types to run"
-        default: '["core", "extended"]'
+  push:
+    branches: [master]
+  pull_request:
+  workflow_dispatch:
 
 env:
   POETRY_VERSION: "1.4.2"
 
 jobs:
   build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -28,7 +19,9 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
-        test_type: ${{ fromJSON(inputs.test_type) }}
+        test_type:
+          - "core"
+          - "extended"
     name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
     steps:
       - uses: actions/checkout@v3
@@ -36,7 +29,6 @@ jobs:
         uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
-          working-directory: ${{ inputs.working-directory }}
           poetry-version: "1.4.2"
           cache-key: ${{ matrix.test_type }}
           install-command: |
@@ -47,10 +39,6 @@ jobs:
                 echo "Running extended tests, installing dependencies with poetry..."
                 poetry install -E extended_testing
               fi
-      - name: Install langchain editable
-        if: ${{ inputs.working-directory != 'langchain' }}
-        run: |
-          pip install -e ../langchain
       - name: Run ${{matrix.test_type}} tests
         run: |
           if [ "${{ matrix.test_type }}" == "core" ]; then

.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

@@ -24,6 +24,6 @@ sphinx:
 # Optionally declare the Python requirements required to build your docs
 python:
    install:
-   - requirements: docs/api_reference/requirements.txt
+   - requirements: docs/requirements.txt
    - method: pip
      path: .

MIGRATE.md

@@ -1,47 +0,0 @@
-# Migrating to `langchain.experimental`
-
-We are moving any experimental components of langchain, or components with vulnerability issues, into `langchain.experimental`.
-This guide covers how to migrate.
-
-## Installation
-
-Previously:
-
-`pip install -U langchain`
-
-Now:
-
-`pip install -U langchain langchain.experimental`
-
-## PALChain
-
-Previously:
-
-`from langchain.chains import PALChain`
-
-Now:
-
-`from langchain.experimental.pal_chain import PALChain`
-
-## SQLDatabaseChain
-
-Previously:
-
-`from langchain.chains import SQLDatabaseChain`
-
-Now:
-
-`from langchain.experimental.sql import SQLDatabaseChain`
-
-## `load_prompt` for Python files
-
-Note: this only applies if you want to load Python files as prompts.
-If you want to load json/yaml files, no change is needed.
-
-Previously:
-
-`from langchain.prompts import load_prompt`
-
-Now:
-
-`from langchain.experimental.prompts import load_prompt`

Makefile

@@ -1,45 +1,60 @@
-.PHONY: all clean docs_build docs_clean docs_linkcheck api_docs_build api_docs_clean api_docs_linkcheck
+.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
 
+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 .
 
-spell_check:
-	poetry run codespell --toml pyproject.toml
+TEST_FILE ?= tests/unit_tests/
 
-spell_fix:
-	poetry run codespell --toml pyproject.toml -w
+test:
+	poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
 
-######################
-# HELP
-######################
+tests: 
+	poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+extended_tests:
+	poetry run pytest --disable-socket --allow-unix-socket --only-extended tests/unit_tests
+
+test_watch:
+	poetry run ptw --now . -- tests/unit_tests
+
+integration_tests:
+	poetry run pytest tests/integration_tests
+
+docker_tests:
+	docker build -t my-langchain-image:test .
+	docker run --rm my-langchain-image:test
 
 help:
 	@echo '----'
@@ -47,3 +62,12 @@ help:
 	@echo 'docs_build                   - build the documentation'
 	@echo 'docs_clean                   - clean the documentation build artifacts'
 	@echo 'docs_linkcheck               - run linkchecker on the documentation'
+	@echo 'format                       - run code formatters'
+	@echo 'lint                         - run linters'
+	@echo 'test                         - run unit tests'
+	@echo 'tests                        - run unit tests'
+	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
+	@echo 'extended_tests               - run only extended unit tests'
+	@echo 'test_watch                   - run unit tests in watch mode'
+	@echo 'integration_tests            - run integration tests'
+	@echo 'docker_tests                 - run unit tests in docker'

README.md

@@ -3,8 +3,8 @@
 ⚡ Building applications with LLMs through composability ⚡
 
 [![Release Notes](https://img.shields.io/github/release/hwchase17/langchain)](https://github.com/hwchase17/langchain/releases)
-[![CI](https://github.com/hwchase17/langchain/actions/workflows/langchain_ci.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/langchain_ci.yml)
-[![Experimental CI](https://github.com/hwchase17/langchain/actions/workflows/langchain_experimental_ci.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/langchain_experimental_ci.yml)
+[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml)
+[![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml)
 [![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
@@ -25,7 +25,7 @@ Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set u
 
 `pip install langchain`
 or
-`pip install langsmith && conda install langchain -c conda-forge`
+`conda install langchain -c conda-forge`
 
 ## 🤔 What is this?
 

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/conf.py

@@ -17,9 +17,8 @@ import sys
 import toml
 
 sys.path.insert(0, os.path.abspath("."))
-sys.path.insert(0, os.path.abspath("../../libs/langchain"))
 
-with open("../../libs/langchain/pyproject.toml") as f:
+with open("../../pyproject.toml") as f:
     data = toml.load(f)
 
 # -- Project information -----------------------------------------------------

docs/api_reference/create_api_rst.py

@@ -4,7 +4,7 @@ import re
 from pathlib import Path
 
 ROOT_DIR = Path(__file__).parents[2].absolute()
-PKG_DIR = ROOT_DIR / "libs" / "langchain" / "langchain"
+PKG_DIR = ROOT_DIR / "langchain"
 WRITE_FILE = Path(__file__).parent / "api_reference.rst"
 
 
@@ -20,9 +20,7 @@ def load_members() -> dict:
                 cls = re.findall(r"^class ([^_].*)\(", line)
                 members[top_level]["classes"].extend([module + "." + c for c in cls])
                 func = re.findall(r"^def ([^_].*)\(", line)
-                afunc = re.findall(r"^async def ([^_].*)\(", line)
-                func_strings = [module + "." + f for f in func + afunc]
-                members[top_level]["functions"].extend(func_strings)
+                members[top_level]["functions"].extend([module + "." + f for f in func])
     return members
 
 

docs/api_reference/modules/evaluation.rst

@@ -1,9 +0,0 @@
-Evaluation
-=======================
-
-LangChain has a number of convenient evaluation chains you can use off the shelf to grade your models' oupputs.
-
-.. automodule:: langchain.evaluation
-   :members:
-   :undoc-members:
-   :inherited-members:

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

@@ -16,6 +16,22 @@
   {%- set development_attrs = '' %}
 {%- endif %}
 
+{# title, link, link_attrs #}
+{%- set drop_down_navigation = [
+  ('Getting Started', pathto('getting_started'), ''),
+  ('Tutorial', pathto('tutorial/index'), ''),
+  ("What's new", pathto('whats_new/v' + version), ''),
+  ('Glossary', pathto('glossary'), ''),
+  ('Development', development_link, development_attrs),
+  ('FAQ', pathto('faq'), ''),
+  ('Support', pathto('support'), ''),
+  ('Related packages', pathto('related_projects'), ''),
+  ('Roadmap', pathto('roadmap'), ''),
+  ('Governance', pathto('governance'), ''),
+  ('About us', pathto('about'), ''),
+  ('GitHub', 'https://github.com/scikit-learn/scikit-learn', ''),
+  ('Other Versions and Download', 'https://scikit-learn.org/dev/versions.html', '')]
+-%}
 
 <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">

docs/docs_skeleton/docs/ecosystem/integrations/index.mdx

@@ -1,8 +1,8 @@
 ---
-sidebar_position: 2 
+sidebar_position: 0
 ---
-# String Evaluators
+# Integrations
 
 import DocCardList from "@theme/DocCardList";
 
-<DocCardList />
+<DocCardList />

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/guides/langsmith/index.md

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

docs/docs_skeleton/docs/modules/callbacks/integrations/promptlayer.ipynb

@@ -0,0 +1,216 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PromptLayer\n",
+    "\n",
+    "<img src=\"https://promptlayer.com/logo.png\"  height=\"300\">\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[PromptLayer](https://promptlayer.com) is a an observability platform for prompts and LLMs. In this guide we will go over how to setup the `PromptLayerCallbackHandler`. While PromptLayer does have LLMs that integrate directly with LangChain (eg [`PromptLayerOpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/promptlayer_openai)), this callback will be an easier and more feature rich way to integrate PromptLayer with any model on LangChain. \n",
+    "\n",
+    "This callback is also the recommended way to connect with PromptLayer when building Chains and Agents on LangChain."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Installation and Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install promptlayer --upgrade"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting API Credentials\n",
+    "\n",
+    "If you have not already create an account on [PromptLayer](https://www.promptlayer.com) and get an API key by clicking on the settings cog in the navbar\n",
+    "Set it as an environment variabled called `PROMPTLAYER_API_KEY`\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Usage\n",
+    "\n",
+    "To get started with `PromptLayerCallbackHandler` is fairly simple, it takes two optional arguments:\n",
+    "1. `pl_tags` - an optional list of strings that will be tags tracked on PromptLayer\n",
+    "2. `pl_id_callback` - an optional function that will get a `promptlayer_request_id` as an argument. This id can be used with all of PromptLayers tracking features to track, metadata, scores, and prompt usage."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Example\n",
+    "\n",
+    "In this simple example we use `PromptLayerCallbackHandler` with `ChatOpenAI`. We add a PromptLayer tag named `chatopenai`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content=\"Sure, here's one:\\n\\nWhy did the tomato turn red?\\n\\nBecause it saw the salad dressing!\" additional_kwargs={} example=False\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.schema import (\n",
+    "    HumanMessage,\n",
+    ")\n",
+    "from langchain.callbacks import PromptLayerCallbackHandler\n",
+    "\n",
+    "chat_llm = ChatOpenAI(\n",
+    "    temperature=0,\n",
+    "    callbacks=[PromptLayerCallbackHandler(pl_tags=[\"chatopenai\"])],\n",
+    ")\n",
+    "llm_results = chat_llm(\n",
+    "    [\n",
+    "        HumanMessage(content=\"What comes after 1,2,3 ?\"),\n",
+    "        HumanMessage(content=\"Tell me another joke?\"),\n",
+    "    ]\n",
+    ")\n",
+    "print(llm_results)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Full Featured Example\n",
+    "\n",
+    "In this example we unlock more of the power of PromptLayer.\n",
+    "\n",
+    "We are using the Prompt Registry and fetching the prompt called `example`.\n",
+    "\n",
+    "We also define a `pl_id_callback` function that tracks a score, metadata and the prompt used. Read more about tracking on [our docs](docs.promptlayer.com)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "prompt layer id  6050929\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\nToasterCo.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.callbacks import PromptLayerCallbackHandler\n",
+    "import promptlayer\n",
+    "\n",
+    "def pl_id_callback(promptlayer_request_id):\n",
+    "    print(\"prompt layer id \", promptlayer_request_id)\n",
+    "    promptlayer.track.score(\n",
+    "        request_id=promptlayer_request_id, score=100\n",
+    "    )  # score is an integer 0-100\n",
+    "    promptlayer.track.metadata(\n",
+    "        request_id=promptlayer_request_id, metadata={\"foo\": \"bar\"}\n",
+    "    )  # metadata is a dictionary of key value pairs that is tracked on PromptLayer\n",
+    "    promptlayer.track.prompt(\n",
+    "        request_id=promptlayer_request_id,\n",
+    "        prompt_name=\"example\",\n",
+    "        prompt_input_variables={\"product\": \"toasters\"},\n",
+    "        version=1,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "openai_llm = OpenAI(\n",
+    "    model_name=\"text-davinci-002\",\n",
+    "    callbacks=[PromptLayerCallbackHandler(pl_id_callback=pl_id_callback)],\n",
+    ")\n",
+    "\n",
+    "example_prompt = promptlayer.prompts.get(\"example\", version=1, langchain=True)\n",
+    "openai_llm(example_prompt.format(product=\"toasters\"))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That is all it takes! After setup all your requests will show up on the PromptLayer dasahboard.\n",
+    "This callback also works with any LLM implemented on LangChain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.8.8"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "c4fe2cd85a8d9e8baaec5340ce66faff1c77581a9f43e6c45e85e09b6fced008"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

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/document_transformers/index.mdx

@@ -24,7 +24,7 @@ That means there are two different axes along which you can customize your text
 1. How the text is split
 2. How the chunk size is measured
 
-### Get started with text splitters
+## Get started with text splitters
 
 import GetStarted from "@snippets/modules/data_connection/document_transformers/get_started.mdx"
 

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

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

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

@@ -8,7 +8,7 @@ Many LLM applications require user-specific data that is not part of the model's
 building blocks to load, transform, store and query your data via:
 
 - [Document loaders](/docs/modules/data_connection/document_loaders/): Load documents from many different sources
-- [Document transformers](/docs/modules/data_connection/document_transformers/): Split documents, convert documents into Q&A format, drop redundant documents, and more
+- [Document transformers](/docs/modules/data_connection/document_transformers/): Split documents, drop redundant documents, and more
 - [Text embedding models](/docs/modules/data_connection/text_embedding/): Take unstructured text and turn it into a list of floating point numbers
 - [Vector stores](/docs/modules/data_connection/vectorstores/): Store and search over embedded data
 - [Retrievers](/docs/modules/data_connection/retrievers/): Query your data

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

@@ -8,20 +8,10 @@ vectors, and then at query time to embed the unstructured query and retrieve the
 'most similar' to the embedded query. A vector store takes care of storing embedded data and performing vector search
 for you.
 
-![vector store diagram](/img/vector_stores.jpg)
-
 ## 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"
 
 <GetStarted/>
-
-## Asynchronous operations
-
-Vector stores are usually run as a separate service that requires some IO operations, and therefore they might be called asynchronously. That gives performance benefits as you don't waste time waiting for responses from external services. That might also be important if you work with an asynchronous framework, such as [FastAPI](https://fastapi.tiangolo.com/).
-
-import AsyncVectorStore from "@snippets/modules/data_connection/vectorstores/async.mdx"
-
-<AsyncVectorStore/>

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

@@ -1,8 +0,0 @@
----
-sidebar_position: 3 
----
-# Comparison Evaluators
-
-import DocCardList from "@theme/DocCardList";
-
-<DocCardList />

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

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

docs/docs_skeleton/docs/modules/evaluation/index.mdx

@@ -1,28 +0,0 @@
----
-sidebar_position: 6
----
-
-import DocCardList from "@theme/DocCardList";
-
-# Evaluation
-
-Language models can be unpredictable. This makes it challenging to ship reliable applications to production, where repeatable, useful outcomes across diverse inputs are a minimum requirement. Tests help demonstrate each component in an LLM application can produce the required or expected functionality. These tests also safeguard against regressions while you improve interconnected pieces of an integrated system. However, measuring the quality of generated text can be challenging. It can be hard to agree on the right set of metrics for your application, and it can be difficult to translate those into better performance. Furthermore, it's common to lack sufficient evaluation data adequately test the range of inputs and expected outputs for each component when you're just getting started. The LangChain community is building open source tools and guides to help address these challenges.
-
-LangChain exposes different types of evaluators for common types of evaluation. Each type has off-the-shelf implementations you can use to get started, as well as an
- extensible API so you can create your own or contribute improvements for everyone to use. The following sections have example notebooks for you to get started.
-
-- [String Evaluators](/docs/modules/evaluation/string/): Evaluate the predicted string for a given input, usually against a reference string
-- [Trajectory Evaluators](/docs/modules/evaluation/trajectory/): Evaluate the whole trajectory of agent actions
-- [Comparison Evaluators](/docs/modules/evaluation/comparison/): Compare predictions from two runs on a common input
-
-
-This section also provides some additional examples of how you could use these evaluators for different scenarios or apply to different chain implementations in the LangChain library. Some examples include:
-
-- [Preference Scoring Chain Outputs](/docs/modules/evaluation/examples/comparisons): An example using a comparison evaluator on different models or prompts to select statistically significant differences in aggregate preference scores
-
-
-## Reference Docs
-
-For detailed information of the available evaluators, including how to instantiate, configure, and customize them. Check out the [reference documentation](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.evaluation) directly.
-
-<DocCardList />

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

@@ -1,8 +0,0 @@
----
-sidebar_position: 4
----
-# Trajectory Evaluators
-
-import DocCardList from "@theme/DocCardList";
-
-<DocCardList />

docs/docs_skeleton/docs/modules/index.mdx

@@ -17,6 +17,4 @@ Let chains choose which tools to use given high-level directives
 #### [Memory](/docs/modules/memory/)
 Persist application state between runs of a chain
 #### [Callbacks](/docs/modules/callbacks/)
-Log and stream intermediate steps of any chain
-#### [Evaluation](/docs/modules/evaluation/)
-Evaluate the performance of a chain.
+Log and stream intermediate steps of any chain

docs/docs_skeleton/docusaurus.config.js

@@ -87,6 +87,7 @@ 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 }],
           ],
@@ -148,33 +149,6 @@ const config = {
       navbar: {
         title: "🦜️🔗 LangChain",
         items: [
-          {
-            to: "/docs/get_started/introduction",
-            label: "Docs",
-            position: "left",
-          },
-          {
-            type: 'docSidebar',
-            position: 'left',
-            sidebarId: 'use_cases',
-            label: 'Use cases',
-          },
-          {
-            type: 'docSidebar',
-            position: 'left',
-            sidebarId: 'integrations',
-            label: 'Integrations',
-          },
-          {
-            href: "https://api.python.langchain.com",
-            label: "API",
-            position: "left",
-          },
-          {
-            to: "https://smith.langchain.com",
-            label: "LangSmith",
-            position: "right",
-          },
           {
             to: "https://js.langchain.com/docs",
             label: "JS/TS Docs",
@@ -183,9 +157,8 @@ const config = {
           // Please keep GitHub link to the right for consistency.
           {
             href: "https://github.com/hwchase17/langchain",
-            position: 'right',
-            className: 'header-github-link',
-            'aria-label': 'GitHub repository',
+            label: "GitHub",
+            position: "right",
           },
         ],
       },

docs/docs_skeleton/package.json

@@ -23,7 +23,7 @@
     "@docusaurus/preset-classic": "2.4.0",
     "@docusaurus/remark-plugin-npm2yarn": "^2.4.0",
     "@mdx-js/react": "^1.6.22",
-    "@mendable/search": "^0.0.125",
+    "@mendable/search": "^0.0.102",
     "clsx": "^1.2.1",
     "json-loader": "^0.5.7",
     "process": "^0.11.10",

docs/docs_skeleton/sidebars.js

@@ -20,7 +20,7 @@
 
 module.exports = {
   // By default, Docusaurus generates a sidebar from the docs folder structure
-  docs: [
+  sidebar: [
     {
       type: "category",
       label: "Get started",
@@ -30,7 +30,7 @@ module.exports = {
       link: {
         type: 'generated-index',
         description: 'Get started with LangChain',
-      slug: "get_started",
+	    slug: "get_started",
       },
     },
     {
@@ -44,6 +44,17 @@ module.exports = {
         id: "modules/index"
       },
     },
+    {
+      type: "category",
+      label: "Use cases",
+      collapsed: true,
+      items: [{ type: "autogenerated", dirName: "use_cases" }],
+      link: {
+        type: 'generated-index',
+        description: 'Walkthroughs of common end-to-end use cases',
+	    slug: "use_cases",
+      },
+    },
     {
       type: "category",
       label: "Guides",
@@ -52,7 +63,7 @@ module.exports = {
       link: {
         type: 'generated-index',
         description: 'Design guides for key parts of the development process',
-      slug: "guides",
+	    slug: "guides",
       },
     },
     {
@@ -62,7 +73,7 @@ module.exports = {
       items: [{ type: "autogenerated", dirName: "ecosystem" }],
       link: {
         type: 'generated-index',
-      slug: "ecosystem",
+	    slug: "ecosystem",
       },
     },
     {
@@ -72,32 +83,18 @@ module.exports = {
       items: [{ type: "autogenerated", dirName: "additional_resources" }, { type: "link", label: "Gallery", href: "https://github.com/kyrolabs/awesome-langchain" }],
       link: {
         type: 'generated-index',
-      slug: "additional_resources",
+	    slug: "additional_resources",
       },
     },
-  ],
-  integrations: [
     {
-      type: "category",
-      label: "Integrations",
-      collapsible: false,
-      items: [{ type: "autogenerated", dirName: "integrations" }],
-      link: {
-        type: 'generated-index',
-      slug: "integrations",
-      },
+      type: "html",
+      value: "<hr>",
+      defaultStyle: true,
     },
-  ],
-  use_cases: [
     {
-      type: "category",
-      label: "Use cases",
-      collapsible: false,
-      items: [{ type: "autogenerated", dirName: "use_cases" }],
-      link: {
-        type: 'generated-index',
-      slug: "use_cases",
-      },
+       type: "link",
+       href: "https://api.python.langchain.com",
+       label: "API reference",
     },
   ],
 };

docs/docs_skeleton/src/css/custom.css

@@ -139,22 +139,4 @@
 
 .hidden {
     display: none !important;
-}
-
-.header-github-link:hover {
-  opacity: 0.6;
-}
-
-.header-github-link::before {
-  content: '';
-  width: 24px;
-  height: 24px;
-  display: flex;
-  background: url("data:image/svg+xml,%3Csvg viewBox='0 0 24 24' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12'/%3E%3C/svg%3E")
-    no-repeat;
-}
-
-[data-theme='dark'] .header-github-link::before {
-  background: url("data:image/svg+xml,%3Csvg viewBox='0 0 24 24' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill='white' d='M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12'/%3E%3C/svg%3E")
-    no-repeat;
 }

docs/docs_skeleton/src/theme/SearchBar.js

@@ -22,7 +22,6 @@ export default function SearchBarWrapper() {
         placeholder="Search..."
         dialogPlaceholder="How do I use a LLM Chain?"
         messageSettings={{ openSourcesInNewTab: false, prettySources: true }}
-        isPinnable
         showSimpleSearch
       />
     </div>

docs/docs_skeleton/vercel.json

@@ -6,551 +6,535 @@
     },
     {
       "source": "/en/latest/integrations/agent_with_wandb_tracing.html",
-      "destination": "/docs/integrations/agent_with_wandb_tracing"
+      "destination": "/docs/ecosystem/integrations/agent_with_wandb_tracing"
     },
     {
       "source": "/en/latest/integrations/ai21.html",
-      "destination": "/docs/integrations/ai21"
+      "destination": "/docs/ecosystem/integrations/ai21"
     },
     {
       "source": "/en/latest/integrations/aim_tracking.html",
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       "source": "/en/latest/integrations/whylabs_profiling.html",
-      "destination": "/docs/integrations/whylabs_profiling"
+      "destination": "/docs/ecosystem/integrations/whylabs_profiling"
     },
     {
       "source": "/en/latest/integrations/wikipedia.html",
-      "destination": "/docs/integrations/wikipedia"
+      "destination": "/docs/ecosystem/integrations/wikipedia"
     },
     {
       "source": "/en/latest/integrations/wolfram_alpha.html",
-      "destination": "/docs/integrations/wolfram_alpha"
+      "destination": "/docs/ecosystem/integrations/wolfram_alpha"
     },
     {
       "source": "/en/latest/integrations/writer.html",
-      "destination": "/docs/integrations/writer"
+      "destination": "/docs/ecosystem/integrations/writer"
     },
     {
       "source": "/en/latest/integrations/yeagerai.html",
-      "destination": "/docs/integrations/yeagerai"
+      "destination": "/docs/ecosystem/integrations/yeagerai"
     },
     {
       "source": "/en/latest/integrations/youtube.html",
-      "destination": "/docs/integrations/youtube"
+      "destination": "/docs/ecosystem/integrations/youtube"
     },
     {
       "source": "/en/latest/integrations/zep.html",
-      "destination": "/docs/integrations/zep"
+      "destination": "/docs/ecosystem/integrations/zep"
     },
     {
       "source": "/en/latest/integrations/zilliz.html",
-      "destination": "/docs/integrations/zilliz"
-    },
-    {
-      "source": "/docs/ecosystem/integrations/:path*",
-      "destination": "/docs/integrations/:path*"
-    },
-    {
-      "source": "/docs/ecosystem/integrations/",
-      "destination": "/docs/integrations/"
-    },
-    {
-      "source": "/docs/ecosystem/integrations",
-      "destination": "/docs/integrations"
+      "destination": "/docs/ecosystem/integrations/zilliz"
     },
     {
       "source": "/en/latest/ecosystem/deployments.html",
@@ -1312,10 +1296,6 @@
       "source": "/en/latest/modules/indexes/text_splitters/examples/markdown_header_metadata.html",
       "destination": "/docs/modules/data_connection/document_transformers/text_splitters/markdown_header_metadata"
     },
-    {
-      "source": "/en/latest/modules/indexes/text_splitters.html",
-      "destination": "/docs/modules/data_connection/document_transformers/"
-    },
     {
       "source": "/en/latest/modules/indexes/retrievers/examples/chroma_self_query.html",
       "destination": "/docs/modules/data_connection/retrievers/how_to/self_query/chroma_self_query"
@@ -1350,11 +1330,7 @@
     },
     {
       "source": "/en/latest/modules/indexes/retrievers/examples/databerry.html",
-      "destination": "/docs/modules/data_connection/retrievers/integrations/chaindesk"
-    },
-    {
-      "source": "/docs/modules/data_connection/retrievers/integrations/databerry",
-      "destination": "/docs/modules/data_connection/retrievers/integrations/chaindesk"
+      "destination": "/docs/modules/data_connection/retrievers/integrations/databerry"
     },
     {
       "source": "/en/latest/modules/indexes/retrievers/examples/elastic_search_bm25.html",
@@ -1888,14 +1864,6 @@
       "source": "/en/latest/modules/models/llms/integrations/writer.html",
       "destination": "/docs/modules/model_io/models/llms/integrations/writer"
     },
-    {
-      "source": "/en/latest/modules/prompts/output_parsers.html",
-      "destination": "/docs/modules/model_io/output_parsers/"
-    },
-    {
-      "source": "/docs/modules/prompts/output_parsers.html",
-      "destination": "/docs/modules/model_io/output_parsers/"
-    },
     {
       "source": "/en/latest/modules/prompts/output_parsers/examples/datetime.html",
       "destination": "/docs/modules/model_io/output_parsers/datetime"
@@ -2149,4 +2117,4 @@
       "destination": "/docs/:path*"
     }
   ]
-}
+}

docs/docs_skeleton/vercel_build.sh

@@ -4,7 +4,7 @@ cd ..
 python3 --version
 python3 -m venv .venv
 source .venv/bin/activate
-python3 -m pip install -r vercel_requirements.txt
+python3 -m pip install -r requirements.txt
 cp -r extras/* docs_skeleton/docs
 cd docs_skeleton
 nbdoc_build

docs/extras/additional_resources/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/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/arthur_tracking.ipynb

@@ -0,0 +1,464 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "944e4194",
+   "metadata": {},
+   "source": [
+    "# Arthur LangChain integration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1ccdfe8",
+   "metadata": {},
+   "source": [
+    "[Arthur](https://www.arthur.ai/) is a model monitoring and observability platform.\n",
+    "\n",
+    "This notebook shows how to register LLMs (chat and non-chat) as models with the Arthur platform. Then we show how to set up langchain LLMs with an Arthur callback that will automatically log model inferences to Arthur.\n",
+    "\n",
+    "For more information about how to use the Arthur SDK, visit our [docs](http://docs.arthur.ai), in particular our [model onboarding guide](https://docs.arthur.ai/user-guide/walkthroughs/model-onboarding/index.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "961c6691",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks import ArthurCallbackHandler\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.chat_models import ChatOpenAI, ChatAnthropic\n",
+    "from langchain.schema import HumanMessage\n",
+    "from langchain.llms import OpenAI, Cohere, HuggingFacePipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a23d1963",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from arthurai import ArthurAI\n",
+    "from arthurai.common.constants import InputType, OutputType, Stage, ValueType\n",
+    "from arthurai.core.attributes import ArthurAttribute, AttributeCategory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d1b90c0",
+   "metadata": {},
+   "source": [
+    "# ArthurModel for chatbot with only input text and output text attributes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a4a4a8a",
+   "metadata": {},
+   "source": [
+    "Connect to Arthur client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f49e9b79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arthur_url = \"https://app.arthur.ai\"\n",
+    "arthur_login = \"your-username-here\"\n",
+    "arthur = ArthurAI(url=arthur_url, login=arthur_login)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6e063bf",
+   "metadata": {},
+   "source": [
+    "Before you can register model inferences to Arthur, you must have a registered model with an ID in the Arthur platform. We will provide this ID to the ArthurCallbackHandler.\n",
+    "\n",
+    "You can register a model with Arthur here in the notebook using this `register_chat_llm()` function. This function returns the ID of the model saved to the platform. To use the function, uncomment `arthur_model_chatbot_id = register_chat_llm()` in the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "31b17b5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def register_chat_llm():\n",
+    "\n",
+    "    arthur_model = arthur.model(\n",
+    "        display_name=\"LangChainChat\",\n",
+    "        input_type=InputType.NLP,\n",
+    "        output_type=OutputType.TokenSequence\n",
+    "    )\n",
+    "\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"my_input_text\",\n",
+    "        stage=Stage.ModelPipelineInput,\n",
+    "        value_type=ValueType.Unstructured_Text,\n",
+    "        categorical=True,\n",
+    "        is_unique=True\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"my_output_text\",\n",
+    "        stage=Stage.PredictedValue,\n",
+    "        value_type=ValueType.Unstructured_Text,\n",
+    "        categorical=True,\n",
+    "        is_unique=False,\n",
+    "    ))\n",
+    "    \n",
+    "    return arthur_model.save()\n",
+    "# arthur_model_chatbot_id = register_chat_llm()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d1d1e60",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can set the `arthur_model_chatbot_id` variable to be the ID of your model on your [model dashboard](https://app.arthur.ai/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cdfa02c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arthur_model_chatbot_id = \"your-model-id-here\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58be5234",
+   "metadata": {},
+   "source": [
+    "This function creates a Langchain chat LLM with the ArthurCallbackHandler to log inferences to Arthur. We provide our `arthur_model_chatbot_id`, as well as the Arthur url and login we are using."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "448a8fee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_langchain_chat_llm(chat_model=ChatOpenAI):\n",
+    "    if chat_model not in [ChatOpenAI, ChatAnthropic]:\n",
+    "        raise ValueError(\"For this notebook, use one of the chat models imported from langchain.chat_models\")\n",
+    "    return chat_model(\n",
+    "        streaming=True, \n",
+    "        temperature=0.1,\n",
+    "        callbacks=[\n",
+    "            StreamingStdOutCallbackHandler(), \n",
+    "            ArthurCallbackHandler.from_credentials(arthur_model_chatbot_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
+    "        ])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17c182da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2dfc00ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat_llm = make_langchain_chat_llm()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "139291f2",
+   "metadata": {},
+   "source": [
+    "Run the chatbot (it will save the chat history in the `history` list so that the conversation can reference earlier messages)\n",
+    "\n",
+    "Type `q` to quit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7480a443",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_langchain_chat_llm(llm):\n",
+    "    history = []\n",
+    "    while True:\n",
+    "        user_input = input(\"\\n>>> input >>>\\n>>>: \")\n",
+    "        if user_input == 'q': break\n",
+    "        history.append(HumanMessage(content=user_input))\n",
+    "        history.append(llm(history))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6868ce71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_langchain_chat_llm(chat_llm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0be7d01",
+   "metadata": {},
+   "source": [
+    "# ArthurModel with input text, output text, token likelihoods, finish reason, and amount of token usage attributes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ee4b741",
+   "metadata": {},
+   "source": [
+    "This function registers an LLM with additional metadata attributes to log to Arthur with each inference\n",
+    "\n",
+    "As above, you can register your callback handler for an LLM using this function here in the notebook or by pasting the ID of an already-registered model from your [model dashboard](https://app.arthur.ai/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e671836c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def register_llm():\n",
+    "\n",
+    "    arthur_model = arthur.model(\n",
+    "        display_name=\"LangChainLLM\",\n",
+    "        input_type=InputType.NLP,\n",
+    "        output_type=OutputType.TokenSequence\n",
+    "    )\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"my_input_text\",\n",
+    "        stage=Stage.ModelPipelineInput,\n",
+    "        value_type=ValueType.Unstructured_Text,\n",
+    "        categorical=True,\n",
+    "        is_unique=True\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"my_output_text\",\n",
+    "        stage=Stage.PredictedValue,\n",
+    "        value_type=ValueType.Unstructured_Text,\n",
+    "        categorical=True,\n",
+    "        is_unique=False,\n",
+    "        token_attribute_link=\"my_output_likelihoods\"\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"my_output_likelihoods\",\n",
+    "        stage=Stage.PredictedValue,\n",
+    "        value_type=ValueType.TokenLikelihoods,\n",
+    "        token_attribute_link=\"my_output_text\"\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"finish_reason\",\n",
+    "        stage=Stage.NonInputData,\n",
+    "        value_type=ValueType.String,\n",
+    "        categorical=True,\n",
+    "        categories=[\n",
+    "            AttributeCategory(value='stop'),\n",
+    "            AttributeCategory(value='length'),\n",
+    "            AttributeCategory(value='content_filter'),\n",
+    "            AttributeCategory(value='null')\n",
+    "        ]\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"prompt_tokens\",\n",
+    "        stage=Stage.NonInputData,\n",
+    "        value_type=ValueType.Integer\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"completion_tokens\",\n",
+    "        stage=Stage.NonInputData,\n",
+    "        value_type=ValueType.Integer\n",
+    "    ))\n",
+    "    arthur_model._add_attribute_to_model(ArthurAttribute(\n",
+    "        name=\"duration\",\n",
+    "        stage=Stage.NonInputData,\n",
+    "        value_type=ValueType.Float\n",
+    "    ))\n",
+    "    \n",
+    "    return arthur_model.save()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2a6686f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arthur_model_llm_id = \"your-model-id-here\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dcacb96",
+   "metadata": {},
+   "source": [
+    "These functions create Langchain LLMs with the ArthurCallbackHandler to log inferences to Arthur.\n",
+    "\n",
+    "There are small differences in the underlying Langchain integrations with these libraries and the available metadata for model inputs & outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "34cf0072",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_langchain_openai_llm():\n",
+    "    return OpenAI(\n",
+    "        temperature=0.1,\n",
+    "        model_kwargs = {'logprobs': 3},\n",
+    "        callbacks=[\n",
+    "            ArthurCallbackHandler.from_credentials(arthur_model_llm_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
+    "        ])\n",
+    "\n",
+    "def make_langchain_cohere_llm():\n",
+    "    return Cohere(\n",
+    "        temperature=0.1,\n",
+    "        callbacks=[\n",
+    "            ArthurCallbackHandler.from_credentials(arthur_model_chatbot_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
+    "        ])\n",
+    "\n",
+    "def make_langchain_huggingface_llm():\n",
+    "    llm = HuggingFacePipeline.from_model_id(\n",
+    "        model_id=\"bert-base-uncased\", \n",
+    "        task=\"text-generation\", \n",
+    "        model_kwargs={\"temperature\":2.5, \"max_length\":64})\n",
+    "    llm.callbacks = [\n",
+    "        ArthurCallbackHandler.from_credentials(arthur_model_chatbot_id, arthur_url=arthur_url, arthur_login=arthur_login)\n",
+    "    ]\n",
+    "    return llm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f40c3ce0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai_llm = make_langchain_openai_llm()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8476d531",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cohere_llm = make_langchain_cohere_llm()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7483b9d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "huggingface_llm = make_langchain_huggingface_llm()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c17d8e86",
+   "metadata": {},
+   "source": [
+    "Run the LLM (each completion is independent, no chat history is saved as we were doing above with the chat llms)\n",
+    "\n",
+    "Type `q` to quit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "72ee0790",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_langchain_llm(llm):\n",
+    "    while True:\n",
+    "        print(\"Type your text for completion:\\n\")\n",
+    "        user_input = input(\"\\n>>> input >>>\\n>>>: \")\n",
+    "        if user_input == 'q': break\n",
+    "        print(llm(user_input), \"\\n================\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fb864057",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_langchain_llm(openai_llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e6673769",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_langchain_llm(cohere_llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "85541f1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_langchain_llm(huggingface_llm)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/ecosystem/integrations/azure_openai.mdx

@@ -22,7 +22,7 @@ import os
 os.environ["OPENAI_API_TYPE"] = "azure"
 os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
 os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
-os.environ["OPENAI_API_VERSION"] = "2023-05-15"
+os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
 ```
 
 ## LLM

docs/extras/ecosystem/integrations/cassandra.mdx

@@ -1,10 +1,10 @@
 # Cassandra
 
->[Apache Cassandra®](https://cassandra.apache.org/) is a free and open-source, distributed, wide-column
+>[Apache Cassandra®](https://cassandra.apache.org/) 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
+> 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
@@ -16,16 +16,6 @@ pip install cassio
 
 
 
-## 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/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
 ```